trentmkelly/LessWrong-43k huggingface dataset:
Reflections on Premium Poker Tools: Part 2 - Deciding to call it quits
by Adam Zerner — 2019-10-09T04:17:25.259Z

For the backstory, see part one.

There's a part of me that thinks that quitting should be an easy decision. A big part of me.

The way I'm thinking about it, there are two reasons to continue working on this:

1. Swinging for a home run. If there's a chance for high upside. If not, there are other ideas I could be working on that do provide the upside I'm looking for.

2. Swinging for a single. If there's a high likelihood of making a few hundred thousand dollars. Something comparable to what I'd make working as a programmer.

Swinging for a home run

Throughout the time I was working on Premium Poker Tools, I had been assuming that the market was big enough to provide high upside. Eg. I thought that 100k users and $100/user was very possible.

In being amongst the poker community, you get the impression that Flopzilla (market leading competitor) is ubiquitous. Everyone knows what it is. Everyone has it. Everyone needs it.
The poker subreddit has over 100k subscribers. Educational YouTube videos get over 100k views. Some posts on TwoPlusTwo have over 1M views. The type of person to watch an educational YouTube video on poker is serious enough to want to have Flopzilla or a similar app, you would think. So maybe there are a few hundred thousand of these people in the world, and maybe I can capture 100k of them.
On top of that, poker seems to be a game that is growing. And imagine if it is legalized in more states?
At $100, the app pays for itself. And poker players are the type to understand +ROI decisions, you would think.
Poker players tend to be on the wealthy side. Older people with money. Younger people in high paying fields like tech. Younger people who are playing professionally. Well, most of them aren't actually wealthy, but it's especially important for them to invest in tools.
Maybe there's potential to be making hundreds of dollars per user. After all, that's still +ROI for a lot of people.

But more recently, I've come to believe that that all just isn't true.

One reliable source said he'd estimate that the market leader, Flopzilla, has between 1,000 and 10,000 users. And they charge $25-35. Which would imply an upper extreme of just $300k or so. Before marketing costs.
Four other people in the industry I've spoken with all say the same thing, although they didn't want to get into specific numbers.
Looking at Flopzilla and the other competitors, they're mostly one man

...
Categories: models of models
by countedblessings — 2019-10-09T02:45:29.617Z

Let me clarify what I mean when I say that math consists of nouns and verbs. Think about elementary school mathematics like addition and subtraction. What you learn to do is take a bunch of nouns—1, 2, 3, etc.—and a bunch of verbs—addition, subtraction—and make sentences. “1 + 2 = 3.”

When you make a sentence like that, what you're doing is taking an object, 1, and observing how it changes when it interacts—specifically, adds—with another object, 2. You observe that becomes a 3. Just like how you can observe a person (object) bump their head (interaction) into a wall (other object) and the corresponding change (equals sign): the bluish bump emerging on their forehead.

Well, it turns out that no matter how far you go in math, you’re still taking objects and observing how they change when they interact with other objects. You learn about other kinds of numbers like 2.5 and 3.333 repeating, and other kinds of interactions like multiplication and division.

Eventually things start getting more abstract. You learn about matrices and the kinds of interactions they can have. You learn about sets and functions in a deep way. Complex numbers, topologies, etc.

But you never get away from nouns and verbs.

What are we going to do with all of these nouns and verbs? Well, at some point, when you have a bunch of things that feel similar in an important way, you often want to take a step back, lump all of the individual things together, and talk about them in general. Why do we have the concept of fruit? Because we live in a world where apples, oranges, lemons, papayas, etc., exist.

Why do we have the concept of numbers? Because we have a bunch of them, not just 1, 2, and 3, but also 2.5, 3.333 repeating, irrational numbers, and even complex numbers. Why do we have the concept of “operations?” Because we have addition, subtraction, multiplication, and division. Even though all of these give you very different answers for the same input of numbers, (1 + 2 does not equal 1 - 2 does not equal 1 times 2 does not equal 1 divided by 2), they still have something very similar in common, so it’s worth coming up with the concept of “operation” to study them independently of their individual characteristics. Just like how we can talk about “fruit” without having to reference the shape of an apple.

If you lived in a world with only apples, you wouldn’t have the concept of fruit. If you live...
Reflections on Premium Poker Tools: Part 1 - My journey
by Adam Zerner — 2019-10-09T00:42:05.694Z

I've been working on Premium Poker Tools for over two years now. I'm at a point where things aren't looking so hot, and I need to sit down and do some hard thinking. What better way to do that then to write on LessWrong!

Background

To understand my journey with Premium Poker Tools, I think you have to first understand a little bit about me and what I'm doing with my life. In short, I want to start startups, make a lot of money, and use the money to a) be financially independent, and b) do good for the world.

