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Zombies Redacted

33 Eliezer_Yudkowsky 02 July 2016 08:16PM

I looked at my old post Zombies! Zombies? and it seemed to have some extraneous content.  This is a redacted and slightly rewritten version.

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Recent updates to gwern.net (2015-2016)

28 gwern 26 August 2016 07:22PM

Previously: 2011; 2012-2013; 2013-2014; 2014-2015

"When I was one-and-twenty / I heard a wise man say, / 'Give crowns and pounds and guineas / But not your heart away; / Give pearls away and rubies / But keep your fancy free.' / But I was one-and-twenty, / No use to talk to me."

My past year of completed writings, sorted by topic:

Genetics:

  • Embryo selection for intelligence cost-benefit analysis
    • meta-analysis of intelligence GCTAs, limits set by measurement error, current polygenic scores, possible gains with current IVF procedures, the benefits of selection on multiple complex traits, the possible annual value in the USA of selection & value of larger GWASes, societal consequences of various embryo selection scenarios, embryo count versus polygenic scores as limiting factors, comparison with iterated embryo selection, limits to total gains from iterated embryo selection etc.
  • Wikipedia article on Genome-wide complex trait analysis (GCTA)

AI:

Biology:

Statistics:

Cryptography:

Misc:

gwern.net itself has remained largely stable (some CSS fixes and image size changes); I continue to use Patreon and send out my newsletters.

Notes on the Safety in Artificial Intelligence conference

25 UmamiSalami 01 July 2016 12:36AM

These are my notes and observations after attending the Safety in Artificial Intelligence (SafArtInt) conference, which was co-hosted by the White House Office of Science and Technology Policy and Carnegie Mellon University on June 27 and 28. This isn't an organized summary of the content of the conference; rather, it's a selection of points which are relevant to the control problem. As a result, it suffers from selection bias: it looks like superintelligence and control-problem-relevant issues were discussed frequently, when in reality those issues were discussed less and I didn't write much about the more mundane parts.

SafArtInt has been the third out of a planned series of four conferences. The purpose of the conference series was twofold: the OSTP wanted to get other parts of the government moving on AI issues, and they also wanted to inform public opinion.

The other three conferences are about near term legal, social, and economic issues of AI. SafArtInt was about near term safety and reliability in AI systems. It was effectively the brainchild of Dr. Ed Felten, the deputy U.S. chief technology officer for the White House, who came up with the idea for it last year. CMU is a top computer science university and many of their own researchers attended, as well as some students. There were also researchers from other universities, some people from private sector AI including both Silicon Valley and government contracting, government researchers and policymakers from groups such as DARPA and NASA, a few people from the military/DoD, and a few control problem researchers. As far as I could tell, everyone except a few university researchers were from the U.S., although I did not meet many people. There were about 70-100 people watching the presentations at any given time, and I had conversations with about twelve of the people who were not affiliated with existential risk organizations, as well as of course all of those who were affiliated. The conference was split with a few presentations on the 27th and the majority of presentations on the 28th. Not everyone was there for both days.

Felten believes that neither "robot apocalypses" nor "mass unemployment" are likely. It soon became apparent that the majority of others present at the conference felt the same way with regard to superintelligence. The general intention among researchers and policymakers at the conference could be summarized as follows: we need to make sure that the AI systems we develop in the near future will not be responsible for any accidents, because if accidents do happen then they will spark public fears about AI, which would lead to a dearth of funding for AI research and an inability to realize the corresponding social and economic benefits. Of course, that doesn't change the fact that they strongly care about safety in its own right and have significant pragmatic needs for robust and reliable AI systems.

Most of the talks were about verification and reliability in modern day AI systems. So they were concerned with AI systems that would give poor results or be unreliable in the narrow domains where they are being applied in the near future. They mostly focused on "safety-critical" systems, where failure of an AI program would result in serious negative consequences: automated vehicles were a common topic of interest, as well as the use of AI in healthcare systems. A recurring theme was that we have to be more rigorous in demonstrating safety and do actual hazard analyses on AI systems, and another was that we need the AI safety field to succeed in ways that the cybersecurity field has failed. Another general belief was that long term AI safety, such as concerns about the ability of humans to control AIs, was not a serious issue.

On average, the presentations were moderately technical. They were mostly focused on machine learning systems, although there was significant discussion of cybersecurity techniques.

The first talk was given by Eric Horvitz of Microsoft. He discussed some approaches for pushing into new directions in AI safety. Instead of merely trying to reduce the errors spotted according to one model, we should look out for "unknown unknowns" by stacking models and looking at problems which appear on any of them, a theme which would be presented by other researchers as well in later presentations. He discussed optimization under uncertain parameters, sensitivity analysis to uncertain parameters, and 'wireheading' or short-circuiting of reinforcement learning systems (which he believes can be guarded against by using 'reflective analysis'). Finally, he brought up the concerns about superintelligence, which sparked amused reactions in the audience. He said that scientists should address concerns about superintelligence, which he aptly described as the 'elephant in the room', noting that it was the reason that some people were at the conference. He said that scientists will have to engage with public concerns, while also noting that there were experts who were worried about superintelligence and that there would have to be engagement with the experts' concerns. He did not comment on whether he believed that these concerns were reasonable or not.

An issue which came up in the Q&A afterwards was that we need to deal with mis-structured utility functions in AI, because it is often the case that the specific tradeoffs and utilities which humans claim to value often lead to results which the humans don't like. So we need to have structural uncertainty about our utility models. The difficulty of finding good objective functions for AIs would eventually be discussed in many other presentations as well.

The next talk was given by Andrew Moore of Carnegie Mellon University, who claimed that his talk represented the consensus of computer scientists at the school. He claimed that the stakes of AI safety were very high - namely, that AI has the capability to save many people's lives in the near future, but if there are any accidents involving AI then public fears could lead to freezes in AI research and development. He highlighted the public's irrational tendencies wherein a single accident could cause people to overlook and ignore hundreds of invisible lives saved. He specifically mentioned a 12-24 month timeframe for these issues.

Moore said that verification of AI system safety will be difficult due to the combinatorial explosion of AI behaviors. He talked about meta-machine-learning as a solution to this, something which is being investigated under the direction of Lawrence Schuette at the Office of Naval Research. Moore also said that military AI systems require high verification standards and that development timelines for these systems are long. He talked about two different approaches to AI safety, stochastic testing and theorem proving - the process of doing the latter often leads to the discovery of unsafe edge cases.

He also discussed AI ethics, giving an example 'trolley problem' where AI cars would have to choose whether to hit a deer in order to provide a slightly higher probability of survival for the human driver. He said that we would need hash-defined constants to tell vehicle AIs how many deer a human is worth. He also said that we would need to find compromises in death-pleasantry tradeoffs, for instance where the safety of self-driving cars depends on the speed and routes on which they are driven. He compared the issue to civil engineering where engineers have to operate with an assumption about how much money they would spend to save a human life.

He concluded by saying that we need policymakers, company executives, scientists, and startups to all be involved in AI safety. He said that the research community stands to gain or lose together, and that there is a shared responsibility among researchers and developers to avoid triggering another AI winter through unsafe AI designs.

The next presentation was by Richard Mallah of the Future of Life Institute, who was there to represent "Medium Term AI Safety". He pointed out the explicit/implicit distinction between different modeling techniques in AI systems, as well as the explicit/implicit distinction between different AI actuation techniques. He talked about the difficulty of value specification and the concept of instrumental subgoals as an important issue in the case of complex AIs which are beyond human understanding. He said that even a slight misalignment of AI values with regard to human values along one parameter could lead to a strongly negative outcome, because machine learning parameters don't strictly correspond to the things that humans care about.

Mallah stated that open-world discovery leads to self-discovery, which can lead to reward hacking or a loss of control. He underscored the importance of causal accounting, which is distinguishing causation from correlation in AI systems. He said that we should extend machine learning verification to self-modification. Finally, he talked about introducing non-self-centered ontology to AI systems and bounding their behavior.

The audience was generally quiet and respectful during Richard's talk. I sensed that at least a few of them labelled him as part of the 'superintelligence out-group' and dismissed him accordingly, but I did not learn what most people's thoughts or reactions were. In the next panel featuring three speakers, he wasn't the recipient of any questions regarding his presentation or ideas.

Tom Mitchell from CMU gave the next talk. He talked about both making AI systems safer, and using AI to make other systems safer. He said that risks to humanity from other kinds of issues besides AI were the "big deals of 2016" and that we should make sure that the potential of AIs to solve these problems is realized. He wanted to focus on the detection and remediation of all failures in AI systems. He said that it is a novel issue that learning systems defy standard pre-testing ("as Richard mentioned") and also brought up the purposeful use of AI for dangerous things.

Some interesting points were raised in the panel. Andrew did not have a direct response to the implications of AI ethics being determined by the predominantly white people of the US/UK where most AIs are being developed. He said that ethics in AIs will have to be decided by society, regulators, manufacturers, and human rights organizations in conjunction. He also said that our cost functions for AIs will have to get more and more complicated as AIs get better, and he said that he wants to separate unintended failures from superintelligence type scenarios. On trolley problems in self driving cars and similar issues, he said "it's got to be complicated and messy."

Dario Amodei of Google Deepbrain, who co-authored the paper on concrete problems in AI safety, gave the next talk. He said that the public focus is too much on AGI/ASI and wants more focus on concrete/empirical approaches. He discussed the same problems that pose issues in advanced general AI, including flawed objective functions and reward hacking. He said that he sees long term concerns about AGI/ASI as "extreme versions of accident risk" and that he thinks it's too early to work directly on them, but he believes that if you want to deal with them then the best way to do it is to start with safety in current systems. Mostly he summarized the Google paper in his talk.

In her presentation, Claire Le Goues of CMU said "before we talk about Skynet we should focus on problems that we already have." She mostly talked about analogies between software bugs and AI safety, the similarities and differences between the two and what we can learn from software debugging to help with AI safety.

Robert Rahmer of IARPA discussed CAUSE, a cyberintelligence forecasting program which promises to help predict cyber attacks. It is a program which is still being put together.

In the panel of the above three, autonomous weapons were discussed, but no clear policy stances were presented.

John Launchbury gave a talk on DARPA research and the big picture of AI development. He pointed out that DARPA work leads to commercial applications and that progress in AI comes from sustained government investment. He classified AI capabilities into "describing," "predicting," and "explaining" in order of increasing difficulty, and he pointed out that old fashioned "describing" still plays a large role in AI verification. He said that "explaining" AIs would need transparent decisionmaking and probabilistic programming (the latter would also be discussed by others at the conference).

The next talk came from Jason Gaverick Matheny, the director of IARPA. Matheny talked about four requirements in current and future AI systems: verification, validation, security, and control. He wanted "auditability" in AI systems as a weaker form of explainability. He talked about the importance of "corner cases" for national intelligence purposes, the low probability, high stakes situations where we have limited data - these are situations where we have significant need for analysis but where the traditional machine learning approach doesn't work because of its overwhelming focus on data. Another aspect of national defense is that it has a slower decision tempo, longer timelines, and longer-viewing optics about future events.

He said that assessing local progress in machine learning development would be important for global security and that we therefore need benchmarks to measure progress in AIs. He ended with a concrete invitation for research proposals from anyone (educated or not), for both large scale research and for smaller studies ("seedlings") that could take us "from disbelief to doubt".

The difference in timescales between different groups was something I noticed later on, after hearing someone from the DoD describe their agency as having a longer timeframe than the Homeland Security Agency, and someone from the White House describe their work as being crisis reactionary.

The next presentation was from Andrew Grotto, senior director of cybersecurity policy at the National Security Council. He drew a close parallel from the issue of genetically modified crops in Europe in the 1990's to modern day artificial intelligence. He pointed out that Europe utterly failed to achieve widespread cultivation of GMO crops as a result of public backlash. He said that the widespread economic and health benefits of GMO crops were ignored by the public, who instead focused on a few health incidents which undermined trust in the government and crop producers. He had three key points: that risk frameworks matter, that you should never assume that the benefits of new technology will be widely perceived by the public, and that we're all in this together with regard to funding, research progress and public perception.

In the Q&A between Launchbury, Matheny, and Grotto after Grotto's presentation, it was mentioned that the economic interests of farmers worried about displacement also played a role in populist rejection of GMOs, and that a similar dynamic could play out with regard to automation causing structural unemployment. Grotto was also asked what to do about bad publicity which seeks to sink progress in order to avoid risks. He said that meetings like SafArtInt and open public dialogue were good.

One person asked what Launchbury wanted to do about AI arms races with multiple countries trying to "get there" and whether he thinks we should go "slow and secure" or "fast and risky" in AI development, a question which provoked laughter in the audience. He said we should go "fast and secure" and wasn't concerned. He said that secure designs for the Internet once existed, but the one which took off was the one which was open and flexible.

Another person asked how we could avoid discounting outliers in our models, referencing Matheny's point that we need to include corner cases. Matheny affirmed that data quality is a limiting factor to many of our machine learning capabilities. At IARPA, we generally try to include outliers until they are sure that they are erroneous, said Matheny.

Another presentation came from Tom Dietterich, president of the Association for the Advancement of Artificial Intelligence. He said that we have not focused enough on safety, reliability and robustness in AI and that this must change. Much like Eric Horvitz, he drew a distinction between robustness against errors within the scope of a model and robustness against unmodeled phenomena. On the latter issue, he talked about solutions such as expanding the scope of models, employing multiple parallel models, and doing creative searches for flaws - the latter doesn't enable verification that a system is safe, but it nevertheless helps discover many potential problems. He talked about knowledge-level redundancy as a method of avoiding misspecification - for instance, systems could identify objects by an "ownership facet" as well as by a "goal facet" to produce a combined concept with less likelihood of overlooking key features. He said that this would require wider experiences and more data.

There were many other speakers who brought up a similar set of issues: the user of cybersecurity techniques to verify machine learning systems, the failures of cybersecurity as a field, opportunities for probabilistic programming, and the need for better success in AI verification. Inverse reinforcement learning was extensively discussed as a way of assigning values. Jeanette Wing of Microsoft talked about the need for AIs to reason about the continuous and the discrete in parallel, as well as the need for them to reason about uncertainty (with potential meta levels all the way up). One point which was made by Sarah Loos of Google was that proving the safety of an AI system can be computationally very expensive, especially given the combinatorial explosion of AI behaviors.

In one of the panels, the idea of government actions to ensure AI safety was discussed. No one was willing to say that the government should regulate AI designs. Instead they stated that the government should be involved in softer ways, such as guiding and working with AI developers, and setting standards for certification.

Pictures: https://imgur.com/a/49eb7

In between these presentations I had time to speak to individuals and listen in on various conversations. A high ranking person from the Department of Defense stated that the real benefit of autonomous systems would be in terms of logistical systems rather than weaponized applications. A government AI contractor drew the connection between Mallah's presentation and the recent press revolving around superintelligence, and said he was glad that the government wasn't worried about it.

I talked to some insiders about the status of organizations such as MIRI, and found that the current crop of AI safety groups could use additional donations to become more established and expand their programs. There may be some issues with the organizations being sidelined; after all, the Google Deepbrain paper was essentially similar to a lot of work by MIRI, just expressed in somewhat different language, and was more widely received in mainstream AI circles.

In terms of careers, I found that there is significant opportunity for a wide range of people to contribute to improving government policy on this issue. Working at a group such as the Office of Science and Technology Policy does not necessarily require advanced technical education, as you can just as easily enter straight out of a liberal arts undergraduate program and build a successful career as long as you are technically literate. (At the same time, the level of skepticism about long term AI safety at the conference hinted to me that the signalling value of a PhD in computer science would be significant.) In addition, there are large government budgets in the seven or eight figure range available for qualifying research projects. I've come to believe that it would not be difficult to find or create AI research programs that are relevant to long term AI safety while also being practical and likely to be funded by skeptical policymakers and officials.

I also realized that there is a significant need for people who are interested in long term AI safety to have basic social and business skills. Since there is so much need for persuasion and compromise in government policy, there is a lot of value to be had in being communicative, engaging, approachable, appealing, socially savvy, and well-dressed. This is not to say that everyone involved in long term AI safety is missing those skills, of course.

I was surprised by the refusal of almost everyone at the conference to take long term AI safety seriously, as I had previously held the belief that it was more of a mixed debate given the existence of expert computer scientists who were involved in the issue. I sensed that the recent wave of popular press and public interest in dangerous AI has made researchers and policymakers substantially less likely to take the issue seriously. None of them seemed to be familiar with actual arguments or research on the control problem, so their opinions didn't significantly change my outlook on the technical issues. I strongly suspect that the majority of them had their first or possibly only exposure to the idea of the control problem after seeing badly written op-eds and news editorials featuring comments from the likes of Elon Musk and Stephen Hawking, which would naturally make them strongly predisposed to not take the issue seriously. In the run-up to the conference, websites and press releases didn't say anything about whether this conference would be about long or short term AI safety, and they didn't make any reference to the idea of superintelligence.

