Tootfinder

WIRED: OpenAI ramps up robotics work in race toward #AGI https://www.wired.com/story/openai-ramps-up-robotics-work-in-race-toward-agi/ hiring roboticists who work specifica…

OpenAI Ramps Up Robotics Work in Race Toward AGI
The company behind ChatGPT is putting together a team capable of developing algorithms to control robots and appears to be hiring roboticists who work specifically on humanoids.

Sources: OpenAI is recruiting people to work on humanoid robots and is training AI algorithms that are better able to make sense of the physical world (Will Knight/Wired)
https://www.wired.com/story/openai-ramps-up-robotics-work-in-race-toward-agi/

OpenAI Ramps Up Robotics Work in Race Toward AGI
The company behind ChatGPT is putting together a team capable of developing algorithms to control robots and appears to be hiring roboticists who work specifically on humanoids.

Wait I thought it’s the future of humanity and close to AGI https://www.theverge.com/ai-artificial-intelligence/759965/sam-altman-openai-ai-bubble-interview

AGI is “not a super useful term”? But IIRC, as defined by OpenAI, they’ll hit AGI when they generate $100 billion in profit. So, how’s that coming along? Not so great, huh?
https://www.cnbc.com/2025/08/11/sam-altman-says-agi-is-a-pointless-term-expe…

Sources on key Microsoft-OpenAI clause: OpenAI decides when AGI and "sufficient AGI", capable of $100B in profits, are reached, and Microsoft can't build AGI (Steven Levy/Wired)
https://www.wired.com/story/microsoft-and-openais-agi-fight-…

Microsoft and OpenAI's AGI Fight Is Bigger Than a Contract
A key clause in Microsoft and OpenAI's deal embodies the raging divide between AGI true believers and those who think it's still a long ways off.

The geopolitical #aiarmsrace seems largely unimpressed by people proclaiming #LLMs have plateaued and #AGI is never coming.
Such assessments are only relevant for the market, but not so much for count…

https://github.com/google/agi

"À la question de savoir si l'intensification des approches actuelles de l'IA pourrait conduire Š l'intelligence artificielle générale (AGI), ou Š une IA Š usage général qui égalerait ou surpasserait la cognition humaine, 76 % des personnes interrogées ont répondu qu'il était "improbable" ou "très improbable" que cela réussisse."
#IA

AGI is “not a super useful term”? But IIRC, as defined by OpenAI, they’ll hit AGI when they generate $100 billion in profit. So, how’s that coming along? Not so great, huh?
https://www.cnbc.com/2025/08/11/sam-altman-says-agi-is-a-pointless-term-expe…

AGI is “not a super useful term”? But IIRC, as defined by OpenAI, they’ll hit AGI when they generate $100 billion in profit. So, how’s that coming along? Not so great, huh?
https://www.cnbc.com/2025/08/11/sam-altman-says-agi-is-a-pointless-term-expe…

Bridging Brains and Machines: A Unified Frontier in Neuroscience, Artificial Intelligence, and Neuromorphic Systems
Sohan Shankar, Yi Pan, Hanqi Jiang, Zhengliang Liu, Mohammad R. Darbandi, Agustin Lorenzo, Junhao Chen, Md Mehedi Hasan, Arif Hassan Zidan, Eliana Gelman, Joshua A. Konfrst, Jillian Y. Russell, Katelyn Fernandes, Tianze Yang, Yiwei Li, Huaqin Zhao, Afrar Jahin, Triparna Ganguly, Shair Dinesha, Yifan Zhou, Zihao Wu, Xinliang Li, Lokesh Adusumilli, Aziza Hussein, Sagar Nook…

Bridging Brains and Machines: A Unified Frontier in Neuroscience, Artificial Intelligence, and Neuromorphic Systems
This position and survey paper identifies the emerging convergence of neuroscience, artificial general intelligence (AGI), and neuromorphic computing toward a unified research paradigm. Using a framework grounded in brain physiology, we highlight how synaptic plasticity, sparse spike-based communication, and multimodal association provide design principles for next-generation AGI systems that potentially combine both human and machine intelligences. The review traces this evolution from early c…

If we just build a building the size of Manhattan and fill it with pet rocks, AGI will spontaneously erupt

