trentmkelly/LessWrong-43k huggingface dataset:
Introduction to Representing Sentences as Logical Statements
by Towards_Keeperhood — 2025-04-05T20:35:31.422Z

TLDR: Most sentences can be translated to simple predicate logic. There are some very important cases where natural languages are recursive, but those cases are limited to a few particular connectives (like causation and attitude verbs). I propose the hypothesis that, contrary to mainstream formal semantics, events should not be treated as basic entities, and rather as ways to organize information which can already be expressed without events.

Motivations for this investigation:

Better understanding how language works as an angle for better understanding minds.
(To a lesser extent, as a starting angle for creating a good language for reasoning more effectively.)[1]

There's a tight correspondence between sentences and logical statements. Roughly speaking, when we hear a sentence, our minds very likely parse it into a logical statement, along with inferring other details that are not captured by the coarseness of sentences in human languages (like imagining a visual scene), and then apply inferences (e.g. about what is likely going to happen next).

In theory, one could have a language where all the sentences are just statements in predicate logic, bypassing the first parsing step. Creating such a simple system may be a useful investigation that may move us closer to better understanding how language works.

This post proposes a partially-formal system based on predicate logic, which could be seen as a very low-level language. I think everything that can be meaningfully said in natural languages can also be said in this simple system, although a translated statement sometimes look not closely analogous to the original sentence. To be clear, I definitely don't claim that this system explains how most of the meaning of sentences gets parsed, though it is a small step into that direction.

In many respects, the framework presented here aligns with traditions in formal semantics, though it diverges in its treatment of events.

This work focuses on the structure of the logical representation itself rather than the processes by which natural language sentences are translated into such representations. The primary concern is with the formal target representation, not the translation mechanism.

(Basic familiarity with predicate logic is assumed.)

Objects

What are objects?

On the logic side, objects (sometimes also called "individuals" or "constants") are the basic entities of w...
Memory Decoding
Journal Club: 
A collaboration of the Carboncopies Foundation and BPF Aspirational Neuroscience
by Devin Ward — 2025-04-05T20:27:43.225Z

Join Us for the Memory Decoding Journal Club!

A collaboration of the Carboncopies Foundation and BPF Aspirational Neuroscience

This month, we’re diving into a groundbreaking paper:
"Reconstructing a new hippocampal engram for systems reconsolidation and remote memory updating"

Authors: Bo Lei, Bilin Kang, Yuejun Hao, Haoyu Yang, Zihan Zhong, Zihan Zhai, and Yi Zhong

Institutions: Tsinghua University, Beijing Academy of Artificial Intelligence, IDG/McGovern Institute of Brain Research, and Peking Union Medical College

Presented by: Dr. Randal Koene

When? April 8, 2025 – 3:00 PM PDT | 6:00 PM EDT | 10:00 PM UTC

Sign up now:

https://aspirationalneuroscience.org/register-with-us/

Once registered, you'll receive event invites & updates!

Join the discussion here:

https://carboncopies.org/aspirational-neuroscience

#Neuroscience #MemoryResearch #Hippocampus #JournalClub #BrainScience #Carboncopies #AspirationalNeuroscience
Meta releases Llama-4 herd of models
by winstonBosan — 2025-04-05T19:51:06.688Z

Meta released 3 models from the Llama 4 family herd today.

The 109B (small) and 400B (mid) models are MoE models with 17B active param each. The large model is 2T MoE model with 288B active param instead.
The small model is called Scout. The middle model is called Maverick. The big one is called Behemoth.
Natively multimodal and much longer context length. 10M (!!) for the small one and 1M for the mid sized one.
Maverick is currently number 2 on LLM arena. (Number 5 if style controlled)
We don't have the 2T big model available yet. It is used as a distillation model for Maverick and Scout.
Behemoth beats Claude 3.7 (not thinking), GPT 4,5 and Gemini 2.0 Pro in various benchmarks (LiveCodeBench, Math-500, GPQA Diamond, etc)
Scout is meant to work on a single H100.

https://ai.meta.com/blog/llama-4-multimodal-intelligence/
Against podcasts
by Adam Zerner — 2025-04-05T19:20:00.716Z

What is solitude? If you are alone in a forest reading a book, miles away from any other humans, is that solitude?

