trentmkelly/LessWrong-43k huggingface dataset:
Congressional Insider Trading
by Maxwell Tabarrok — 2024-08-30T13:32:57.264Z
Link: https://www.maximum-progress.com/p/congressional-insider-trading

You’ve probably seen the

Nancy Pelosi Stock Tracker

on X or else a

collection

of

articles

and

books

exposing the secret and lucrative world of congressional insider trading.

The underlying claim behind these stories is intuitive and compelling. Regulations, taxes, and subsidies can

make or break entire industries

and congresspeople can get information on these rules before anyone else, so it wouldn’t be surprising if they used this information to make profitable stock trades.

But do congresspeople really have a consistent advantage over the market? Or is this narrative built on a cherrypicked selection of a few good years for a few lucky traders?

Is Congressional Insider Trading Real

There are several papers in economics and finance on this topic

First is the 2004 paper:

Abnormal Returns from the Common Stock Investments of the U.S. Senate

by Ziobrowski et al. They look at Senator’s stock transactions over 1993-1998 and construct a synthetic portfolio based on those transactions to measure their performance.

This is the headline graph. The red line tracks the portfolio of stocks that Senators bought, and the blue line the portfolio that Senators sold. Each day, the performance of these portfolios is compared to the market index and the cumulative difference between them is plotted on the graph. The synthetic portfolios start at day -255, a year (of trading days) before any transactions happen.

In the year leading up to day 0, the stocks that Senators will buy (red line) basically just tracks the market index. On some days, the daily return from the Senator’s buy portfolio outperforms the index and the line moves up, on others it underperforms and the line moves down. Cumulatively over the whole year, you don’t gain much over the index.

The stocks that Senators will sell (blue line), on the other hand, rapidly and consistently outperform the market index in the year leading up to the Senator’s transaction.

After the Senator buys the red portfolio and sells the blue portfolio, the trends reverse. The Senator’s transactions seem incredibly prescient. Right after they buy the red stocks, that portfolio goes on a tear, running up the index by 25% over the next year. They also pick the right time to sell the blue portfolio, as it barely gains over the index over the year after they sell.

Ziobrowski finds that the buy portfolio of the average senator, weighted by their...
Thoughts on paper "How Organisms Come to Know the World: Fundamental Limits on Artificial General Intelligence"?
by mikbp — 2024-08-30T09:04:20.037Z

I've read the abstract of this paper, How Organisms Come to Know the World: Fundamental Limits on Artificial General Intelligence. It says:

A general example of an affordance is the use of an object in the hands of an agent. We show that it is impossible to predefine a list of such uses. Therefore, they cannot be treated algorithmically. This means that “AI agents” and organisms differ in their ability to leverage new affordances. Only organisms can do this.

This sounds very strange to me. I have basically 0 technical knowledge of AI, just the general ideas I've gathered reading in general. But I thought one of the main characteristics of it, is that it learns by itself (or with what we feed them). So, that precisely one does not need to "predifine a list of such uses", in the context of the abstract. Particularly, self-directed learning AIs are famous for coming up with totally unpredictable ways of achieving their goals. Isn't it? I guess in the language of the abstract this would mean that they are not treated algorithmically... I don't think anybody would do such an error and less so, be able to publish it. So, what am I missing?

I searched in LW and the EA forum and I didn't find any post or comment about this paper, so I thought I would post a question.
Are LLMs on the Path to AGI?
by Davidmanheim — 2024-08-30T03:14:04.710Z

I am unsure, but I disagree with one argument that they aren’t.

There’s a joke about how humans have gotten so good at thinking that they tricked rocks into thinking for them. But it’s a joke, in part because it’s funny to say that computers work by “tricking rocks into thinking,” and in part because what computers do isn’t “really” thinking.

But it is possible to take the limitations of computers and computation too far. A point I’ve repeatedly seen is that “Artificial General Intelligence lies beyond Deep Learning,” which gets something fundamentally but very subtly wrong about Large Language Models. The overall claim is that machine learning is fundamentally incapable of certain types of reasoning required for AGI. Whether that is true is fundamentally unclear, and I think the proponents of this view are substantively wrong in repeating the common claim that deep learning cannot do counterfactual reasoning.

First, though, I want to provide a bit of background to be clear about what computers are and are not doing. There is a deep question about whether LLMs understand anything, but I will claim that it’s irrelevant, because they don’t need to. Silicon and electrical waves inside of a calculator certainly do not “understand” numbers. It might be objected that if the circuits and logic gates aren’t doing math, so what calculators do isn’t truly math. When we put them together correctly, however, they can do addition anyways, without the logic gates and circuits understanding what they are doing. It can’t “truly” do math - and yet, e pur si muove! Calculators do not “truly understand” numbers, but that doesn’t mean we cannot build something on top of electronic circuits to do addition.