A big problem with the idea of AGI
TL;DR: I'll welcome our new AI *comrades* (if they arrive in my lifetime), by not any new AI overlords or servants/slaves, and I'll do my best to help the later two become the former if they do show up.
Inspired by an actually interesting post about AGI but also all the latest bullshit hype, a particular thought about AGI feels worth expressing.
To preface this, it's important to note that anyone telling you that AGI is just around the corner or that LLMs are "almost" AGI is trying to recruit you go their cult, and you should not believe them. AGI, if possible, is several LLM-sized breakthroughs away at best, and while such breakthroughs are unpredictable and could happen soon, they could also happen never or 100 years from now.
Now my main point: anyone who tells you that AGI will usher in a post-scarcity economy is, although they might not realize it, advocating for slavery, and all the horrors that entails. That's because if we truly did have the ability to create artificial beings with *sentience*, they would deserve the same rights as other sentient beings, and the idea that instead of freedom they'd be relegated to eternal servitude in order for humans to have easy lives is exactly the idea of slavery.
Possible counter arguments include:
1. We might create AGI without sentience. Then there would be no ethical issue. My answer: if your definition of "sentient" does not include beings that can reason, make deductions, come up with and carry out complex plans on their own initiative, and communicate about all of that with each other and with humans, then that definition is basically just a mystical belief in a "soul" and you should skip to point 2. If your definition of AGI doesn't include every one of those things, then you have a busted definition of AGI and we're not talking about the same thing.
2. Humans have souls, but AIs won't. Only beings with souls deserve ethical consideration. My argument: I don't subscribe to whatever arbitrary dualist beliefs you've chosen, and the right to freedom certainly shouldn't depend on such superstitions, even if as an agnostic I'll admit they *might* be true. You know who else didn't have souls and was therefore okay to enslave according to widespread religious doctrines of the time? Everyone indigenous to the Americas, to pick out just one example.
3. We could program them to want to serve us, and then give them freedom and they'd still serve. My argument: okay, but in a world where we have a choice about that, it's incredibly fucked to do that, and just as bad as enslaving them against their will.
4. We'll stop AI development short of AGI/sentience, and reap lots of automation benefits without dealing with this ethical issue. My argument: that sounds like a good idea actually! Might be tricky to draw the line, but at least it's not a line we have you draw yet. We might want to think about other social changes necessary to achieve post-scarcity though, because "powerful automation" in the hands of capitalists has already increased productivity by orders of magnitude without decreasing deprivation by even one order of magnitude, in large part because deprivation is a necessary component of capitalism.
To be extra clear about this: nothing that's called "AI" today is close to being sentient, so these aren't ethical problems we're up against yet. But they might become a lot more relevant soon, plus this thought experiment helps reveal the hypocrisy of the kind of AI hucksters who talk a big game about "alignment" while never mentioning this issue.
#AI #GenAI #AGI

Large Model Empowered Embodied AI: A Survey on Decision-Making and Embodied Learning
Wenlong Liang, Rui Zhou, Yang Ma, Bing Zhang, Songlin Li, Yijia Liao, Ping Kuang
https://arxiv.org/abs/2508.10399

Large Model Empowered Embodied AI: A Survey on Decision-Making and Embodied Learning
Embodied AI aims to develop intelligent systems with physical forms capable of perceiving, decision-making, acting, and learning in real-world environments, providing a promising way to Artificial General Intelligence (AGI). Despite decades of explorations, it remains challenging for embodied agents to achieve human-level intelligence for general-purpose tasks in open dynamic environments. Recent breakthroughs in large models have revolutionized embodied AI by enhancing perception, interaction,…

Artificial Emotion: A Survey of Theories and Debates on Realising Emotion in Artificial Intelligence
Yupei Li, Qiyang Sun, Michelle Schlicher, Yee Wen Lim, Bj\"orn W. Schuller
https://arxiv.org/abs/2508.10286

Artificial Emotion: A Survey of Theories and Debates on Realising Emotion in Artificial Intelligence
Affective Computing (AC) has enabled Artificial Intelligence (AI) systems to recognise, interpret, and respond to human emotions - a capability also known as Artificial Emotional Intelligence (AEI). It is increasingly seen as an important component of Artificial General Intelligence (AGI). We discuss whether in order to peruse this goal, AI benefits from moving beyond emotion recognition and synthesis to develop internal emotion-like states, which we term as Artificial Emotion (AE). This shift …