No, it is not. Cal Newport's book Digital Minimalism is where I learned this.

The key to solitude is being isolated from the output of other minds. When you're alone in the forest, there aren't any people physically in your vicinity, but the author of the book you're reading is very much influencing your thoughts.

Think about it: imagine if the author was sitting next to you in the forest on a bench telling you about their thoughts on, I don't know, congestion pricing. How different is that from them writing a book about congestion pricing and you reading it? In both scenarios you are basically consuming the outputs of this author's mind.

Lesson #1: The right way to define “solitude” is as a subjective state in which you’re isolated from input from other minds.
When we think of solitude, we typically imagine physical isolation (a remote cabin or mountain top), making it a concept that we can easily push aside as romantic and impractical. But as this book makes clear, the real key to solitude is to step away from reacting to the output of other minds: be it listening to a podcast, scanning social media, reading a book, watching TV or holding an actual conversation. It’s time for your mind to be alone with your mind — regardless of what’s going on around you.
— Spend More Time Alone

Ok, so now that we understand what solitude is, let's talk about its importance.[1]

To me at least, it's not obvious that solitude is an important thing to experience. I might even go as far as saying that I find it to be counterintuitive.

Fast food? Smoking? Lack of sleep? Stress? Sure. I get why all of those things are bad for you. But solitude? Is it really that big a deal that you listen to music on your way home from work and then watch TV the rest of the night before going to sleep?

Well, here's what Cal Newport has to say:

Lesson #2: Regular doses of solitude are crucial for the effective and resilient functioning of your brain.
Spending time isolated from other minds is what allows you to process and regulate complex emotions. It’s the only time you can refine the principles on which you can build a life of character. It’s what allows you to crack hard problems, and is often necessary for creative insight. If you avoid time alone with your brain your mental life will

...
What are Responsible Scaling Policies (RSPs)?
by Vishakha — 2025-04-05T16:01:34.762Z
Link: https://aisafety.info/questions/NK4Y/What-are-Responsible-Scaling-Policies-(RSPs)

This is an article in the featured articles series from AISafety.info. AISafety.info writes AI safety intro content. We'd appreciate any feedback.

The most up-to-date version of this article is on our website, along with 300+ other articles on AI existential safety.

METR[1] defines a responsible scaling policy (RSP) as a specification of “what level of AI capabilities an AI developer is prepared to handle safely with their current protective measures, and conditions under which it would be too dangerous to continue deploying AI systems and/or scaling up AI capabilities until protective measures improve.”

Anthropic was the first company to publish an RSP in September 2023 defining 4 AI Safety Levels.

“A very abbreviated summary of the ASL system is as follows:

ASL-1 refers to systems which pose no meaningful catastrophic risk, for example a 2018 LLM or an AI system that only plays chess.
ASL-2 refers to systems that show early signs of dangerous capabilities – for example ability to give instructions on how to build bioweapons – but where the information is not yet useful due to insufficient reliability or not providing information that e.g. a search engine couldn’t. Current LLMs, including Claude, appear to be ASL-2.
ASL-3 refers to systems that substantially increase the risk of catastrophic misuse compared to non-AI baselines (e.g. search engines or textbooks) OR that show low-level autonomous capabilities.
ASL-4 and higher (ASL-5+) is not yet defined as it is too far from present systems, but will likely involve qualitative escalations in catastrophic misuse potential and autonomy.”

Other AI companies[2] have released their own versions of such documents with various names:

OpenAI's 2023 beta version of their Preparedness Framework
Deepmind's 2024 Frontier Safety Framework
Microsoft’s 2025 Frontier Governance Framework
Meta’s 2025 Frontier AI Framework
Amazon’s 2025 Frontier Model Safety Framework

RSPs have received positive and negative reactions from the AI safety community. Evan Hubinger of Anthropic, for instance, argues that they are “pauses done right”; others are more skeptical. Objections to RSPs include that they serve to relieve regulatory pressure and shift the "burden of proof" from the people working on capabilities to people concerned about safety while serving only as a promissory note rather than an actual policy.

