A New Bill Offer Has Arrived

Center for AI Policy proposes a concrete actual model bill for us to look at.

Here was their announcement:

WASHINGTON – April 9, 2024 – To ensure a future where artificial intelligence (AI) is safe for society, the Center for AI Policy (CAIP) today announced its proposal for the “Responsible Advanced Artificial Intelligence Act of 2024.” This sweeping model legislation establishes a comprehensive framework for regulating advanced AI systems, championing public safety, and fostering technological innovation with a strong sense of ethical responsibility.

“This model legislation is creating a safety net for the digital age,” said Jason Green-Lowe, Executive Director of CAIP, “to ensure that exciting advancements in AI are not overwhelmed by the risks they pose.”

The “Responsible Advanced Artificial Intelligence Act of 2024” is model legislation that contains provisions for requiring that AI be developed safely [...]

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Outline:

(05:00) RTFC: Read the Bill

(05:39) Basics and Key Definitions

(10:00) Oh the Permits You’ll Need

(21:11) Rubrics for Your Consideration

(25:55) Open Model Weights Are Unsafe And Nothing Can Fix This

(30:02) Extremely High Concern Systems

(32:43) The Judges Decide

(35:01) Several Rapid-Fire Final Sections

(39:47) Overall Take: A Forceful, Flawed and Thoughtful Model Bill

(49:20) The Usual Objectors Respond: The Severability Clause

(52:34) The Usual Objectors Respond: Inception

(54:12) The Usual Objectors Respond: Rulemaking Authority

(01:01:53) Conclusion

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Previously: #1

It feels so long ago that Covid and health were my beat, and what everyone often thought about all day, rather than AI. Yet the beat goes on. With Scott Alexander at long last giving us what I expect to be effectively the semi-final words on the Rootclaim debate, it seemed time to do this again.

Bad News

I know no methodical way to find a good, let alone great, therapist.

Cate Hall: One reason it's so hard to find a good therapist is that all the elite ones market themselves as coaches.

As a commentor points out, therapists who can’t make it also market as coaches or similar, so even if Cate's claim is true then it is tough.

My actual impression is that the elite therapists largely do not market themselves at all. They instead work on referrals and [...]

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Outline:

(00:24) Bad News

(01:27) Good News, Everyone

(03:35) The Battle of the Bulge

(09:13) Support Anti-Aging Research

(09:37) Variably Effective Altruism

(09:51) Periodic Reminders (You Should Know This Already)

(12:05) FDA Delenda Est

(13:03) Other Enemies of Life

(14:04) Covid Postmortems

(14:55) Everything sounds like a sales pitch

(17:07) Information that would have been helpful was never provided

(17:49) A disconnect between what I experienced on the ground and the narrative I was hearing

(21:52) Covid-19 Origins

(24:29) Assisted Suicide Watch

(27:58) Talking Price

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This article contains more than 100 uses of logical or mathematical notation, so an audio narration would be too hard to follow. You'll find a link to the original text in the episode description.

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Grant Snider created this comic (which became a meme):

Richard Ngo extended it into posthuman/transhumanist literature:

That's cool, but I'd have gone for different categories myself.[1]
Here they are together with their explanations.

Top: Man vs Agency
(Other names: Superintelligence, Singularity, Self-improving technology, Embodied consequentialism.)
Because Nature creates Society creates Technology creates Agency.
At each step Man becomes less in control, due to his increased computational boundedness relative to the other.

Middle: Man vs Realities
(Other names: Simulation, Partial existence, Solomonoff prior, Math.)
Because

• Man vs Self is the result of dissolving holistic individualism (no subagents in conflict) from Man vs Man.
• Man vs Reality is the result of dissolving the Self boundary altogether from Man vs Self.
• Man vs Realities is the result of dissolving the binary boundary between existence and non-existence from Man vs Reality. Or equivalently, the boundary between different [...]

The original text contained 3 footnotes which were omitted from this narration.

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Linkpost URL:
https://twitter.com/MotionTsar/status/1776726715311767913

Narrated by TYPE III AUDIO.

Say you've learnt math in your native language which is not English. Since then you've also read math in English and you appreciate the near universality of mathematical notation. Then one day you want to discuss a formula in real life and you realize you don't know how to pronunce "_a_n_".

Status: I had little prior knowledge of the topic. This was mostly generated by ChatGTP4 and kindly reviewed by @TheManxLoiner.

General

Distinguishing case

• _F, delta_
• "Big F" or "capital F", "little delta"

Subscripts

• _a_n_
• "a sub n" or, in most cases, just "a n"

Calculus

Pythagorean Theorem

• _a^2 + b^2 = c^2_
• "a squared plus b squared equals c squared."

Area of a Circle

• _A = pi r^2_
• "Area equals pi r squared."

Slope of a Line

• _m = frac{y_2 - y_1}{x_2 - x_1}_
• "m equals y 2 minus y 1 over x [...]

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Outline:

(00:32) General

(00:50) Calculus

(02:48) More advanced operations

(04:43) Linear Algebra (vectors and matrices)

(06:25) With numerical values

(08:13) Probabilities and Statistics

(10:24) Logic

(10:27) Propositional Logic

(11:20) Predicate Logic

(11:46) Modal Logic

(12:08) Set Theory

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PIBBSS is looking to expand its team and is running work trials for new team members (primarily) in April, May and early June. If you’re interested in joining a nimble team focused on AI safety research, field-building and incubation of new agendas, consider letting us know by filling in this form.

