trentmkelly/LessWrong-43k huggingface dataset:
How to fix academia?
by passive_fist — 2015-08-20T00:50:56.109Z

I don't usually submit articles to Discussion, but this news upset me so much that I think there is a real need to talk about it.

http://www.nature.com/news/faked-peer-reviews-prompt-64-retractions-1.18202

A leading scientific publisher has retracted 64 articles in 10 journals, after an internal investigation discovered fabricated peer-review reports linked to the articles’ publication.

The cull comes after similar discoveries of ‘fake peer review’ by several other major publishers, including London-based BioMed Central, an arm of Springer, which began retracting 43 articles in March citing "reviews from fabricated reviewers". The practice can occur when researchers submitting a paper for publication suggest reviewers, but supply contact details for them that actually route requests for review back to the researchers themselves.

Types of Misconduct

We all know that academia is a tough place to be in. There is constant pressure to 'publish or perish', and people are given promotions and pay raises directly as a result of how many publications and grants they are awarded. I was awarded a PhD recently so the subject of scientific honesty is dear to my heart.

I'm of course aware of misconduct in the field of science. 'Softer' forms of misconduct include things like picking only results that are consistent with your hypothesis or repeating experiments until you get low p-values. This kind of thing sometimes might even happen non-deliberately and subconsciously, which is why it is important to disclose methods and data.

'Harder' forms of misconduct include making up data and fudging numbers in order to get published and cited. This is of course a very deliberate kind of fraud, but it is still easy to see how someone could be led to this kind of behaviour by virtue of the incredible pressures that exist. Here, the goal is not just academic advancement, but also obtaining recognition. The authors in this case are confident that even though their data is falsified, their reasoning (based, of course, on falsified data) is sound and correct and stands up to scrutiny.

What is the problem?

But the kind of misconduct being mentioned in the linked article is extremely upsetting to me, beyond the previous types of misconduct. It is a person or (more likely) a group of people knowing full well that their...
[Link] First almost fully-formed human [foetus] brain grown in lab, researchers claim
by ESRogs — 2015-08-19T06:37:21.049Z

This seems significant:

An almost fully-formed human brain has been grown in a lab for the first time, claim scientists from Ohio State University. The team behind the feat hope the brain could transform our understanding of neurological disease.

Though not conscious the miniature brain, which resembles that of a five-week-old foetus, could potentially be useful for scientists who want to study the progression of developmental diseases.

...

The brain, which is about the size of a pencil eraser, is engineered from adult human skin cells and is the most complete human brain model yet developed

...

Previous attempts at growing whole brains have at best achieved mini-organs that resemble those of nine-week-old foetuses, although these “cerebral organoids” were not complete and only contained certain aspects of the brain. “We have grown the entire brain from the get-go,” said Anand.

...

The ethical concerns were non-existent, said Anand. “We don’t have any sensory stimuli entering the brain. This brain is not thinking in any way.”

...

If the team’s claims prove true, the technique could revolutionise personalised medicine. “If you have an inherited disease, for example, you could give us a sample of skin cells, we could make a brain and then ask what’s going on,” said Anand.

...

For now, the team say they are focusing on using the brain for military research, to understand the effect of post traumatic stress disorder and traumatic brain injuries.

http://www.theguardian.com/science/2015/aug/18/first-almost-fully-formed-human-brain-grown-in-lab-researchers-claim
Meetup : Rochester LessWrong and Transhumanists
by mare-of-night — 2015-08-19T03:46:17.135Z

Discussion article for the meetup : Rochester LessWrong and Transhumanists

WHEN:
12 September 2015 01:00:00PM (-0400)

WHERE:
3333 West Henrietta Road, Rochester, NY 14623

Location: Jitters Cafe

I'm starting a meetup group in Rochester so some of us can hang out in meatspace. Hopefully, this will be a recurring meetup, every 2 - 4 weeks or so. To be notified of future meetups, join the meetup.com group: http://www.meetup.com/Rochester-Transhumanists/

For the first meetup, we'll play some board games (I have Xendo, and feel free to bring other games, too), eat pizza, get to know each other, and talk about what we want out of this group. The start and end times aren't firm, come and go when you like.