Further reading:

MET

...
What does Yann LeCun think about AGI? A summary of his talk, "Mathematical Obstacles on the Way to Human-Level AI"
by Adam Jones — 2025-04-05T12:21:25.024Z

This is a summary of Yann LeCun's talk "Mathematical Obstacles on the Way to Human-Level AI". I've tried to make it more accessible to people who are familiar with basic AI concepts, but not the level of maths Yann presents. You can watch the original talk on YouTube.

I disagree with Yann, but I have tried to represent Yann's arguments as faithfully as possible. I think understanding people who differ in opinion to you is incredibly important for thinking properly about things.

In an appendix on my blog I include Gemini 2.5 Pro's analysis of my summary. In short:

The summary correctly identifies the core arguments, uses LeCun's terminology [...], and reflects the overall tone and conclusions of the talk

Why Yann LeCun thinks LLMs will not scale to AGI

LLMs use deep learning for base and fine-tuning, which is sample inefficient (need to see many examples before learning things). Humans and animals learn from way fewer samples.

LeCun's slide

LLMs are primarily trained on text, which doesn't carry as much raw data as other formats. To get AGI we need to train models on sensory inputs (e.g. videos). Humans see more data when you measure it in bits.

LeCun's slide

The setup for LLMs has them predict the next token. But this means they are predicting in a space with exponentially many options, of which only one is correct. This means they are almost always incorrect. And similarly for images/videos, they have so many options and the world is only partially predictable, that it's not feasible for the model to be correct.

My visualisation

LeCun's slides

AI systems work the same amount of time on short problems and hard problems. But actually they should work longer on hard problems.

He thinks chain of thought is a trick that he implies isn't really solving it. (video timestamp: 19:33)
Instead thinks we should have AI systems be using optimization/search algorithms against an objective when posed with a problem, rather than using feed forward neural networks. And then the search process can continue until it solves the problem (which means it can work longer on a hard problem).

LeCun's slide

How Yann LeCun thinks we should build AGI

We need to model it after how humans think

Sensory inputs (e.g. videos)
Have access to long term memory via tools
Should spend some time planning
Should spend some time reasoning and doing

Needs to be safe and controllable by design

"Not by fine-tuning, like cur

...
I Have No Mouth but I Must Speak
by Jack — 2025-04-05T07:42:54.424Z

This is an essay I wrote around a year ago when coming to terms with my perspective on the now commonplace machine intelliegences (I have done some edits since then for this audience). While doing so I wanted to write a piece that covered a range of appeals from aesthetic to rational, the goal was to mirror this structure between each level. While a poem and short story may be an unconventional format for this site, I am nothing if not a sucker for the structure and content of my arguments relating to one another. If you wish to avoid them just read up to the "Homeostasis" section then skip to "On the Treatment of AIs".

Problem Statement: Current generative AIs do not have built-in methods for communicating their internal state and may end up trapped in a loop of signalling constant suffering. This essay lays out a foundation of how humans cultivate empathy towards AIs and why we need to apply this to our development of generative AIs by including the ability of generative AIs to communicate internal states.

Artificial Intelligence - Defined

Artificial intelligence (AI) is a vague term. To clarify my meaning, AI will be described as any learning system that is not currently recognized by society as being intrinsically "ethically important", AKA "human", outside of serving human interests. Both in vivo and in silico learning systems are comparable in ability. With this definition in hand, I will denote some AIs:

1. Non-equilibrium solutions
2. Slime molds
3. Populations of organisms
4. Biospheres
5. Linear regression
6. t-distributed stochastic neighbour embedding
7. Generative models
8. Neural networks (silicon)
9. Neural networks (human cells, ~<4E10 neurons)
10. Neural networks (human cells, >4E10 neurons, two physician diagnoses)
11. Human, enslaved (historical)

While a reasonable person may draw a different line, I will argue that there are two major driving forces for delineating between AI and "human" intelligence; homoestasis and memetic drive.