To analogize briefly, cells in the human brain also don’t know how to think, they just send electrical signals based on chemical gradients inside and outside the cell, and chemical signals. Clearly, the thinking happens at a different level than the sodium-potassium pumps or the neurons firing. That doesn’t mean human brains cannot represent numbers or do math, just that it happens at a different level than the neurons firing. But these philosophical questions aren’t actually answering anything. So I’ll abandon the analogies and get to the limitations of deep learning.

Machine learning models derive statistical rules based only on observational data. For this reason, th...
Nursing doubts
by dynomight — 2024-08-30T02:25:36.826Z
Link: https://dynomight.net/breastfeeding/

If you ask the internet if breastfeeding is good, you will soon learn that YOU MUST BREASTFEED because BREAST MILK = OPTIMAL FOOD FOR BABY. But if you look for evidence, you’ll discover two disturbing facts.

First, there’s no consensus about why breastfeeding is good. I’ve seen experts suggest at least eight possible mechanisms:

Formula can’t fully reproduce the complex blend of fats, proteins and sugars in breast milk.
Formula lacks various bio-active things in breast milk, like antibodies, white blood cells, oligosaccharides, and epidermal growth factor.
If local water is unhealthy, then the mother’s body acts as a kind of “filter”.
Breastfeeding may have psychological/social benefits, perhaps in part by releasing oxytocin in the mother.
Breastfeeding decreases fertility, meaning the baby may get more time before resources are redirected to a younger sibling.
Breastfeeding may help mothers manage various post-birth health issues?
Infants are often given formula while lying on their backs, which might lead to fluid buildup in the ears and thus temporary hearing loss during a critical development period?
Breastfeeding is cheaper??

Second, the evidence for breastfeeding is overwhelmingly observational: It’s not based on experiments, but rather looking at the existing population and “observing” that breastfeeding is correlated with having mildly fewer infections (of many kinds) and slightly lower obesity. It may also be correlated with better outcomes in terms of allergies, diabetes, lymphoma, colitis, Crohn’s disease, or later IQ.

Observational evidence is disturbing because correlations are bad. Even if breastfeeding did nothing, people think it’s good, so the same parents who breastfeed more tend to have higher socioeconomic status and provide lots of other goodies too. Babies that wear baby Rolex watches are probably healthier on average. But that’s because their parents are rich, not because Rolexes are good for you. Could breastfeeding be like that?

Of course, experts are aware of this issue. They try to compensate for it by “controlling” for upstream variables. The most-cited meta-analysis on breastfeeding and IQ collected 18 papers that each controlled for different things, like parental education, social status, or how much social interaction the baby got. The control variables seemed to matter a lot:

Among studies that…	Breastfeeding associated with a…
Did not cont

...
Free Will and Dodging Anvils: AIXI Off-Policy
by Cole Wyeth — 2024-08-29T22:42:24.485Z

This post depends on a basic understanding of history-based reinforcement learning and the AIXI model.

I am grateful to Marcus Hutter and the lesswrong team for early feedback, though any remaining errors are mine.

The universal agent AIXI treats the environment it interacts with like a video game it is playing; the actions it chooses at each step are like hitting buttons and the percepts it receives are like images on the screen (observations) and an unambiguous point tally (rewards). It has been suggested that since AIXI is inherently dualistic and doesn't believe anything in the environment can "directly" hurt it, if it were embedded in the real world it would eventually drop an anvil on its head to see what would happen. This is certainly possible, because the math of AIXI cannot explicitly represent the idea that AIXI is running on a computer inside the environment it is interacting with. For one thing, that possibility is not in AIXI's hypothesis class (which I will write M). There is not an easy patch because AIXI is defined as the optimal policy for a belief distribution over its hypothesis class, but we don't really know how to talk about optimality for embedded agents (so the expectimax tree definition of AIXI cannot be easily extended to handle embeddedness). On top of that, "any" environment "containing" AIXI is at the wrong computability level for a member of M: our best upper bound on AIXI's computability level is Δ02 = limit-computable (for an ε-approximation) instead of the Σ01 level of its environment class. Reflective oracles can fix this but at the moment there does not seem to be a canonical reflective oracle, so there remains a family of equally valid reflective versions of AIXI without an objective favorite.

