This summary was posted to LW Main on September 30th. The following week's summary is here.

Irregularly scheduled Less Wrong meetups are taking place in:

• Stockholm: When to stop making a decision: 07 October 2016 03:00PM

The remaining meetups take place in cities with regular scheduling, but involve a change in time or location, special meeting content, or simply a helpful reminder about the meetup:

• Bay Area Winter Solstice 2016: 17 December 2016 07:00PM
• Melbourne: A Bayesian Guide on How to Read a Scientific Paper: 08 October 2016 03:30PM
• Sydney Rationality Dojo - October 2016: 02 October 2016 04:00PM

Locations with regularly scheduled meetups: Austin, Berlin, Boston, Brussels, Buffalo, Canberra, Columbus, Denver, Kraków, London, Madison WI, Melbourne, Moscow, New Hampshire, New York, Philadelphia, Research Triangle NC, San Francisco Bay Area, Seattle, Sydney, Tel Aviv, Toronto, Vienna, Washington DC, and West Los Angeles. There's also a 24/7 online study hall for coworking LWers and a Slack channel for daily discussion and online meetups on Sunday night US time.

This summary was posted to LW Main on September 23rd. The following week's summary is here.

The following meetups take place in cities with regular scheduling, but involve a change in time or location, special meeting content, or simply a helpful reminder about the meetup:

• Baltimore Area / UMBC Weekly Meetup: 25 September 2016 07:00PM
• Bay Area Winter Solstice 2016: 17 December 2016 07:00PM
• [Moscow] Games in Kocherga club: FallacyMania, Zendo, Tower of Chaos: 28 September 2016 07:40PM
• San Francisco Meetup: Mini Talks: 26 September 2016 06:15PM
• Sydney Rationality Dojo - October 2016: 02 October 2016 04:00PM
• Vienna: 24 September 2016 03:00PM
• Washington, D.C.: Outdoor Fun & Games: 25 September 2016 03:30PM

Locations with regularly scheduled meetups: Austin, Berlin, Boston, Brussels, Buffalo, Canberra, Columbus, Denver, Kraków, London, Madison WI, Melbourne, Moscow, New Hampshire, New York, Philadelphia, Research Triangle NC, San Francisco Bay Area, Seattle, Sydney, Tel Aviv, Toronto, Vienna, Washington DC, and West Los Angeles. There's also a 24/7 online study hall for coworking LWers and a Slack channel for daily discussion and online meetups on Sunday night US time.

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

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"The answer is that the universe is governed by a tiny subset of all possible functions. In other words, when the laws of physics are written down mathematically, they can all be described by functions that have a remarkable set of simple properties."

“For reasons that are still not fully understood, our universe can be accurately described by polynomial Hamiltonians of low order.” These properties mean that neural networks do not need to approximate an infinitude of possible mathematical functions but only a tiny subset of the simplest ones."

Interesting article, and just diving into the paper now, but it looks like this is a big boost to the simulation argument. If the universe is built like a game engine, with stacked sets like Mandelbrots, then the simplicity itself becomes a driver in a fabricated reality.

https://www.technologyreview.com/s/602344/the-extraordinary-link-between-deep-neural-networks-and-the-nature-of-the-universe/

Why does deep and cheap learning work so well?

http://arxiv.org/abs/1608.08225

Solving the value learning problem is (IMO) the key technical challenge for AI safety.
How good or bad is an approximate solution?

EDIT for clarity:
By "approximate value learning" I mean something which does a good (but suboptimal from the perspective of safety) job of learning values.  So it may do a good enough job of learning values to behave well most of the time, and be useful for solving tasks, but it still has a non-trivial chance of developing dangerous instrumental goals, and is hence an Xrisk.

Considerations:

1. How would developing good approximate value learning algorithms effect AI research/deployment?
It would enable more AI applications.  For instance, many many robotics tasks such as "smooth grasping motion" are difficult to manually specify a utility function for.  This could have positive or negative effects:

Positive:
* It could encourage more mainstream AI researchers to work on value-learning.