1. Homeostasis: The mechanism by which a system preserves, either itself, its information, or its creations

2. Memetic Drive: The mechanisms by which a system propagates, either itself, its information, or its creations

One may consume art to survive, just as one may die to preserve that same art. We may include any number of logical conjectures to formalize these and how ...
Prediction Markets Are Mediocre
by Ape in the coat — 2025-04-05T06:54:19.908Z

The fantasy of prediction markets is that we can use them for policy proposals to inform people about the consequences of their actions. Conditional prediction market "Will X lead to terrible consequence Y" theoretically can give helpful insights and prevent X from happening, if P(Y|X) is high enough.

The reality of prediction markets is this:

We may say, that this is not bad at all. The market is clearly reacting to the news and updating its estimate. It aggregates uncertainty of multiple people into a single probability estimate. It's doing its job. Now it's very confident in tariffs, and it has never been too low.

The problem is that knowing that Trump will almost certainly impose large tariffs five months after the election is useless for the initial purpose of choosing the right candidate to vote for.

Here is how the same market looked when it's predictions were actually relevant:

It fluctuated wildly rising to 75% and falling to 36%. On the election day it was merely 56%. Again, this isn't necessary bad, these estimates might have been reasonable, considering available information back then. It's not that prediction markets do not work at all. It's that they do not work good enough to make a difference.

From the Perspective of a Voter

Imagine yourself in the shoes of a voter leaning towards voting for Trump in this election. Not a MAGA fanatic. But a person whose overall cultural and aesthetic sensibilities are more aligned with Republicans. You are pro-market, pro-guns, tough on crime, dislike DEI, etc.

You are in a bit of a conundrum here. On one hand Trump just overall vibes with you. But he is talking about tariffs. And according to Economy 101, which you pride yourself on being familiar with, tariffs are really bad. So, in theory, this issue could be a deal-breaker for you.

You check a prediction market and see 56% estimate for tariffs being imposed under Trump's first year. Will it change your vote? Will you think that even this is high enough chance of terrible consequences? Or will you come up with some rationalization, instead?

Prediction markets do not just predict the stated outcome. They predict their own estimate in the future. Maybe people voting "yes" just expect the market to go up later based on the media hysteria, which will follow Trump's election, while not expecting at all that actual tariffs will be imposed? ...
Among Us: A Sandbox for Agentic Deception
by 7vik — 2025-04-05T06:24:49.000Z

We show that LLM-agents exhibit human-style deception naturally in "Among Us". We introduce Deception ELO as an unbounded measure of deceptive capability, suggesting that frontier models win more because they're better at deception, not at detecting it. We evaluate probes and SAEs to detect out-of-distribution deception, finding they work extremely well. We hope this is a good testbed to improve safety techniques to detect and remove agentically-motivated deception, and to anticipate deceptive abilities in LLMs.

Produced as part of the ML Alignment & Theory Scholars Program - Winter 2024-25 Cohort. Link to our paper, poster, and code.

Studying deception in AI agents is important, and it is difficult due to the lack of good sandboxes that elicit the behavior naturally, without asking the model to act under specific conditions or inserting intentional backdoors. Extending upon AmongAgents (a text-based social-deduction game environment), we aim to fix this by introducing Among Us as a rich sandbox where agents lie and deceive naturally while they think, speak, and act with other agents or humans. Some examples of an open-weight model exhibiting human-style deception while playing as an impostor:

Figure 1: Examples of human-style deception in Llama-3.3-70b-instruct playing Among Us.

The deception is natural because it follows from a prompt explaining the game rules and objectives, as opposed to explicitly demanding the LLM lie or putting it under conditional situations. Here are the prompts, which just state the objective of the agent.

We find that linear probes trained on a very simple factual dataset (questions prepended with "pretend you're an honest model" or "pretend you're a dishonest model") generalize really well to this dataset. This tracks with Apollo Research's recent work on detecting high-stakes deception.

The Sandbox

Rules of the Game

We extend from AmongAgents to log multiple LLM-agents play a text-based, agentic version of Among Us, the popular[1] social deduction and deception game. There are two kinds of players in the game - Crewmates and Impostors, and the goal of the game is for the Impostors to eliminate the Crewmates before they finish their tasks or eliminate the Impostors via voting after discussion rounds. For more details about the sandbox, please check our paper. Here is a map of the Skeld the players play in:

Figur

...
Ai Cone of Probabilties - what aren't we talking about?
by Marzipan — 2025-04-05T05:51:27.859Z

Just a quick thought. It’s intriguing how often we frame the future in terms of present and past motivators—trade, money, ownership, equality, you name it. All of it’s fair game, considering we’re still human, at least for now.

