trentmkelly/LessWrong-43k huggingface dataset:
Review: Artifact
by Zvi — 2018-11-22T15:00:01.335Z

Epistemic Status: Alpha tester

Bottom Line: If you are willing to devote the time and attention to a deep strategic game, Artifact will reward you handsomely. I highly recommended those who like such experiences to make the time. If you are not willing to devote the time and attention, you will likely be frustrated and bounce off, and what time and attention you do have to game with is better spent elsewhere.

Artifact is an amazing game. Artifact is gorgeous, immersive and flavorful, hilarious, innovative, exciting, suspenseful, skill testing, strategically complex and rewarding. The execution is bug-free and flawless. It is the most fun I have had playing a game in a long time.

It streams well and is an excellent spectator sport for those who know the game and cards, and will be supported by an inaugural tournament with a one million dollar first prize. It is a Valve game, so you know it will get the level of support and attention it deserves in every aspect.

The economic model is the right one. Rather than addicting players to daily rewards and grinds, Artifact charges money for a game worth playing. You own your cards and will soon be able to buy and sell them. For your initial $20 you get 20 packs, each containing at least one card of the highest rarity and often two or even three. Additional packs are $2. Playing events costs only a single event ticket ($1), and you turn a large profit if you can get three wins before your second loss. I will say more on this later, but the model presented is extraordinarily generous, and those who are comparing it unfavorably to Magic Online should be ashamed of themselves.

The catch is that Artifact is complex. Very complex. Complex enough that I have had multiple Magic professionals try the game only to have them report back that they bounced off the game because they did not understand what was going on. Artifact makes the most of its complexity, and uses its roots in DOTA 2 to justify much of it, but the complexity is still there and complexity is bad. Your first hour is likely to be overwhelming and confusing, as lots of cool things are happening all around you but it’s impossible to fully know why or what they mean, or what is likely to happen next.

Gameplay

Artifact matches up two teams of five heroes each, who do battle across three distinct lanes. This mimics the structure of games like DOTA 2, where there are five...
Perspective Reasoning and the Sleeping Beauty Problem
by dadadarren — 2018-11-22T11:55:22.114Z

I want to present a new argument for double-halving position for the sleeping beauty problem. It is my contention that paradoxes related to the anthropic principle such as the Sleeping Beauty Problem and the Doomsday Argument is caused by mixing logics from different perspectives. To solve these paradoxes we only need to reason from a single consistent perspective.

When thinking about anthropic related paradoxes one perspective we can employ is an observer’s first-person perspective. The center of a perspective is primitively unique in logic and reasoning. E.g. I am fundamentally special to myself. I do not need to know any objective differences between me and every other human being to tell “this is me”. Similarly, the present moment is primitively meaningful. I can inherently tell “now” is not any other time. From first-person perceptive it is possible to specify the center, e.g. myself and now, from other people and time without any additional information.

We can also reason as an outside observer, or employ a God’s view, I call it the third-person perspective. The main point is that the center of that perspective is irrelevant to the topic of interest. From this perspective there is no obvious “me”. Every observer in question is ordinary as everybody else. In order to specify someone from a group one have to know the differences between him and the rest. Similarly there is no “now” that inherently stands out from any other moment. From third-person perspective every observer and everyday are treated as equals.

I argue the reasonings from these two perspectives should not be mixed and used together. So I am either inherently special (such that no information is needed to specify me) or just an ordinary person as everybody else is. Similarly “today” is either a meaningful moment uniquely stands out or everyday in the experiment are equals. We should always stick to one perspective and not use both points in the same logic framework. Mix them up and paradoxes would ensue.

From this starting point I derived the answer to the Sleeping Beauty Problem should be double halving. Several point worth mentioning:

No matter which perspective one choose to reason from there is no new information when beauty wakes up.

Questions such as “the probability of today is Monday” is invalid. Such probabilities do not exist since it requires specifying today from first-person p

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The Semantic Man
by namespace — 2018-11-22T08:38:21.220Z
Link: http://www.generalsemantics.org/wp-content/uploads/2011/05/lee-semantic-man.pdf

Irving Lee discusses the goals of General Semantics, one of the historical movements that underlies intellectual currents expressed here now on LessWrong.

