trentmkelly/LessWrong-43k huggingface dataset:
Another way to look at consciousness
by JanetK — 2010-05-20T13:58:04.856Z

Edit: First paragraph removed and small changes made to the rest.

I am putting forth a hypothesis is about the nature of consciousness. First I will have to tell you how I am using certain words because they are generally used in a number of ways. 'Brain' is an biological organ and it has a function, 'mind'. Mind is not an object; it is what brains do. It is not a property of the brain, let alone an emergent property, whatever that is. It is a function - so mind-is-to-brain as circulation-is-to-heart or digestion-is-to- intestine. There is one brain in any head and there is one mind being produced by that brain – not two. (Assuming sanity) The different parts of the cortex work together; the two hemispheres work together; the fore-brain structures work together with the mid-brain structures. The mind includes at least: perception, cognition, learning, intention, motor control, remembering, and most importantly, the forming a model of the environment and the person in that environment. The division between 'conscious mind' and 'unconscious mind' is meaningless. The brain does its mind-function which maintains the model. Some but not all of this model is made globally accessible to all of the brain and remembered. That edit of the model is what we experience as conscious experience, in other words, is our 'consciousness'. Consciousness is awareness not thought. Consciousness is not separate but part of a single mind-function. Now that the words are straight, I can describe the hypothesis.

How is the model edited for consciousness?

There is an attention focus that is triggered by the on-going work of the mind and the events that happen in the environment. I may concentrate on some task so that I am not conscious of other parts of the model but a loud noise will cause my attention focus to shift to the source of noise in the model. The level of attention is variable from non-existent (coma) to intense. This level depends on the signals coming from the lower parts of the brain, through the thalamus, into the cortex. A common analogue for attention is a searchlight scanning the mind-model of reality. We cannot be aware of the whole of the model at any instant of time.

How is the model formed?

The fragments for the model are forced together into a best fit global model. The perception of the various senses, inbo...
Summer vs Winter Strategies
by  — 2010-05-20T00:31:14.588Z

Abstract: I have a hypothesis that there are two different general strategies for life that humans might switch between predicated on the way general resource availability change in the society. If it is constant or increasing one strategy pays off, if predictably increasing then decreasing, another is good. These strategies would have been selected for at different times and environments in prehistory but humans are mainly plastic in which strategy they adopt. Culture reinforces them and can create lags. For value neutral purposes I will call them by seasons, the Summer strategy and the Winter strategy. The summer is for times of plenty and partying, and the winter for when resources regularly become scarcer and life becomes harsher. These strategies affect every part of society from mating to the way people plan.

The above is an idea that seems to tie up a few loose threads I have been seeing around the place. I am mainly channelling Robin Hanson here, so some familiarity with him would be useful. I'd also recommend this paper on sexuality and character traits. And the red queen.

Note: I don't have time to write a properly researched paper, so you are going to have to settle for a blog style post. So it is not front page material. But I would rather it got more coverage than in the open thread. If someone is enamoured with the idea, feel free to make a well written riff on this theme. I won't get off

ended.

The summer strategy

This is selected for by sexual selection. Women want to mate with attractive powerful men when they don't have to worry about the babies being provided for by that man. Attractive powerful men have to signal there attractiveness and power, they can use lots of resources to do so. This [sexual strategies theory paper](http://www.psy.cmu.edu/~rakison/bussandschmitt.pdf) gives lots of good reasons why men may have been selected for what they call short term sexuality.

It is characterised by:

- Less planning needed. Credit cards. Significant Debt.

- Near thinking

- Extroversion

- Babies without fathers will more likely to survive. Women gathering resources by themselves if they lack male relatives. This leads to more promiscuous women and more promiscuous men as well, as social mores change. Short-term mating strategies do well. Breakdown of traditional monogamy.

- More extroversion in men. Due to less need for planning ahead and gathering reso...
Blame Theory
by cousin_it — 2010-05-19T21:49:14.867Z

EDIT: this post, like many other posts of mine, is wrong. See the comments by Yvain below. Maybe "Regret Theory" would've been a better title. But I'm keeping this as it is because I like having reminders of my mistakes.

Platitudes notwithstanding, "personal responsibility" doesn't adequately summarize my relationship with the universe. Other people are to blame for some of my troubles because, as Shepherd from MW2 put it, "what happens over here matters over there". Let's talk about it.

When other people's actions can affect you and vice versa, individual utility maximization stops working and you must use game theory. The Prisoner's Dilemma stresses our intuitions by making a mockery of personal responsibility: each player holds the power to screw up the other player's welfare which they don't care about. Economists call such things "externalities", political scientists talk of "special interests". You can press a button that gives you $10 but makes 10000 random people lose $0.01 each (due to environmental pollution or something like that), you care enough to vote for this proposal, other people don't care enough to vote against, haha democracy fail.