I spent about two years in college working on a startup that went nowhere. After that, I managed to get an entry level job as a web developer. I was a pretty bad at the time. I spent about 11 months at that job before getting fired. I quickly got another job, but got fired 5 months into that one. In both cases, it was a mix of a) me clearly not being a "company guy", and instead wanting to be done at 5pm so I could go off and do what's best for me, b) clashes with authority figures, and c) being a slow programmer.

After the second job, I moved to Vegas to be with my now girlfriend. I had about $85,000 saved up in the bank, and my plan was the following: 1) get better at programming, 2) freelance a little and save up some more money, 3) start a startup. And then from there repeat steps 2 and 3 (and maybe 1) until I succeed with a startup. After that, the plan would be some mix of 1) figuring out how to use the money for good, 2) starting or investing in more startups, and 3) enjoying myself.

Self-studying computer science

I spent my first year in Vegas self-studying computer science. It was alright. Idk. I feel like I should have came away from that year making a lot more progress than I did, but at the same time, I did learn a lot.

In general, I've always planned on living the digital nomad life. At least until I find a place I like enough to want to settle down in. But I'm pretty sure that'll take some time, and that I'll end up spending time in a lot of different places. Anyway, I find myself living in Vegas. Poker is a thing in Vegas. I figure that I should spend some time on hobbies, and poker seems cool, so I feel like I should give poker a shot while I happen to be in Vegas. I won't have another opportunity like this. Or so my thought process was at the time.

So, during that first year, I spend so...
Misconceptions about continuous takeoff
by Matthew Barnett — 2019-10-08T21:31:37.876Z

There has been considerable debate over whether development in AI will experience a discontinuity, or whether it will follow a more continuous growth curve. Given the lack of consensus and the confusing, diverse terminology, it is natural to hypothesize that much of the debate is due to simple misunderstandings. Here, I seek to dissolve some misconceptions about the continuous perspective, based mostly on how I have seen people misinterpret it in my own experience.

First, we need to know what I even mean by continuous takeoff. When I say it, I mean a scenario where the development of competent, powerful AI follows a trajectory that is roughly in line with what we would have expected by extrapolating from past progress. That is, there is no point at which a single project lunges forward in development and creates an AI that is much more competent than any other project before it. This leads to the first clarification,

Continuous doesn't necessarily mean slow

The position I am calling "continuous" has been called a number of different names over the years. Many refer to it as "slow" or "soft." I think continuous is preferable to these terms because it focuses attention on the strategically relevant part of the question. It seems to matter less what the actual clock-time is from AGI to superintelligence, and instead matters more if there are will be single projects who break previous technological trends and gain capabilities that are highly unusual relative to the past.

Moreover, there are examples of rapid technological developments that I consider to be continuous. As an example, consider GANs. In 2014, GANs were used to generate low quality black-and-white photos of human faces. By late 2018, they were used to create nearly-photorealistic images of human faces.

Yet, at no point during this development did any project leap forward by a huge margin. Instead, every paper built upon the last one by making minor improvements and increasing the compute involved. Since these minor improvements nonetheless happened rapidly, the result is that the GANs followed a fast development relative to the lifetimes of humans.

Extrapolating from this progress, we can assume that GAN video generation will follow a similar trajectory, starting with simple low resolution clips, and gradually transitioning to the creation of HD videos. What would be...
Book summary: Unlocking the Emotional Brain
by Kaj_Sotala — 2019-10-08T19:11:23.578Z

If the thesis in

Unlocking the Emotional Brain

(UtEB) is even half-right, it may be one of the most important books that I have read. Written by the psychotherapists Bruce Ecker, Robin Ticic and Laurel Hulley, it claims to offer a neuroscience-grounded, comprehensive model of how effective therapy works. In so doing, it also happens to formulate its theory in terms of belief updating, helping explain how the brain models the world and what kinds of techniques allow us to

actually change our minds

. Furthermore, if UtEB is correct, it also explains why rationalist techniques such as Internal Double Crux [

1

2

3

] work.

UtEB’s premise is that much if not most of our behavior is driven by emotional learning. Intense emotions generate unconscious predictive models of how the world functions and what caused those emotions to occur. The brain then uses those models to guide our future behavior. Emotional issues and seemingly irrational behaviors are generated from implicit world-models (schemas) which have been formed in response to various external challenges. Each schema contains memories relating to times when the challenge has been encountered and mental structures describing both the problem and a solution to it.