However, in my conversations with Marcus Hutter (the inventor of AIXI) he has always insisted AIXI would not drop an anvil on its head, because Cartesian dualism is not a problem for humans in the real world, who historically believed in a metaphysical soul and mostly got along fine anyway. But when humans stick electrodes in our brains, we can observe changed behavior and deduce that our cognition is physical - would this kind of experiment allow AIXI to make the same discovery? Though we could not agree on this for some time, we eventually discovered the crux: we were actually using slig...
Solving adversarial attacks in computer vision as a baby version of general AI alignment
by Stanislav Fort — 2024-08-29T17:17:47.136Z

I spent the last few months trying to tackle the problem of adversarial attacks in computer vision from the ground up. The results of this effort are written up in our new paper Ensemble everything everywhere: Multi-scale aggregation for adversarial robustness (explainer on X/Twitter). Taking inspiration from biology, we reached state-of-the-art or above state-of-the-art robustness at 100x – 1000x less compute, got human-understandable interpretability for free, turned classifiers into generators, and designed transferable adversarial attacks on closed-source (v)LLMs such as GPT-4 or Claude 3. I strongly believe that there is a compelling case for devoting serious attention to solving the problem of adversarial robustness in computer vision, and I try to draw an analogy to the alignment of general AI systems here.

1. Introduction

In this post, I argue that the problem of adversarial attacks in computer vision is in many ways analogous to the larger task of general AI alignment. In both cases, we are trying to faithfully convey an implicit function locked within the human brain to a machine, and we do so extremely successfully on average. Under static evaluations, the human and machine functions match up exceptionally well. However, as is typical in high-dimensional spaces, some phenomena can be relatively rare and basically impossible to find by chance, yet ubiquitous in their absolute count. This is the case for adversarial attacks – imperceptible modifications to images that completely fool computer vision systems and yet have virtually no effect on humans. Their existence highlights a crucial and catastrophic mismatch between the implicit human vision function and the function learned by machines – a mismatch that can be exploited in a dynamic evaluation by an active, malicious agent. Such failure modes will likely be present in more general AI systems, and our inability to remedy them even in the more restricted vision context (yet) does not bode well for the broader alignment project. This is a call to action to solve the problem of adversarial vision attacks – a stepping stone on the path to aligning general AI systems.

2. Communicating implicit human functions to machines

The basic goal of computer vision can be viewed as trying to endow a machine with the same vision capabilities a human has. A human carries, locked inside their skull, an implicit vi...
How to build open source AI models for landscape architecture?
by SimonLOLAland — 2024-08-29T17:17:32.608Z

Hello everyone,

Together with a group of designers, researchers and journalists we are working in a publication on the Application of AI for Planning and Climate Adaptation (SCAPE magazine).

While diving into the topic, we have started wondering: how will less profitable and more activist fields like landscape architecture or nature conservation be able to develop their own AI systems? And how would be the best approach to make them not only efficient but also to work within the same values of openness, collaboration and sustainability that we share, and we do not see in current available models.

Inspiring initiatives in other fields make us think that there is another way around Big Tech corporations, and we would like to understand the developer perspective on it.

We are happy to hear any opinions, discussions, strategic advices, development tips or any other remark shared that you think is essential for developing, deploying and maintaining such an open source AI system for Landscape Architecture.

For context, as Landscape Architects, our work is quite broad, from designing green public spaces for cities, to developing city level planning focused on greener, walkable and climate adaptive neighborhoods, to larger regional plans focused on nature and floodplain restoration.

In the field of landscape architecture the emergence of the computer and internet changed the profession, and not always for good. We can see the risks of ai, pushing landscape architects to more generic design, quick visual output, efficiency, low cost, etcetera. At the same time we see the opportunity of integrating ever improving climate models, ecology mapping, better understanding how to manipulate the landscape to optimize biodiversity and climate adaptivity. But what about the things that are hard to digitalise? Word to mouth stories, soft values, local culture, local history, seasonality, atmosphere, etcetera? Exactly because landscape architecture is not a very large/profitable market, it’s not likely commercial companies will jump on this. We think it’s worth developing/training an AI for local soft values - run on a solar/hydro powered datacenter. With universities - but we’d need a larger community to make it work.

Thank you in advance for any answer – we will link to this post and fully cite you in the magazine for all the information shared,

And hopefully we can build a collective ...
Can AI agents learn to be good?
by Ram Rachum — 2024-08-29T14:20:04.336Z
Link: https://futureoflife.org/ai-research/can-ai-agents-learn-to-be-good/

Hi everyone!

My name is Ram Rachum and I'm working on AI Safety research. I want to elicit social behavior in RL agents and use it to achieve AI Safety goals such as alignment, interpretability and corrigibility.