When the shit hits the fan in a multiplayer setting, we clearly need a theory for assigning blame in correct proportion. For example, what should we make of the democratic credo that people are responsible for the leaders they have? Exactly how much responsible? How many people did I personally kill by voting for Hitler, and how is this responsibility shared between Hitler and me? The "naive counterfactual" answer goes like this: if I hadn't voted for Hitler, he'd still have been elected (since I wasn't the marginal deciding voter), therefore I'm blameless. Clearly this answer is not satisfactory. We need more sophisticated game theory concepts.

First of all, we would do well to assume transferable utility. To understand why, consider Clippy. Clippy is perfectly willing to kill a million Armenians to gain one paperclip. We can blame him (her?) for it all day, but it's probably safe to say that Clippy's internal sense of guilt isn't denominated in Armenians. We must reach a mutual understanding by employing a common currency of guilt, which is just another way of saying "convertable utils". You feel guilty toward me = you owe me dough. Too bad, knowing you, you probably won't pay.

Our second ass...
Backchaining causes wishful thinking
by PhilGoetz — 2010-05-19T19:01:34.503Z

Wishful thinking - believing things that make you happy - may be a result of adapting an old cognitive mechanism to new content.

Obvious, well-known stuff

The world is a complicated place. When we first arrive, we don't understand it at all; we can't even recognize objects or move our arms and legs reliably. Gradually, we make sense of it by building categories of perceptions and objects and events and feelings that resemble each other. Then, instead of processing every detail of a new situation, we just have to decide which category it's closest to, and what we do with things in that category. Most, possibly all, categories can be built using unsupervised learning, just by noting statistical regularities and clustering.

If we want to be more than finite-state automata, we also need to learn how to notice which things and events might be useful or dangerous, and make predictions, and form plans. There are logic-based ways of doing this, and there are also statistical methods. There's good evidence that the human dopaminergic system uses one of these statistical methods, temporal difference learning (TD). TD is a backchaining method: First it learns what state or action Gn-1 usually comes just before reaching a goal Gn, and then what Gn-2 usually comes just before Gn-1, etc. Many other learning methods use backchaining, including backpropagation, bucket brigade, and spreading activation. These learning methods need a label or signal, during or after some series of events, saying whether the result was good or bad.

I don't know why we have consciousness, and I don't know what determines which kinds of learning require conscious attention. For those that do, the signals produce some variety of pleasure or pain. We learn to pay attention to things associated with pleasure or pain, and for planning, we may use TD to build something analogous to a Markov process (sorry, I found no good link; and Wikipedia's entry on Markov chain is not what you want) where, given a sequence of the previous n states or actions (A1, A2, ... An), the probability of taking action A is proportional to the expected (pleasure - pain) for the sequence (A1, ... An, A). In short, we learn to do things that make us feel better.

Less-obvious stuff

Here's a key point which is overlooked (or specifically denied) by most...
Physicalism: consciousness as the last sense
by Academian — 2010-05-19T16:31:13.459Z

Follow-up to There just has to be something more, you know? and The two insights of materialism.

I have alluded that one cause for the common reluctance to consider physicalism — in particular, that our minds can in principle be characterized entirely by physical states — is an asymmetry in how people perceive characterization. This can be alleviated by analogy to how our external senses can supervene on each other, and how abstract manipulations of those senses using recording, playback, and editing technologies have made such characterizations useful and intuitive.

We have numerous external senses, and at least one internal sense that people call "thinking" or "consciousness". In part because you and I can point our external senses at the same objects, collaborative science has done a great job characterizing them in terms of each other. The first thing is to realize the symmetry and non-triviality of this situation.

First, at a personal level: say you've never sensed a musical instrument in any way, and for the first time, in the dark, you hear a cello playing. Then later, you see the actual cello. You probably wouldn't immediately recognize these perceptions as being of the same physical object. But watching and listening to the cello playing at the same time would certainly help, and physically intervening yourself to see that you can change the pitch of the note by placing your fingers on the strings would be a deal breaker: you'd start thinking of that sound, that sight, and that tactile sense as all coming from one object "cello".

Before moving on, note how in these circumstances we don't conclude that "only sight is real" and that sound is merely a derivate of it, but simply that the two senses are related and can characterize each other, at least roughly speaking: when you see a cello, you know what sort of sounds to expect, and conversely.

Next, consider the more precise correspondence that collaborative science has provided, which follows a similar trend: in the theory of characterizing sound as logitudinal compression waves, first came recording, then playback, and finally editing. In fact, the first intelligible recording of a human voice, in 1860, was played back for the first time in 2008, using computers. So, suppose it's 1810, well before the inve...
What is Wei Dai's Updateless Decision Theory?
by AlephNeil — 2010-05-19T10:16:51.228Z

As a newcomer to LessWrong, I quite often see references to 'UDT' or 'updateless decision theory'. The very name is like crack - I'm irresistably compelled to find out what the fuss is about.