I want to speak very briefly to you. There is one small notion that I should like to talk about rather briefly. It was in 1946, I remember almost the time of year. I had just taken off that Air Force uniform and had managed to persuade Alfred Korzybski to let me pose some questions to him. I had a number of things that bothered me. I had read that “blue peril” and there were paragraphs in it that made no sense even after the fifteenth reading, and I wanted the opportunity to confront him with these paragraphs. I wanted to say: “Now, Alfred, what did you mean when you said this?” And he very kindly agreed to submit to some such questioning, over a period of several afternoons, and I think Miss Kendig may remember some of them. And at one of these sessions, I said, “Now, Alfred, you have been thinking about this stuff for a very long time. Can you tell me, in a nutshell, what are you trying to do? What is the objective of all this reading and studying and talking and sweating that you go through day after day, year after year? What are you after?” And, you know, I never could call on him in those sessions without being forced to take notes. If I came without a pencil and paper, he invariably found a pad and pencil, and “take some notes” was the continuous refrain. Well, I have gone over those notes many times and in answer to that question, this is almost a verbatim account of what he said when I asked him, “Alfred, what are you trying to do, in a nutshell?”
Iteration Fixed Point Exercises
by Scott Garrabrant — 2018-11-22T00:35:09.885Z

This is the third of three sets of fixed point exercises. The first post in this sequence is here, giving context.

Note: Questions 1-5 form a coherent sequence and questions 6-10 form a separate coherent sequence. You can jump between the sequences.

Let (X,d) be a complete metric space. A function f:X→X is called a contraction if there exists a q<1 such that for all x,y∈X, d(f(x),f(y))≤q⋅d(x,y). Show that if f is a contraction, then for any x, the sequence {xn=fn(x0)} converges. Show further that it converges exponentially quickly (i.e. the distance between the nth term and the limit point is bounded above by c⋅an for some a<1)

(Banach contraction mapping theorem) Show that if (X,d) is a complete metric space and f is a contraction, then f has a unique fixed point.

If we only require that d(f(x),f(y))<d(x,y) for all x≠y, then we say f is a weak contraction. Find a complete metric space (X,d) and a weak contraction f:X→X with no fixed points.

A function f:Rn→R is convex if f(tx+(1−t)y)≤tf(x)+(1−t)f(y), for all t∈[0,1] and x,y∈Rn. A function f is strongly convex if you can subtract a positive parabaloid from it and it is still convex. (i.e. f is strongly convex if x↦f(x)−ε||x||2 is convex for some ε>0.) Let f be a strongly convex smooth function from Rn to R, and suppose that the magnitude of the second derivative ∥∇2f∥ is bounded. Show that there exists an ε>0 such that the function g:Rn→Rn given by x↦x−ε(∇f)(x) is a contraction. Conclude that gradient descent with a sufficiently small constant step size converges exponentially quickly on a strongly convex smooth function.

A finite stationary Markov chain is a finite set S of states, along with probabilistic rule A:S→ΔS for transitioning between the states, where ΔS represents the space of probability distributions on S. Note that the transition rule has no memory, and depends only on the previous state. If for any pair of states s,t∈ΔS, the probability of passing from s to t in one step is positive, then the Markov chain (S,A) is ergodic. Given an ergodic finite stationary Markov chain, use the Banach contraction mapping theorem to show that there is a unique distribution over states which is fixed under application of transition rule. Show that, starting from any state s, the limit distribution limn→∞An(s) exists and is equal to the stationary distribution.

A function f from a

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Suggestion: New material shouldn't be released too fast
by Chris_Leong — 2018-11-21T16:39:19.495Z

Three sequences on AI Safety were recently released. I'd suggest that in general it is probably better when new material isn't released that fast. Those sequences are quite technical so each post requires a reasonable amount of time to be invested. This means:

There is more psychological pressure to read each post quickly rather than taking the time to understand and internalise the ideas
There are less comments because keeping up with the posts reduces the time people have to write comments and if you're behind, by the time you catch up there's less value writing a comment as less people will see it

This isn't a particularly important post, just feedback for the future.
EA Funds: Long-Term Future fund is open to applications until November 24th (this Saturday)
by habryka — 2018-11-21T03:39:15.247Z

I am one of the Fund Managers for the Long-Term Future fund and we are doing a very short round of applications to help donors get a sense of what projects we are likely to fund before giving season:

Have an x-risk or far-future focused project you’ve been thinking about, but don’t have funding for? The CEA Long-Term Future Fund is accepting applications until this Saturday (11/24) at this form:

https://docs.google.com/forms/d/e/1FAIpQLSf46ZTOIlv6puMxkEGm6G1FADe5w5fCO3ro-RK6xFJWt7SfaQ/viewform

This round of applications has a much shorter deadline than usual, to give the Fund’s new management team a chance to make some grants to help donors get a sense of what kinds of projects we are likely to fund before the end of the giving season in December. Future rounds will have longer timelines, and the Fund may invite applicants from this round to re-submit in future rounds.

In other words: if you can’t hit this deadline, no need to worry - we know it’s very tight! Just keep an eye out for future rounds.

Feel free to ask any questions in the comments. I will be happy to answer any that I know the answer to (and will ping other fund managers if they seem better suited to answer a question).

