trentmkelly/LessWrong-43k huggingface dataset:
Category Theory Without The Baggage
by johnswentworth — 2020-02-03T20:03:13.586Z

If you are an algebraic abstractologist, this post is probably not for you. Further meta-commentary can be found in the “meta” section, at the bottom of the post.

So you’ve heard of this thing called “category theory”. Maybe you’ve met some smart people who say that’s it’s really useful and powerful for… something. Maybe you’ve even cracked open a book or watched some lectures, only to find that the entire subject seems to have been generated by training

GPT-2

on a mix of algebraic optometry and output from

theproofistrivial.com

.

What is this subject? What could one do with it, other than write opaque math papers?

This introduction is for you.

This post will cover just the bare-bones foundational pieces: categories, functors, and natural transformations. I will mostly eschew the typical presentation; my goal is just to convey intuition for what these things mean. Depending on interest, I may post a few more pieces in this vein, covering e.g. limits, adjunction, Yoneda lemma, symmetric monoidal categories, types and programming, etc - leave a comment if you want to see more.

Outline:

Category theory is the study of paths in graphs, so I’ll briefly talk about that and highlight some relevant aspects.
What’s a category? A category is just a graph with some notion of equivalence of paths; we’ll see a few examples.
Pattern matching: find a sub-category with a particular shape. Matches are called “functors”.
One sub-category modelling another: commutative squares and natural transformations.

Paths in Graphs

Here’s a graph:

Here are some paths in that graph:

A -> B
B -> C
A -> B -> C
A -> A
A -> A -> A (twice around the loop)
A -> A -> A -> B (twice around the loop, then to B)
(trivial path - start at D and don’t go anywhere)
(trivial path - start at A and don’t go anywhere)

In category theory, we usually care more about the edges and paths than the vertices themselves, so let’s give our edges their own names:

We can then write paths like this:

A -> B is written y
B -> C is written z
A -> B -> C is written yz
A -> A is written x
A -> A -> A is written xx
A -> A -> A -> B is written xxy
The trivial path at D is written id_D (this is roughly a standard notation)
The trivial path at A is written id_A

We can build longer paths by “composing” shorter paths. For instance, we can compose y (aka A -> B) with z (aka B -> C) to ...
Welcome to SSC Zurich [Edit With Your Details]
by ssczurich — 2020-02-03T18:56:53.255Z

(The following are our suggestions for what kind of information is best to include in the welcome post of your group, feel free to replace them with whatever you think is best)

What kind of events does your group usually run? What does it usually do?

How frequently does your group organize events or meet?

Who would be a good fit for you group?

Should they have any particular skills or have done some specific background reading?
Protecting Large Projects Against Mazedom
by Zvi — 2020-02-03T17:10:00.520Z

If we wish to accomplish something that would benefit from or require a larger organization or more levels of management and bureaucracy, what should we do in light of the dangers of mazes?

There are no easy answers. Real tradeoffs with real sacrifice are the order of the day. But we can do some things to expand the production possibilities frontier, and choose wisely along that frontier.

As is often the case, this starts with admitting you have a problem.

Too often, it is assumed that one should scale without worrying about the costs of scaling, or without counting becoming a maze as one of the biggest costs. Not stretching oneself maximally thin, or getting in the way of this process, becomes the sin of not maximizing effectiveness or profits.

That ensures failure and rapid descent into a maze. Start by getting out of this mindset.

If you are looking to accomplish a big thing that requires lots of organization, management and bureaucracy, here are ways to help contain the damage.

None of them should come as a surprise by this point. This is more of a synthesis of points already made, and not a place I feel I have special additional insights. So I will keep this short.

Solution 1: Do Less Things and Be Smaller

Recognize the threat and its seriousness, and the resulting risks and costs of scaling even if handled wisely. Understand that you almost certainly want to be smaller and do less things due to this concern. This is a real trade-off.