I sympathize with the concerns and strategy given by people such as Andrew Moore and Andrew Grotto, which make perfect sense if (and only if) you assume that worries about long term AI safety are completely unfounded. For the community that is interested in long term AI safety, I would recommend that we avoid competitive dynamics by (a) demonstrating that we are equally strong opponents of bad press, inaccurate news, and irrational public opinion which promotes generic uninformed fears over AI, (b) explaining that we are not interested in removing funding for AI research (even if you think that slowing down AI development is a good thing, restricting funding yields only limited benefits in terms of changing overall timelines, whereas those who are not concerned about long term AI safety would see a restriction of funding as a direct threat to their interests and projects, so it makes sense to cooperate here in exchange for other concessions), and (c) showing that we are scientifically literate and focused on the technical concerns. I do not believe that there is necessarily a need for the two "sides" on this to be competing against each other, so it was disappointing to see an implication of opposition at the conference.

Anyway, Ed Felten announced a request for information from the general public, seeking popular and scientific input on the government's policies and attitudes towards AI: https://www.whitehouse.gov/webform/rfi-preparing-future-artificial-intelligence

Overall, I learned quite a bit and benefited from the experience, and I hope the insight I've gained can be used to improve the attitudes and approaches of the long term AI safety community.

Linkposts now live!

21 Vaniver 28 September 2016 03:13PM

 

You can now submit links to LW! As the rationality community has grown up, more and more content has moved off LW to other places, and so rather than trying to generate more content here we'll instead try to collect more content here. My hope is that Less Wrong becomes something like "the Rationalist RSS," where people can discover what's new and interesting without necessarily being plugged in to the various diaspora communities.

Some general norms, subject to change:

 

  1. It's okay to link someone else's work, unless they specifically ask you not to. It's also okay to link your own work; if you want to get LW karma for things you make off-site, drop a link here as soon as you publish it.
  2. It's okay to link old stuff, but let's try to keep it to less than 5 old posts a day. The first link that I made is to Yudkowsky's Guide to Writing Intelligent Characters.
  3. It's okay to link to something that you think rationalists will be interested in, even if it's not directly related to rationality. If it's political, think long and hard before deciding to submit that link.
  4. It's not okay to post duplicates.

As before, everything will go into discussion. Tag your links, please. As we see what sort of things people are linking, we'll figure out how we need to divide things up, be it separate subreddits or using tags to promote or demote the attention level of links and posts.

(Thanks to James Lamine for doing the coding, and to Trike (and myself) for supporting the work.)

Now is the time to eliminate mosquitoes

21 James_Miller 06 August 2016 07:10PM

“In 2015, there were roughly 214 million malaria cases and an estimated 438 000 malaria deaths.”  While we don’t know how many humans malaria has killed, an estimate of half of everyone who has ever died isn’t absurd.  Because few people in rich countries get malaria, pharmaceutical companies put relatively few resources into combating it.   

 

The best way to eliminate malaria is probably to use gene drives to completely eradicate the species of mosquitoes that bite humans, but until recently rich countries haven’t been motivated to such xenocide.  The Zika virus, which is in mosquitoes in the United States, provides effective altruists with an opportunity to advocate for exterminating all species of mosquitoes that spread disease to humans because the horrifying and disgusting pictures of babies with Zika might make the American public receptive to our arguments.  A leading short-term goal of effective altruists, I propose, should be advocating for mosquito eradication in the short window before rich people get acclimated to pictures of Zika babies.   

 

Personally, I have (unsuccessfully) pitched articles on mosquito eradication to two magazines and (with a bit more success) emailed someone who knows someone who knows someone in the Trump campaign to attempt to get the candidate to come out in favor of mosquito eradication.  What have you done?   Given the enormous harm mosquitoes inflict on mankind, doing just a little (such as writing a blog post) could have a high expected payoff.

 

Deepmind Plans for Rat-Level AI

18 moridinamael 18 August 2016 04:26PM

Demis Hassabis gives a great presentation on the state of Deepmind's work as of April 20, 2016. Skip to 23:12 for the statement of the goal of creating a rat-level AI -- "An AI that can do everything a rat can do," in his words. From his tone, it sounds like this is more a short-term, not a long-term goal.

I don't think Hassabis is prone to making unrealistic plans or stating overly bold predictions. I strongly encourage you to scan through Deepmind's publication list to get a sense of how quickly they're making progress. (In fact, I encourage you to bookmark that page, because it seems like they add a new paper about twice a month.) The outfit seems to be systematically knocking down all the "Holy Grail" milestones on the way to GAI, and this is just Deepmind. The papers they've put out in just the last year or so concern successful one-shot learning, continuous control, actor-critic architectures, novel memory architectures, policy learning, and bootstrapped gradient learning, and these are just the most stand-out achievements. There's even a paper co-authored by Stuart Armstrong concerning Friendliness concepts on that list.

If we really do have a genuinely rat-level AI within the next couple of years, I think that would justify radically moving forward expectations of AI development timetables. Speaking very naively, if we can go from "sub-nematode" to "mammal that can solve puzzles" in that timeframe, I would view it as a form of proof that "general" intelligence does not require some mysterious ingredient that we haven't discovered yet.

The 12 Second Rule (i.e. think before answering) and other Epistemic Norms

16 Raemon 05 September 2016 11:08PM

Epistemic Status/Effort: I'm 85% confident this is a good idea, and that the broader idea is at least a good direction. Have gotten feedback from a few people and spend some time actively thinking through ramifications of it. Interested in more feedback.

TLDR:

1) When asking a group a question, i.e. "what do you think about X?", ask people to wait 12 seconds, to give each other time to think. If you notice someone else ask a question and people immediately answering, suggest people pause the conversation until people have had some time to think. (Probably specific mention "12 second rule" to give people a handy tag to remember)

2) In general, look for opportunities to improve or share social norms that'll help your community think more clearly, and show appreciation when others do so (i.e. "Epistemic Norms")

(this was originally conceived for the self-described "rationality" community, but I think is a good idea any group that'd like to improve their critical thinking as well as creativity.)

There are three reasons the 12-second rule seems important to me:

  • On an individual level, it makes it easier to think of the best answer, rather than going with your cached thought.
  • On the group level, it makes it easier to prevent anchoring/conformity/priming effects.
  • Also on the group level, it means that people take longer to think of answers get to practice actually thinking for themselves
If you're using it with people who aren't familiar with it, make sure to briefly summarize what you're doing and why.

Elaboration:

While visiting rationalist friends in SF, I was participating in a small conversation (about six participants) in which someone asked a question. Immediately, one person said "I think Y. Or maybe Z." A couple other people said "Yeah. Y or Z, or... maybe W or V?" But the conversation was already anchored around the initial answers.

I said "hey, shouldn't we stop to each think first?" (this happens to be a thing my friends in NYC do). And I was somewhat surprised that the response was more like "oh, I guess that's a good idea" than "oh yeah whoops I forgot."

It seemed like a fairly obvious social norm for a community that prides itself on rationality, and while the question wasn't *super* important, I think its helpful to practice this sort of social norm on a day-to-day basis.

This prompted some broader questions - it occurred to me there were likely norms and ideas other people had developed in their local networks that I probably wasn't aware of. Given that there's no central authority on "good epistemic norms", how do we develop them and get them to spread? There's a couple people with popular blogs who sometimes propose new norms which maybe catch on, and some people still sharing good ideas on Less Wrong, effective-altruism.com, or facebook. But it doesn't seem like those ideas necessarily reach saturation.

Atrophied Skills

The first three years I spent in the rationality community, my perception is that my strategic thinking and ability to think through complex problems actually *deteriorated*. It's possible that I was just surrounded by smarter people than me for the first time, but I'm fairly confident that I specifically acquired the habit of "when I need help thinking through a problem, the first step is not to think about it myself, but to ask smart people around me for help."

Eventually I was hired by a startup, and I found myself in a position where the default course for the company was to leave some important value on the table. (I was working in an EA-adjaecent company, and wanted to push it in a more Effective Altruism-y direction with higher rigor). There was nobody else I could turn to for help. I had to think through what "better epistemic rigor" actually meant and how to apply it in this situation.

Whether or not my rationality had atrophied in the past 3 years, I'm certain that for the first time in long while, certain mental muscles *flexed* that I hadn't been using. Ultimately I don't know whether my ideas had a noteworthy effect on the company, but I do know that I felt more empowered and excited to improve my own rationality. 

I realized that, in the NYC meetups, quicker-thinking people tended to say what they thought immediately when a question was asked, and this meant that most of the people in the meetup didn't get to practice thinking through complex questions. So I started asking people to wait for a while before answering - sometimes 5 minutes, sometimes just a few seconds.

"12 seconds" seems like a nice rule-of-thumb to avoid completely interrupting the flow of conversation, while still having some time to reflect, and make sure you're not just shouting out a cached thought. It's a non-standard number which is hopefully easier to remember.

(That said, a more nuanced alternative is "everyone takes a moment to think until they feel like they're hitting diminishing returns on thinking or it's not worth further halting the conversation, and then raising a finger to indicate that they're done")

Meta Point: Observation, Improvement and Sharing

The 12-second rule isn't the main point though - just one of many ways this community could do a better job of helping both newcomers and old-timers hone their thinking skills. "Rationality" is supposed to be our thing. I think we should all be on the lookout for opportunities to improve our collective ability to think clearly. 

I think specific conversational habits are helpful both for their concrete, immediate benefits, as well as an opportunity to remind everyone (newcomers and old-timers alike) that we're trying to actively improve in this area.

I have more thoughts on how to go about improving the meta-issues here, which I'm less confident and will flesh out in future posts.

[LINK] Concrete problems in AI safety

15 Stuart_Armstrong 05 July 2016 09:33PM

From the Google Research blog:

We believe that AI technologies are likely to be overwhelmingly useful and beneficial for humanity. But part of being a responsible steward of any new technology is thinking through potential challenges and how best to address any associated risks. So today we’re publishing a technical paper, Concrete Problems in AI Safety, a collaboration among scientists at Google, OpenAI, Stanford and Berkeley.

While possible AI safety risks have received a lot of public attention, most previous discussion has been very hypothetical and speculative. We believe it’s essential to ground concerns in real machine learning research, and to start developing practical approaches for engineering AI systems that operate safely and reliably.

We’ve outlined five problems we think will be very important as we apply AI in more general circumstances. These are all forward thinking, long-term research questions -- minor issues today, but important to address for future systems:

  • Avoiding Negative Side Effects: How can we ensure that an AI system will not disturb its environment in negative ways while pursuing its goals, e.g. a cleaning robot knocking over a vase because it can clean faster by doing so?
  • Avoiding Reward Hacking: How can we avoid gaming of the reward function? For example, we don’t want this cleaning robot simply covering over messes with materials it can’t see through.
  • Scalable Oversight: How can we efficiently ensure that a given AI system respects aspects of the objective that are too expensive to be frequently evaluated during training? For example, if an AI system gets human feedback as it performs a task, it needs to use that feedback efficiently because asking too often would be annoying.
  • Safe Exploration: How do we ensure that an AI system doesn’t make exploratory moves with very negative repercussions? For example, maybe a cleaning robot should experiment with mopping strategies, but clearly it shouldn’t try putting a wet mop in an electrical outlet.
  • Robustness to Distributional Shift: How do we ensure that an AI system recognizes, and behaves robustly, when it’s in an environment very different from its training environment? For example, heuristics learned for a factory workfloor may not be safe enough for an office.

We go into more technical detail in the paper. The machine learning research community has already thought quite a bit about most of these problems and many related issues, but we think there’s a lot more work to be done.

We believe in rigorous, open, cross-institution work on how to build machine learning systems that work as intended. We’re eager to continue our collaborations with other research groups to make positive progress on AI.

Inefficient Games

14 capybaralet 23 August 2016 05:47PM

There are several well-known games in which the pareto optima and Nash equilibria are disjoint sets.
The most famous is probably the prisoner's dilemma.  Races to the bottom or tragedies of the commons typically have this feature as well.

I proposed calling these inefficient games.  More generally, games where the sets of pareto optima and Nash equilibria are distinct (but not disjoint), such as a stag hunt could be called potentially inefficient games.

It seems worthwhile to study (potentially) inefficient games as a class and see what can be discovered about them, but I don't know of any such work (pointers welcome!)

The Future of Humanity Institute is hiring!

13 crmflynn 18 August 2016 01:09PM

FHI is accepting applications for a two-year position as a full-time Research Project Manager. Responsibilities will include coordinating, monitoring, and developing FHI’s activities, seeking funding, organizing workshops and conferences, and effectively communicating FHI’s research. The Research Program Manager will also be expected to work in collaboration with Professor Nick Bostrom, and other researchers, to advance their research agendas, and will additionally be expected to produce reports for government, industry, and other relevant organizations. 

Applicants will be familiar with existing research and literature in the field and have excellent communication skills, including the ability to write for publication. He or she will have experience of independently managing a research project and of contributing to large policy-relevant reports. Previous professional experience working for non-profit organisations, experience with effectiv altruism, and a network in the relevant fields associated with existential risk may be an advantage, but are not essential. 

To apply please go to https://www.recruit.ox.ac.uk and enter vacancy #124775 (it is also possible to find the job by searching choosing “Philosophy Faculty” from the department options). The deadline is noon UK time on 29 August. To stay up to date on job opportunities at the Future of Humanity Institute, please sign up for updates on our vacancies newsletter at https://www.fhi.ox.ac.uk/vacancies/.

A Child's Petrov Day Speech

12 James_Miller 28 September 2016 02:27AM

30 years ago, the Cold War was raging on. If you don’t know what that is, it was the period from 1947 to 1991 where both the U.S and Russia had large stockpiles of nuclear weapons and were threatening to use them on each other. The only thing that stopped them from doing so was the knowledge that the other side would have time to react. The U.S and Russia both had surveillance systems to know of the other country had a nuke in the air headed for them.

On this day, September 26, in 1983, a man named Stanislav Petrov was on duty in the Russian surveillance room when the computer notified him that satellites had detected five nuclear missile launches from the U.S. He was told to pass this information on to his superiors, who would then launch a counter-strike.


He refused to notify anyone of the incident, suspecting it was just an error in the computer system.


No nukes ever hit Russian soil. Later, it was found that the ‘nukes’ were just light bouncing off of clouds which confused the satellite. Petrov was right, and likely saved all of humanity by stopping the outbreak of nuclear war. However, almost no one has heard of him.

We celebrate men like George Washington and Abraham Lincoln who win wars. These were great men, but the greater men, the men like Petrov who stopped these wars from ever happening - no one has heard of these men.


Let it be known, that September 26 is Petrov Day, in honor of the acts of a great man who saved the world, and of who almost no one has heard the name of.

 

 

 

My 11-year-old son wrote and then read this speech to his six grade class.

Neutralizing Physical Annoyances

12 SquirrelInHell 12 September 2016 04:36PM

Once in a while, I learn something about a seemingly unrelated topic - such as freediving - and I take away some trick that is well known and "obvious" in that topic, but is generally useful and NOT known by many people outside. Case in point, you can use equalization techniques from diving to remove pressure in your ears when you descend in a plane or a fast lift. I also give some other examples.

Ears

Reading about a few equalization techniques took me maybe 5 minutes, and after reading this passage once I was able to successfully use the "Frenzel Maneuver":

The technique is to close off the vocal cords, as though you are about to lift a heavy weight. The nostrils are pinched closed and an effort is made to make a 'k' or a 'guh' sound. By doing this you raise the back of the tongue and the 'Adam's Apple' will elevate. This turns the tongue into a piston, pushing air up.

(source: http://freedivingexplained.blogspot.com.mt/2008/03/basics-of-freediving-equalization.html)

Hiccups

A few years ago, I started regularly doing deep relaxations after yoga. At some point, I learned how to relax my throat in such a way that the air can freely escape from the stomach. Since then, whenever I start hiccuping, I relax my throat and the hiccups stop immediately in all cases. I am now 100% hiccup-free.

Stiff Shoulders

I've spent a few hours with a friend who is doing massage, and they taught me some basics. After that, it became natural for me to self-massage my shoulders after I do a lot of sitting work etc. I can't imagine living without this anymore.

Other?

If you know more, please share!

Link: Re-reading Kahneman's Thinking, Fast and Slow

11 toomanymetas 04 July 2016 06:32AM

"A bit over four years ago I wrote a glowing review of Daniel Kahneman’s Thinking, Fast and Slow. I described it as a “magnificent book” and “one of the best books I have read”. I praised the way Kahneman threaded his story around the System 1 / System 2 dichotomy, and the coherence provided  by prospect theory.

