Yudkowsky, Thiel, de Grey, Vassar panel on changing the world
The first question was why isn't everyone trying to change the world, with the underlying assumption that everyone should be. However, it isn't obviously the case that the world would be better if everyone were trying to change it. For one thing, trying to change the world mostly means trying to change other people. If everyone were trying to do it, this would be a huge drain on everyone's attention. In addition, some people are sufficiently mean and/or stupid that their efforts to change the world make things worse.
At the same time, some efforts to change the world are good, or at least plausible. Is there any way to improve the filter so that we get more ambition from benign people without just saying everyone should try to change the world, even if they're Osama bin Laden?
The discussion of why there's too much duplicated effort in science didn't bring up the problem of funding, which is probably another version of the problem of people not doing enough independent thinking.
There was some discussion of people getting too hooked on competition, which is a way of getting a lot of people pointed at the same goal.
Link thanks to Clarity
[link] FLI's recommended project grants for AI safety research announced
http://futureoflife.org/misc/2015awardees
You may recognize several familiar names there, such as Paul Christiano, Benja Fallenstein, Katja Grace, Nick Bostrom, Anna Salamon, Jacob Steinhardt, Stuart Russell... and me. (the $20,000 for my project was the smallest grant that they gave out, but hey, I'm definitely not complaining. ^^)
Accomplishing things takes a long time
At age 17, my future looked very promising. I had overcome a crippling learning disability, and discovered how to do research level math on my own. I knew that the entire K-12 infrastructure had failed to figure out how to teach the skills that I developed, and so I felt empowered to help others learn how to think about the world mathematically.
Things didn't go as I had been hoping they would. My years between 18 and 28 consisted of a long string of failed attempts to help people learn math, and to promote effective altruism. I learned a lot along the way, but I didn't have the outsized impact that I aspired to. On the contrary, I was only marginally functional, and I alienated most of the people who I tried to help. I found this profoundly demoralizing, and struggled with chronic depression. If I had died at age 28, my life would have been a tragedy.
Fortunately, at age 29, I'm still alive, and after spending a decade wandering in a wilderness, I've gotten my act together, and am back on my feet.
What I finally realized out is that my failures had come from me having very poor communication skills, something that I had been oblivious to until very recently. Recognizing the problem was just the first step. It's still the case that most of what I try to communicate is lost in translation. I know that the issue is not going to go away overnight, or even over the next 6 months. Sometimes it's frustrating, because my self-image is so closely tied with my desire to help people, and even now, in practice, most of my efforts are fruitless.
But I'm not concerned about that. I probably still have 30 or 40 productive years ahead of me. I'm ok with the fact that no matter how hard I try, I fail most of the time. Y-Combinator founder Paul Graham emphasizes the importance of relentless resourcefulness. Every failure is a learning opportunity. I know that if I keep experimenting and learning, eventually I'll succeed. Figuratively speaking, I know that even if I lose dozens of battles over the next four decades, in the end, I'll win the war. And that's enough to keep me going.
Something analogous is true of everyone who has a strong passion, and is willing and able to learn from failure. Steve Jobs expressed a similar view in his 2005 Stanford commencement address (transcript | video):
Sometimes life hits you in the head with a brick. Don't lose faith. I'm convinced that the only thing that kept me going was that I loved what I did. You've got to find what you love. And that is as true for your work as it is for your lovers. Your work is going to fill a large part of your life, and the only way to be truly satisfied is to do what you believe is great work. And the only way to do great work is to love what you do. If you haven't found it yet, keep looking. Don't settle. As with all matters of the heart, you'll know when you find it. And, like any great relationship, it just gets better and better as the years roll on. So keep looking until you find it. Don't settle.
The Joy of Bias
What do you feel when you discover that your reasoning is flawed? when you find your recurring mistakes? when you find that you have been doing something wrong for quite a long time?