Think of the actions, priorities, skills and members of a group as being increasingly inherently expensive as the group grows – adding new elements has increasing marginal costs. Changing anything, or preventing anything from taking its natural course (including towards being more of a maze) also becomes increasingly expensive. Under such circumstances, the final marginal cost of every action or member is equal to the marginal cost of the final action taken or member added.

One must think about the future scale and the future costs, solve for the resulting budget constraints and trade-offs, and spend wisely. The same way that one must avoid accumulating technical debt, an organization should worry greatly about complexity and culture long before the bills are presented.

Remember that not doing something does not condemn that thing to never being done.

Encourage others to form distinct groups and organizat

...
Pessimism About Unknown Unknowns Inspires Conservatism
by michaelcohen — 2020-02-03T14:48:14.824Z

This [EDIT: final version, presentation] is a design for a conservative agent that I worked on with Marcus Hutter. Conservative agents are reluctant to make unprecedented things happen. The agent also approaches at least human-level reward acquisition.

The agent is made conservative by being pessimistic. Pessimism is tuned by a scalar parameter β∈(0,1). When the agent is more pessimistic, it is more conservative. When it is made more pessimistic, it would be less likely to exceed human-level reward acquisition (almost definitely, but I haven’t tried to prove that). It would also require more observations before it started acting, the more pessimistic it is. It is not clear to me how useful the agent would be at the level of pessimism where we could be confident it is safe. At 0 pessimism, it is similar to AIXI (although technically stronger, because AIXI doesn’t have the performance guarantee of matching or exceeding human-level reward acquisition).

The agent has access to a human mentor, and at every timestep, it can either act or defer to the mentor. The only assumption we make is that the true environment belongs to the agent’s (countable) set of world-models. First a bit of math, then the main results.

A bit of math:

An event E is a subset of interaction histories that end with an action. Letting A, O, and R be the action, observation, and reward spaces, an event E⊂(A×O×R)∗×A. An element of E would look like a1o1r1a2o2r2…at−1ot−1rt−1at. Below, I will say “[to] take an action which immediately causes event E”, by which I mean “to take an action such that now the interaction history is an element of E.”

The main results:

1) (At least) mentor-level reward acquisition,

2) Probability of querying the mentor →0,

and this will take some time read, but I figured I’d spell it all out properly:

3) For any complexity class C (defined on normal Turing machines, not e.g. non-deterministic ones), we can construct a set of world-models M such that for all events E in the complexity class C and for all δ>0, there exists a β such that: when the pessimistic agent has the model class M and a pessimism β, the following holds with probability 1−δ: for the whole lifetime of the pessimistic agent, if E has never happened...
UML IX: Kernels and Boosting
by Rafael Harth — 2020-02-02T21:51:25.114Z

(This is the ninth post in a sequence on Machine Learning based on this book. Click here for part I.)

Kernels

To motivate this chapter, consider some training sequence S=((x1,y1),...,(xm,ym)) with instances in some domain set X. Suppose we wish to use an embedding ψ:X→Rd of the kind discussed in the previous post (i.e., to make the representation of our points more expressive, so that they can be classified by a hyperplane). Most importantly, suppose that d is significantly larger than m. In such a case, we're describing each point ψ(xi) in terms of d coordinates, even though our space only has m points, which means that there can, in some sense, only be m "relevant" directions. In particular, let

U:=span(ψ(Sx))={p∈Rd|∃a∈Rm:p=∑di=1αiψ(xi)}

where Sx is the training sequence without labels, so that ψ(Sx)=(ψ(x1),...,ψ(xm)). Then U is an (at most) m-dimensional subspace of Rd, and we would like to prove that we can work in U rather than in Rd.