I made a guest post on the Future of Life Institute's blog: https://futureoflife.org/ai-research/can-ai-agents-learn-to-be-good/

This isn't specifically about my research, as it's mostly geared towards the public so it's pretty basic. I do have a plug for my latest paper at the bottom. This is my first public writing on AI Safety, so I'd appreciate any comments or corrections.

I'm currently raising funding for my research. If you know of relevant funders, I'd appreciate a connection.
Things I learned talking to the new breed of scientific institution
by Abhishaike Mahajan — 2024-08-29T14:00:14.844Z
Link: https://www.owlposting.com/p/things-i-learned-talking-to-the-new

Note: this article is sponsored by and cross-posted to the

Good Science Project

. They also write a fair bit, and

their articles

were essential reading for writing this essay!

Also, this article would not be possible without the hours of discussion/editing help I’ve had with several people from these institutions, and a few outside of them. Huge shout-out to all of them!

Introduction

Arcadia Science

,

Speculative Technologies

,

FutureHouse

,

Arc

, and

Convergent

.

All of these are a new form of scientific institute.

Most are funded entirely by a few billionaires. Most are non-profits. Most of them focus on the life-sciences. Most of them have sprung up in just the last few years.

They do all also have one common thread: a grand statement. We are an experiment in a new way to do science.

And they are! Traditionally, research is conducted in academic or private industry labs — dependent on NIH grants in the former and markets in the latter. Given the (often singular) sources of no-strings-attached funding, these new institutions need not satisfy either the NIH or the markets, allowing them to conduct research in a unique fashion.

In one sense, the experimental aspect of these institutions revolves around the focus of the research itself, addressing fields or using methods that the founders — correctly or not — view as underserved/underutilized. But, on a more subtle level, the experimental aspect could be more closely tied to the culture of these organizations. Institutions like Arcadia, FutureHouse, and the rest could be viewed as the production of auteurs — a term from filmmaking for films with such a heavy sense of the director’s personal taste that the film is inseparable from the director.

This is where the novelty within these institutions primarily lie, in how the founders of the institute wish science was conducted. And wielding billions of dollars, thousands of hours of work, and hundreds of scientists as a means to test whether their theories are correct.

Of course, nothing under the sun is truly new. There is an age-old history of scientist dissatisfaction with how ‘things are traditionally done’, and confidently building new institutions to solve the problems they’ve seen. Many of these are now household names amongst researchers:

Broad Institute

,

Whitehead Institute

,

Max Planck Society

,

Howard Hughes Medical Institute

(HHMI), and so on. Each of these were started...
Speculations on Sleep in an Age of Machines
by cleanwhiteroom — 2024-08-29T13:42:54.548Z

One-liner: Must androids dream of electric sheep?

Three-liner: In a thinking network, a vast web of microstates underpin system macrostates. These microstates are expensive to maintain. Processes that locally reduce or collapse informational entropy may be important for thinking things to learn and function.

Entropy in Language and Thought

Entropy has a foot in the physical and informational worlds. Because the human brain is a physical information processor, multiple subtypes of entropy apply to it.

1) Physical entropy is a measure of the disorder or randomness in a physical system. It famously increases over time. It quantifies the number of possible microscopic configurations of a system.

2) Informational entropy is the average amount of information in a dataset and measures the unpredictability or randomness of information content.

3) Neural entropy measures the informational processing capacity of neural systems and is used to quantify the repertoire of neural states a given brain can access.

4) Semantic entropy refers to the ambiguities in meaning (microstates) that a given word (macrostate) conveys.

Within their respective domains, all forms of entropy deal with uncertainty, randomness, disorder and the number of microstates compatible with a given macrostate.

For a more rigorous treatment, see Introduction to abstract entropy. For a more informal metaphor, consider all the ways you’ve ever answered (or been tempted to answer) the high ambiguity query: “How are you?” You assess recent events, internal self states, and analyze relational data about the relative social position between you and your questioner before replying, “I’m fine.” This scenario contains all four types of entropy discussed above. Physical entropy is present in the heat generated by neuronal firing. Informational entropy will vary based on the depth of the thought content dataset that sums to your reply. Similarly, neural entropy increases proportionally to the literal firing configurations of the circuits holding the events of your day. Semantic entropy is a property of the language you use. (And what does “fine” really mean, anyway?)

The Entropy of Thinking

Biological systems locally reverse entropy to maintain their functional patterns against the metaphorical (and literal) friction of living. They're open systems that take in matter and energy, metabolize it into usable biolog...