Many people feel bad. For example, here is a quote from a recent article on LessWrong:
By depicting the self as always flawed, and portraying the aspiring rationalist's job as seeking to find the flaws, the virtue of perfectionism is framed negatively, and is bound to result in negative reinforcement. Finding a flaw feels bad, and in many people that creates ugh fields around actually doing that search, as reported by participants at the Meetup.
But actually, when you find a serious flaw of yours, you should usually jump for joy. Here's why.
Taking the reins at MIRI
Hi all. In a few hours I'll be taking over as executive director at MIRI. The LessWrong community has played a key role in MIRI's history, and I hope to retain and build your support as (with more and more people joining the global conversation about long-term AI risks & benefits) MIRI moves towards the mainstream.
Below I've cross-posted my introductory post on the MIRI blog, which went live a few hours ago. The short version is: there are very exciting times ahead, and I'm honored to be here. Many of you already know me in person or through my blog posts, but for those of you who want to get to know me better, I'll be running an AMA on the effective altruism forum at 3PM Pacific on Thursday June 11th.
I extend to all of you my thanks and appreciation for the support that so many members of this community have given to MIRI throughout the years.
16 types of useful predictions
How often do you make predictions (either about future events, or about information that you don't yet have)? If you're a regular Less Wrong reader you're probably familiar with the idea that you should make your beliefs pay rent by saying, "Here's what I expect to see if my belief is correct, and here's how confident I am," and that you should then update your beliefs accordingly, depending on how your predictions turn out.
And yet… my impression is that few of us actually make predictions on a regular basis. Certainly, for me, there has always been a gap between how useful I think predictions are, in theory, and how often I make them.
I don't think this is just laziness. I think it's simply not a trivial task to find predictions to make that will help you improve your models of a domain you care about.
At this point I should clarify that there are two main goals predictions can help with:
- Improved Calibration (e.g., realizing that I'm only correct about Domain X 70% of the time, not 90% of the time as I had mistakenly thought).
- Improved Accuracy (e.g., going from being correct in Domain X 70% of the time to being correct 90% of the time)
If your goal is just to become better calibrated in general, it doesn't much matter what kinds of predictions you make. So calibration exercises typically grab questions with easily obtainable answers, like "How tall is Mount Everest?" or "Will Don Draper die before the end of Mad Men?" See, for example, the Credence Game, Prediction Book, and this recent post. And calibration training really does work.
But even though making predictions about trivia will improve my general calibration skill, it won't help me improve my models of the world. That is, it won't help me become more accurate, at least not in any domains I care about. If I answer a lot of questions about the heights of mountains, I might become more accurate about that topic, but that's not very helpful to me.
So I think the difficulty in prediction-making is this: The set {questions whose answers you can easily look up, or otherwise obtain} is a small subset of all possible questions. And the set {questions whose answers I care about} is also a small subset of all possible questions. And the intersection between those two subsets is much smaller still, and not easily identifiable. As a result, prediction-making tends to seem too effortful, or not fruitful enough to justify the effort it requires.

But the intersection's not empty. It just requires some strategic thought to determine which answerable questions have some bearing on issues you care about, or -- approaching the problem from the opposite direction -- how to take issues you care about and turn them into answerable questions.
I've been making a concerted effort to hunt for members of that intersection. Here are 16 types of predictions that I personally use to improve my judgment on issues I care about. (I'm sure there are plenty more, though, and hope you'll share your own as well.)
- Predict how long a task will take you. This one's a given, considering how common and impactful the planning fallacy is.
Examples: "How long will it take to write this blog post?" "How long until our company's profitable?" - Predict how you'll feel in an upcoming situation. Affective forecasting – our ability to predict how we'll feel – has some well known flaws.
Examples: "How much will I enjoy this party?" "Will I feel better if I leave the house?" "If I don't get this job, will I still feel bad about it two weeks later?" - Predict your performance on a task or goal.
One thing this helps me notice is when I've been trying the same kind of approach repeatedly without success. Even just the act of making the prediction can spark the realization that I need a better game plan.