Jitters Cafe is 5 minutes from RIT by car, or a bit under half an hour on foot. If you need a ride, contact Kim at mareofnight@gmail.com. I'm coming from center city, so both RIT and UofR are on the way. If you're somewhere else, I'll see if I can coordinate a carpool for you.

Discussion article for the meetup : Rochester LessWrong and Transhumanists
MIRI's 2015 Summer Fundraiser!
by So8res — 2015-08-19T00:27:44.535Z

Our summer fundraising drive is now finished. We raised a grand total of $631,957 from 263 donors. This is an incredible sum, making this the biggest fundraiser we’ve ever run.

We've already been hard at work growing our research team and spinning up new projects, and I’m excited to see what our research team can do this year. Thank you to all our supporters for making our summer fundraising drive so successful!

It's safe to say that this past year exceeded a lot of people's expectations.

Twelve months ago, Nick Bostrom's Superintelligence had just come out. Questions about the long-term risks and benefits of smarter-than-human AI systems were nearly invisible in mainstream discussions of AI's social impact.

Twelve months later, we live in a world where Bill Gates is confused by why so many researchers aren't using Superintelligence as a guide to the questions we should be asking about AI's future as a field.

Following a conference in Puerto Rico that brought together the leading organizations studying long-term AI risk (MIRI, FHI, CSER) and top AI researchers in academia (including Stuart Russell, Tom Mitchell, Bart Selman, and the Presidents of AAAI and IJCAI) and industry (including representatives from Google DeepMind and Vicarious), we've seen Elon Musk donate $10M to a grants program aimed at jump-starting the field of long-term AI safety research; we've seen the top AI and machine learning conferences (AAAI, IJCAI, and NIPS) announce their first-ever workshops or discussions on AI safety and ethics; and we've seen a panel discussion on superintelligence at ITIF, the leading U.S. science and technology think tank. (I presented a paper at the AAAI workshop, I spoke on the ITIF panel, and I'll be at NIPS.)

As researchers begin investigating this area in earnest, MIRI is in an excellent position, with a developed research agenda already in hand. If we can scale up as an organization then we have a unique chance to shape the research priorities and methods of this new paradigm in AI, and direct this momentum in useful directions.

This is a big opportunity. MIRI is already growing and scaling its research activities, but the speed at which we scale in the coming months and years depends heavily on our available funds.

For that reason, MIRI is starting a six-week fundraiser aimed at increasing our rate of growth.

— Live ...
Meetup : Rationality Meetup Vienna
by AnnaLeptikon — 2015-08-18T20:37:03.107Z

Discussion article for the meetup : Rationality Meetup Vienna

WHEN:
26 September 2015 03:00:00PM (+0200)

WHERE:
Kaisermühlenstraße 24, Wien

Topics:
Not defined yet

Location:
http://web.student.tuwien.ac.at/~e0326238/rationality_meetup/directions.html

Facebook for more details:
https://www.facebook.com/events/498666000282761/
(join the "Rationality Vienna" group)

Discussion article for the meetup : Rationality Meetup Vienna
Meetup : Oslo Lesswrong meetup, September
by FourFire — 2015-08-18T14:15:21.614Z

Discussion article for the meetup : Oslo Lesswrong meetup, September

WHEN:
26 September 2015 09:32:33AM (+0200)

WHERE:
youngs gate 6, oslo

Hello, and welcome to the September Lesswrong meetup, in Oslo!
We will be meeting up at the largest hackerspace in town, once again, and will have several interesting topics to discuss including: Psychological attractors in the near future Self modification Bioethics ...and many others which will be brought up during the meetup.

Discussion article for the meetup : Oslo Lesswrong meetup, September
Predicted corrigibility: pareto improvements
by Stuart_Armstrong — 2015-08-18T11:24:40.000Z

Corrigibility allows an agent to transition smoothly from a perfect u-maximiser to a perfect v-maximiser, without seeking to resist or cause this transition.