I

As a first justification for this goal, observe that ψ(xi)∈U for all i∈[m]. (The symbol [n] for any n∈N denotes the set {1,...,n}.) Recall that we wish to learn a hyperplane parametrized by some w∈Rd that can then be used to predict a new instance ψ(y) for some y∈X by checking whether ⟨w,ψ(y)⟩>0. The bulk of the difficulty, however, lies in finding the vector w; this is generally much harder than computing a single inner product ⟨w,ψ(y)⟩.

Thus, our primary goals are to show that

(1) w will lie in U

(2) w can somehow be computed by only working in U

To demonstrate this, we need to look at how w is chosen, which depends on the algorithm we use. In the case of Soft Support Vector Machines (previous post), we choose

w∈argminw∈Rd(λ||w||2+1mm∑k=1max[0,1−yk⟨w,ψ(xk)⟩]).

This rule shows that we only care about the inner product between w and our mapped training points, the ψ(xi). Thus, if we could somehow prove (1), then (2) would seem to follow: if w∈U, then, according to the rule above, we would only end up caring about inner products between points that are both in U.

Therefore, we now turn to proving (1) formally. To have the result be a bit more general (so that it also applies to alg...
A point of clarification on infohazard terminology
by eukaryote — 2020-02-02T17:43:56.601Z
Link: https://eukaryotewritesblog.com/2020/02/02/a-point-of-clarification-on-infohazard-terminology/

TL;DR: “Infohazard” means any kind of information that could be harmful in some fashion. Let’s use “cognitohazard” to describe information that could specifically harm the person who knows it.

Some people in my circle like to talk about the idea of information hazards or infohazards, which are dangerous information. This isn’t a fictional concept – Nick Bostrom characterizes a number of different types of infohazards in his 2011 paper that introduces the term (PDF available here). Lots of kinds of information can be dangerous or harmful in some fashion – detailed instructions for making a nuclear bomb. A signal or hint that a person is a member of a marginalized group. An extremist ideology. A spoiler for your favorite TV show. (Listen, an infohazard is a kind of hazard, not a measure of intensity. A papercut is still a kind of injury!)

I’ve been in places where “infohazard” is used in the Bostromian sense casually – to talk about, say, dual-use research of concern in the biological sciences, and describe the specific dangers that might come from publishing procedures of results.

I’ve also been in more esoteric conversations where people use the word “infohazard” to talk about a specific kind of Bostromian information hazard: information that may harm the person who knows it. This is a stranger concept, but there are still lots of apparent examples – a catchy earworm. “You just lost the game.” More seriously, an easy method of committing suicide for a suicidal person. A prototypical fictional example is the “basilisk” fractal from David Langford’s 1988 short story BLIT, which kills you if you see it.

This is a subset of the original definition because it is harmful information, but it’s expected to harm the person who knows it in particular. For instance, detailed schematics for a nuclear weapon aren’t really expected to bring harm to a potential weaponeer – the danger is that the weaponeer will use them to harm others. But fully internalizing the information that Amazon will deliver you a 5-pound bag of Swedish Fish whenever you want is specifically a danger to you. (…Me.)

This disparate use of terms is confusing. I think Bostrom and his intellectual kith get the broader definition of “infohazard”, since they coined the word and are actually using it professionally.*

I propose we call the second thing – information that harms the knower – a cognitohazard.

Pictured

...
Money isn't real.  When you donate money to a charity, how does it actually help? 
by Dagon — 2020-02-02T17:03:04.426Z

Money is a shared illusion. Neither paper, gold, nor electron patterns can stop a mosquito or feed a hungry person directly. Transfer of money is a temporary, low friction way to motivate unidentified strangers to preform an action.

So what's the mechanism by which monetary charity works? Are we "just" exploiting a comparative advantage in dollar-gathering in order to force the people on the ground to behave the way we prefer? Do we have any evidence that it can create channels of behavior that are self-sustaining?