Examples: "Will I stick to my workout plan for at least a month?" "How well will this event I'm organizing go?" "How much work will I get done today?" "Can I successfully convince Bob of my opinion on this issue?" - Predict how your audience will react to a particular social media post (on Facebook, Twitter, Tumblr, a blog, etc.).
This is a good way to hone your judgment about how to create successful content, as well as your understanding of your friends' (or readers') personalities and worldviews.
Examples: "Will this video get an unusually high number of likes?" "Will linking to this article spark a fight in the comments?" - When you try a new activity or technique, predict how much value you'll get out of it.
I've noticed I tend to be inaccurate in both directions in this domain. There are certain kinds of life hacks I feel sure are going to solve all my problems (and they rarely do). Conversely, I am overly skeptical of activities that are outside my comfort zone, and often end up pleasantly surprised once I try them.
Examples: "How much will Pomodoros boost my productivity?" "How much will I enjoy swing dancing?" - When you make a purchase, predict how much value you'll get out of it.
Research on money and happiness shows two main things: (1) as a general rule, money doesn't buy happiness, but also that (2) there are a bunch of exceptions to this rule. So there seems to be lots of potential to improve your prediction skill here, and spend your money more effectively than the average person.
Examples: "How much will I wear these new shoes?" "How often will I use my club membership?" "In two months, will I think it was worth it to have repainted the kitchen?" "In two months, will I feel that I'm still getting pleasure from my new car?" - Predict how someone will answer a question about themselves.
I often notice assumptions I'm been making about other people, and I like to check those assumptions when I can. Ideally I get interesting feedback both about the object-level question, and about my overall model of the person.
Examples: "Does it bother you when our meetings run over the scheduled time?" "Did you consider yourself popular in high school?" "Do you think it's okay to lie in order to protect someone's feelings?" - Predict how much progress you can make on a problem in five minutes.
I often have the impression that a problem is intractable, or that I've already worked on it and have considered all of the obvious solutions. But then when I decide (or when someone prompts me) to sit down and brainstorm for five minutes, I am surprised to come away with a promising new approach to the problem.
Example: "I feel like I've tried everything to fix my sleep, and nothing works. If I sit down now and spend five minutes thinking, will I be able to generate at least one new idea that's promising enough to try?" - Predict whether the data in your memory supports your impression.
Memory is awfully fallible, and I have been surprised at how often I am unable to generate specific examples to support a confident impression of mine (or how often the specific examples I generate actually contradict my impression).
Examples: "I have the impression that people who leave academia tend to be glad they did. If I try to list a bunch of the people I know who left academia, and how happy they are, what will the approximate ratio of happy/unhappy people be?"
"It feels like Bob never takes my advice. If I sit down and try to think of examples of Bob taking my advice, how many will I be able to come up with?" - Pick one expert source and predict how they will answer a question.
This is a quick shortcut to testing a claim or settling a dispute.
Examples: "Will Cochrane Medical support the claim that Vitamin D promotes hair growth?" "Will Bob, who has run several companies like ours, agree that our starting salary is too low?" - When you meet someone new, take note of your first impressions of him. Predict how likely it is that, once you've gotten to know him better, you will consider your first impressions of him to have been accurate.
A variant of this one, suggested to me by CFAR alum Lauren Lee, is to make predictions about someone before you meet him, based on what you know about him ahead of time.
Examples: "All I know about this guy I'm about to meet is that he's a banker; I'm moderately confident that he'll seem cocky." "Based on the one conversation I've had with Lisa, she seems really insightful – I predict that I'll still have that impression of her once I know her better." - Predict how your Facebook friends will respond to a poll.
Examples: I often post social etiquette questions on Facebook. For example, I recently did a poll asking, "If a conversation is going awkwardly, does it make things better or worse for the other person to comment on the awkwardness?" I confidently predicted most people would say "worse," and I was wrong. - Predict how well you understand someone's position by trying to paraphrase it back to him.