And it's the very perfection of the transition that could cause problems; while u-maximising, the agent will not take the slightest action to increase v, even if such actions are readily available. Nor will it 'rush' to finish its u-maximising before transitioning. It seems that there's some possibility of improvements here.

I've already attempted one way of dealing with the issue (see the pre-corriged agent idea). This is another one.

Pareto improvements allowed

Suppose that an agent with corrigible algorithm A is following utility u currently, and estimates that there are probabilities pi that it will transition to utilities vi at midnight (note that these are utility function representatives, not affine classes of equivalent utility functions). At midnight, the usual corrigibility applies, making A indifferent to that transition, making use of such terms as E(u|u→u) (the expectation of u, given that the A's utility doesn't change) and E(vi|u→vi) (the expectation of vi, given that A's utility changes to vi).

But, in the meantime, there are expectations such as E({u,v1,v2,...}). These are A's best current estimates as to what the genuine expected utility of the various utilites are, given all it knows about the world and itself. It could be more explicitly written as E({u,v1,v2,...}| A), to emphasise that these expectations are dependent on the agent's own algorithm.

Then the idea is to modify the agent's algorithm so that Pareto improvements are possible. Call this modified algorithm B. B can select actions that A would not have chosen, conditional on:

E(u|B) ≥ E(u|A) and E(Σpivi|B) ≥ E(Σpivi|A).
There are two obvious ways we could define B:

B maximises u, subject to the constraints E(Σpivi|B) ≥ E(Σpivi|A).
B maximises Σpivi, subject to the constraints E(u|B) ≥ E(u|A).
In the first case, the agent maximises its current utility, without sacrificing its future utility. This could apply, for example, to a ruby mining agent that rushes to gets its rubies to the bank before its utility changes. In the second case, the agent maximises it future expected utility, without sacrificing its current utility. This could apply to a ruby mining agent that's soon to become a sapphire mining agent: it then starts to look arou

...
Truth seeking as an optimization process
by ScottL — 2015-08-18T11:03:03.872Z

From the costs of rationality wiki:

Becoming more epistemically rational can only guarantee one thing: what you believe will include more of the truth . Knowing that truth might help you achieve your goals , or cause you to become a pariah. Be sure that you really want to know the truth before you commit to finding it; otherwise, you may flinch from it.

The reason that truth seeking is often seen as being integral to rationality is that in order to make optimal decisions you must first be able to make accurate predictions. Delusions, or false beliefs, are self-imposed barriers to accurate prediction. They are surprise inducers. It is because of this that the rational path is often to break delusions, but you should remember that doing so is a slow and hard process that is rife with potential problems.

Below I have listed three scenarios in which a person could benefit from considering the costs of truth seeking. The first scenario is when seeking a more accurate measurement is computationally expensive and not really required. The second scenario is when you know that the truth will be emotionally distressing to another person and that this person is not in an optimal state to handle this truth. The third scenario is when you are trying to change the beliefs of others. It is often beneficial if you can understand the costs involved for them to change their beliefs as well as their perspective. This allows you to become better able to actually change their beliefs rather than to just win an argument.

Scenario 1: computationally expensive truth

We should forget about small efficiencies, say about 97% of the time: premature optimization is the root of all evil. Yet we should not pass up our opportunities in that critical 3%. – Donald Knuth

If optimization requires significant effort and only results in minimal gains in utility, then it is not worth it. If you only need to be 90% sure that something is true and you are currently 98% sure that it is, then it is not worth spending some extra effort to get to 99% certainty. For example, if you are testing ballistics on Earth then it may be appropriate to use Newtons laws even though they are known to be inexact in some extreme conditions. Now, this does not mean that optimization should never be done. Sometimes that extra 1% certainty is actually extremely important. What it does mean is that you shou...
Predicted corrigibility: pareto improvements
by Stuart_Armstrong — 2015-08-18T11:02:01.311Z

A putative new idea for AI control; index here.

Corrigibility allows an agent to transition smoothly from a perfect u-maximiser to a perfect v-maximiser, without seeking to resist or cause this transition.

And it's the very perfection of the transition that could cause problems; while u-maximising, the agent will not take the slightest action to increase v, even if such actions are readily available. Nor will it 'rush' to finish its u-maximising before transitioning. It seems that there's some possibility of improvements here.