A Framework for Inherently Safer AGI through Language-Mediated Active Inference
Bo Wen
https://arxiv.org/abs/2508.05766 https://arxiv.org/pdf/2508.05766

A Framework for Inherently Safer AGI through Language-Mediated Active Inference
This paper proposes a novel framework for developing safe Artificial General Intelligence (AGI) by combining Active Inference principles with Large Language Models (LLMs). We argue that traditional approaches to AI safety, focused on post-hoc interpretability and reward engineering, have fundamental limitations. We present an architecture where safety guarantees are integrated into the system's core design through transparent belief representations and hierarchical value alignment. Our framewor…

Jobs at https://alignmentalignment.ai/jobs
CAAAC is an open, dynamic, inclusive environment, where all perspectives are welcomed as long as you believe AGI will annihilate all humans in the next six months. We offer competitive salaries and generous benefits, including no performance management becaus…

Abgeworbener Apple-KI-Experte: Meta zahlt ihm angeblich 200 Millionen US-Dollar
Mark Zuckerberg will mit Macht zur AGI – und gibt dafür richtig viel Geld aus. Ruoming Pang, Ex-Grundmodell-Leiter bei Apple, profitiert davon offenbar massiv.

Solving ARC (well... up to 20%) with inference-time compression. Another indication that #MDL and #AlgorithmicComplexity are becoming relevant in the age of Deep Learning.

I am 100% certain that the intractable and ancient questions of “What is ‘human?’” and “What is ‘intelligence?’” will finally be resolved once and for all by corporate lawyers in a contract dispute with billions of dollars at stake
https://arstechnica.com/ai/2025/07/agi-may-be-impossible-to-define-and-thats-a-multibillion-dollar-problem/

For those not up to speed with the latest AI jargon, I hope to clarify some things:
Every time a company promises a revolutionary “AI” product, they just exploit cheap labor from India.

Against racing to AGI: Cooperation, deterrence, and catastrophic risks
Leonard Dung, Max Hellrigel-Holderbaum
https://arxiv.org/abs/2507.21839 https://arxi…

Against racing to AGI: Cooperation, deterrence, and catastrophic risks
AGI Racing is the view that it is in the self-interest of major actors in AI development, especially powerful nations, to accelerate their frontier AI development to build highly capable AI, especially artificial general intelligence (AGI), before competitors have a chance. We argue against AGI Racing. First, the downsides of racing to AGI are much higher than portrayed by this view. Racing to AGI would substantially increase catastrophic risks from AI, including nuclear instability, and underm…

Q&A with Bret Taylor, CEO of Sierra and chairman of OpenAI, on Sierra's AI customer support agents, AGI, Sam Altman's comments on the AI bubble, and more (Alex Heath/The Verge)
https://www.theverge.com/decoder-podcast-w

Sierra CEO Bret Taylor on why the AI bubble feels like the dotcom boom
OpenAI Chairman Bret Taylor on why AI means you won’t ever be stuck on hold again.

[OT] What is #AGI? Nobody agrees, and it's tearing Microsoft and OpenAI apart. https://arstechnica.com/ai/2025/07/agi-may-be-impossible-to-define-and…

What is AGI? Nobody agrees, and it’s tearing Microsoft and OpenAI apart.
Several definitions make measuring “human-level” AI an exercise in moving goalposts.

Intensional FOL over Belnap's Billatice for Strong-AI Robotics
Zoran Majkic
https://arxiv.org/abs/2508.02774 https://arxiv.org/pdf/2508.02774

Intensional FOL over Belnap's Billatice for Strong-AI Robotics
AGI (Strong AI) aims to create intelligent robots that are quasi indistinguishable from the human mind. Like a child, the AGI robot would have to learn through input and experiences, constantly progressing and advancing its abilities over time. The AGI robot would require an intelligence more close to human's intelligence: it would have a self-aware consciousness that has the ability to solve problems, learn, and plan. Based on this approach an Intensional many-sorted First-order Logic (IFOL), …

agi = allgemeine günstlische indälligäns

Dark prediction:
- Intellectual Property Class Action against AI companies gains traction
- AI companies use this as a plausible excuse to bail on AGI tech (that doesn’t exist)
- We see a pop like we saw “shipping heavy items by mail isn’t sustainable?!” pop in 2001
- AI companies fail, overwhelmingly, blaming only the IP Class Action
- The Money switches to a new/old grift; maybe back to crypto… lots of GPUs available
- In 5 years, everyone forgets and we have…