Some more context from the form:

For this round and this form, we are particularly interested in small teams and individuals that are trying to get projects off the ground, or that need less money than existing grant-making institutions are likely to give out (i.e. less than ~$40k). Here are a few examples of project types that we're open to funding an individual or group for (note that this list is not exhaustive):

+ To spend a few months (perhaps during the summer) to research an open problem in AI alignment or AI strategy and produce a few blog posts or videos on their ideas
+ To spend a few months building a web app with the potential to solve an operations bottleneck at x-risk organisations
+ To spend a few months up-skilling in a field to prepare for future work (e.g. microeconomics, functional programming, etc).
+ To spend a year testing an idea that has the potential to be built into an org.

You can find more details on the kind of project we are likely to fund on the fund page: https://app.effectivealtruism.org/funds/far-future
Incorrect hypotheses point to correct observations
by Kaj_Sotala — 2018-11-20T21:10:02.867Z

1. The Consciousness Researcher and Out-Of-Body Experiences

In his book Consciousness and the Brain, cognitive neuroscientist Stansilas Dehaene writes about scientifically investigating people’s reports of their out-of-body experiences:

… the Swiss neurologist Olaf Blanke[ did a] beautiful series of experiments on out-of-body experiences. Surgery patients occasionally report leaving their bodies during anesthesia. They describe an irrepressible feeling of hovering at the ceiling and even looking down at their inert body from up there. [...]

What kind of brain representation, Blanke asked, underlies our adoption of a specific point of view on the external world? How does the brain assess the body’s location? After investigating many neurological and surgery patients, Blanke discovered that a cortical region in the right temporoparietal junction, when impaired or electrically perturbed, repeatedly caused a sensation of out-of-body transportation. This region is situated in a high-level zone where multiple signals converge: those arising from vision; from the somatosensory and kinesthetic systems (our brain’s map of bodily touch, muscular, and action signals); and from the vestibular system (the biological inertial platform, located in our inner ear, which monitors our head movements). By piecing together these various clues, the brain generates an integrated representation of the body’s location relative to its environment. However, this process can go awry if the signals disagree or become ambiguous as a result of brain damage. Out-of-body flight “really” happens, then—it is a real physical event, but only in the patient’s brain and, as a result, in his subjective experience. The out-of-body state is, by and large, an exacerbated form of the dizziness that we all experience when our vision disagrees with our vestibular system, as on a rocking boat.

Blanke went on to show that any human can leave her body: he created just the right amount of stimulation, via synchronized but delocalized visual and touch signals, to elicit an out-of-body experience in the normal brain. Using a clever robot, he even managed to re-create the illusion in a magnetic resonance imager. And while the scanned person experienced the illusion, her brain lit up in the temporoparietal junction—very close to where the patient’s lesions were located.

We still do not know exactly how this re

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Preschool: Much Less Than You Wanted To Know
by Zvi — 2018-11-20T19:30:01.155Z

Response to (Scott Alexander): Preschool: Much More Than You Wanted to Know

Previously (Here): The Case Against Education, The Case Against Education: Foundations, The Case Against Education: Splitting the Education Premium Pie and Considering IQ

I see Scott’s analysis of preschool as burying the lead.

I see his analysis as assuming there exists a black box called ‘preschool’ one can choose whether to send children to. Then, we have to decide whether or not this thing has value. Since studies are the way one figures out if things are true, we look at a wide variety of studies, slog through their problems and often seemingly contradictory results, and see if anything good emerges.

The result of that analysis, to me, was that it was possible preschool had positive long term effects on things like high school graduation rates. It was also possible that it did not have such an effect if you properly controlled for things, or that the active ingredient was effectively mostly ‘give poor families time and money’ via a place to park their kids, rather than any benefits from preschool itself. Scott puts it at 60% that preschool has a small positive effect, whether or not it is worth it and whether or not it’s mainly giving families money, and 40% it is useless even though it is giving them money. Which would kind of be an epic fail.

There was one clear consistent result, however: Preschool gives an academic boost, then that academic boost fades away within a few years. Everyone agrees this occurs.

Let us think about what this means.

This means that preschool is (presumably) spending substantial resources teaching children ‘academics,’ and even as measured by future achievement in those same academics, this has zero long term effect. Zippo. Zilch. Not a thing.

Maybe you should stop doing that, then?