Wei Dai's post is certainly interesting, but it seemed to me (as a naive observer) that a fairly small 'mathematical signal' was in danger of being lost in a lot of AI-noise. Or to put it less confrontationally: I saw a simple 'lesson' on how to attack many of the problems that frequently get discussed here, which can easily be detached from the rest of the theory. Hence this short note, the purpose of which is to present and motivate UDT in the context of 'naive decision theory' (NDT), and to pre-empt what I think is a possible misunderstanding.

First, a quick review of the basic Bayesian decision-making recipe.

What is Naïve Decision Theory?

You take the prior and some empirical data and calculate a posterior by (i) working out the 'likelihood function' of the data and (ii) calculating prior times likelihood and renormalising. Then you calculate expected utilities for every possible action (wrt to this posterior) and maximize.

Of course there's a lot more to conventional decision theory than this, but I think one can best get a handle on UDT by considering it as an alternative to the above procedure, in order to handle situations where some of its presuppositions fail.

(Note: NDT is especially 'naïve' in that it takes the existence of a 'likelihood function' for granted. Therefore, in decision problems where EDT and CDT diverge, one must 'dogmatically' choose between them at the outset just to obtain a problem that NDT regards as being well-defined.)

When does NDT fail?

The above procedure is extremely limited. Taking it exactly as stated, it only applies to games with a single player and a single opportunity to act at some stage in the game. The following diagram illustrates the kind of situation for which NDT is adequate:

This is a tree diagram (as opposed to a causal graph). The blue and orange boxes show 'information states', so that any player-instance within the blue box sees exactly the same 'data'. Hence, their strategy (whether pure or mixed) must be the same throughout the box. The branches on the right have been greyed out to depict the Bayesian 'updating' that the player following NDT would do upon seeing 'blue' rather than 'orange'--a branch ...
Be a Visiting Fellow at the Singularity Institute
by AnnaSalamon — 2010-05-19T08:00:54.020Z

Now is the very last minute to apply for a Summer 2010 Visiting Fellowship. If you’ve been interested in SIAI for a while, but haven’t quite managed to make contact -- or if you’re just looking for a good way to spend a week or more of your summer -- drop us a line. See what an SIAI summer might do for you and the world.
(SIAI’s Visiting Fellow program brings volunteers to SIAI for anywhere from a week to three months, to learn, teach, and collaborate. Flights and room and board are covered. We’ve been rolling since June of 2009, with good success.)
Apply because:

SIAI is tackling the world’s most important task -- the task of shaping the Singularity. The task of averting human extinction. We aren’t the only people tackling this, but the total set is frighteningly small.

When numbers are this small, it’s actually plausible that you can tip the balance.

SIAI has some amazing people to learn from -- many report learning and growing more here than in any other period of their lives.

SIAI also has major gaps, and much that desperately needs doing but that we haven’t noticed yet, or have noticed but haven’t managed to fix -- gaps where your own skills, talents, and energy can come into play.

Apply especially if:

You have start-up experience or are otherwise an instigator: someone who can walk into an unstructured environment and create useful projects for yourself and others;

You’re skilled at creating community; you have an open heart; you can learn rapidly, and create contexts for others to learn; you have a serious interest in pioneering more effective ways of thinking;

You care about existential risk, and are searching for long-term career paths that might help;

You have high analytic intelligence, a tendency to win math competitions, or background and thinking skill around AI, probability, anthropics, simulation scenarios, rationality, existential risk, and related topics; (math, compsci, physics, or analytic philosophy background is also a plus)

You have a specific background that is likely to prove helpful: academic research experience; teaching or writing skill; strong personal productivity; programming fluency; a cognitive profile that differs from the usual LW mold; or strong talent of some other sort, in an area we need, that we may not have re

...
Multiple Choice
by Alicorn — 2010-05-17T22:26:41.804Z

When we choose behavior, including verbal behavior, it's sometimes tempting to do what is most likely to be right without paying attention to how costly it is to be wrong in various ways or looking for a safer alternative.

If you've taken a lot of standardized tests, you know that some of them penalize guessing and some don't. That is, leaving a question blank might be better than getting a wrong answer, or they might have the same result. If they're the same, of course you guess, because it can't hurt and may help. If they take off points for wrong answers, then there's some optimal threshold at which a well-calibrated test-taker will answer. For instance, the ability to rule out one of four choices on a one-point question where a wrong answer costs a quarter point means that you should guess from the remaining three - the expected point value of this guess is positive. If you can rule out one of four choices and a wrong answer costs half a point, leave it blank.

If you have ever asked a woman who wasn't pregnant when the baby was due, you might have noticed that life penalizes guessing.