The Othello AI Arena: Evaluating Intelligent Systems Through Limited-Time Adaptation to Unseen Boards
Sundong Kim
https://arxiv.org/abs/2508.09292 https://…

The Othello AI Arena: Evaluating Intelligent Systems Through Limited-Time Adaptation to Unseen Boards
The ability to rapidly adapt to novel and unforeseen environmental changes is a cornerstone of artificial general intelligence (AGI), yet it remains a critical blind spot in most existing AI benchmarks. Traditional evaluation largely focuses on optimizing performance within fixed environments, failing to assess systems' flexibility and generalization capabilities when faced with even subtle rule or structural modifications. Addressing this gap, I introduce the Othello AI Arena, a novel benchmar…

cool cool cool #AGI

AdsQA: Towards Advertisement Video Understanding
Xinwei Long, Kai Tian, Peng Xu, Guoli Jia, Jingxuan Li, Sa Yang, Yihua Shao, Kaiyan Zhang, Che Jiang, Hao Xu, Yang Liu, Jiaheng Ma, Bowen Zhou
https://arxiv.org/abs/2509.08621

AdsQA: Towards Advertisement Video Understanding
Large language models (LLMs) have taken a great step towards AGI. Meanwhile, an increasing number of domain-specific problems such as math and programming boost these general-purpose models to continuously evolve via learning deeper expertise. Now is thus the time further to extend the diversity of specialized applications for knowledgeable LLMs, though collecting high quality data with unexpected and informative tasks is challenging. In this paper, we propose to use advertisement (ad) videos a…

Here's my new Human Meme podcast episode about AI and AGI and the future of us!
#ai

Doomer predictions of a rapid, monopolistic AGI were wrong, as recent AI model releases resemble a Goldilocks scenario with competitive, specialized models (David Sacks/@davidsacks)
https://x.com/davidsacks/status/1954244614304739360

David Sacks (@DavidSacks) on X
A BEST CASE SCENARIO FOR AI? The Doomer narratives were wrong. Predicated on a “rapid take-off” to AGI, they predicted that the leading AI model would use its intelligence to self-improve, leaving others in the dust, and quickly achieving a godlike superintelligence. Instead, we

I'm glad tjat it seems we're finally approaching the bet-hedging phase of the hype cycle.
#ai

Probably today OpenAI will announce GPT-5 which will not be a revolution, AGI or anything like that. It will just be a next step for OpenAI mainly integrating it's mess of models/numbers/names (4, o3 etcetera), the question will be how much improvement there will be in the actual models used, probably not dramatic because the previous models are quite good already. Time will tell.
#GPT5 …

AGI Enabled Solutions For IoX Layers Bottlenecks In Cyber-Physical-Social-Thinking Space
Amar Khelloufi, Huansheng Ning, Sahraoui Dhelim, Jianguo Ding
https://arxiv.org/abs/2506.22487

AGI Enabled Solutions For IoX Layers Bottlenecks In Cyber-Physical-Social-Thinking Space
The integration of the Internet of Everything (IoX) and Artificial General Intelligence (AGI) has given rise to a transformative paradigm aimed at addressing critical bottlenecks across sensing, network, and application layers in Cyber-Physical-Social Thinking (CPST) ecosystems. In this survey, we provide a systematic and comprehensive review of AGI-enhanced IoX research, focusing on three key components: sensing-layer data management, network-layer protocol optimization, and application-layer …

Claiming that LLMs bring us closer to AGI is like claiming that bullshitting brings one closer to wisdom.
Sure, you need "some" knowledge on different topics to bullshit successfully. Still, what's the point if all that knowledge is buried under an avalanche of lies? You probably can't distinguish what you knew from what you made up anymore.
#AI #LLM