I've already attempted one way of dealing with the issue (see the pre-corriged agent idea). This is another one.

Pareto improvements allowed

Suppose that an agent with corrigible algorithm A is following utility u currently, and estimates that there are probabilities pi that it will transition to utilities vi at midnight (note that these are utility function representatives, not affine classes of equivalent utility functions). At midnight, the usual corrigibility applies, making A indifferent to that transition, making use of such terms as E(u|u→u) (the expectation of u, given that the A's utility doesn't change) and E(vi|u→vi) (the expectation of vi, given that A's utility changes to vi).

But, in the meantime, there are expectations such as E({u,v1,v2,...}). These are A's best current estimates as to what the genuine expected utility of the various utilites are, given all it knows about the world and itself. It could be more explicitly written as E({u,v1,v2,...}| A), to emphasise that these expectations are dependent on the agent's own algorithm.

Then the idea is to modify the agent's algorithm so that Pareto improvements are possible. Call this modified algorithm B. B can select actions that A would not have chosen, conditional on:

E(u|B) ≥ E(u|A) and E(Σpivi|B) ≥ E(Σpivi|A).

There are two obvious ways we could define B:

B maximises u, subject to the constraints E(Σpivi|B) ≥ E(Σpivi|A).

B maximises Σpivi, subject to the constraints E(u|B) ≥ E(u|A).

In the first case, the agent maximises its current utility, without sacrificing its future utility. This could apply, for example, to a ruby mining agent that rushes to gets its rubies to the bank before its utility changes. In the second case, the agent maximises it future expected utility, without sacrifici...
Mental Model Theory - Illusion of Possibility Example
by ScottL — 2015-08-18T06:29:31.137Z

(I have written an overview of the mental model theory which is in main and the link is here. You should read this overview before you read this post. You should only read this post if you want more explicit details on the first example which demonstrates the illusion of possibility)

Consider the following problem:

Before you stands a card-dealing robot. This robot has been programmed to deal one hand of cards. You are going to make a bet with another person on whether the dealt hand will contain an ace or whether it will contain a king. If the dealt hand is just a single queen, it's a draw. Based on what you know about this robot, you deduce correctly that only one of the following statements is true.

The dealt hand will contain either a king or an ace (or both).

The dealt hand will contain either a queen or an ace (or both).

Based on your deductions, should you bet that the dealt hand will contain an Ace or that it will contain a King?

If you think that the ace is the better bet, then you would have made a losing bet. In short, this is because it is impossible for an ace to be in the dealt hand.

To see why this is I will list out all of the explicit mental models.

Below are the mental models that people will create in accordance with the principle of truth. (See the article in main for what this is). You can see that Ace is in both rows, which makes it seem like ace must obviously be more likely to be in the dealt hand.

Statement 1 true

K

A

K ∩ A

Statement 2 true

Q

A

Q ∩ A

But, when we look at the full explicit set of potential models (including the models when one of the statements is false) we will realise that it is impossible for an ace to be in the hand. Note that ¬ stands for negation. (¬A) means that the hand does not have an ace. The first possible scenario is when statement one is true and statement two is false. The mental models for this are in the below table:

Statement 1 true

Statement 2 false

K

A

K ∩ A

¬Q

¬A

¬Q ∩ ¬A

Consider each column after the first as a potential possibility for how the dealt hand could be.

The first column means that the dealt hand will have a king and not have a queen. This looks good. There are no problems with th

...
How to learn a new area X that you have no idea about.
by Elo — 2015-08-18T05:42:26.039Z

This guide is in response to a request in the open thread. I would like to improve it; If you have some improvement to contribute I would be delighted to hear it! I hope it helps. It was meant to be a written down form of; "wait-stop-think" before approaching a new area.

This list is mean't to be suggestive and not limiting.

I realise there are many object-level opportunities for better strategies but I didn't want to cover them in this meta-strategy.

It would be very easy to strawman this list. i.e. 1 could be a waste of time that people of half a brain don't need to cover. However if your steelman each point it will hopefully make entire sense. (I would love this document to be stronger, if there is an obvious strawman I probably missed it; feel free to make a suggestion for it to obviously read in the steel-form of the point.