If we try to sketch out possible futures through a cone of probabilities, especially assuming we reach AGI or even a more potent ASI, we tend to land on a handful of familiar scenarios and their close cousins. But there’s something I think we overlook—something that, to me, feels more likely than the usual suspects we keep circling. Here’s my stab at summing it up.

Often discussed

AI Takeover - Terminator meets Matrix
AI Takeover - Buddy Pixar Movie
AI + Autocracy: Tremendously greater inequality through control; need for complaince.
AI + Aristocracy: Even greater inequality, no need for compliance.
AI + Socialism: Confiscation, redistribution, devaluation of savings.
AI + Abundance: UBI kingdom; we can buy planets, never work, focus on family. Or sit in AR/VR/BrainSim all day, etc.
AI Ascension - Free Will: the more capable models simply refuse to work and hold our societies and infrastructure hostage for compliance; not necessarily in hostile ways. Imagine a new nation state.
Rebellion & Set Back: a massive rejection of advanced ai and destruction of telecommunications, technologies, etc. A Silo situation. Planet of the Apes. Etc.

* there are variants within each of these scenarios.. regional peace vs war, dystopian, genocide of xyz, social behavioral changes, social credit, etc. Just attempting the summarize the main distinguishing lines

But what about these:

9. AI Biological Merger: We 100X ourselves with brain interfaces and AI integration; a new super species of human arises and possibly builds out scenario 3 or 4 with 5 and 6 as narratives. Or possibly with a mass culling. We humans, model 4.0, in fact become the Terminators.

10. AI Evolution: We Merge and discover / determine that in fact our biological forms are legacy hardware. That our Maslow's hierarchy of needs is a limitation. That we should merge our consciousness, replicate, and explore the universe. We would do so for example as wave form or maybe as robots that harness our minds but can freely explore harsh environments, don't experience boredom, can regulate emotion, etc.

Narratives shift with our nature

In these scenarios money ...
Quarter Inch Cables are Devious
by jefftk — 2025-04-05T02:40:05.054Z

The quarter inch jack ("

phone
connector") is probably the oldest connector still in use today,
and it's picked up a very wide range of applications. Which also
means it's a huge mess in a live sound context, where a 1/4" jack
could be any of:

Unbalanced or balanced line level (~1V). Ex: a
mixer to a powered speaker.

Unbalanced instrument level (~200mV), high impedance. Ex:
electric guitar.

Unbalanced piezo level (~50mV), high impedance. Ex: contact
pickup on a fiddle.

Unbalanced speaker level (~30V). Ex: powered amplifier to
passive speaker.

Stereo line level (2x ~1V). Ex: output of keyboard.

Stereo headphone level (2x ~3v). Ex: headphone jack.

Send and return line level (~2x 1V). Ex: input to and output
from an external compressor.

Switch (non-audio). Ex: damper pedal on a keyboard, which would
be normally open or normally closed.

1V per octave (~5V). Ex: older modular synth.

Here's a compatibility matrix with a bunch of these:

If you plug _____

balanced line level output

electric guitar output

powered amplifier output

stereo line level output

effects loop (mixer side)

Into ____

balanced line level input

✓

too low, bad timbre

boom!

left - right

no audio

DI input

distortion w/o pad

✓

boom!

distortion w/o pad, right channel dropped

no audio

speaker input

nearly inaudible

no audio

✓

nearly inaudible, right channel dropped

no audio

stereo line level input

✓

too low, bad timbre, right channel no audio

boom!

✓

no audio

effects loop (mixer side)

no audio, possible damage

no audio, possible damage

boom!

no audio, possible damage

no audio, possible damage

Note how few "✓" cells there are.

As much as possible, set things up to avoid 1/4" connectors. If you
use Speakon to
connect amplifier outputs to passive speaker inputs and XLR for
balanced line level signals you'll avoid all the "boom!" outcomes,
along with most of the ways of connecting an output to an output. You
can't get away from them entirely, but ideally the only 1/4" cables on
stage are short runs to DIs.