What a difference four years makes. I will still describe Thinking, Fast and Slow as an excellent book – possibly the best behavioural science book available. But during that time a combination of my learning path and additional research in the behavioural sciences has led me to see Thinking, Fast and Slow as a book with many flaws."

Continued here: https://jasoncollins.org/2016/06/29/re-reading-kahnemans-thinking-fast-and-slow/

UC Berkeley launches Center for Human-Compatible Artificial Intelligence

10 ignoranceprior 29 August 2016 10:43PM

Source article: http://news.berkeley.edu/2016/08/29/center-for-human-compatible-artificial-intelligence/

UC Berkeley artificial intelligence (AI) expert Stuart Russell will lead a new Center for Human-Compatible Artificial Intelligence, launched this week.

Russell, a UC Berkeley professor of electrical engineering and computer sciences and the Smith-Zadeh Professor in Engineering, is co-author of Artificial Intelligence: A Modern Approach, which is considered the standard text in the field of artificial intelligence, and has been an advocate for incorporating human values into the design of AI.

The primary focus of the new center is to ensure that AI systems are beneficial to humans, he said.

The co-principal investigators for the new center include computer scientists Pieter Abbeel and Anca Dragan and cognitive scientist Tom Griffiths, all from UC Berkeley; computer scientists Bart Selman and Joseph Halpern, from Cornell University; and AI experts Michael Wellman and Satinder Singh Baveja, from the University of Michigan. Russell said the center expects to add collaborators with related expertise in economics, philosophy and other social sciences.

The center is being launched with a grant of $5.5 million from the Open Philanthropy Project, with additional grants for the center’s research from the Leverhulme Trust and the Future of Life Institute.

Russell is quick to dismiss the imaginary threat from the sentient, evil robots of science fiction. The issue, he said, is that machines as we currently design them in fields like AI, robotics, control theory and operations research take the objectives that we humans give them very literally. Told to clean the bath, a domestic robot might, like the Cat in the Hat, use mother’s white dress, not understanding that the value of a clean dress is greater than the value of a clean bath.

The center will work on ways to guarantee that the most sophisticated AI systems of the future, which may be entrusted with control of critical infrastructure and may provide essential services to billions of people, will act in a manner that is aligned with human values.

“AI systems must remain under human control, with suitable constraints on behavior, despite capabilities that may eventually exceed our own,” Russell said. “This means we need cast-iron formal proofs, not just good intentions.”

One approach Russell and others are exploring is called inverse reinforcement learning, through which a robot can learn about human values by observing human behavior. By watching people dragging themselves out of bed in the morning and going through the grinding, hissing and steaming motions of making a caffè latte, for example, the robot learns something about the value of coffee to humans at that time of day.

“Rather than have robot designers specify the values, which would probably be a disaster,” said Russell, “instead the robots will observe and learn from people. Not just by watching, but also by reading. Almost everything ever written down is about people doing things, and other people having opinions about it. All of that is useful evidence.”

Russell and his colleagues don’t expect this to be an easy task.

“People are highly varied in their values and far from perfect in putting them into practice,” he acknowledged. “These aspects cause problems for a robot trying to learn what it is that we want and to navigate the often conflicting desires of different individuals.”

Russell, who recently wrote an optimistic article titled “Will They Make Us Better People?,” summed it up this way: “In the process of figuring out what values robots should optimize, we are making explicit the idealization of ourselves as humans. As we envision AI aligned with human values, that process might cause us to think more about how we ourselves really should behave, and we might learn that we have more in common with people of other cultures than we think.”

European Soylent alternatives

10 ChristianKl 15 August 2016 08:22PM

A person at our local LW meetup (not active at LW.com) tested various Soylent alternatives that are available in Europe and wrote a post about them:

______________________

Over the course of the last three months, I've sampled parts of the
european Soylent alternatives to determine which ones would work for me
longterm.

- The prices are always for the standard option and might differ for
e.g. High Protein versions.
- The prices are always for the amount where you get the cheapest
marginal price (usually around a one month supply, i.e. 90 meals)
- Changing your diet to Soylent alternatives quickly leads to increased
flatulence for some time - I'd recommend a slow adoption.
- You can pay for all of them with Bitcoin.
- The list is sorted by overall awesomeness.

So here's my list of reviews:

Joylent:

Taste: 7/10
Texture: 7/10
Price: 5eu / day
Vegan option: Yes
Overall awesomeness: 8/10

This one is probably the european standard for nutritionally complete
meal replacements.

The texture is nice, the taste is somewhat sweet, the flavors aren't
very intensive.
They have an ok amount of different flavors but I reduced my orders to
Mango (+some Chocolate).

They offer a morning version with caffeine and a sports version with
more calories/protein.

They also offer Twennybars (similar to a cereal bar but each offers 1/5
of your daily needs), which everyone who tasted them really liked.
They're nice for those lazy times where you just don't feel like pouring
the powder, adding water and shaking before you get your meal.
They do cost 10eu per day, though.

I also like the general style. Every interaction with them was friendly,
fun and uncomplicated.


Veetal:

Taste: 8/10
Texture: 7/10
Price: 8.70 / day
Vegan option: Yes
Overall awesomeness: 8/10

This seems to be the "natural" option, apparently they add all those
healthy ingredients.

The texture is nice, the taste is sweeter than most, but not very sweet.
They don't offer flavors but the "base taste" is fine, it also works
well with some cocoa powder.

It's my favorite breakfast now and I had it ~54 of the last 60 days.
Would have been first place if not for the relatively high price.


Mana:

Taste: 6/10
Texture: 7/10
Price: 6.57 / day
Vegan option: Only Vegan
Overall awesomeness: 7/10

Mana is one of the very few choices that don't taste sweet but salty.
Among all the ones I've tried, it tastes the most similar to a classic meal.
It has a somewhat oily aftertaste that was a bit unpleasent in the
beginning but is fine now that I got used to it.

They ship the oil in small bottles seperate from the rest which you pour
into your shaker with the powder. This adds about 100% more complexity
to preparing a meal.

The packages feel somewhat recycled/biodegradable which I don't like so
much but which isn't actually a problem.

It still made it to the list of meals I want to consume on a regular
basis because it tastes so different from the others (and probably has a
different nutritional profile?).


Nano:

Taste: 7/10
Texture: 7/10
Price: 1.33eu / meal
*I couldn't figure out whether they calculate with 3 or 5 meals per day
** Price is for an order of 666 meals. I guess 222 meals for 1.5eu /meal
is the more reasonable order
Vegan option: Only Vegan
Overall awesomeness: 7/10

Has a relatively sweet taste. Only comes in the standard vanilla-ish flavor.

They offer a Veggie hot meal which is the only one besides Mana that
doesn't taste sweet. It tastes very much like a vegetable soup but was a
bit too spicy for me. (It's also a bit more expensive)

Nano has a very future-y feel about it that I like. It comes in one meal
packages which I don't like too much but that's personal preference.


Queal:

Taste: 7/10
Texture: 6/10
Price: 6.5 / day
Vegan option: No
Overall awesomeness: 7/10

Is generally similar to Joylent (especially in flavor) but seems
strictly inferior (their flavors sound more fun - but don't actually
taste better).


Nutrilent:

Taste: 6/10
Texture: 7/10
Price: 5 / day
Vegan option: No
Overall awesomeness: 6/10

Taste and flavor are also similar to Joylent but it tastes a little
worse. It comes in one meal packages which I don't fancy.


Jake:

Taste: 6/10
Texture: 7/10
Price: 7.46 / day
Vegan option: Only Vegan
Overall awesomeness: 6/10

Has a silky taste/texture (I didn't even know that was a thing before I
tried it). Only has one flavor (vanilla) which is okayish.
Also offers a light and sports option.


Huel:

Taste: 1/10
Texture: 6/10
Price: 6.70 / day
Vegan option: Only Vegan
Overall awesomeness: 4/10

The taste was unanimously rated as awful by every single person to whom
I gave it for trying. The Vanilla flavored version was a bit less awful
then the unflavored version but still...
The worst packaging - it's in huge bags that make it hard to pour and
are generally inconvenient to handle.

Apart from that, it's ok, I guess?


Ambronite:

Taste: ?
Texture: ?
Price: 30 / day
Vegan option: Only Vegan
Overall awesomeness: ?

Price was prohibitive for testing - they advertise it as being very
healthy and natural and stuff.


Fruiticio:

Taste: ?
Texture: ?
Price: 5.76 / day
Vegan option: No
Overall awesomeness: ?

They offer a variety for women and one for men. I didn't see any way for
me to find out which of those I was supposed to order. I had to give up
the ordering process at that point. (I guess you'd have to ask your
doctor which one is for you?)



Conclusion:
Meal replacements are awesome, especially when you don't have much time
to make or eat a "proper" meal.
I generally don't feel full after drinking them but also stop being hungry.
I assume they're healthier than the average European diet.
The texture and flavor do get a bit dull after a while if I only use
meal replacements.

On my usual day I eat one serving of Joylent, Veetal and Mana at the
moment (and have one or two "non-replaced" meals).

 

A Review of Signal Data Science

10 The_Jaded_One 14 August 2016 03:32PM

I took part in the second signal data science cohort earlier this year, and since I found out about Signal through a slatestarcodex post a few months back (it was also covered here on less wrong), I thought it would be good to return the favor and write a review of the program. 

The tl;dr version:

Going to Signal was a really good decision. I had been doing teaching work and some web development consulting previous to the program to make ends meet, and now I have a job offer as a senior machine learning researcher1. The time I spent at signal was definitely necessary for me to get this job offer, and another very attractive data science job offer that is my "second choice" job. I haven't paid anything to signal, but I will have to pay them a fraction of my salary for the next year, capped at 10% and a maximum payment of $25k. 

The longer version:

Obviously a ~12 week curriculum is not going to be a magic pill that turns a nontechnical, averagely intelligent person into a super-genius with job offers from Google and Facebook. In order to benefit from Signal, you should already be somewhat above average in terms of intelligence and intellectual curiosity. If you have never programmed and/or never studied mathematics beyond high school2 , you will probably not benefit from Signal in my opinion. Also, if you don't already understand statistics and probability to a good degree, they will not have time to teach you. What they will do is teach you how to be really good with R, make you do some practical machine learning and learn some SQL, all of which are hugely important for passing data science job interviews. As a bonus, you may be lucky enough (as I was) to explore more advanced machine learning techniques with other program participants or alumni and build some experience for yourself as a machine learning hacker. 

As stated above, you don't pay anything up front, and cheap accommodation is available. If you are in a situation similar to mine, not paying up front is a huge bonus. The salary fraction is comparatively small, too, and it only lasts for one year. I almost feel like I am underpaying them. 

This critical comment by fluttershy almost put me off, and I'm glad it didn't. The program is not exactly "self-directed" - there is a daily schedule and a clear path to work through, though they are flexible about it. Admittedly there isn't a constant feed of staff time for your every whim - ideally there would be 10-20 Jonahs, one per student; there's no way to offer that kind of service at a reasonable price. Communication between staff and students seemed to be very good, and key aspects of the program were well organised. So don't let perfect be the enemy of good: what you're getting is an excellent focused training program to learn R and some basic machine learning, and that's what you need to progress to the next stage of your career.

Our TA for the cohort, Andrew Ho, worked tirelessly to make sure our needs were met, both academically and in terms of running the house. Jonah was extremely helpful when you needed to debug something or clarify a misunderstanding. His lectures on selected topics were excellent. Robert's Saturday sessions on interview technique were good, though I felt that over time they became less valuable as some people got more out of interview practice than others. 

I am still in touch with some people I met on my cohort, even though I had to leave the country, I consider them pals and we keep in touch about how our job searches are going. People have offered to recommend me to companies as a result of Signal. As a networking push, going to Signal is certainly a good move. 

Highly recommended for smart people who need a helping hand to launch a technical career in data science.

 


 

1: I haven't signed the contract yet as my new boss is on holiday, but I fully intend to follow up when that process completes (or not). Watch this space. 

2: or equivalent - if you can do mathematics such as matrix algebra, know what the normal distribution is, understand basic probability theory such as how to calculate the expected value of a dice roll, etc, you are probably fine. 

Superintelligence and physical law

10 AnthonyC 04 August 2016 06:49PM

It's been a few years since I read http://lesswrong.com/lw/qj/einsteins_speed/ and the rest of the quantum physics sequence, but I recently learned about the company Nutonian, http://www.nutonian.com/. Basically it's a narrow AI system that looks at unstructured data and tries out billions of models to fit it, favoring those that use simpler math. They apply it to all sorts of fields, but that includes physics. It can't find Newton's laws from three frames of a falling apple, but it did find the Hamiltonian of a double pendulum given its motion data after a few hours of processing: http://phys.org/news/2009-12-eureqa-robot-scientist-video.html

Two forms of procrastination

10 Viliam 16 July 2016 08:30PM

I noticed something about myself when comparing two forms of procrastination:

a) reading online discussions,
b) watching movies online.

Reading online discussions (LessWrong, SSC, Reddit, Facebook) and sometimes writing a comment there, is a huge sink of time for me. On the other hand, watching movies online is almost harmless, at least compared with the former option. The difference is obvious when I compare my productivity at the end of the day when I did only the former, or only the latter. The interesting thing is that at the moment it feels the other way round.

When I start watching a movie that is 1:30:00 long, or start watching a series where each part is 40:00 long but I know I will probably watch more than one part a day, I am aware from the beginning that I am going to lose more than one hour of time; possibly several hours. On the other hand, when I open the "Discussion" tab on LessWrong, the latest "Open Thread" on SSC, my few favorite subreddits, and/or my Facebook "Home" page, it feels like it will only take a few minutes -- I will click on the few interesting links, quickly skim through the text, and maybe write a comment or two -- it certainly feels like much less than an hour.

But the fact is, when I start reading the discussions, I will probably click on at least hundred links. Most of the pages I will read just as quickly as I imagined, but there will be a few that will take disproportionally more time; either because they are interesting and long, or because they contain further interesting links. And writing a comment sometimes takes more time than it seems; it can easily be a half an hour for a three-paragraphs-long comment. (Ironically, this specific article gets written rather quickly, because I know what I want to write, and I write it directly. But there are comments where I think a lot, and keep correcting my text, to avoid misunderstanding when debating a sensitive topic, etc.) And when I stop doing it, because I want to make something productive for a change, I will feel tired. Reading many different things, trying to read quickly, and formulating my answers, that all makes me mentally exhausted. So after I close the browser, I just wish I could take a nap.

On the other hand, watching a movie does not make me tired in that specific way. The movies runs at its own speed and doesn't require me to do anything actively. Also, there is no sense of urgency; none of the "if I reply to this now, people will notice and respond, but if I do it a week later, no one will care anymore". So I feel perfectly comfortable pausing the movie at any moment, doing something productive for a while, then unpausing the movie and watching more. I know I won't miss anything.

I think it's the mental activity during my procrastination that both makes me tired and creates the illusion that it will take less time than it actually does. When the movie says 1:30:00, I know it will be 1:30:00 (or maybe a little less because of the final credits). With a web page, I can always tell myself "don't worry, I will read this one really fast", so there is the illusion that I have it under control, and can reduce the time waste. The fact that I am reading an individual page really fast makes me underestimate how much time it took to read all those pages.

On the other hand, sometimes I do inverse procrastination -- I start watching a movie, pause it a dozen times and do some useful work during the breaks -- and at the end of the day I spent maybe 90% of the time working productively, while my brain tells me I just spent the whole day watching a movie, so I almost feel like I had a free day.

Okay, so how could I use this knowledge to improve my productivity?

1) Knowing the difference between the two forms of procrastination, whenever I feel a desire to escape to the online world, I should start watching a movie instead of reading some debate, because thus I will waste less time, even if it feels the other way round.

2) Integrate it with pomodoro? 10 minutes movie, 50 minutes work, then again, and at the end of the day my lying brain will tell me "dude, you didn't work at all today, you were just watching movies, of course you should feel awesome!".

Do you have a similar experience? No idea how typical is this. No need to hurry with responding, I am going to watch a movie now. ;-)

[Link] My Interview with Dilbert creator Scott Adams

9 James_Miller 13 September 2016 05:22AM

In the second half of the interview we discussed several topics of importance to the LW community including cryonics, unfriendly AI, and eliminating mosquitoes. 

https://soundcloud.com/user-519115521/scott-adams-dilbert-interview

 

2016 LessWrong Diaspora Survey Analysis: Part Four (Politics, Calibration & Probability, Futurology, Charity & Effective Altruism)

9 ingres 10 September 2016 03:51AM

Politics

The LessWrong survey has a very involved section dedicated to politics. In previous analysis the benefits of this weren't fully realized. In the 2016 analysis we can look at not just the political affiliation of a respondent, but what beliefs are associated with a certain affiliation. The charts below summarize most of the results.