Negative:
* It could encourage more mainstream AI developers to use reinforcement learning to solve tasks for which "good-enough" utility functions can be learned.
Consider a value-learning algorithm which is "good-enough" to learn how to perform complicated, ill-specified tasks (e.g. folding a towel).  But it's still not quite perfect, and so every second, there is a 1/100,000,000 chance that it decides to take over the world. A robot using this algorithm would likely pass a year-long series of safety tests and seem like a viable product, but would be expected to decide to take over the world in ~3 years.
Without good-enough value learning, these tasks might just not be solved, or might be solved with safer approaches involving more engineering and less performance, e.g. using a collection of supervised learning modules and hand-crafted interfaces/heuristics.

2. What would a partially aligned AI do? 
An AI programmed with an approximately correct value function might fail 
* dramatically (see, e.g. Eliezer, on AIs "tiling the solar system with tiny smiley faces.")
or
* relatively benignly (see, e.g. my example of an AI that doesn't understand gustatory pleasure)

Perhaps a more significant example of benign partial-alignment would be an AI that has not learned all human values, but is corrigible and handles its uncertainty about its utility in a desirable way.

This summary was posted to LW Main on August 19th. The following week's summary is here.

New meetups (or meetups with a hiatus of more than a year) are happening in:

• Bay City Meetup: 19 August 2016 01:25PM

Irregularly scheduled Less Wrong meetups are taking place in:

• Australian-ish Online Hangout: 20 August 2016 07:30PM
• Baltimore Area Weekly Meetup: 21 August 2016 08:00PM
• European Community Weekend: 02 September 2016 03:35PM

The remaining meetups take place in cities with regular scheduling, but involve a change in time or location, special meeting content, or simply a helpful reminder about the meetup:

• [Moscow] Games in Kocherga club: FallacyMania, Zendo, Tower of Chaos: 24 August 2016 07:40PM
• San Jose Meetup: Park Day (VII): 21 August 2016 03:00PM
• Sydney Rationality Dojo - September 2016: 04 September 2016 04:00PM
• Sydney Rationality Dojo - October 2016: 02 October 2016 04:00PM
• Vienna: 20 August 2016 03:00PM
• Washington, D.C.: Mini Talks: 21 August 2016 03:30PM

Locations with regularly scheduled meetups: Austin, Berlin, Boston, Brussels, Buffalo, Canberra, Columbus, Denver, Kraków, London, Madison WI, Melbourne, Moscow, New Hampshire, New York, Philadelphia, Research Triangle NC, San Francisco Bay Area, Seattle, Sydney, Tel Aviv, Toronto, Vienna, Washington DC, and West Los Angeles. There's also a 24/7 online study hall for coworking LWers and a Slack channel for daily discussion and online meetups on Sunday night US time.

"We maintain that humanity’s grand challenge is solving the intertwined problems of human population growth and overconsumption, climate change, pollution, ecosystem destruction, disease spillovers, and extinction, in order to avoid environmental tipping points that would make human life more difficult and would irrevocably damage planetary life support systems."

pdf onsite

https://elementascience.org/articles/94

Hi all,

Please read the Disclaimers at the end of the post first, if you're easily offended.