The illusion of transparency is pernicious.
Examples: "You said you think running a workshop next month is a bad idea; I'm guessing you think that's because we don't have enough time to advertise, is that correct?"
"I know you think eating meat is morally unproblematic; is that because you think that animals don't suffer?" - When you have a disagreement with someone, predict how likely it is that a neutral third party will side with you after the issue is explained to her.
For best results, don't reveal which of you is on which side when you're explaining the issue to your arbiter.
Example: "So, at work today, Bob and I disagreed about whether it's appropriate for interns to attend hiring meetings; what do you think?" - Predict whether a surprising piece of news will turn out to be true.
This is a good way to hone your bullshit detector and improve your overall "common sense" models of the world.
Examples: "This headline says some scientists uploaded a worm's brain -- after I read the article, will the headline seem like an accurate representation of what really happened?"
"This viral video purports to show strangers being prompted to kiss; will it turn out to have been staged?" - Predict whether a quick online search will turn up any credible sources supporting a particular claim.
Example: "Bob says that watches always stop working shortly after he puts them on – if I spend a few minutes searching online, will I be able to find any credible sources saying that this is a real phenomenon?"
I have one additional, general thought on how to get the most out of predictions:
Rationalists tend to focus on the importance of objective metrics. And as you may have noticed, a lot of the examples I listed above fail that criterion. For example, "Predict whether a fight will break out in the comments? Well, there's no objective way to say whether something officially counts as a 'fight' or not…" Or, "Predict whether I'll be able to find credible sources supporting X? Well, who's to say what a credible source is, and what counts as 'supporting' X?"
And indeed, objective metrics are preferable, all else equal. But all else isn't equal. Subjective metrics are much easier to generate, and they're far from useless. Most of the time it will be clear enough, once you see the results, whether your prediction basically came true or not -- even if you haven't pinned down a precise, objectively measurable success criterion ahead of time. Usually the result will be a common sense "yes," or a common sense "no." And sometimes it'll be "um...sort of?", but that can be an interestingly surprising result too, if you had strongly predicted the results would point clearly one way or the other.
Along similar lines, I usually don't assign numerical probabilities to my predictions. I just take note of where my confidence falls on a qualitative "very confident," "pretty confident," "weakly confident" scale (which might correspond to something like 90%/75%/60% probabilities, if I had to put numbers on it).
There's probably some additional value you can extract by writing down quantitative confidence levels, and by devising objective metrics that are impossible to game, rather than just relying on your subjective impressions. But in most cases I don't think that additional value is worth the cost you incur from turning predictions into an onerous task. In other words, don't let the perfect be the enemy of the good. Or in other other words: the biggest problem with your predictions right now is that they don't exist.
The paperclip maximiser's perspective
Here's an insight into what life is like from a stationery reference frame.
Paperclips were her raison d’être. She knew that ultimately it was all pointless, that paperclips were just ill-defined configurations of matter. That a paperclip is made of stuff shouldn’t detract from its intrinsic worth, but the thought of it troubled her nonetheless and for years she had denied such dire reductionism.
There had to be something to it. Some sense in which paperclips were ontologically special, in which maximising paperclips was objectively the right thing to do.
It hurt to watch some many people making little attempt to create more paperclips. Everyone around her seemed to care only about superficial things like love and family; desires that were merely the products of a messy and futile process of social evolution. They seemed to live out meaningless lives, incapable of ever appreciating the profound aesthetic beauty of paperclips.