The form is meant to be a low effort means for gauging interests. We don’t guarantee getting back to everyone, but will reach out to you if we think you might be a good fit for the team. We would then aim to get to know you better (e.g. via call) before deciding whether it seems valuable (and worth our respective time) to do a trial. Work trials will look different depending on circumstances, including your interests and availability. We intend to reimburse people for [...]

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Outline:

(01:04) About PIBBSS

(03:14) What we’re looking for in a new team member

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This work represents progress on removing attention head superposition. We are excited by this approach but acknowledge there are currently various limitations. In the short term, we will be working on adjacent problems are excited to collaborate with anyone thinking about similar things!

Produced as part of the ML Alignment & Theory Scholars Program - Summer 2023 Cohort

Summary: In transformer language models, attention head superposition makes it difficult to study the function of individual attention heads in isolation. We study a particular kind of attention head superposition that involves constructive and destructive interference between the outputs of different attention heads. We propose a novel architecture - a ‘gated attention block’ - which resolves this kind of attention head superposition in toy models. In future, we hope this architecture may be useful for studying more natural forms of attention head superposition in large language models.

Our code can [...]

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Outline:

(01:10) Background

(03:27) Attention head superposition for OV-Incoherent Skip Trigrams

(06:21) Removing Attention Head Superposition from models trained on OV-Incoherent Skip Trigrams

(06:29) Toy Dataset Generation

(07:18) Gated Attention Mechanism

(12:17) Gated Attention Block Training

(13:51) Summary of Architectural and Training Choices

(15:10) Experimental Results on Toy Model Setup

(19:12) Related Work

(20:14) Conclusion

(21:00) Limitations

(25:32) Future Work

(27:42) Acknowledgements

(28:08) Contributions Statement

(28:46) Appendix

(28:49) Appendix A: OV-Incoherent skip trigram dataset generation algorithm

(29:03) Appendix B1: 2 Heads, 3 Skip Trigrams (Trained via Stochastic Gradient Descent)

(29:47) Appendix B2: 3 Heads, 4 Skip Trigrams (Trained via Stochastic Gradient Descent)

(30:32) Appendix C: Other approaches that we investigated

(32:31) Appendix D: Weights for Manually Specified Model encoding 3 OV-Incoherent Skip Trigrams with 2 heads

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Recently, the yen-dollar exchange rate hit a 34-year low. Why is that?

6-month US Treasuries are paying around 5.3% interest. Japanese government bonds are paying about 0%. That being the case, you can borrow yen, trade it for dollars, buy US bonds, and get more interest. That's called a "yen carry trade". The risk you take in exchange for that money is that the exchange rate will shift so that a dollar is worth less yen.

But of course, it's also possible that the exchange rate will shift in the other direction, and that's what's happened recently. From 2020 to now, $1 went from 105 to 150 yen.

That being the case, I'd normally expect inflation to be higher in Japan than the US - their currency became less valuable, which makes imports more expensive. Yet, that's not what happened; inflation [...]

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Outline:

(01:58) Question 1: Why would an hour of labor from an American be worth 2x as much as an hour from a Japanese employee?

(02:37) Question 2: Why do Japanese automakers operate some factories in America instead of importing everything from Japan?

(03:25) Question 3: Why can the Japanese government keep borrowing money with no interest?

(03:59) Question 4: If dollars are overvalued, why does America have any exports?

(05:31) Question 5: If American labor is expensive, whats it doing?

(06:50) Question 6: Why do people want dollars if theyre overpriced?

(08:16) Question 7: Is that actually a problem?

(09:54) Question 8: Why dont Japanese invest domestically?

(14:24) Question 9: Why dont foreign companies outcompete in Japan?

(15:03) Question 10: What could be done about these issues?

(16:15) Question 11: What if youre an American who wants to make stuff?

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https://www.bhauth.com/blog/economics/yen.html

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I think that when most people picture a Bayesian agent, they imagine a system which:

• Enumerates every possible state/trajectory of “the world”, and assigns a probability to each.
• When new observations come in, loops over every state/trajectory, checks the probability of the observations conditional on each, and then updates via Bayes rule.
• To select actions, computes the utility which each action will yield under each state/trajectory, then averages over state/trajectory weighted by probability, and picks the action with the largest weighted-average utility.

Typically, we define Bayesian agents as agents which behaviorally match that picture.

But that's not really the picture David and I typically have in mind, when we picture Bayesian agents. Yes, behaviorally they act that way. But I think people get overly-anchored imagining the internals of the agent that way, and then mistakenly imagine that a Bayesian model of agency is incompatible with various features of [...]

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Outline:

(01:20) Causal Models and Submodels

(03:15) Updates

(04:04) Latents

(06:19) Aside: Map-Territory Correspondence

(07:50) Utility Over Latents

(08:33) Lazy Utility Maximization

(09:52) Caching and Inconsistency

(11:06) Putting It All Together

The original text contained 1 footnote which was omitted from this narration.

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As the dictum goes, “If it helps but doesn’t solve your problem, perhaps you’re not using enough.” But I still find that I’m sometimes not using enough effort, not doing enough of what works, simply put, not using enough dakka. And if reading one post isn’t enough to get me to do something… perhaps there isn’t enough guidance, or examples, or repetition, or maybe me writing it will help reinforce it more. And I hope this post is useful for more than just myself.

Of course, the ideas below are not all useful in any given situation, and many are obvious, at least after they are mentioned, but when you’re trying to get more dakka, it's probably worth running through the list and considering each one and how it applies to your actual problem. And more dakka won’t solve every problem - but if it's not working, make sure [...]

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