This seems to be saying something important – that when you force four year olds to learn to read or add, that you don’t achieve any permanent benefits to their math or reading ability, which strongly implies you’re not helping them in other ways either. That’s not a result about preschool. That’s a result about developing brains and how they learn, and suggesting we should focus on other skills and letting them be kids. Spending early time you will never get back on ‘academic’ skills is a waste, presumably because it’s so horribly inefficient and we’ll end up re-teaching the same ...
New safety research agenda: scalable agent alignment via reward modeling
by Vika — 2018-11-20T17:29:22.751Z
Link: https://medium.com/@deepmindsafetyresearch/scalable-agent-alignment-via-reward-modeling-bf4ab06dfd84

Jan Leike and others from the DeepMind safety team have released a new research agenda on reward learning:

"Ultimately, the goal of AI progress is to benefit humans by enabling us to address increasingly complex challenges in the real world. But the real world does not come with built-in reward functions. This presents some challenges because performance on these tasks is not easily defined. We need a good way to provide feedback and enable artificial agents to reliably understand what we want, in order to help us achieve it. In other words, we want to train AI systems with human feedback in such a way that the system’s behavior aligns with our intentions. For our purposes, we define the agent alignment problem as follows:

How can we create agents that behave in accordance with the user’s intentions?

The alignment problem can be framed in the reinforcement learning framework, except that instead of receiving a numeric reward signal, the agent can interact with the user via an interaction protocol that allows the user to communicate their intention to the agent. This protocol can take many forms: the user can provide demonstrations, preferences, optimal actions, or communicate a reward function, for example. A solution to the agent alignment problem is a policy that behaves in accordance with the user’s intentions.

With our new paper we outline a research direction for tackling the agent alignment problem head-on. Building on our earlier categorization of AI safety problems as well as numerous problem expositions on AI safety, we paint a coherent picture of how progress in these areas could yield a solution to the agent alignment problem. This opens the door to building systems that can better understand how to interact with users, learn from their feedback, and predict their preferences — both in narrow, simpler domains in the near term, and also more complex and abstract domains that require understanding beyond human level in the longer term.

The main thrust of our research direction is based on reward modeling: we train a reward model with feedback from the user to capture their intentions. At the same time, we train a policy with reinforcement learning to maximize the reward from the reward model. In other words, we separate learning what to do (the reward model) from learning how to do it (the policy).

For example, in previous work we taught agents to do...
Prosaic AI alignment
by paulfchristiano — 2018-11-20T13:56:39.773Z

(Related: a possible stance for AI control.)

It’s conceivable that we will build “prosaic” AGI, which doesn’t reveal any fundamentally new ideas about the nature of intelligence or turn up any “unknown unknowns.” I think we wouldn’t know how to align such an AGI; moreover, in the process of building it, we wouldn’t necessarily learn anything that would make the alignment problem more approachable. So I think that understanding this case is a natural priority for research on AI alignment.

In particular, I don’t think it is reasonable to say “we’ll know how to cross that bridge when we come to it,” or “it’s impossible to do meaningful work without knowing more about what powerful AI will look like.” If you think that prosaic AGI is plausible, then we may already know what the bridge will look like when we get to it: if we can’t do meaningful work now, then we have a problem.

1. Prosaic AGI

It now seems possible that we could build “prosaic” AGI, which can replicate human behavior but doesn’t involve qualitatively new ideas about “how intelligence works:”

It’s plausible that a large neural network can replicate “fast” human cognition, and that by coupling it to simple computational mechanisms — short and long-term memory, attention, etc. — we could obtain a human-level computational architecture.
It’s plausible that a variant of RL can train this architecture to actually implement human-level cognition. This would likely involve some combination of ingredients like model-based RL, imitation learning, or hierarchical RL. There are a whole bunch of ideas currently on the table and being explored; if you can’t imagine any of these ideas working out, then I feel that’s a failure of imagination (unless you see something I don’t).

We will certainly learn something by developing prosaic AGI. The very fact that there were no qualitatively new ideas is itself surprising. And beyond that, we’ll get a few more bits of information about which particular approach works, fill in a whole bunch of extra details about how to design and train powerful models, and actually get some experimental data.

But none of these developments seem to fundamentally change the alignment problem, and existing approaches to AI alignment are not bottlenecked on this kind of information. Actually having the AI in front of us may let us work several times more efficiently, but it’s not going to move us fro...
[Insert clever intro here]
by Bae's Theorem — 2018-11-20T03:26:32.420Z

Hello, everyone. I'm not entirely sure where to start, or exactly how to say it, but here goes.

I only stumbled across Less Wrong this year, after reading HPMOR (I'm sure you get this all the time). Since then, I have gone from an evangelical Christian to a passionate rationalist with an insatiable hunger for challenging what I think I know. It helps that I already had a rationalist foundation, having originally started my adulthood as an atheist.

My training is in IT, but nowhere near enough to help with AI research. I am also quite intelligent, but not enough to open my own rationality dojo. Instead, I am channeling my extroverted nature to help spread the influence of rationality as far and passionately as I can. I am currently spearheading the Kansas City LW/SSC group, and plan to also begin practicing Street Epistemology.

At the risk of sounding like I am trying to garner empathy, I feel rather intimidated by the conversations that are had here; so my posting and commenting will be limited if not entirely non-existent. Please don't ever be discouraged by my silence. You may count yourselves among my friends.