On small (and not-so-small, but not universal) margins, money is a fine way to change some behaviors. I'm wondering if there are ways to shift the equilibria at the core rather than the margin.
[Link] Beyond the hill: thoughts on ontologies for thinking, essay-completeness and forecasting 
by Bird Concept — 2020-02-02T12:39:06.563Z

This is a link-post for: https://www.foretold.io/c/1bea107b-6a7f-4f39-a599-0a2d285ae101/n/5ceba5ae-60fc-4bd3-93aa-eeb333a15464

---

Epistemic status: gesturing at something that feels very important. Based on a true story. Show, don't tell. Release early.

Why are documents and spreadsheets so successful?

Why does code, which is many times more powerful than spreadsheets, have many times fewer users?

I think it's because code not just forces you to express your ideas in code, but also to think in code. It imposes constraints on your ontology for thinking.

Having spent the last year working on forecasting, I got some experience with how ontologies can significantly constrain technology projects.

I think such constraints have...

heavily limited the usefulness of past forecasting efforts
resulted in broad misconceptions about what forecasting could be used for
hidden a large space of interesting work that can be unlocked if we solved them

So the link-post is an interactive essay where I attempt to show what solving them might look like in practice, using some technology which is currently not supported on LessWrong.

(Note that the link will not work well on mobile.)
The Case for Artificial Expert Intelligence (AXI): What lies between narrow and general AI?
by Yuli_Ban — 2020-02-02T05:55:27.188Z

For years, I've felt that our AI categories have been missing an important step: what comes between narrow AI & general AI. With the rise of synthetic media (especially natural-language generation) and game-playing AI, we're finally forced to confront this architecture.

Based on my lack of knowledge in the field of machine learning and bare observations of OpenAI's GPT-2, I propose a hypothesis: between narrow artificial intelligence and general artificial intelligence, there lies a sort of architecture capable of generalized learning in narrow fields of tasks. I don't claim to be an AI expert or even an amateur. Indeed, I likely lack so much understanding of data science that literally everything I'm about to say is actually wrong on a fundamental level.

But I do feel like, at least when it comes to mainstream discussions of AI, there's a big problem. Several big problems, in fact.

How does media talk about AI? Typically by reducing it to three architectural categories:

Artificial narrow intelligence (ANI). This is AI that can do one thing— and only one thing. If there's a network that you notice does more than one thing, it's actually just a bundle of ANIs all doing different things at the same time. In technical parlance, most of what is called ANI isn't actually artificial intelligence at all— basic scripts, Markov chains, Monte Carlo Tree Searches, conversation trees, stochastic gradient descent, autoencoding, etc. are part of data science & optimization more than AI, but this really comes down to the fact "AI" carries connotations of humanlike cognition. For the sake of this post, we'll consider them AI anyway.

Artificial general intelligence (AGI). The holy grail of data science. The cybernetic messiah. The solution to all our problems (which includes nuking all our problems). This is AI that can do anything, presumably as well as a human can.

Artificial superintelligence (ASI). The rapture of the nerds and your new God. This is an AGI on crack, if that crack was also on crack. Take the limits of human intelligence: fusion-ha'ing Einstein, Euler, Newton, Mozart, the whole lot of them. Push human intelligence as far as it can go genetically, to the absolute limit of standard deviations. ASI is everything even further beyond. It's a level of intelligence no human, eith...
"Memento Mori", Said The Confessor
by namespace — 2020-02-02T03:37:30.573Z
Link: https://www.thelastrationalist.com/memento-mori-said-the-confessor.html

Abstract

The fear of death acts as a sort of master key for introductory rationality concepts. Examining the fear of death ties all the rationality basics together into a coherent framework, including:

Map/Territory Errors

Something To Protect

Keeping Your Identity Small

Atheism

X-Risk

Read more

Small brain: Don't think about death.

Shining tomagraph: "After I die I'll go to heaven because I'm a good person."

Expanding brain: "God isn't real, I find it more comforting to think that this isn't all a test."