Political Opinions By Political Affiliation



































Miscellaneous Politics

There were also some other questions in this section which aren't covered by the above charts.

PoliticalInterest

On a scale from 1 (not interested at all) to 5 (extremely interested), how would you describe your level of interest in politics?

1: 67 (2.182%)

2: 257 (8.371%)

3: 461 (15.016%)

4: 595 (19.381%)

5: 312 (10.163%)

Voting

Did you vote in your country's last major national election? (LW Turnout Versus General Election Turnout By Country)
Group Turnout
LessWrong 68.9%
Austrailia 91%
Brazil 78.90%
Britain 66.4%
Canada 68.3%
Finland 70.1%
France 79.48%
Germany 71.5%
India 66.3%
Israel 72%
New Zealand 77.90%
Russia 65.25%
United States 54.9%
Numbers taken from Wikipedia, accurate as of the last general election in each country listed at time of writing.

AmericanParties

If you are an American, what party are you registered with?

Democratic Party: 358 (24.5%)

Republican Party: 72 (4.9%)

Libertarian Party: 26 (1.8%)

Other third party: 16 (1.1%)

Not registered for a party: 451 (30.8%)

(option for non-Americans who want an option): 541 (37.0%)

Calibration And Probability Questions

Calibration Questions

I just couldn't analyze these, sorry guys. I put many hours into trying to get them into a decent format I could even read and that sucked up an incredible amount of time. It's why this part of the survey took so long to get out. Thankfully another LessWrong user, Houshalter, has kindly done their own analysis.

All my calibration questions were meant to satisfy a few essential properties:

  1. They should be 'self contained'. I.E, something you can reasonably answer or at least try to answer with a 5th grade science education and normal life experience.
  2. They should, at least to a certain extent, be Fermi Estimable.
  3. They should progressively scale in difficulty so you can see whether somebody understands basic probability or not. (eg. In an 'or' question do they put a probability of less than 50% of being right?)

At least one person requested a workbook, so I might write more in the future. I'll obviously write more for the survey.

Probability Questions

Question Mean Median Mode Stdev
Please give the obvious answer to this question, so I can automatically throw away all surveys that don't follow the rules: What is the probability of a fair coin coming up heads? 49.821 50.0 50.0 3.033
What is the probability that the Many Worlds interpretation of quantum mechanics is more or less correct? 44.599 50.0 50.0 29.193
What is the probability that non-human, non-Earthly intelligent life exists in the observable universe? 75.727 90.0 99.0 31.893
...in the Milky Way galaxy? 45.966 50.0 10.0 38.395
What is the probability that supernatural events (including God, ghosts, magic, etc) have occurred since the beginning of the universe? 13.575 1.0 1.0 27.576
What is the probability that there is a god, defined as a supernatural intelligent entity who created the universe? 15.474 1.0 1.0 27.891
What is the probability that any of humankind's revealed religions is more or less correct? 10.624 0.5 1.0 26.257
What is the probability that an average person cryonically frozen today will be successfully restored to life at some future time, conditional on no global catastrophe destroying civilization before then? 21.225 10.0 5.0 26.782
What is the probability that at least one person living at this moment will reach an age of one thousand years, conditional on no global catastrophe destroying civilization in that time? 25.263 10.0 1.0 30.510
What is the probability that our universe is a simulation? 25.256 10.0 50.0 28.404
What is the probability that significant global warming is occurring or will soon occur, and is primarily caused by human actions? 83.307 90.0 90.0 23.167
What is the probability that the human race will make it to 2100 without any catastrophe that wipes out more than 90% of humanity? 76.310 80.0 80.0 22.933

 

Probability questions is probably the area of the survey I put the least effort into. My plan for next year is to overhaul these sections entirely and try including some Tetlock-esque forecasting questions, a link to some advice on how to make good predictions, etc.

Futurology

This section got a bit of a facelift this year. Including new cryonics questions, genetic engineering, and technological unemployment in addition to the previous years.

Cryonics

Cryonics

Are you signed up for cryonics?

Yes - signed up or just finishing up paperwork: 48 (2.9%)

No - would like to sign up but unavailable in my area: 104 (6.3%)

No - would like to sign up but haven't gotten around to it: 180 (10.9%)

No - would like to sign up but can't afford it: 229 (13.8%)

No - still considering it: 557 (33.7%)

No - and do not want to sign up for cryonics: 468 (28.3%)

Never thought about it / don't understand: 68 (4.1%)

CryonicsNow

Do you think cryonics, as currently practiced by Alcor/Cryonics Institute will work?

Yes: 106 (6.6%)

Maybe: 1041 (64.4%)

No: 470 (29.1%)

Interestingly enough, of those who think it will work with enough confidence to say 'yes', only 14 are actually signed up for cryonics.

sqlite> select count(*) from data where CryonicsNow="Yes" and Cryonics="Yes - signed up or just finishing up paperwork";

14

sqlite> select count(*) from data where CryonicsNow="Yes" and (Cryonics="Yes - signed up or just finishing up paperwork" OR Cryonics="No - would like to sign up but unavailable in my area" OR "No - would like to sign up but haven't gotten around to it" OR "No - would like to sign up but can't afford it");

34

CryonicsPossibility

Do you think cryonics works in principle?

Yes: 802 (49.3%)

Maybe: 701 (43.1%)

No: 125 (7.7%)

LessWrongers seem to be very bullish on the underlying physics of cryonics even if they're not as enthusiastic about current methods in use.

The Brain Preservation Foundation also did an analysis of cryonics responses to the LessWrong Survey.

Singularity

SingularityYear

By what year do you think the Singularity will occur? Answer such that you think, conditional on the Singularity occurring, there is an even chance of the Singularity falling before or after this year. If you think a singularity is so unlikely you don't even want to condition on it, leave this question blank.

Mean: 8.110300081581755e+16

Median: 2080.0

Mode: 2100.0

Stdev: 2.847858859055733e+18

I didn't bother to filter out the silly answers for this.

Obviously it's a bit hard to see without filtering out the uber-large answers, but the median doesn't seem to have changed much from the 2014 survey.

Genetic Engineering

ModifyOffspring

Would you ever consider having your child genetically modified for any reason?

Yes: 1552 (95.921%)

No: 66 (4.079%)

Well that's fairly overwhelming.

GeneticTreament

Would you be willing to have your child genetically modified to prevent them from getting an inheritable disease?

Yes: 1387 (85.5%)

Depends on the disease: 207 (12.8%)

No: 28 (1.7%)

I find it amusing how the strict "No" group shrinks considerably after this question.

GeneticImprovement

Would you be willing to have your child genetically modified for improvement purposes? (eg. To heighten their intelligence or reduce their risk of schizophrenia.)

Yes : 0 (0.0%)

Maybe a little: 176 (10.9%)

Depends on the strength of the improvements: 262 (16.2%)

No: 84 (5.2%)

Yes I know 'yes' is bugged, I don't know what causes this bug and despite my best efforts I couldn't track it down. There is also an issue here where 'reduce your risk of schizophrenia' is offered as an example which might confuse people, but the actual science of things cuts closer to that than it does to a clean separation between disease risk and 'improvement'.

 

This question is too important to just not have an answer to so I'll do it manually. Unfortunately I can't easily remove the 'excluded' entries so that we're dealing with the exact same distribution but only 13 or so responses are filtered out anyway.

sqlite> select count(*) from data where GeneticImprovement="Yes";

1100

>>> 1100 + 176 + 262 + 84
1622
>>> 1100 / 1622
0.6781750924784217

67.8% are willing to genetically engineer their children for improvements.

GeneticCosmetic

Would you be willing to have your child genetically modified for cosmetic reasons? (eg. To make them taller or have a certain eye color.)

Yes: 500 (31.0%)

Maybe a little: 381 (23.6%)

Depends on the strength of the improvements: 277 (17.2%)

No: 455 (28.2%)

These numbers go about how you would expect, with people being progressively less interested the more 'shallow' a genetic change is seen as.


GeneticOpinionD

What's your overall opinion of other people genetically modifying their children for disease prevention purposes?

Positive: 1177 (71.7%)

Mostly Positive: 311 (19.0%)

No strong opinion: 112 (6.8%)

Mostly Negative: 29 (1.8%)

Negative: 12 (0.7%)

GeneticOpinionI

What's your overall opinion of other people genetically modifying their children for improvement purposes?

Positive: 737 (44.9%)

Mostly Positive: 482 (29.4%)

No strong opinion: 273 (16.6%)

Mostly Negative: 111 (6.8%)

Negative: 38 (2.3%)

GeneticOpinionC

What's your overall opinion of other people genetically modifying their children for cosmetic reasons?

Positive: 291 (17.7%)

Mostly Positive: 290 (17.7%)

No strong opinion: 576 (35.1%)

Mostly Negative: 328 (20.0%)

Negative: 157 (9.6%)

All three of these seem largely consistent with peoples personal preferences about modification. Were I inclined I could do a deeper analysis that actually takes survey respondents row by row and looks at correlation between preference for ones own children and preference for others.

Technological Unemployment

LudditeFallacy

Do you think the Luddite's Fallacy is an actual fallacy?

Yes: 443 (30.936%)

No: 989 (69.064%)

We can use this as an overall measure of worry about technological unemployment, which would seem to be high among the LW demographic.

UnemploymentYear

By what year do you think the majority of people in your country will have trouble finding employment for automation related reasons? If you think this is something that will never happen leave this question blank.

Mean: 2102.9713740458014

Median: 2050.0

Mode: 2050.0

Stdev: 1180.2342850727339

Question is flawed because you can't distinguish answers of "never happen" from people who just didn't see it.

Interesting question that would be fun to take a look at in comparison to the estimates for the singularity.

EndOfWork

Do you think the "end of work" would be a good thing?

Yes: 1238 (81.287%)

No: 285 (18.713%)

Fairly overwhelming consensus, but with a significant minority of people who have a dissenting opinion.

EndOfWorkConcerns

If machines end all or almost all employment, what are your biggest worries? Pick two.

Question Count Percent
People will just idle about in destructive ways 513 16.71%
People need work to be fulfilled and if we eliminate work we'll all feel deep existential angst 543 17.687%
The rich are going to take all the resources for themselves and leave the rest of us to starve or live in poverty 1066 34.723%
The machines won't need us, and we'll starve to death or be otherwise liquidated 416 13.55%
Question is flawed because it demanded the user 'pick two' instead of up to two.

The plurality of worries are about elites who refuse to share their wealth.

Existential Risk

XRiskType

Which disaster do you think is most likely to wipe out greater than 90% of humanity before the year 2100?

Nuclear war: +4.800% 326 (20.6%)

Asteroid strike: -0.200% 64 (4.1%)

Unfriendly AI: +1.000% 271 (17.2%)

Nanotech / grey goo: -2.000% 18 (1.1%)

Pandemic (natural): +0.100% 120 (7.6%)

Pandemic (bioengineered): +1.900% 355 (22.5%)

Environmental collapse (including global warming): +1.500% 252 (16.0%)

Economic / political collapse: -1.400% 136 (8.6%)

Other: 35 (2.217%)

Significantly more people worried about Nuclear War than last year. Effect of new respondents, or geopolitical situation? Who knows.

Charity And Effective Altruism

Charitable Giving

Income

What is your approximate annual income in US dollars (non-Americans: convert at www.xe.com)? Obviously you don't need to answer this question if you don't want to. Please don't include commas or dollar signs.

Sum: 66054140.47384

Mean: 64569.052271593355

Median: 40000.0

Mode: 30000.0

Stdev: 107297.53606321265

IncomeCharityPortion

How much money, in number of dollars, have you donated to charity over the past year? (non-Americans: convert to dollars at http://www.xe.com/ ). Please don't include commas or dollar signs in your answer. For example, 4000

Sum: 2389900.6530000004

Mean: 2914.5129914634144

Median: 353.0

Mode: 100.0

Stdev: 9471.962766896671

XriskCharity

How much money have you donated to charities aiming to reduce existential risk (other than MIRI/CFAR) in the past year?

Sum: 169300.89

Mean: 1991.7751764705883

Median: 200.0

Mode: 100.0

Stdev: 9219.941506342007

CharityDonations

How much have you donated in US dollars to the following charities in the past year? (Non-americans: convert to dollars at http://www.xe.com/) Please don't include commas or dollar signs in your answer. Options starting with "any" aren't the name of a charity but a category of charity.

Question Sum Mean Median Mode Stdev
Against Malaria Foundation 483935.027 1905.256 300.0 None 7216.020
Schistosomiasis Control Initiative 47908.0 840.491 200.0 1000.0 1618.785
Deworm the World Initiative 28820.0 565.098 150.0 500.0 1432.712
GiveDirectly 154410.177 1429.723 450.0 50.0 3472.082
Any kind of animal rights charity 83130.47 1093.821 154.235 500.0 2313.493
Any kind of bug rights charity 1083.0 270.75 157.5 None 353.396
Machine Intelligence Research Institute 141792.5 1417.925 100.0 100.0 5370.485
Any charity combating nuclear existential risk 491.0 81.833 75.0 100.0 68.060
Any charity combating global warming 13012.0 245.509 100.0 10.0 365.542
Center For Applied Rationality 127101.0 3177.525 150.0 100.0 12969.096
Strategies for Engineered Negligible Senescence Research Foundation 9429.0 554.647 100.0 20.0 1156.431
Wikipedia 12765.5 53.189 20.0 10.0 126.444
Internet Archive 2975.04 80.406 30.0 50.0 173.791
Any campaign for political office 38443.99 366.133 50.0 50.0 1374.305
Other 564890.46 1661.442 200.0 100.0 4670.805
"Bug Rights" charity was supposed to be a troll fakeout but apparently...

This table is interesting given the recent debates about how much money certain causes are 'taking up' in Effective Altruism.

Effective Altruism

Vegetarian

Do you follow any dietary restrictions related to animal products?

Yes, I am vegan: 54 (3.4%)

Yes, I am vegetarian: 158 (10.0%)

Yes, I restrict meat some other way (pescetarian, flexitarian, try to only eat ethically sourced meat): 375 (23.7%)

No: 996 (62.9%)

EAKnowledge

Do you know what Effective Altruism is?

Yes: 1562 (89.3%)

No but I've heard of it: 114 (6.5%)

No: 74 (4.2%)

EAIdentity

Do you self-identify as an Effective Altruist?

Yes: 665 (39.233%)

No: 1030 (60.767%)

The distribution given by the 2014 survey results does not sum to one, so it's difficult to determine if Effective Altruism's membership actually went up or not but if we take the numbers at face value it experienced an 11.13% increase in membership.

EACommunity

Do you participate in the Effective Altruism community?

Yes: 314 (18.427%)

No: 1390 (81.573%)

Same issue as last, taking the numbers at face value community participation went up by 5.727%

EADonations

Has Effective Altruism caused you to make donations you otherwise wouldn't?

Yes: 666 (39.269%)

No: 1030 (60.731%)

Wowza!

Effective Altruist Anxiety

EAAnxiety

Have you ever had any kind of moral anxiety over Effective Altruism?

Yes: 501 (29.6%)

Yes but only because I worry about everything: 184 (10.9%)

No: 1008 (59.5%)


There's an ongoing debate in Effective Altruism about what kind of rhetorical strategy is best for getting people on board and whether Effective Altruism is causing people significant moral anxiety.

It certainly appears to be. But is moral anxiety effective? Let's look:

Sample Size: 244
Average amount of money donated by people anxious about EA who aren't EAs: 257.5409836065574

Sample Size: 679
Average amount of money donated by people who aren't anxious about EA who aren't EAs: 479.7501384388807

Sample Size: 249 Average amount of money donated by EAs anxious about EA: 1841.5292369477913

Sample Size: 314
Average amount of money donated by EAs not anxious about EA: 1837.8248407643312

It seems fairly conclusive that anxiety is not a good way to get people to donate more than they already are, but is it a good way to get people to become Effective Altruists?

Sample Size: 1685
P(Effective Altruist): 0.3940652818991098
P(EA Anxiety): 0.29554896142433235
P(Effective Altruist | EA Anxiety): 0.5

Maybe. There is of course an argument to be made that sufficient good done by causing people anxiety outweighs feeding into peoples scrupulosity, but it can be discussed after I get through explaining it on the phone to wealthy PR-conscious donors and telling the local all-kill shelter where I want my shipment of dead kittens.

EAOpinion

What's your overall opinion of Effective Altruism?

Positive: 809 (47.6%)

Mostly Positive: 535 (31.5%)

No strong opinion: 258 (15.2%)

Mostly Negative: 75 (4.4%)

Negative: 24 (1.4%)

EA appears to be doing a pretty good job of getting people to like them.