According to the authors, the key for updating such schemas involves a process of memory reconsolidation, originally identified in neuroscience. The emotional brain’s learnings are usually locked and not modifiable. However, once an emotional schema is activated, it is possible to simultaneously bring into awareness knowledge contradicting the active schema. When this happens, the information contained in the schema can be overwritten by the new knowledge.

While I am not convinced that the authors are entirely right, many of the book’s claims definitely feel like they are pointing in the right direction. I will discuss some of my caveats and reservations after summarizing some of the book’s claims in general. I also consider its model in the light of an issue of a psychology/cognitive science journal devoted to discussing a very similar hypothesis.

Emotional learning

In UtEB’s model, emotional learning forms the foundation of much of our behavior. It sets our basic understanding about what situations are safe or unsafe, desirable or undesirable. The authors do not quite say it explicitly, but the general feeling I get is that the subcortical emotional proce...
Welcome to Dustin Space
by agentydragon — 2019-10-08T16:08:05.855Z

Dustin Space is an effective altruist community in Zurich. We are also rationalists, vegans and computer scientists. We care about the world and help each other become the best version of ourselves.

We run:

Weekly coworkings with Pomodoros every Sunday.
Roughly once-monthly personal development meetups under EA Zurich.
Occasional social events or meetups for EA Zurich.
Characterizing Real-World Agents as a Research Meta-Strategy
by johnswentworth — 2019-10-08T15:32:27.896Z

Background

Intuitively, the real world seems to contain agenty systems (e.g. humans), non-agenty systems (e.g. rocks), and ambiguous cases which display some agent-like behavior sometimes (bacteria, neural nets, financial markets, thermostats, etc). There’s a vague idea that agenty systems pursue consistent goals in a wide variety of environments, and that various characteristics are necessary for this flexible goal-oriented behavior.

But once we get into the nitty-gritty, it turns out we don’t really have a full mathematical formalization of these intuitions. We lack a characterization of agents.

To date, the closest we’ve come to characterizing agents in general are the

coherence theorems

underlying Bayesian inference and utility maximization. A

wide variety of theorems

with a wide variety of different assumptions all point towards agents which perform Bayesian inference and choose their actions to maximize expected utility. In this framework, an agent is characterized by two pieces:

A probabilistic world-model
A utility function

The Bayesian utility characterization of agency neatly captures many of our intuitions of agency: the importance of accurate beliefs about the environment, the difference between things which do and don’t consistently pursue a goal (or approximately pursue a goal, or sometimes pursue a goal…), the importance of updating on new information, etc.

Sadly, for purposes of AGI alignment, the standard Bayesian utility characterization is incomplete at best. Some example issues include:

The

need for a cartesian boundary

- a clear separation between “agent” and “environment”, with well-defined input/output channels between the two

Logical omniscience

- the assumption that agents can fully compute all of the implications of the information available to them, and track every possible state of the world

Path independence and complete preferences

- the assumption that an agent doesn’t have a general tendency to stay in the state it’s in

One way to view

agent foundations

research is that it seeks a characterization of agents which resolves problems like the first two above. We want the same sort of benefits offered by the Bayesian utility characterization, but in a wider and more realistic range of agenty systems.

Characterizing Real-World Agents

We want to characterize agency. We ...
What's the dream for giving natural language commands to AI?
by Charlie Steiner — 2019-10-08T13:42:38.928Z

0

I promised in a previous post that I would give a post-mortem for a scheme for learning the intentional stance from natural language. This is that post. But first, I should explain why such an idea might seem good in the first place.

Some people think of AI as a genie. The goal of AI research, in this picture, is to "tell the AI what to do," sometimes explicitly in natural language. And then since the AI is smart, it will understand what we mean and do that, because to do something else would be stupid.

This is, in a sense, very naive. Making an AI that does what we want is not at all like instructing a human - see the relevant Eliezer post - the methods, dangers, and goals are all different. But... if the AI understood what we meant, maybe we could just tell it what to do.

Of course, "understood what we meant" captures more or less the whole problem, because meaning isn't like the charge of the electron, it's nowhere in the words themselves. When you understand moral language, you're implicitly using your morals. But what if we trained an AI so that it functionally understood moral language - would that be implicitly using your morals too, and isn't that exactly what we want?

1 - The Artificial Intentional Stance

I like to think of myself as having preferences, but at the same time I am made of atoms, and my preferences are not-like-the-charge-of-the-electron, they're nowhere in the atoms. Instead, my preferences are an abstraction that I (and others) use when thinking about me.

So part of the this artificial intentional stance stuff can be summed up as: get the AI to think about humans like humans think about humans. (Another part is that abstractions are contagious. If I want to go to the gym, to handle this correctly this you need abstractions not just for me but also for the gym.)