Galaxy Brain: Practice dying.
Bay Winter Solstice seating-scarcity
by Raemon — 2020-02-01T23:09:39.563Z

tl'dr: The last few years, Bay Winter Solstice celebration has sold out (at 240 seats, plus an overflow room). I'm one of the three organizers this year, and am trying to gauge the True Demand.

So, would you be inviting friends to Winter Solstice who wouldn't come on their own? And/or have you not gone to Solstice in the last few years due to scarcity of seating?

The past couple years Bay Solstice has been held in a planetarium, which is a pretty cool aesthetic, but only fit 240 people. It turns out there's a bigger planetarium in San Francisco (seats 290 seats, 50 more than last time. It also has a nicer overflow room that seats 100)

The question is "is 50 more seats enough?"

Last year we ended up fitting everyone who showed up (including overflow people into the room), with 20 people who originally said they were coming who didn't end up showing up, and 10 people on my facebook wall who said they would have come or brought more people if seating didn't feel scarce.

For the past several years, Bay Winter Solstice attendance has clearly been bottlenecked on venue size, and it seems pretty valuable to have a year where there's _zero_ scarcity, to generally fight any perception of solstice-attendance as scarce, as well as to get a clear sense of what the true demand actually is.

The main alternatives seem to be "Ballrooms and theaters that are reasonably nice but don't really hit any particular Solstice Aesthetic that hard."

It so happens the planetarium is currently on-hold (but not officially booked) for Dec 19th, which is the currently planned date for Bay Solstice Celebration, but we might be able to snatch it away if we move quickly.

I have a lot of uncertainty over whether 290 seats is enough, and curious about other people's thoughts.

Does a Nicer Overflow Room Matter?

I also have some uncertainty about the new planetarium's overflow room. Last year, the overflow room doubled as the childcare room, which wasn't a nice experience for those were really trying to get "as close to the Dark Solstice aesthetic as possible."

In we went with the SF Planetarium, a) there'd be childcare room separate from the overflow room, b) the overflow room is really quite nice, nestled right up against the planetarium itself. It has nice mood lighting. It also has a giant projection screen composed out of three projectors. I think there's potential to a legitimately good job live

...
The case for lifelogging as life extension
by Matthew Barnett — 2020-02-01T21:56:38.535Z

Those in the cryonics community want to be frozen upon legal death, in order to preserve the information content in their brain. The hope is that, given good protocol, damage incurred during the freezing process will not destroy enough information about you to prevent people in the future from reconstructing your identity.

As most who want cryonics will understand, death is not an event. Instead, it is a process with intermediate steps. We consider a long-decayed corpse to be dead because it no longer performs the functions associated with a normal living human being, not because any sort of spirit or soul has left the body.

But philosophers have also identified important dilemmas for the view that death is a process rather than an event. If what we call death is simply my body performing different functions, then what do we make of the fact that we also change so much simply due to the passage of time?

I find it easy to believe that I am the 'same person' as I was last night. Enough of the neural pathways are still the same. Memories from my childhood are essentially still identical. My personality has not changed any significant extent. My values and beliefs remain more-or-less intact.

But every day brings small changes to our identity. To what extent would you say that you are still the 'same person' as you were when you were a child? And to what extent are you still going to be the 'same person' when you get old?

In addition to the gradual changes that happen due to every day metabolic processes, and interactions with the outside world, there is also a more sudden change that may happen to your identity as you get old. By the age of 85, something like 25 to 50 percent of the population will get a form of dementia. Alzheimer's is a very harsh transformation to our connectome.

Ironically, those who are healthiest in their youth will have the highest chance of getting Alzhiemers, as it is typically a disease of the very-old, rather than the somewhat old. Furthermore, most forecasters expect that as medical technology advances, the rate of Alzhiemers will go up, since it's among the hardest diseases to fix with our current paradigm of medical technology, and therefore you won't be as likely to die of the others. And Alzhiemers is just one brand of neurodegenerative diseases.

If you care about preserving your current sel...