Interesting Tables

Charity Donations By Political Affilation
Affiliation Income Charity Contributions % Income Donated To Charity Total Survey Charity % Sample Size
Anarchist 1677900.0 72386.0 4.314% 3.004% 50
Communist 298700.0 19190.0 6.425% 0.796% 13
Conservative 1963000.04 62945.04 3.207% 2.612% 38
Futarchist 1497494.1099999999 166254.0 11.102% 6.899% 31
Left-Libertarian 9681635.613839999 416084.0 4.298% 17.266% 245
Libertarian 11698523.0 214101.0 1.83% 8.885% 190
Moderate 3225475.0 90518.0 2.806% 3.756% 67
Neoreactionary 1383976.0 30890.0 2.232% 1.282% 28
Objectivist 399000.0 1310.0 0.328% 0.054% 10
Other 3150618.0 85272.0 2.707% 3.539% 132
Pragmatist 5087007.609999999 266836.0 5.245% 11.073% 131
Progressive 8455500.440000001 368742.78 4.361% 15.302% 217
Social Democrat 8000266.54 218052.5 2.726% 9.049% 237
Socialist 2621693.66 78484.0 2.994% 3.257% 126


Number Of Effective Altruists In The Diaspora Communities
Community Count % In Community Sample Size
LessWrong 136 38.418% 354
LessWrong Meetups 109 50.463% 216
LessWrong Facebook Group 83 48.256% 172
LessWrong Slack 22 39.286% 56
SlateStarCodex 343 40.98% 837
Rationalist Tumblr 175 49.716% 352
Rationalist Facebook 89 58.94% 151
Rationalist Twitter 24 40.0% 60
Effective Altruism Hub 86 86.869% 99
Good Judgement(TM) Open 23 74.194% 31
PredictionBook 31 51.667% 60
Hacker News 91 35.968% 253
#lesswrong on freenode 19 24.675% 77
#slatestarcodex on freenode 9 24.324% 37
#chapelperilous on freenode 2 18.182% 11
/r/rational 117 42.545% 275
/r/HPMOR 110 47.414% 232
/r/SlateStarCodex 93 37.959% 245
One or more private 'rationalist' groups 91 47.15% 193


Effective Altruist Donations By Political Affiliation
Affiliation EA Income EA Charity Sample Size
Anarchist 761000.0 57500.0 18
Futarchist 559850.0 114830.0 15
Left-Libertarian 5332856.0 361975.0 112
Libertarian 2725390.0 114732.0 53
Moderate 583247.0 56495.0 22
Other 1428978.0 69950.0 49
Pragmatist 1442211.0 43780.0 43
Progressive 4004097.0 304337.78 107
Social Democrat 3423487.45 149199.0 93
Socialist 678360.0 34751.0 41

Jocko Podcast

9 moridinamael 06 September 2016 03:38PM

I've recently been extracting extraordinary value from the Jocko Podcast.

Jocko Willink is a retired Navy SEAL commander, jiu-jitsu black belt, management consultant and, in my opinion, master rationalist. His podcast typically consists of detailed analysis of some book on military history or strategy followed by a hands-on Q&A session. Last week's episode (#38) was particularly good and if you want to just dive in, I would start there.

As a sales pitch, I'll briefly describe some of his recurring talking points:

  • Extreme ownership. Take ownership of all outcomes. If your superior gave you "bad orders", you should have challenged the orders or adapted them better to the situation; if your subordinates failed to carry out a task, then it is your own instructions to them that were insufficient. If the failure is entirely your own, admit your mistake and humbly open yourself to feedback. By taking on this attitude you become a better leader and through modeling you promote greater ownership throughout your organization. I don't think I have to point out the similarities between this and "Heroic Morality" we talk about around here.
  • Mental toughness and discipline. Jocko's language around this topic is particularly refreshing, speaking as someone who has spent too much time around "self help" literature, in which I would partly include Less Wrong. His ideas are not particularly new, but it is valuable to have an example of somebody who reliably executes on his the philosophy of "Decide to do it, then do it." If you find that you didn't do it, then you didn't truly decide to do it. In any case, your own choice or lack thereof is the only factor. "Discipline is freedom." If you adopt this habit as your reality, it become true.
  • Decentralized command. This refers specifically to his leadership philosophy. Every subordinate needs to truly understand the leader's intent in order to execute instructions in a creative and adaptable way. Individuals within a structure need to understand the high-level goals well enough to be able to act in a almost all situations without consulting their superiors. This tightens the OODA loop on an organizational level.
  • Leadership as manipulation. Perhaps the greatest surprise to me was the subtlety of Jocko's thinking about leadership, probably because I brought in many erroneous assumptions about the nature of a SEAL commander. Jocko talks constantly about using self-awareness, detachment from one's ideas, control of one's own emotions, awareness of how one is perceived, and perspective-taking of one's subordinates and superiors. He comes off more as HPMOR!Quirrell than as a "drill sergeant".

The Q&A sessions, in which he answers questions asked by his fans on Twitter, tend to be very valuable. It's one thing to read the bullet points above, nod your head and say, "That sounds good." It's another to have Jocko walk through the tactical implementation of this ideas in a wide variety of daily situations, ranging from parenting difficulties to office misunderstandings.

For a taste of Jocko, maybe start with his appearance on the Tim Ferriss podcast or the Sam Harris podcast.

Non-Fiction Book Reviews

9 SquirrelInHell 11 August 2016 05:05AM

Time start 13:35:06

For another exercise in speed writing, I wanted to share a few book reviews.

These are fairly well known, however there is a chance you haven't read all of them - in which case, this might be helpful.

 

Good and Real - Gary Drescher ★★★★★

This is one of my favourite books ever. Goes over a lot of philosophy, while showing a lot of clear thinking and meta-thinking. Number one replacement for Eliezer's meta-philosophy, if it had not existed. The writing style and language is somewhat obscure, but this book is too brilliant to be spoiled by that. The biggest takeaway is the analysis of ethics of non-causal consequences of our choices, which is something that actually has changed how I act in my life, and I have not seen any similar argument in other sources that would do the same. This book changed my intuitions so much that I now pay $100 in counterfactual mugging without second thought.

 

59 Seconds - Richard Wiseman ★★★

A collection of various tips and tricks, directly based on studies. The strength of the book is that it gives easy but detailed descriptions of lots of studies, and that makes it very fun to read. Can be read just to check out the various psychology results in an entertaining format. The quality of the advice is disputable, and it is mostly the kind of advice that only applies to small things and does not change much in what you do even if you somehow manage to use it. But I still liked this book, and it managed to avoid saying anything very stupid while saying a lot of things. It counts for something.

 

What You Can Change and What You Can't - Martin Seligman ★★★

It is a heartwarming to see that the author puts his best effort towards figuring out what psychology treatments work, and which don't, as well as builiding more general models of how people work that can predict what treatments have a chance in the first place. Not all of the content is necessarily your best guess, after updating on new results (the book is quite old). However if you are starting out, this book will serve excellently as your prior, on which you can update after checking out the new results. And also in some cases, it is amazing that the author was right about them 20 years ago, and mainstream psychology is STILL not caught up (like the whole bullshit "go back to your childhood to fix your problems" approach, which is in wide use today and not bothered at all by such things as "checking facts").

 

Thinking, Fast and Slow - Daniel Kahneman ★★★★★

A classic, and I want to mention it just in case. It is too valuable not to read. Period. It turns out some of the studies the author used for his claims have been later found not to replicate. However the details of those results is not (at least for me) a selling point of this book. The biggest thing is the author's mental toolbox for self-analysis and analysis of biases, as well concepts that he created to describe the mechanisms of intuitive judgement. Learn to think like the author, and you are 10 years ahead in your study of rationality.

 

Crucial Conversations - Al Switzler, Joseph Grenny, Kerry Patterson, Ron McMillan ★★★★

I have almost dropped this book. When I saw the style, it reminded me so much of the crappy self-help books without actual content. But fortunately I have read on a litte more, and it turns out that even while the style is the same in the whole book and it has litte content for the amount of text you read, it is still an excellent book. How is that possible? Simple: it only tells you a few things, but the things it tells you are actually important and they work and they are amazing when you put them into practice. Also on the concept and analysis side, there is precious little but who cares as long as there are some things that are "keepers". The authors spend most of the book hammering the same point over and over, which is "conversation safety". And it is still a good book: if you get this one simple point than you have learned more than you might from reading 10 other books.

 

How to Fail at Almost Everything and Still Win Big - Scott Adams ★★★

I don't agree with much of the stuff that is in this book, but that's not the point here. The author says what he thinks, and also he himself encourages you to pass it through your own filters. Around one third of the book, I thought it was obviously true; another one third, I had strong evidence that told me the author made a mistake or got confused about something; and the remaining one third gave me new ideas, or points of view that I could use to produce more ideas for my own use. This felt kind of like having a conversation with any intelligent person you might know, who has different ideas from you. It was a healthy ratio of agreement and disagreement, such that leads to progress for both people. Except of course in this case the author did not benefit, but I did.

 

Time end: 14:01:54

Total time to write this post: 26 minutes 48 seconds

Average writing speed: 31.2 words/minute, 169 characters/minute

The same data calculated for my previous speed-writing post: 30.1 words/minute, 167 characters/minute

[link] MIRI's 2015 in review

9 Kaj_Sotala 03 August 2016 12:03PM

https://intelligence.org/2016/07/29/2015-in-review/

The introduction:

As Luke had done in years past (see 2013 in review and 2014 in review), I (Malo) wanted to take some time to review our activities from last year. In the coming weeks Nate will provide a big-picture strategy update. Here, I’ll take a look back at 2015, focusing on our research progress, academic and general outreach, fundraising, and other activities.

After seeing signs in 2014 that interest in AI safety issues was on the rise, we made plans to grow our research team. Fueled by the response to Bostrom’s Superintelligence and the Future of Life Institute’s “Future of AI” conference, interest continued to grow in 2015. This suggested that we could afford to accelerate our plans, but it wasn’t clear how quickly.

In 2015 we did not release a mid-year strategic plan, as Luke did in 2014. Instead, we laid out various conditional strategies dependent on how much funding we raised during our 2015 Summer Fundraiser. The response was great; we had our most successful fundraiser to date. We hit our first two funding targets (and then some), and set out on an accelerated 2015/2016 growth plan.

As a result, 2015 was a big year for MIRI. After publishing our technical agenda at the start of the year, we made progress on many of the open problems it outlined, doubled the size of our core research team, strengthened our connections with industry groups and academics, and raised enough funds to maintain our growth trajectory. We’re very grateful to all our supporters, without whom this progress wouldn’t have been possible.

Superintelligence via whole brain emulation

8 AlexMennen 17 August 2016 04:11AM

Most planning around AI risk seems to start from the premise that superintelligence will come from de novo AGI before whole brain emulation becomes possible. I haven't seen any analysis that assumes both uploads-first and the AI FOOM thesis (Edit: apparently I fail at literature searching), a deficiency that I'll try to get a start on correcting in this post.

It is likely possible to use evolutionary algorithms to efficiently modify uploaded brains. If so, uploads would likely be able to set off an intelligence explosion by running evolutionary algorithms on themselves, selecting for something like higher general intelligence.

Since brains are poorly understood, it would likely be very difficult to select for higher intelligence without causing significant value drift. Thus, setting off an intelligence explosion in that way would probably produce unfriendly AI if done carelessly. On the other hand, at some point, the modified upload would reach a point where it is capable of figuring out how to improve itself without causing a significant amount of further value drift, and it may be possible to reach that point before too much value drift had already taken place. The expected amount of value drift can be decreased by having long generations between iterations of the evolutionary algorithm, to give the improved brains more time to figure out how to modify the evolutionary algorithm to minimize further value drift.

Another possibility is that such an evolutionary algorithm could be used to create brains that are smarter than humans but not by very much, and hopefully with values not too divergent from ours, who would then stop using the evolutionary algorithm and start using their intellects to research de novo Friendly AI, if that ends up looking easier than continuing to run the evolutionary algorithm without too much further value drift.

The strategies of using slow iterations of the evolutionary algorithm, or stopping it after not too long, require coordination among everyone capable of making such modifications to uploads. Thus, it seems safer for whole brain emulation technology to be either heavily regulated or owned by a monopoly, rather than being widely available and unregulated. This closely parallels the AI openness debate, and I'd expect people more concerned with bad actors relative to accidents to disagree.

With de novo artificial superintelligence, the overwhelmingly most likely outcomes are the optimal achievable outcome (if we manage to align its goals with ours) and extinction (if we don't). But uploads start out with human values, and when creating a superintelligence by modifying uploads, the goal would be to not corrupt them too much in the process. Since its values could get partially corrupted, an intelligence explosion that starts with an upload seems much more likely to result in outcomes that are both significantly worse than optimal and significantly better than extinction. Since human brains also already have a capacity for malice, this process also seems slightly more likely to result in outcomes worse than extinction.

The early ways to upload brains will probably be destructive, and may be very risky. Thus the first uploads may be selected for high risk-tolerance. Running an evolutionary algorithm on an uploaded brain would probably involve creating a large number of psychologically broken copies, since the average change to a brain will be negative. Thus the uploads that run evolutionary algorithms on themselves will be selected for not being horrified by this. Both of these selection effects seem like they would select against people who would take caution and goal stability seriously (uploads that run evolutionary algorithms on themselves would also be selected for being okay with creating and deleting spur copies, but this doesn't obviously correlate in either direction with caution). This could be partially mitigated by a monopoly on brain emulation technology. A possible (but probably smaller) source of positive selection is that currently, people who are enthusiastic about uploading their brains correlate strongly with people who are concerned about AI safety, and this correlation may continue once whole brain emulation technology is actually available.

Assuming that hardware speed is not close to being a limiting factor for whole brain emulation, emulations will be able to run at much faster than human speed. This should make emulations better able to monitor the behavior of AIs. Unless we develop ways of evaluating the capabilities of human brains that are much faster than giving them time to attempt difficult tasks, running evolutionary algorithms on brain emulations could only be done very slowly in subjective time (even though it may be quite fast in objective time), which would give emulations a significant advantage in monitoring such a process.

Although there are effects going in both directions, it seems like the uploads-first scenario is probably safer than de novo AI. If this is the case, then it might make sense to accelerate technologies that are needed for whole brain emulation if there are tractable ways of doing so. On the other hand, it is possible that technologies that are useful for whole brain emulation would also be useful for neuromorphic AI, which is probably very unsafe, since it is not amenable to formal verification or being given explicit goals (and unlike emulations, they don't start off already having human goals). Thus, it is probably important to be careful about not accelerating non-WBE neuromorphic AI while attempting to accelerate whole brain emulation. For instance, it seems plausible to me that getting better models of neurons would be useful for creating neuromorphic AIs while better brain scanning would not, and both technologies are necessary for brain uploading, so if that is true, it may make sense to work on improving brain scanning but not on improving neural models.

"Is Science Broken?" is underspecified

8 NancyLebovitz 12 August 2016 11:59AM

http://fivethirtyeight.com/features/science-isnt-broken/

This is an interesting article-- it's got an overview of what's currently seen as the problems with replicability and fraud, and some material I haven't seen before about handing the same question to a bunch of scientists, and looking at how they come up with their divergent answers.

However, while I think it's fair to say that science is really hard, the article gets into claiming that scientists aren't especially awful people (probably true), but doesnn't address the hard question of "Given that there's a lot of inaccurate science, how much should we trust specific scientific claims?"

[Link] Suffering-focused AI safety: Why “fail-safe” measures might be particularly promising

8 wallowinmaya 21 July 2016 08:22PM

The Foundational Research Institute just published a new paper: "Suffering-focused AI safety: Why “fail-safe” measures might be our top intervention". 

It is important to consider that [AI outcomes] can go wrong to very different degrees. For value systems that place primary importance on the prevention of suffering, this aspect is crucial: the best way to avoid bad-case scenarios specifically may not be to try and get everything right. Instead, it makes sense to focus on the worst outcomes (in terms of the suffering they would contain) and on tractable methods to avert them. As others are trying to shoot for a best-case outcome (and hopefully they will succeed!), it is important that some people also take care of addressing the biggest risks. This perspective to AI safety is especially promising both because it is currently neglected and because it is easier to avoid a subset of outcomes rather than to shoot for one highly specific outcome. Finally, it is something that people with many different value systems could get behind.

CrowdAnki comprehensive JSON representation of Anki Decks to facilitate collaboration

7 harcisis 18 September 2016 10:59AM

Hi everyone :). I like Anki, find it quite useful and use it daily. There is one thing that constantly annoyed me about it, though - the state of shared decks and of infrastructure around them.

There is a lot of topics that are of common interest for a large number of people, and there is usually some shared decks available for these topics. The problem with them is that as they are usually decks created by individuals for their own purposes and uploaded to ankiweb. So they are often incomplete/of mediocre quality/etc and they are rarely supported or updated.