Generalists(general medicine):

1. Get extremely unbeatable at 20 Questions(rationality link). It'll help you make your initial diagnoses(ones based on questions about symptoms) faster and more accurate.
2. Understand probability, bayes theorem and how to apply it^** This will help you interpret the test results, you ordered based on the 20 questions.
3.  Understand base rate fallacy, and how to avoid  being over confident.
4. Understand the upsides and downsides of the drugs you prescribe. Know the probabilities of fatal and adverse side-effects and update them with evidence(Bayes' theorem mentioned above) as you try out different brands and combinations.
5. Know the costs and benefits of any treatment and help the patient make a good decision based on the cost-benefit analysis of treatment combined with the probabilities of outcome.
6. Ask and Keep a history of medical records and allergies of the patient and till their grand parents.*
7. Be willing and able to judge, when a patient is better off with a specialist. Try to keep in touch with Doctors nearby and hopeful all types of specialists.
8. Explain the treatment options and pros and cons in easy language to the patients. It'll reduce misunderstandings and eventually dis-satisfaction with the treatment.
9. Resist the urge to treat patients as NPCs. Involve them in the treatment process.
10. Meditate
11. Find a hobby, that you can keep improving on till the end of life.
12. Be aware of the conflict of interest between the patient and the pharmaceutical companies.
13. Have enough research skills to form opinions on base rates/probabilities in different diseases and treatment methods as needed.
14. If you're in a big hospital setup, make sure you've the best hospital administration. 
15. Medical expertise is only relevant once you see the patient. Your ability to judge the evidence requires getting access to it; this means you need to be able to correctly send requests, get the data back, and keep all this attached to the correct patient.Scheduling, filing and communication. Lacking these, medical expertise is meaningless. 

Specialists:

Basically the same skill sets as above. One difference is in the skill level and you should customize that as needed.

1. For ex: You would need to be able to explain the treatment options and the probabilistic nature of the outcomes to your patients.
2. As for research, keep a track of progress in your area in treatment methods and different outcomes on the "quality of life" for the patients after the treatment.
3. Better applied Bayesian skills. In the sense of figuring out independent variables and their probabilities affecting the outcome.

Some controversial ideas(Better use your common-sense before trying out):

1.  Experiment a little with your bio-chemistry and see how they affect your thought-processes.  To be safe, stick to biologically produced ones. For ex: injecting self with a small adrenalin dose and monitoring bodily response can help keep your thinking clear in emergency situations.
2. Know your self biology better. For ex: male vs female differences mean the adrenalin response is different and peaks later in females.  If you think that's wrong, please go back and check your course work. Also watch this 2 hour video and come back with objections after reading the studies he quotes.
3. Keep regularly(whatever frequency your practice and nature of work demands) checking your(for ex; hormone levels) blood states, so that you  can start regulating your self for optimal decision-making skills.
4. If you're a woman, you'll customize practice on some of the skill sets above differently. For ex: Mastery over emotions might need more practice, while empathizing/connecting with the patient might be easier.

Disclaimers:

1. Most of what follows is based on my experiences(either as a patient myself or a concerned relative) with Indian Doctors. Some of it may be trivial, to others, but most of it is skills a doc will need and ignored in school.
2.  I've split it in two (specialists and generalists) but there's a fair amount of overlap.
3. These are fairly high standards, but worth shooting for and I've kept the focus on smart rather than hard work.
4. I've stayed from a few topics like: bedside manners/social skills, specific medical treatments and conditions(obviously, I'm not a Doctor after all) and a few others, you can add/delete(also specify/pick levels) as you see fit.
5. Pick the skill-levels as demanded by your client population and adjust.
6. I'm assuming generalists, don't have to deal with emergency cases, but in some parts, that's not likely then pick common emergency areas and follow specialist advice.
7. I wrote this based on my experiences and with humans in mind, but veterinary Doctors may find some useful too.

* -- I understand this is difficult in Indian circumstances, but I've seen it being done manually(simply leaves of prescriptions organized alphabetically, link to dr.rathinavel) , so it's possible and worth the effort unless, you practice in area of highly migratory population.(for example rural vs urban areas).

**-- If you're trying to compete on availability for consultation, you'll need to be able to do this after being woken in the middle of the night.

I'm hoping to convert it into a rationalist skills for Doctors Wiki page, so please provide feedback, especially if you're practicing Doctors. If you don't want to post publicly email me(in profile) or comment on wordpress.

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