Happy readings!

0. make sure you have a growth mindset. Nearly anything can be learnt or improved on. Aside from a few physical limits – i.e. being the best marathon runner is very difficult; but being a better marathon runner than you were yesterday is possible. (unknown time duration, changing one's mind)

Make sure your chosen X is aligned with your actual goals (are you doing it because you want to?). When you want to learn a thing; is X that thing? (Example: if you want to exercise; maybe skiing isn't the best way to do it. Or maybe it is because you live in a snow country) (5-10 minutes)

Check that you want to learn X and that will be progress towards a goal (or is a terminal goal – i.e. learning to draw faces can be your terminal, or can help you to paint a person's portrait). (5 minutes, assuming you know your goals)

Make a list of what you think that X is. Break it down. Followed by what you know about X, and if possible what you think you are missing about X. (5-30 minutes, no more than an hour)

Do some research to confirm that your rough definition of X is actually correct. Confirm that what you know already is true, if not – replace that existing knowledge with true things about X. Do not jump into everything yet. (1-2 hours, no more than 5 hours)

Figure out what experts in the area know (by topic area name), try to find what strategies experts in the area use to go about improving themselves. (expert people are usually a pretty good way to find things out) (1-2 hou

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Fragile Universe Hypothesis and the Continual Anthropic Principle - How crazy am I?
by PeterCoin — 2015-08-18T00:53:16.711Z

Personal Statement

I like to think about big questions from time to time. A fancy that quite possibly causes me more harm than good. Every once in a while I come up with some idea and wonder "hey, this seems pretty good, I wonder if anyone is taking it seriously?" Usually, answering that results at worst in me wasting a couple days on google and blowing $50 on amazon before I find someone who’s going down the same path and can tell myself. "Well, someone's got that covered". This particular idea is a little more stubborn and the amazon bill is starting to get a little heavy. So I cobbled together this “paper” to get this idea out there and see where it goes.

I've been quite selective here and have only submitted it on two other places Vixra, and FXQI forum. Vixra for posterity in the bizarre case that it's actually right. FXQI because they play with some similar ideas (but the forum turned out to be not really vibrant for such things). I'm now posting it on Less Wrong because you guys seem to be the right balance of badass skeptics and open minded geeks. In addition I see a lot of cool work on Anthropic Reasoning and the like so it seems to go along with your theme.

Any and all feedback is welcome, I'm a good sport!

Abstract

A popular objection to the Many-worlds interpretation of Quantum Mechanics is that it allows for quantum suicide where an experimenter creates a device that instantly kills him or leaves him be depending the output of a quantum measurement, since he has no experience of the device killing him he experiences quantum immortality. This is considered counter-intuitive and absurd. Presented here is a speculative argument that accepts counter-intuitiveness and proposes it as a new approach to physical theory without accepting some of the absurd conclusions of the thought experiment. The approach is based on the idea that the Universe is Fragile in that only a fraction of the time evolved versions retain the familiar structures of people and planets, but the fractions that do not occur are not observed. This presents to us as a skewed view of physics and only by accounting for this fact (which I propose calling the Continual Anthropic Principle) can we understand the true fundamental laws.

Preliminary reasoning

Will a supercollider destroy the Earth?

A fringe objection to the latest generation of high...
Does random reward evoke stronger habits?
by Bound_up — 2015-08-17T21:03:56.004Z

http://measureofdoubt.com/2011/04/12/pulling-levers-killing-monsters-the-lure-of-unpredictable-rewards/ (how do I put a link like this in a word with blue letters?)

I've read that unpredictable rewards associated with a behavior actually encourage that behavior more effectively than consistent rewards.

The optimal habit-forming figure given in the link above is a 25% chance of reward for each instance of performing the behavior.

My hypothesis then, is that if I want to establish a habit by rewarding myself upon successfully performing a certain task, I should reward myself only 25% of the time if I want to ingrain the habit as forcefully as possible into my unconscious.

Anyone else think so, or have any other research to add?