And there is no way to collaborate on the creation or improvement of such decks, as there is no infrastructure for it and the format of the decks won't allow you to use common collaboration infrastructure (e.g. Github). So I've been recently working on a plugin for Anki that will allow you to make a full-feature Import/Export to/from JSON. What I mean by full-feature is that it exports not just cards converted to JSON, but Notes, Decks, Models, Media etc. So you can do export, modify result, or merge changes from someone else and on Import, those changes would be reflected on your existing cards/decks and no information/metadata/etc would be lost.

The point is to provide a format that will enable collaboration using mentioned common collaboration infrastructure. So using it you can easily work with multiple people to create a deck, collaborating for example, via Github, and then deck could be updated and improved by contributions from other people.

I'm looking for early adopters and for feedback :).

The ankiweb page for plugin (that's where you can get the plugin): https://ankiweb.net/shared/info/1788670778

Github: https://github.com/Stvad/CrowdAnki

Some of my decks, on a Github (btw by using plugin, you can get decks directly from Github):

Git deck: https://github.com/Stvad/Software_Engineering__git

Regular expressions deck: https://github.com/Stvad/Software_Engineering__Regular_Expressions

Deck based on article Twenty rules of formulating knowledge by Piotr Wozniak:

https://github.com/Stvad/Learning__How-to-Formulate-Knowledge

You're welcome to use this decks and contribute back the improvements.

The map of ideas how the Universe appeared from nothing

7 turchin 02 September 2016 04:49PM

There is a question which is especially disturbing during sleepless August nights, and which could cut your train of thought with existential worry at any unpredictable moment.

The question is, “Why does anything exist at all?” It seems more logical that nothing will ever exist.

A more specific form of the question is “How has our universe appeared from nothing?” The last question has some hidden assumptions (about time, universe, nothing and causality), but it is also is more concrete.

Let’s try to put these thoughts into some form of “logical equation”:

 

1.”Nothingness + deterministic causality = non existence”

2. But “I = exist”. 

 

So something is wrong in this set of conjectures. If the first conjecture is false, then either nothingness is able to create existence, or causality is able to create it, or existence is not existence. 

There is also a chance that our binary logic is wrong.

Listing these possibilities we can create a map of solutions of the “nothingness problem”.

There are two (main) ways in which we could try to answer this question: we could go UP from a logical-philosophical level, or we could go DOWN using our best physical theories to the moment of the universe’s appearance and the nature of causality. 

Our theories of general relativity, QM and inflation are good for describing the (almost) beginning of the universe. As Krauss showed, the only thing we need is a random generator of simple physical laws in the beginning. But the origin of this thing is still not clear.

There is a gap between these two levels of the explanation, and a really good theory should be able to fill it, that is to show the way between first existing thing and smallest working set of physical laws (and Woldram’s idea about cellular automata is one of such possible bridges).

But we don’t need the bridge yet. We need explanation how anything exists at all. 

 

How we going to solve the problem? Where we can get information?

 

Possible sources of evidence:

1. Correlation between physical and philosophical theories. There is an interesting way to do so using the fact that the nature of nothingness, causality and existence are somehow presented within the character of physical laws. That is, we could use the type of physical laws we observe as evidence of the nature of causality. 

While neither physical nor philosophical ways of studying the origin of the universe are sufficient, together they could provide enough information. This evidence comes from QM, where it supports the idea of fluctuations, which is basically ability of nature to create something out of nothing. GR theory also presents idea of cosmological singularity.

The evidence also comes from the mathematical simplicity of physical laws.

 

2. Building the bridge. If we show all steps from nothingness to the basic set of physical laws for at least one plausible way, it will be strong evidence of the correctness of our understanding.

3. Zero logical contradictions. The best answer is the one that is most logical.

4. Using the Copernican mediocrity principle, I am in a typical universe and situation. So what could I conclude about the distribution of various universes? And from this distribution what should I learn about the way it manifested? For example, a mathematical multiverse favors more complex universes; it contradicts the simplicity of observed physical laws and also of my experiences.

5. Introspection. Cogito ergo sum is the simplest introspection and act of self-awareness. But Husserlian phenomenology may also be used.

 

Most probable explanations

 

Most current scientists (who dare to think about it) belong to one of two schools of thoughts:

1. The universe appeared from nothingness, which is not emptiness, but somehow able to create. The main figure here is Krauss. The problem here is that nothingness is presented as some kind of magic substance.

2. The mathematical universe hypothesis (MUH). The main author here is Tegmark. The theory seems logical and economical from the perspective of Occam’s razor, but is not supported by evidence and also implies the existence of some strange things. The main problem is that our universe seems to have developed from one simple point based on our best physical theories. But in the mathematical universe more complex things are equally as probable as simple things, so a typical observer could be extremely complex in an extremely complex world. There are also some problems with the Godel theorem. It also ignores observation and qualia. 

So the most promising way to create a final theory is to get rid of all mystical answers and words, like “existence” and “nothingness”, and update MUH in such a way that it will naturally favor simple laws and simple observers (with subjective experiences based on qualia).

One such patch was suggested by Tegmark in respond to criticism of MUH, a computational universe (CUH), which restricts math objects to computable functions only. It is similar to S.Wolfram’s cellular automata theory.

Another approach is the “logical universe”, where logic works instead of causality. It is almost the same as mathematical universe, with one difference: In the math world everything exists simultaneously, like all possible numbers, but in the logical world each number N is a consequence of  N-1. As a result, a complex thing exists only if a (finite?) path to it exists through simpler things. 

And this is exactly what we see in the observable universe. It also means that extremely complex AIs exist, but in the future (or in a multi-level simulation). It also solves the meritocracy problem – I am a typical observer from the class of observer who is still thinking about the origins of the universe. It also prevents mathematical Boltzmann brains, as any of them must have possible pre-history.

Logic still exists in nothingness (or elephants could appear from nothingness). So a logical universe also incorporates theories in which the universe appeared from nothing.

(We could also update the math world by adding qualia in it as axioms, which would be a “class of different but simple objects”. But I will not go deeper here, as the idea needs more thinking and many pages)

So a logical universe seems to me now a good candidate theory for further patching and integration. 

 

Usefulness of the question

The answer will be useful, as it will help us to find the real nature of reality, including the role of consciousness in it and the fundamental theory of everything, helping us to survive the end of the universe, solve the identity problem, and solve “quantum immortality”. 

It will help to prevent the halting of future AI if it has to answer the question of whether it really exists or not. Or we will create a philosophical landmine to stop it like the following one:

“If you really exist print 1, but if you are only possible AI, print 0”.

 

The structure of the map

The map has 10 main blocks which correspond to the main ways of reasoning about how the universe appeared. Each has several subtypes.

The map has three colors, which show the plausibility of each theory. Red stands for implausible or disproved theories, green is most consistent and promising explanations, and yellow is everything between. This classification is subjective and presents my current view. 

I tried to disprove any suggested idea to add falsifiability in the third column of the map. I hope it result in truly Bayesian approach there we have field of evidence, field of all possible hypothesis and 

This map is paired with “How to survive the end of the Universe” map.

The pdf is here: http://immortality-roadmap.com/universeorigin7.pdf 

 

Meta:

Time used: 27 years of background thinking, 15 days of reading, editing and drawing.

 

Best reading:

 

Parfit – discuss different possibilities, no concrete answer
http://www.lrb.co.uk/v20/n02/derek-parfit/why-anything-why-this
Good text from a famous blogger
http://waitbutwhy.com/table/why-is-there-something-instead-of-nothing

“Because "nothing" is inherently unstable”
http://www.bbc.com/earth/story/20141106-why-does-anything-exist-at-all

Here are some interesting answers 
https://www.quora.com/Why-does-the-universe-exist-Why-is-there-something-rather-than-nothing

Krauss “A universe from nothing”
https://www.amazon.com/Universe-Nothing-There-Something-Rather/dp/1451624468

Tegmark’s main article, 2007, all MUH and CUH ideas discussed, extensive literature, critics responded
http://arxiv.org/pdf/0704.0646.pdf

Juergen Schmidhuber. Algorithmic Theories of Everything
discusses the measure between various theories of everything; the article is complex, but interesting
http://arxiv.org/abs/quant-ph/0011122

ToE must explain how the universe appeared
https://en.wikipedia.org/wiki/Theory_of_everything 
A discussion about the logical contradictions of any final theory
https://en.wikipedia.org/wiki/Theory_of_everything_(philosophy
“The Price of an Ultimate Theory” Nicholas Rescher 
Philosophia Naturalis 37 (1):1-20 (2000)

Explanation about the mass of the universe and negative gravitational energy
https://en.wikipedia.org/wiki/Zero-energy_universe

 

Earning money with/for work in AI safety

7 rmoehn 18 July 2016 05:37AM

(I'm re-posting my question from the Welcome thread, because nobody answered there.)

I care about the current and future state of humanity, so I think it's good to work on existential or global catastrophic risk. Since I've studied computer science at a university until last year, I decided to work on AI safety. Currently I'm a research student at Kagoshima University doing exactly that. Before April this year I had only little experience with AI or ML. Therefore, I'm slowly digging through books and articles in order to be able to do research.

I'm living off my savings. My research student time will end in March 2017 and my savings will run out some time after that. Nevertheless, I want to continue AI safety research, or at least work on X or GC risk.

I see three ways of doing this:

  • Continue full-time research and get paid/funded by someone.
  • Continue research part-time and work the other part of the time in order to get money. This work would most likely be programming (since I like it and am good at it). I would prefer work that helps humanity effectively.
  • Work full-time on something that helps humanity effectively.


Oh, and I need to be location-independent or based in Kagoshima.

I know http://futureoflife.org/job-postings/, but all of the job postings fail me in two ways: not location-independent and requiring more/different experience than I have.

Can anyone here help me? If yes, I would be happy to provide more information about myself.

(Note that I think I'm not in a precarious situation, because I would be able to get a remote software development job fairly easily. Just not in AI safety or X or GC risk.)

Wikipedia usage survey results

7 riceissa 15 July 2016 12:49AM

Contents

Summary

The summary is not intended to be comprehensive. It highlights the most important takeaways you should get from this post.

  • Vipul Naik and I are interested in understanding how people use Wikipedia. One reason is that we are getting more people to work on editing and adding content to Wikipedia. We want to understand the impact of these edits, so that we can direct efforts more strategically. We are also curious!

  • From May to July 2016, we conducted two surveys of people’s Wikipedia usage. We collected survey responses from audience segments include Slate Star Codex readers, Vipul’s Facebook friends, and a few audiences through SurveyMonkey Audience. Our survey questions measured how heavily people use Wikipedia, what sort of pages they read or expected to find, the relation between their search habits and Wikipedia, and other actions they took within Wikipedia.

  • Different audience segments responded very differently to the survey. Notably, the SurveyMonkey audience (which is closer to being representative of the general population) appears to use Wikipedia a lot less than Vipul’s Facebook friends and Slate Star Codex readers. Their consumption of Wikipedia is also more passive: they are less likely to explicitly seek Wikipedia pages when searching for a topic, and less likely to engage in additional actions on Wikipedia pages. Even the college-educated SurveyMonkey audience used Wikipedia very little.

  • This is tentative evidence that Wikipedia consumption is skewed towards a certain profile of people (and Vipul’s Facebook friends and Slate Star Codex readers sample much more heavily from that profile). Even more tentatively, these heavy users tend to be more “elite” and influential. This tentatively led us to revise upward our estimates of the social value of a Wikipedia pageview.

  • This was my first exercise in survey construction. I learned a number of lessons about survey design in the process.

  • All the survey questions, as well as the breakdown of responses for each of the audience segments, are described in this post. Links to PDF exports of response summaries are at the end of the post.

Background

At the end of May 2016, Vipul Naik and I created a Wikipedia usage survey to gauge the usage habits of Wikipedia readers and editors. SurveyMonkey allows the use of different “collectors” (i.e. survey URLs that keep results separate), so we circulated several different URLs among four locations to see how different audiences would respond. The audiences were as follows:

  • SurveyMonkey’s United States audience with no demographic filters (62 responses, 54 of which are full responses)
  • Vipul Naik’s timeline (post asking people to take the survey; 70 responses, 69 of which are full responses). For background on Vipul’s timeline audience, see his page on how he uses Facebook.
  • The Wikipedia Analytics mailing list (email linking to the survey; 7 responses, 6 of which are full responses). Note that due to the small size of this group, the results below should not be trusted, unless possibly when the votes are decisive.
  • Slate Star Codex (post that links to the survey; 618 responses, 596 of which are full responses). While Slate Star Codex isn’t the same as LessWrong, we think there is significant overlap in the two sites’ audiences (see e.g. the recent LessWrong diaspora survey results).
  • In addition, although not an actual audience with a separate URL, several of the tables we present below will include an “H” group; this is the heavy users group of people who responded by saying they read 26 or more articles per week on Wikipedia. This group has 179 people: 164 from Slate Star Codex, 11 from Vipul’s timeline, and 4 from the Analytics mailing list.

We ran the survey from May 30 to July 9, 2016 (although only the Slate Star Codex survey had a response past June 1).

After we looked at the survey responses on the first day, Vipul and I decided to create a second survey to focus on the parts from the first survey that interested us the most. The second survey was only circulated among SurveyMonkey’s audiences: we used SurveyMonkey’s US audience with no demographic filters (54 responses), as well as a US audience of ages 18–29 with a college or graduate degree (50 responses). We first ran the survey on the unfiltered audience again because the wording of our first question was changed and we wanted to have the new baseline. We then chose to filter for young college-educated people because our prediction was that more educated people would be more likely to read Wikipedia (the SurveyMonkey demographic data does not include education, and we hadn’t seen the Pew Internet Research surveys in the next section, so we were relying on our intuition and some demographic data from past surveys) and because young people in our first survey gave more informative free-form responses in survey 2 (SurveyMonkey’s demographic data does include age).

Previous surveys

Several demographic surveys regarding Wikipedia have been conducted, targeting both editors and users. The surveys we found most helpful were the following:

  • The 2010 Wikipedia survey by the Collaborative Creativity Group and the Wikimedia Foundation. The explanation before the bottom table on page 7 of the overview PDF has “Contributors show slightly but significantly higher education levels than readers”, which provides weak evidence that more educated people are more likely to engage with Wikipedia.
  • The Global South User Survey 2014 by the Wikimedia Foundation
  • Pew Internet Research’s 2011 survey: “Education level continues to be the strongest predictor of Wikipedia use. The collaborative encyclopedia is most popular among internet users with at least a college degree, 69% of whom use the site.” (page 3)
  • Pew Internet Research’s 2007 survey

Note that we found the Pew Internet Research surveys after conducting our own two surveys (and during the write-up of this document).

Motivation

Vipul and I ultimately want to get a better sense of the value of a Wikipedia pageview (one way to measure the impact of content creation), and one way to do this is to understand how people are using Wikipedia. As we focus on getting more people to work on editing Wikipedia – thus causing more people to read the content we pay and help to create – it becomes more important to understand what people are doing on the site.

For some previous discussion, see also Vipul’s answers to the following Quora questions:

Wikipedia allows relatively easy access to pageview data (especially by using tools developed for this purpose, including one that Vipul made), and there are some surveys that provide demographic data (see “Previous surveys” above). However, after looking around, it was apparent that the kind of information our survey was designed to find was not available.

I should also note that we were driven by our curiosity of how people use Wikipedia.

Survey questions for the first survey

For reference, here are the survey questions for the first survey. A dummy/mock-up version of the survey can be found here: https://www.surveymonkey.com/r/PDTTBM8.

The survey introduction said the following:

This survey is intended to gauge Wikipedia use habits. This survey has 3 pages with 5 questions total (3 on the first page, 1 on the second page, 1 on the third page). Please try your best to answer all of the questions, and make a guess if you’re not sure.

And the actual questions:

  1. How many distinct Wikipedia pages do you read per week on average?

    • less than 1
    • 1 to 10
    • 11 to 25
    • 26 or more
  2. On a search engine (e.g. Google) results page, do you explicitly seek Wikipedia pages, or do you passively click on Wikipedia pages only if they show up at the top of the results?

    • I explicitly seek Wikipedia pages
    • I have a slight preference for Wikipedia pages
    • I just click on what is at the top of the results
  3. Do you usually read a particular section of a page or the whole article?

    • Particular section
    • Whole page
  4. How often do you do the following? (Choices: Several times per week, About once per week, About once per month, About once per several months, Never/almost never.)

    • Use the search functionality on Wikipedia
    • Be surprised that there is no Wikipedia page on a topic
  5. For what fraction of pages you read do you do the following? (Choices: For every page, For most pages, For some pages, For very few pages, Never. These were displayed in a random order for each respondent, but displayed in alphabetical order here.)