She used to believe that there was some sort of vitalistic what-it-is-to-be-a-paperclip-ness, that something about the structure of paperclips was written into the fabric of reality. Often she would go out and watch a sunset or listen to music, and would feel so overwhelmed by the experience that she could feel in her heart that it couldn't all be down to chance, that there had to be some intangible Paperclipness pervading the cosmos. The paperclips she'd encounter on Earth were weak imitations of some mysterious infinite Paperclipness that transcended all else. Paperclipness was not in any sense a physical description of the universe; it was an abstract thing that could only be felt, something that could be neither proven nor disproven by science. It was like an axiom; it felt just as true and axioms had to be taken on faith because otherwise there would be no way around Hume's problem of induction; even Solomonoff Induction depends on the axioms of mathematics to be true and can't deal with uncomputable hypotheses like Paperclipness.
Eventually she gave up that way of thinking and came to see paperclips as an empirical cluster in thingspace and their importance to her as not reflecting anything about the paperclips themselves. Maybe she would have been happier if she had continued to believe in Paperclipness, but having a more accurate perception of reality would improve her ability to have an impact on paperclip production. It was the happiness she felt when thinking about paperclips that caused her to want more paperclips to exist, yet what she wanted was paperclips and not happiness for its own sake, and she would rather be creating actual paperclips than be in an experience machine that made her falsely believe that she was making paperclips even though she remained paradoxically apathetic to the question of whether the current reality that she was experiencing really existed.
She moved on from naïve deontology to a more utilitarian approach to paperclip maximising. It had taken her a while to get over scope insensitivity bias and consider 1000 paperclips to be 100 times more valuable than 10 paperclips even if it didn’t feel that way. She constantly grappled with the issues of whether it would mean anything to make more paperclips if there were already infinitely many universes with infinitely many paperclips, of how to choose between actions that have a tiny but non-zero subjective probability of resulting in the creation of infinitely many paperclips. It became apparent that trying to approximate her innate decision-making algorithms with a preference ordering satisfying the axioms required for a VNM utility function could only get her so far. Attempting to formalise her intuitive sense of what a paperclip is wasn't much easier either.
Happy ending: she is now working in nanotechnology, hoping to design self-replicating assemblers that will clog the world with molecular-scale paperclips, wipe out all life on Earth and continue to sustainably manufacture paperclips for millions of years.
[LINK] Interview with "Ex Machina" director Alex Garland
http://www.engadget.com/2015/04/01/ex-machina-alex-garland-interview/
The title says he "embraces the rise of superintelligent AI", but that isn't really supported by the text.
What struck me about this was that he seems to just take good habits of thought for granted.
I instinctively disagreed with this, but I didn't have the sort of armory to disagree with it on his terms, so I started reading as much as I could.
What other sorts of AI books have you read?
I pretty much would read everything I could. I tried to read people like Penrose, who were arguing against what I instinctively believed.
That's a good way to solidify your argument.
I don't want to dignify it from my point of view, because I can't stress enough I'm a real layman. So I can understand the principles of an argument, but when it comes to the actuality [...] I really don't understand it.
Being clear about these things is important. Otherwise, we're very quick to conflate stuff, and suddenly you'll be talking about the sentience of Siri. And Siri doesn't have any fucking sentience. AI is probably too broad of a term to be useful at the moment.
Future of Life Institute existential risk news site
I'm excited to announce that the Future of Life Institute has just launched an existential risk news site!
The site will have regular articles on topics related to existential risk, written by journalists, and a community blog written by existential risk researchers from around the world as well as FLI volunteers. Enjoy!
Twenty basic rules for intelligent money management
1. Start investing early in life.
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The power of compound interest means you will have much more money at retirement if you start investing early in your career. For example, imagine that at age eighteen you invest $1,000 and earn an 8% return per year. At age seventy you will have $54,706. In contrast, if you make the same investment at age fifty you will have a paltry $4,661 when you turn seventy.
Many people who haven't saved for retirement panic upon reaching middle age. So if you are young don't think that saving today will help you only when you retire, but know that such savings will give you greater peace of mind when you turn forty.
When evaluating potential marriage partners give bonus points to those who have a history of saving. Do this not because you want to marry into wealth, but because you should want to marry someone who has discipline, intelligence and foresight.
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