We often put too much magic into the word "understand." If the AI can hold a good conversation and extract real-world information from human speech, it's reasonable to say it understands what we're saying. And then once it understands us, you might think "communicating our goals to it is a lot like communicating with a human."

But it's easy to hold a decent conversation without taking the intentional stance towards humans, and easier still to extract real-world information from human speech without the intentional stance. This leads to problems that become clear if you try to take ...
Artifact: What Went Wrong?
by Zvi — 2019-10-08T12:10:01.019Z

Previously: Card Balance and Artifact, Artifact Embraces Card Balance Changes, Review: Artifact

Epistemic Status: Looks pretty dead

Artifact had every advantage. Artifact should have been great. Artifact was great for the right players, and had generally positive reviews. Then Artifact fell flat, its players bled out, its cards lost most of their value, and the game died.

Valve takes the long view, so the game is being retooled and might return. But for now, for all practical purposes, the game is dead.

Richard Garfield and Skaff Elias have one take on this podcast. They follow up with more thoughts in this interview.

Here’s my take, which is that multiple major mistakes were made, all of which mattered, and all of which will be key to avoid if we are to bring the combination of strategic depth and player-friendly economic models back to collectible card games.

I see ten key mistakes, which I will detail below.

Alas, we do not get to run controlled experiments. The lack of ability to experiment and iterate was the meta-level problem with Artifact. The parts of the game that Valve knew how to test, and knew to test, were outstanding, finely crafted and polished. The parts that Valve did not test had severe problems.

We will never know for sure which reasons were most critical, and which ones were minor setbacks. I will make it clear what my guesses are, but they are just that, guesses.

Reason 1: Artifact Was Too Complex, Complicated and Confusing

Artifact streams were difficult to follow even if you knew the game. As teaching tools they didn’t work at all. I heard multiple reports that excellent streamers were unable to explain to viewers what was going on in their Artifact games.

I was mostly able to follow streams when I had a strong strategic understanding of the game and all of its cards and recognized all the cards on sight. Mostly.

I was fortunate to learn Artifact in person with those deeply committed to it, at the Valve offices, and in a setting where I had several days with no distractions to become comfortable with the game and how it worked. All eight of us who were introduced to the game that week got what it was about and had fun. We were able to pass through hours of not knowing why our heroes had died or our cards hadn’t worked the way we expected.

But that’s all an extraordinary bar to entry. It also helped that we were playing against each other, rather...
When is pair-programming superior to regular programming?
by ChristianKl — 2019-10-08T11:48:46.734Z

When doing software development there are many choices that can affect your productivity. There are choices about how a company works. One of those is doing pair-programming over doing solo-programming. We recently had a consultant at our company who claimed that pair-programming is proven to be better but who couldn't point to evidence.

How does the case for pair-programming look like? What do we know? How can we rationally think about the question?
Realigning Housing Coalitions
by jefftk — 2019-10-08T11:10:01.584Z

There are currently two main coalitions in US housing politics in high
cost-of-living areas:

Developer / Pro-business / YIMBY: make it
easier to build more housing. Allow higher densities, more
subdivision, and smaller units. Lower affordable housing minimums,
lower parking minimums, lower anything else that acts as a tax on
development.

Anti-displacement / Affordable housing / Landlord / NIMBY: new housing
is luxury housing, they're building units that are too small for
families, this construction only benefits the small slice of
people rich enough to afford it. There's already too much traffic,
not enough parking, and construction means tree removal. Stop condo
conversions and evictions. Impose a transfer tax to stop
speculators.

This division has been shifting some, however, with the development of
density bonus programs. The idea is, developers are allowed to build
more units than zoning would normally allow, but they also have to
make a larger fraction of them available below market rate. For
example, an affordable housing bonus density program could apply to a
property where current zoning would allow eight $1M units and two
affordable $400k units, and offer an option of ten $1M units and ten
affordable $400k units. Programs like this have several advantages:

The number of new affordable units built is higher than it
would be under either current zoning or a new zoning that simply
increased the required fraction of affordable units.

The total number of units available, regardless of
affordability, is higher, taking pressure off the market
elsewhere.

The housing actually gets built, because construction is
profitable.

Other programs along these lines could include:

Remove parking minimums for new housing that's near
transit, allowing more housing to be built in a way that doesn't cause
as much traffic.

Remove parking minimums for new housing that
isn't entitled to street
parking and pre-pays for decades of public transit passes

Raise the cap on on floor-area-ratio more than the cap on
allowed units, leading developers to build more family-sized
units.

For buildings with tenants, allow higher density for landlords
that retain their tenants while rebuilding, providing interim housing
and similarly affordable units in the new building.

These have the same structure, allowing higher density in exchan...