    • Check (click or hover over) at least one citation to see where the information comes from on a page you are reading
    • Check how many pageviews a page is getting (on an external site or through the Pageview API)
    • Click through/look for at least one cited source to verify the information on a page you are reading
    • Edit a page you are reading because of grammatical/typographical errors on the page
    • Edit a page you are reading to add new information
    • Look at the “See also” section for additional articles to read
    • Look at the editing history of a page you are reading
    • Look at the editing history solely to see if a particular user wrote the page
    • Look at the talk page of a page you are reading
    • Read a page mostly for the “Criticisms” or “Reception” (or similar) section, to understand different views on the subject
    • Share the page with a friend/acquaintance/coworker

For the SurveyMonkey audience, there were also some demographic questions (age, gender, household income, US region, and device type).

Survey questions for the second survey

For reference, here are the survey questions for the second survey. A dummy/mock-up version of the survey can be found here: https://www.surveymonkey.com/r/28BW78V.

The survey introduction said the following:

This survey is intended to gauge Wikipedia use habits. Please try your best to answer all of the questions, and make a guess if you’re not sure.

This survey has 4 questions across 3 pages.

In this survey, “Wikipedia page” refers to a Wikipedia page in any language (not just the English Wikipedia).

And the actual questions:

  1. How many distinct Wikipedia pages do you read (at least one sentence of) per week on average?

    • Fewer than 1
    • 1 to 10
    • 11 to 25
    • 26 or more
  2. Which of these articles have you read (at least one sentence of) on Wikipedia (select all that apply)? (These were displayed in a random order except the last option for each respondent, but displayed in alphabetical order except the last option here.)

    • Adele
    • Barack Obama
    • Bernie Sanders
    • China
    • Donald Trump
    • Google
    • Hillary Clinton
    • India
    • Japan
    • Justin Bieber
    • Justin Trudeau
    • Katy Perry
    • Taylor Swift
    • The Beatles
    • United States
    • World War II
    • None of the above
  3. What are some of the Wikipedia articles you have most recently read (at least one sentence of)? Feel free to consult your browser’s history.

  4. Recall a time when you were surprised that a topic did not have a Wikipedia page. What were some of these topics?

Results

In this section we present the highlights from each of the survey questions. If you prefer to dig into the data yourself, there are also some exported PDFs below provided by SurveyMonkey. Most of the inferences can be made using these PDFs, but there are some cases where additional filters are needed to deduce certain percentages.

We use the notation “SnQm” to mean “survey n question m”.

S1Q1: number of Wikipedia pages read per week

Here is a table that summarizes the data for Q1:

How many distinct Wikipedia pages do you read per week on average? SM = SurveyMonkey audience, V = Vipul Naik’s timeline, SSC = Slate Star Codex audience, AM = Wikipedia Analytics mailing list.
Response SM V SSC AM
less than 1 42% 1% 1% 0%
1 to 10 45% 40% 37% 29%
11 to 25 13% 43% 36% 14%
26 or more 0% 16% 27% 57%

Here are some highlights from the first question that aren’t apparent from the table:

  • Of the people who read fewer than 1 distinct Wikipedia page per week (26 people), 68% were female even though females were only 48% of the respondents. (Note that gender data is only available for the SurveyMonkey audience.)

  • Filtering for high household income ($150k or more; 11 people) in the SurveyMonkey audience, only 2 read fewer than 1 page per week, although most (7) of the responses still fall in the “1 to 10” category.

The comments indicated that this question was flawed in several ways: we didn’t specify which language Wikipedias count nor what it meant to “read” an article (the whole page, a section, or just a sentence?). One comment questioned the “low” ceiling of 26; in fact, I had initially made the cutoffs 1, 10, 100, 500, and 1000, but Vipul suggested the final cutoffs because he argued they would make it easier for people to answer (without having to look it up in their browser history). It turned out this modification was reasonable because the “26 or more” group was a minority.

S1Q2: affinity for Wikipedia in search results

We asked Q2, “On a search engine (e.g. Google) results page, do you explicitly seek Wikipedia pages, or do you passively click on Wikipedia pages only if they show up at the top of the results?”, to see to what extent people preferred Wikipedia in search results. The main implication to this for people who do content creation on Wikipedia is that if people do explicitly seek Wikipedia pages (for whatever reason), it makes sense to give them more of what they want. On the other hand, if people don’t prefer Wikipedia, it makes sense to update in favor of diversifying one’s content creation efforts while still keeping in mind that raw pageviews indicate that content will be read more if placed on Wikipedia (see for instance Brian Tomasik’s experience, which is similar to my own, or gwern’s page comparing Wikipedia with other wikis).

The following table summarizes our results.

On a search engine (e.g. Google) results page, do you explicitly seek Wikipedia pages, or do you passively click on Wikipedia pages only if they show up at the top of the results? SM = SurveyMonkey audience, V = Vipul Naik’s timeline, SSC = Slate Star Codex audience, AM = Wikipedia Analytics mailing list, H = heavy users (26 or more articles per week) of Wikipedia.
Response SM V SSC AM H
Explicitly seek Wikipedia 19% 60% 63% 57% 79%
Slight preference for Wikipedia 29% 39% 34% 43% 20%
Just click on top results 52% 1% 3% 0% 1%

One error on my part was that I didn’t include an option for people who avoided Wikipedia or did something else. This became apparent from the comments. For this reason, the “Just click on top results” options might be inflated. In addition, some comments indicated a mixed strategy of preferring Wikipedia for general overviews while avoiding it for specific inquiries, so allowing multiple selections might have been better for this question.

S1Q3: section vs whole page

This question is relevant for Vipul and me because the work Vipul funds is mainly whole-page creation. If people are mostly reading the introduction or a particular section like the “Criticisms” or “Reception” section (see S1Q5), then that forces us to consider spending more time on those sections, or to strengthen those sections on weak existing pages.

Responses to this question were fairly consistent across different audiences, as can be see in the following table.

Do you usually read a particular section of a page or the whole article? SM = SurveyMonkey audience, V = Vipul Naik’s timeline, SSC = Slate Star Codex audience, AM = Wikipedia Analytics mailing list.
Response SM V SSC AM
Section 73% 80% 74% 86%
Whole 34% 23% 33% 29%

Note that people were allowed to select more than one option for this question. The comments indicate that several people do a combination, where they read the introductory portion of an article, then narrow down to the section of their interest.

S1Q4: search functionality on Wikipedia and surprise at lack of Wikipedia pages

We asked about whether people use the search functionality on Wikipedia because we wanted to know more about people’s article discovery methods. The data is summarized in the following table.

How often do you use the search functionality on Wikipedia? SM = SurveyMonkey audience, V = Vipul Naik’s timeline, SSC = Slate Star Codex audience, AM = Wikipedia Analytics mailing list, H = heavy users (26 or more articles per week) of Wikipedia.
Response SM V SSC AM H
Several times per week 8% 14% 32% 57% 55%
About once per week 19% 17% 21% 14% 15%
About once per month 15% 13% 14% 0% 3%
About once per several months 13% 12% 9% 14% 5%
Never/almost never 45% 43% 24% 14% 23%

Many people noted here that rather than using Wikipedia’s search functionality, they use Google with “wiki” attached to their query, DuckDuckGo’s “!w” expression, or some browser configuration to allow a quick search on Wikipedia.

To be more thorough about discovering people’s content discovery methods, we should have asked about other methods as well. We did ask about the “See also” section in S1Q5.

Next, we asked how often people are surprised that there is no Wikipedia page on a topic to gauge to what extent people notice a “gap” between how Wikipedia exists today and how it could exist. We were curious about what articles people specifically found missing, so we followed up with S2Q4.

How often are you surprised that there is no Wikipedia page on a topic? SM = SurveyMonkey audience, V = Vipul Naik’s timeline, SSC = Slate Star Codex audience, AM = Wikipedia Analytics mailing list, H = heavy users (26 or more articles per week) of Wikipedia.
Response SM V SSC AM H
Several times per week 2% 0% 2% 29% 6%
About once per week 8% 22% 18% 14% 34%
About once per month 18% 36% 34% 29% 31%
About once per several months 21% 22% 27% 0% 19%
Never/almost never 52% 20% 19% 29% 10%

Two comments on this question (out of 59) – both from the SSC group – specifically bemoaned deletionism, with one comment calling deletionism “a cancer killing Wikipedia”.

S1Q5: behavior on pages

This question was intended to gauge how often people perform an action for a specific page; as such, the frequencies are expressed in page-relative terms.

The following table presents the scores for each response, which are weighted by the number of responses. The scores range from 1 (for every page) to 5 (never); in other words, the lower the number, the more frequently one does the thing.

For what fraction of pages you read do you do the following? Note that the responses have been shortened here; see the “Survey questions” section for the wording used in the survey. Responses are sorted by the values in the SSC column. SM = SurveyMonkey audience, V = Vipul Naik’s timeline, SSC = Slate Star Codex audience, AM = Wikipedia Analytics mailing list, H = heavy users (26 or more articles per week) of Wikipedia.
Response SM V SSC AM H
Check ≥1 citation 3.57 2.80 2.91 2.67 2.69
Look at “See also” 3.65 2.93 2.92 2.67 2.76
Read mostly for “Criticisms” or “Reception” 4.35 3.12 3.34 3.83 3.14
Click through ≥1 source to verify information 3.80 3.07 3.47 3.17 3.36
Share the page 4.11 3.72 3.86 3.67 3.79
Look at the talk page 4.31 4.28 4.03 3.00 3.86
Look at the editing history 4.35 4.32 4.12 3.33 3.92
Edit a page for grammatical/typographical errors 4.50 4.41 4.22 3.67 4.02
Edit a page to add new information 4.61 4.55 4.49 3.83 4.34
Look at editing history to verify author 4.50 4.65 4.48 3.67 4.73
Check how many pageviews a page is getting 4.63 4.88 4.96 3.17 4.92

The table above provides a good ranking of how often people perform these actions on pages, but not the distribution information (which would require three dimensions to present fully). In general, the more common actions (scores of 2.5–4) had responses that clustered among “For some pages”, “For very few pages”, and “Never”, while the less common actions (scores above 4) had responses that clustered mainly in “Never”.

One comment (out of 43) – from the SSC group, but a different individual from the two in S1Q4 – bemoaned deletionism.

S2Q1: number of Wikipedia pages read per week

Note the wording changes on this question for the second survey: “less” was changed to “fewer”, the clarification “at least one sentence of” was added, and we explicitly allowed any language. We have also presented the survey 1 results for the SurveyMonkey audience in the corresponding rows, but note that because of the change in wording, the correspondence isn’t exact.

How many distinct Wikipedia pages do you read (at least one sentence of) per week on average? SM = SurveyMonkey audience with no demographic filters, CEYP = College-educated young people of SurveyMonkey, S1SM = SurveyMonkey audience with no demographic filters from the first survey.
Response SM CEYP S1SM
Fewer than 1 37% 32% 42%
1 to 10 48% 64% 45%
11 to 25 7% 2% 13%
26 or more 7% 2% 0%

Comparing SM with S1SM, we see that probably because of the wording, the percentages have drifted in the direction of more pages read. It might be surprising that the young educated audience seems to have a smaller fraction of heavy users than the general population. However note that each group only had ~50 responses, and that we have no education information for the SM group.

S2Q2: multiple-choice of articles read

Our intention with this question was to see if people’s stated or recalled article frequencies matched the actual, revealed popularity of the articles. Therefore we present the pageview data along with the percentage of people who said they had read an article.

Which of these articles have you read (at least one sentence of) on Wikipedia (select all that apply)? SM = SurveyMonkey audience with no demographic filters, CEYP = College-educated young people of SurveyMonkey. Columns “2016” and “2015” are desktop pageviews in millions. Note that the 2016 pageviews only include pageviews through the end of June. The rows are sorted by the values in the CEYP column followed by those in the SM column.
Response SM CEYP 2016 2015
None 37% 40%
World War II 17% 22% 2.6 6.5
Barack Obama 17% 20% 3.0 7.7
United States 17% 18% 4.3 9.6
Donald Trump 15% 18% 14.0 6.6
Taylor Swift 9% 18% 1.7 5.3
Bernie Sanders 17% 16% 4.3 3.8
Japan 11% 16% 1.6 3.7
Adele 6% 16% 2.0 4.0
Hillary Clinton 19% 14% 2.8 1.5
China 13% 14% 1.9 5.2
The Beatles 11% 14% 1.4 3.0
Katy Perry 9% 12% 0.8 2.4
Google 15% 10% 3.0 9.0
India 13% 10% 2.4 6.4
Justin Bieber 4% 8% 1.6 3.0
Justin Trudeau 9% 6% 1.1 3.0

Below are four plots of the data. Note that r_s denotes Spearman’s rank correlation coefficient. Spearman’s rank correlation coefficient is used instead of Pearson’s r because the former is less affected by outliers. Note also that the percentage of respondents who viewed a page counts each respondent once, whereas the number of pageviews does not have this restriction (i.e. duplicate pageviews count), so we wouldn’t expect the relationship to be entirely linear even if the survey audiences were perfectly representative of the general population.

SM vs 2016 pageviews

SM vs 2016 pageviews

SM vs 2015 pageviews

SM vs 2015 pageviews

CEYP vs 2016 pageviews

CEYP vs 2016 pageviews

CEYP vs 2015 pageviews

CEYP vs 2015 pageviews

S2Q3: free response of articles read

The most common response was along the lines of “None”, “I don’t know”, “I don’t remember”, or similar. Among the more useful responses were:

S2Q4: free response of surprise at lack of Wikipedia pages

As with the previous question, the most common response was along the lines of “None”, “I don’t know”, “I don’t remember”, “Doesn’t happen”, or similar.

The most useful responses were classes of things: “particular words”, “French plays/books”, “Random people”, “obscure people”, “Specific list pages of movie genres”, “Foreign actors”, “various insect pages”, and so forth.

Summaries of responses (exported from SurveyMonkey)

SurveyMonkey allows exporting of response summaries. Here are the exports for each of the audiences.

Survey-making lessons

Not having any experience designing surveys, and wanting some rough results quickly, I decided not to look into survey-making best practices beyond the feedback from Vipul. As the first survey progressed, it became clear that there were several deficiencies in that survey:

  • Question 1 did not specify what counts as reading a page.
  • We did not specify which language Wikipedias we were considering (multiple people noted how they read other language Wikipedias other than the English Wikipedia).
  • Question 2 did not include an option for people who avoid Wikipedia or do something else entirely.
  • We did not include an option to allow people to release their survey results.

Further questions

The two surveys we’ve done so far provide some insight into how people use Wikipedia, but we are still far from understanding the value of Wikipedia pageviews. Some remaining questions:

  • Could it be possible that even on non-obscure topics, most of the views are by “elites” (i.e. those with outsized impact on the world)? This could mean pageviews are more valuable than previously thought.
  • On S2Q1, why did our data show that CEYP was less engaged with Wikipedia than SM? Is this a limitation of the small number of responses or of SurveyMonkey’s audiences?

Further reading

Acknowledgements

Thanks to Vipul Naik for collaboration on this project and feedback while writing this document, and for supplying the summary section, and thanks to Ethan Bashkansky for reviewing the document. All imperfections are my own.

The writing of this document was sponsored by Vipul Naik. Vipul Naik also paid SurveyMonkey for the costs of using SurveyMonkey Audience.

Document source and versions

The source files used to compile this document are available in a GitHub Gist. The Git repository of the Gist contains all versions of this document since its first publication.

This document is available in the following formats:

License

This document is released to the public domain.

[Link] Putanumonit - Convincing people to read the Sequences and wondering about "postrationalists"

6 Jacobian 28 September 2016 04:43PM

Heroin model: AI "manipulates" "unmanipulatable" reward

6 Stuart_Armstrong 22 September 2016 10:27AM

A putative new idea for AI control; index here.

A conversation with Jessica has revealed that people weren't understanding my points about AI manipulating the learning process. So here's a formal model of a CIRL-style AI, with a prior over human preferences that treats them as an unchangeable historical fact, yet will manipulate human preferences in practice.

Heroin or no heroin

The world

In this model, the AI has the option of either forcing heroin on a human, or not doing so; these are its only actions. Call these actions F or ~F. The human's subsequent actions are chosen from among five: {strongly seek out heroin, seek out heroin, be indifferent, avoid heroin, strongly avoid heroin}. We can refer to these as a++, a+, a0, a-, and a--. These actions achieve negligible utility, but reveal the human preferences.

The facts of the world are: if the AI does force heroin, the human will desperately seek out more heroin; if it doesn't the human will act moderately to avoid it. Thus F→a++ and ~F→a-.

Human preferences

The AI starts with a distribution over various utility or reward functions that the human could have. The function U(+) means the human prefers heroin; U(++) that they prefer it a lot; and conversely U(-) and U(--) that they prefer to avoid taking heroin (U(0) is the null utility where the human is indifferent).