If you're risk-neutral, you still can't just do whatever has the highest chance of being right; you must also consider the cost of being wrong. You will probably win a bet that says a fair six-sided die will come up on a number greater than 2. But you shouldn't buy this bet for a dollar if the payoff is only $1.10, even though that purchase can be summarized as "you will probably gain ten cents". That bet is better than a similarly-priced, similarly-paid bet on the opposite outcome; but it's not good.

There's a few factors at work to make guessing tempting anyway:

You can't always leave "questions" blank.

Guessing is inconsistently penalized, and the penalties are often hidden.

Inaction sometimes has negative consequences too.

An increased ability to rule things out relative to the general population causes overconfidence.

We are biased against rating the value of information highly, and prefer to act on what we already know.

However, I maintain that we should refrain from guessing at a higher rate than baseline. This is especially relevant when choosing verbal behavior - we may remember to act according to expected utility, but rarely think to speak according to expected utility, and this is especially significant around ...
Preface to a Proposal for a New Mode of Inquiry
by Daniel_Burfoot — 2010-05-17T02:11:02.211Z

Summary: The problem of AI has turned out to be a lot harder than was originally thought. One hypothesis is that the obstacle is not a shortcoming of mathematics or theory, but limitations in the philosophy of science. This article is a preview of a series of posts that will describe how, by making a minor revision in our understanding of the scientific method, further progress can be achieved by establishing AI as an empirical science.

The field of artificial intelligence has been around for more than fifty years. If one takes an optimistic view of things, its possible to believe that a lot of progress has been made. A chess program defeated the top-ranked human grandmaster. Robotic cars drove autonomously across 132 miles of Mojave desert. And Google seems to have made great strides in machine translation, apparently by feeding massive quantities of data to a statistical learning algorithm.

But even as the field has advanced, the horizon has seemed to recede. In some sense the field's successes make its failures all the more conspicuous. The best chess programs are better than any human, but go is still challenging for computers. Robotic cars can drive across the desert, but they're not ready to share the road with human drivers. And Google is pretty good at translating Spanish to English, but still produces howlers when translating Japanese to English. The failures indicate that, instead of being threads in a majestic general theory, the successes were just narrow, isolated solutions to problems that turned out to be easier than they originally appeared.

So what went wrong, and how to move forward? Most mainstream AI researchers are reluctant to provide clear answers to this question, so instead one must read behind the lines in the literature. Every new paper in AI implicitly suggests that the research subfield of which it is a part will, if vigorously pursued, lead to dramatic progress towards intelligence. People who study reinforcement learning think the answer is to develop better versions of algorithms like Q-Learning and temporal difference (TD) learning. The researchers behind the IBM Blue Brain project think the answer is to conduct massive neural simulations. For some roboticists, the answer involves the idea of embodiment: since the purpose of the brain is to control the body, to understand intelligence one should build robots, put them in t...
Explanatory normality fallacy
by KatjaGrace — 2010-05-16T03:26:40.000Z

Only a psychologist thinks to ask why people laugh at jokes. – Someone (apparently)

A common error in trying to understand human behavior is to think something is explained because it is so intuitively familiar to you. The wrong answer to, ‘I wonder why people laugh at jokes?’ is, ‘They are funny duh’. This is an unrealistically obvious example; it can be harder to see. Why do we like art? Because it’s aesthetically pleasing. Why does sex exist? For reproduction. These are a popular variety of mind projection fallacy.

One thing that makes it much harder to see is emotional or moral overtones. A distinctive feature of morality is that it seems objectively true, so this isn’t surprising. e.g. if I say ‘I wonder why women evolved to be *so* upset about being raped?’ the wrong answer is ‘I can’t believe you just said that – rape is HORRIBLE!!!’. Why don’t humans let their disabled children die? Not ‘because they appreciate that that would be cruel’. Why do we want revenge when others have done us wrong? Not ‘because the others DESERVE IT!’ Why do humans hate incest? Not ‘because they aren’t completely depraved’.

Another thing that makes this error happen more is when the explanation is somewhat complicated even without explaining the key issue. This makes it less obvious that you haven’t said anything. Why do we enjoy some styles and features of music particularly? Because we have advanced as a civilization so much that we appreciate them. Fill this out with some more about how civilization has progressed and what some famous people have said about musical progression through time and nobody will notice you didn’t really answer.

Here’s a common combination of morality and apparent complication: Why do women hate being treated as instrumental to sexual pleasure? Because it objectifies them. Why do women hate being objectified? Because it makes people think of them as objects. Why don’t women like being thought of as objects? They get treated as objects. Why don’t women like being treated as objects? Objects are treated badly. Note that while we have now established that to be treated as instrumental to sexual pleasure is to be treated badly from a woman’s perspective, but since it was stated in the questio...