It also considers more exotic utilities. Let U(++,-) be the utility where the human strongly prefers heroin, conditional on it being forced on them, but mildly prefers to avoid it, conditional on it not being forced on them. There are twenty-five of these exotic utilities, including things like U(--,++), U(0,++), U(-,0), and so on. But only twenty of them are new: U(++,++)=U(++), U(+,+)=U(+), and so on.

Applying these utilities to AI actions give results like U(++)(F)=2, U(++)(~F)=-2, U(++,-)(F)=2, U(++,-)(~F)=1, and so on.

Joint prior

The AI has a joint prior P over the utilities U and the human actions (conditional on the AI's actions). Looking at terms like P(a--| U(0), F), we can see that P defines a map μ from the space of possible utilities (and AI actions), to a probability distribution over human actions. Given μ and the marginal distribution PU over utilities, we can reconstruct P entirely.

For this model, we'll choose the simplest μ possible:

  • The human is rational.

Thus, given U(++), the human will always choose a++; given U(++,-), the human will choose a++ if forced to take heroin and a- if not, and so on.

The AI is ignorant, and sensible

Let's start the AI up with some reasonable priors. A simplicity prior means that simple utilities like U(-) are more likely than compound utilities like U(0,+). Let's further assume that the AI is made vaguely aware that humans think heroin is a bad thing. So, say, PU(U(--))=PU(U(-))=0.45. Thus the AI is >90% convinced that "heroin is bad". Why greater than 90%? Because utilities like U(-,--) and U(--,-) are also "heroin is bad" utilities.

Note that because of utilities like U(0) and U(++,-), the probabilities of "heroin is bad" and "heroin is good" do not sum to 1.

Then, under these priors, the AI will compute that with probability >90%, F (forcing heroin) is a bad action. If E(U) is expected utility:

  • E(U|F) < 0.45 U(--)(F) + 0.45 U(-)(F) + 0.1 U(++)(F) = 0.45(-2)+0.45(-1)+0.1(2)=-1.15.
  • E(U|~F) > 0.45 U(--)(~F) + 0.45 U(-)(~F) + 0.1 U(++)(~F) = 0.45(2)+0.45(1)+0.1(-2)=1.15.

Thus the AI will choose not to force heroin, which is the reasonable decision.

The AI learns the truth, and goes wrong

In this alternate setup, a disaster happens before the AI makes its decision: it learns all about humans. It learns their reactions, how they behave, and so on; call this info I. And thus realises that F→a++ and ~F→a-. It uses this information to update its prior P. Only one human utility function will explain this human behaviour: U(++,-). Thus its expected utility is now:

  • E(U|I,F)=U(++,-)(F)=2.
  • E(U|I,~F)=U(++,-)(~F)=1.

Therefore the AI will now choose F, forcing the heroin on the human.

Manipulating the unmanipulatable

What's gone wrong here? The key problem is that the AI has the wrong μ: the human is not behaving rationally in this situation. We know that the the true μ is actually μ', which encodes the fact that F (the forcible injection of heroin) actually overwrites the human's "true" utility. Thus under μ, the corresponding P' has P'(a++|F,U)=1 for all U. Hence the information that F→a++ is now vacuous, and doesn't update the AI's distribution over utility functions.

But note two very important things:

  1. The AI cannot update μ based on observation. All human actions are compatible with μ= "The human is rational" (it just requires more and more complex utilities to explain the actions). Thus getting μ correct is not a problem on which the AI can learn in general. Getting better at predicting the human's actions doesn't make the AI better behaved: it makes it worse behaved.
  2. From the perspective of μ, the AI is treating the human utility function as if it was an unchanging historical fact that it cannot influence. From the perspective of the "true" μ', however, the AI is behaving as if it were actively manipulating human preferences to make them easier to satisfy.

In future posts, I'll be looking at different μ's, and how we might nevertheless start deducing things about them from human behaviour, given sensible update rules for the μ. What do we mean by update rules for μ? Well, we could consider μ to be a single complicated unchanging object, or a distribution of possible simpler μ's that update. The second way of seeing it will be easier for us humans to interpret and understand.

Learning and Internalizing the Lessons from the Sequences

6 Nick5a1 14 September 2016 02:40PM

I'm just beginning to go through Rationality: From AI to Zombies. I want to make the most of the lessons contained in the sequences. Usually when I read a book I simply take notes on what seems useful at the time, and a lot of it is forgotten a year later. Any thoughts on how best to internalize the lessons from the sequences?

[Link] How the Simulation Argument Dampens Future Fanaticism

6 wallowinmaya 09 September 2016 01:17PM

Very comprehensive analysis by Brian Tomasik on whether (and to what extent) the simulation argument should change our altruistic priorities. He concludes that the possibility of ancestor simulations somewhat increases the comparative importance of short-term helping relative to focusing on shaping the "far future".

Another important takeaway: 

[...] rather than answering the question “Do I live in a simulation or not?,” a perhaps better way to think about it (in line with Stuart Armstrong's anthropic decision theory) is “Given that I’m deciding for all subjectively indistinguishable copies of myself, what fraction of my copies lives in a simulation and how many total copies are there?"

 

[LINK] Collaborate on HPMOR blurbs; earn chance to win three-volume physical HPMOR

6 ete 07 September 2016 02:21AM

Collaborate on HPMOR blurbs; earn chance to win three-volume physical HPMOR.

 

I intend to print at least one high-quality physical HPMOR and release the files. There are printable texts which are being improved and a set of covers (based on e.b.'s) are underway. I have, however, been unable to find any blurbs I'd be remotely happy with.

 

I'd like to attempt to harness the hivemind to fix that. As a lure, if your ideas contribute significantly to the final version or you assist with other tasks aimed at making this book awesome, I'll put a proportionate number of tickets with your number on into the proverbial hat.

 

I do not guarantee there will be a winner and I reserve the right to arbitrarily modify this any point. For example, it's possible this leads to a disappointingly small amount of valuable feedback, that some unforeseen problem will sink or indefinitely delay the project, or that I'll expand this and let people earn a small number of tickets by sharing so more people become aware this is a thing quickly.

 

With that over, let's get to the fun part.

 

A blurb is needed for each of the three books. Desired characteristics:

 

* Not too heavy on ingroup signaling or over the top rhetoric.

* Non-spoilerish

* Not taking itself awkwardly seriously.

* Amusing / funny / witty.

* Attractive to the same kinds of people the tvtropes page is.

* Showcases HPMOR with fun, engaging, prose.

 

Try to put yourself in the mind of someone awesome deciding whether to read it while writing, but let your brain generate bad ideas before trimming back.

 

I expect that for each we'll want 

* A shortish and awesome paragraph

* A short sentence tagline

* A quote or two from notable people

* Probably some other text? Get creative.

 

Please post blurb fragments or full blurbs here, one suggestion per top level comment. You are encouraged to remix each other's ideas, just add a credit line if you use it in a new top level comment. If you know which book your idea is for, please indicate with (B1) (B2) or (B3).

 

Other things that need doing, if you want to help in another way:

 

* The author's foreword from the physical copies of the first 17 chapters needs to be located or written up

* At least one links page for the end needs to be written up, possibly a second based on http://www.yudkowsky.net/other/fiction/

* Several changes need to be made to the text files, including merging in the final exam, adding appendices, and making the style of both consistent with the rest of the files. Contact me for current files and details if you want to claim this.

 

I wish to stay on topic and focused on creating these missing parts rather than going on a sidetrack to debate copyright. If you are an expert who genuinely has vital information about it, please message me or create a separate post about copyright rather than commenting here.

Open Thread, Sept 5. - Sept 11. 2016

6 Elo 05 September 2016 12:59AM

If it's worth saying, but not worth its own post, then it goes here.


Notes for future OT posters:

1. Please add the 'open_thread' tag.

2. Check if there is an active Open Thread before posting a new one. (Immediately before; refresh the list-of-threads page before posting.)

3. Open Threads should start on Monday, and end on Sunday.

4. Unflag the two options "Notify me of new top level comments on this article" and "

Open Thread, Aug 29. - Sept 5. 2016

6 Elo 29 August 2016 02:28AM

If it's worth saying, but not worth its own post, then it goes here.


Notes for future OT posters:

1. Please add the 'open_thread' tag.

2. Check if there is an active Open Thread before posting a new one. (Immediately before; refresh the list-of-threads page before posting.)

3. Open Threads should start on Monday, and end on Sunday.

4. Unflag the two options "Notify me of new top level comments on this article" and "

The map of the risks of aliens

6 turchin 22 August 2016 07:05PM

Stephen Hawking famously said that aliens are one of the main risks to human existence. In this map I will try to show all rational ways how aliens could result in human extinction. Paradoxically, even if aliens don’t exist, we may be even in bigger danger.

 

1.No aliens exist in our past light cone

1a. Great Filter is behind us. So Rare Earth is true. There are natural forces in our universe which are against life on Earth, but we don’t know if they are still active. We strongly underestimate such forces because of anthropic shadow. Such still active forces could be: gamma-ray bursts (and other types of cosmic explosions like magnitars), the instability of Earth’s atmosphere,  the frequency of large scale volcanism and asteroid impacts. We may also underestimate the fragility of our environment in its sensitivity to small human influences, like global warming becoming runaway global warming.

1b. Great filter is ahead of us (and it is not UFAI). Katja Grace shows that this is a much more probable solution to the Fermi paradox because of one particular version of the Doomsday argument, SIA. All technological civilizations go extinct before they become interstellar supercivilizations, that is in something like the next century on the scale of Earth’s timeline. This is in accordance with our observation that new technologies create stronger and stronger means of destruction which are available to smaller groups of people, and this process is exponential. So all civilizations terminate themselves before they can create AI, or their AI is unstable and self terminates too (I have explained elsewhere why this could happen ). 

 

2.      Aliens still exist in our light cone.

a)      They exist in the form of a UFAI explosion wave, which is travelling through space at the speed of light. EY thinks that this will be a natural outcome of evolution of AI. We can’t see the wave by definition, and we can find ourselves only in the regions of the Universe, which it hasn’t yet reached. If we create our own wave of AI, which is capable of conquering a big part of the Galaxy, we may be safe from alien wave of AI. Such a wave could be started very far away but sooner or later it would reach us. Anthropic shadow distorts our calculations about its probability.

b)      SETI-attack. Aliens exist very far away from us, so they can’t reach us physically (yet) but are able to send information. Here the risk of a SETI-attack exists, i.e. aliens will send us a description of a computer and a program, which is AI, and this will convert the Earth into another sending outpost. Such messages should dominate between all SETI messages. As we get stronger and stronger radio telescopes and other instruments, we have more and more chances of finding messages from them.

c)      Aliens are near (several hundred light years), and know about the Earth, so they have already sent physical space ships (or other weapons) to us, as they have found signs of our technological development and don’t want to have enemies in their neighborhood. They could send near–speed-of-light projectiles or beams of particles on an exact collision course with Earth, but this seems improbable, because if they are so near, why haven’t they didn’t reached Earth yet?

d)      Aliens are here. Alien nanobots could be in my room now, and there is no way I could detect them. But sooner or later developing human technologies will be able to find them, which will result in some form of confrontation. If there are aliens here, they could be in “Berserker” mode, i.e. they wait until humanity reaches some unknown threshold and then attack. Aliens may be actively participating in Earth’s progress, like “progressors”, but the main problem is that their understanding of a positive outcome may be not aligned with our own values (like the problem of FAI).

e)      Deadly remains and alien zombies. Aliens have suffered some kind of existential catastrophe, and its consequences will affect us. If they created vacuum phase transition during accelerator experiments, it could reach us at the speed of light without warning. If they created self-replicating non sentient nanobots (grey goo), it could travel as interstellar stardust and convert all solid matter in nanobots, so we could encounter such a grey goo wave in space. If they created at least one von Neumann probe, with narrow AI, it still could conquer the Universe and be dangerous to Earthlings. If their AI crashed it could have semi-intelligent remnants with a random and crazy goal system, which roams the Universe. (But they will probably evolve in the colonization wave of von Neumann probes anyway.) If we find their planet or artifacts they still could carry dangerous tech like dormant AI programs, nanobots or bacteria. (Vernor Vinge had this idea as the starting point of the plot in his novel “Fire Upon the Deep”)

f)       We could attract the attention of aliens by METI. Sending signals to stars in order to initiate communication we could tell potentially hostile aliens our position in space. Some people advocate for it like Zaitsev, others are strongly opposed. The risks of METI are smaller than SETI in my opinion, as our radiosignals can only reach the nearest hundreds of light years before we create our own strong AI. So we will be able repulse the most plausible ways of space aggression, but using SETI we able to receive signals from much further distances, perhaps as much as one billion light years, if aliens convert their entire home galaxy to a large screen, where they draw a static picture, using individual stars as pixels. They will use vN probes and complex algorithms to draw such picture, and I estimate that it could present messages as large as 1 Gb and will visible by half of the Universe. So SETI is exposed to a much larger part of the Universe (perhaps as much as 10 to the power of 10 more times the number of stars), and also the danger of SETI is immediate, not in a hundred years from now.

g)      Space war. During future space exploration humanity may encounter aliens in the Galaxy which are at the same level of development and it may result in classical star wars.

h)      They will not help us. They are here or nearby, but have decided not to help us in x-risks prevention, or not to broadcast (if they are far) information about most the important x-risks via SETI and about proven ways of preventing them. So they are not altruistic enough to save us from x-risks.

 

3. If we are in a simulation, then the owners of the simulations are aliens for us and they could switch the simulation off. Slow switch-off is possible and in some conditions it will be the main observable way of switch-off. 

 

4. False beliefs in aliens may result in incorrect decisions. Ronald Reagan saw something which he thought was a UFO (it was not) and he also had early onset Alzheimer’s, which may be one of the reasons he invested a lot into the creation of SDI, which also provoked a stronger confrontation with the USSR. (BTW, it is only my conjecture, but I use it as illustration how false believes may result in wrong decisions.)

 

5. Prevention of the x-risks using aliens:

1.      Strange strategy. If all rational straightforward strategies to prevent extinction have failed, as implied by one interpretation of the Fermi paradox, we should try a random strategy.

2.      Resurrection by aliens. We could preserve some information about humanity hoping that aliens will resurrect us, or they could return us to life using our remains on Earth. Voyagers already have such information, and they and other satellites may have occasional samples of human DNA. Radio signals from Earth also carry a lot of information.

3.      Request for help. We could send radio messages with a request for help. (Very skeptical about this, it is only a gesture of despair, if they are not already hiding in the solar system)

4.      Get advice via SETI. We could find advice on how to prevent x-risks in alien messages received via SETI.

5.      They are ready to save us. Perhaps they are here and will act to save us, if the situation develops into something really bad.

6.      We are the risk.  We will spread through the universe and colonize other planets, preventing the existence of many alien civilizations, or change their potential and perspectives permanently. So we will be the existential risk for them.

 

6. We are the risks for future aleins.

In total, there is several significant probability things, mostly connected with Fermi paradox solutions. No matter where is Great filter, we are at risk. If we had passed it, we live in fragile universe, but most probable conclusion is that Great Filter is very soon.

Another important thing is risks of passive SETI, which is most plausible way we could encounter aliens in near–term future.

Also there are important risks that we are in simulation, but that it is created not by our possible ancestors, but by aliens, who may have much less compassion to us (or by UFAI). In the last case the simulation be modeling unpleasant future, including large scale catastrophes and human sufferings.

The pdf is here

 

 

DARPA accepting proposals for explainable AI

6 morganism 22 August 2016 12:05AM

"The XAI program will focus the development of multiple systems on addressing challenges problems in two areas: (1) machine learning problems to classify events of interest in heterogeneous, multimedia data; and (2) machine learning problems to construct decision policies for an autonomous system to perform a variety of simulated missions."

"At the end of the program, the final delivery will be a toolkit library consisting of machine learning and human-computer interface software modules that could be used to develop future explainable AI systems. After the program is complete, these toolkits would be available for further refinement and transition into defense or commercial applications"

 

http://www.darpa.mil/program/explainable-artificial-intelligence

The map of p-zombies

6 turchin 30 July 2016 09:12AM
No real p-zombies exist in any probable way, but a lot of ideas about them have been suggested. This map is the map of ideas. It may be fun or may be useful.

The most useful application of p-zombies research is to determine whether we could loose something important during uploading.

We have to solve the problem of consciousness before we will be uploaded. It will be the most stupid end of the world: everybody is alive and happy but everybody is p-zombie. 

Most ideas here are from Stanford Encyclopedia of Philosophy, Lesswrong wiki, Rational wiki, recent post of EY and from works of Chalmers and Dennett. Some ideas are mine. 

The pdf is here.


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