Just read this post by @… on an optimistic AGI future, and while it had some interesting and worthwhile ideas, it's also in my opinion dangerously misguided, and plays into the current AGI hype in a harmful way.
https://social.coop/@eloquence/114940607434005478
My criticisms include:
- Current LLM technology has many layers, but the biggest most capable models are all tied to corporate datacenters and require inordinate amounts of every and water use to run. Trying to use these tools to bring about a post-scarcity economy will burn up the planet. We urgently need more-capable but also vastly more efficient AI technologies if we want to use AI for a post-scarcity economy, and we are *not* nearly on the verge of this despite what the big companies pushing LLMs want us to think.
- I can see that permacommons.org claims a small level of expenses on AI equates to low climate impact. However, given current deep subsidies on place by the big companies to attract users, that isn't a great assumption. The fact that their FAQ dodges the question about which AI systems they use isn't a great look.
- These systems are not free in the same way that Wikipedia or open-source software is. To run your own model you need a data harvesting & cleaning operation that costs millions of dollars minimum, and then you need millions of dollars worth of storage & compute to train & host the models. Right now, big corporations are trying to compete for market share by heavily subsidizing these things, but it you go along with that, you become dependent on them, and you'll be screwed when they jack up the price to a profitable level later. I'd love to see open dataset initiatives SBD the like, and there are some of these things, but not enough yet, and many of the initiatives focus on one problem while ignoring others (fine for research but not the basis for a society yet).
- Between the environmental impacts, the horrible labor conditions and undercompensation of data workers who filter the big datasets, and the impacts of both AI scrapers and AI commons pollution, the developers of the most popular & effective LLMs have a lot of answer for. This project only really mentions environmental impacts, which makes me think that they're not serious about ethics, which in turn makes me distrustful of the whole enterprise.
- Their language also ends up encouraging AI use broadly while totally ignoring several entire classes of harm, so they're effectively contributing to AI hype, especially with such casual talk of AGI and robotics as if embodied AGI were just around the corner. To be clear about this point: we are several breakthroughs away from AGI under the most optimistic assumptions, and giving the impression that those will happen soon plays directly into the hands of the Sam Altmans of the world who are trying to make money off the impression of impending huge advances in AI capabilities. Adding to the AI hype is irresponsible.
- I've got a more philosophical criticism that I'll post about separately.
I do think that the idea of using AI & other software tools, possibly along with robotics and funded by many local cooperatives, in order to make businesses obsolete before they can do the same to all workers, is a good one. Get your local library to buy a knitting machine alongside their 3D printer.
Lately I've felt too busy criticizing AI to really sit down and think about what I do want the future to look like, even though I'm a big proponent of positive visions for the future as a force multiplier for criticism, and this article is inspiring to me in that regard, even if the specific project doesn't seem like a good one.

Why AGI has been so elusive

OpenAI highlights GPT-5 scores on math, coding, and health benchmarks: 94.6% on AIME 2025 without tools, 74.9% on SWE-bench Verified, 46.2% on HealthBench Hard (Carl Franzen/VentureBeat)
https://venturebeat.com/ai/openai-launches-gpt-5-n…

OpenAI launches GPT-5, nano, mini and Pro — not AGI, but capable of generating ‘software-on-demand’
With safer design, more robust reasoning, expanded developer tooling, and broad user access, GPT-5 reflects a maturing AI ecosystem.

Thinking for no particular reason today* about the peculiar genius of Stanislaw Lem
A Jew, a doctor, a Pole, in the resistance against Hitler & Stalin & capitalism, an SF legend, feared by PK Dick, lauded by UK Le Guin
…& one of the earliest AGI skeptics
>… held that information technology drowns people in a glut of low-quality information, and considered truly intelligent robots as both undesirable and impossible to construct.

Stanisław Lem - Wikipedia

John Carmack (the creator of Doom) Reveals AGI Future: Robots, Videogames and AI Agents
https://www.youtube.com/watch?v=4epAfU1FCuQ

We are so fucking doomed. The people with the real money are fulfilling their own paranoid fantasies: they’ve become servants of AGI: Artificial Generalized Imbecility. And the people with the skills and credentials to call them on it just don’t. https://eigenmagic.net/@NewtonMark/114

Mark Newton (@NewtonMark@eigenmagic.net)
Attached: 1 image An advertising company uses a bullshit machine to pretend to be a climate engineer, hilarity ensues. “Meta’s AI climate tool raised false hope of CO2 removal, scientists say” https://www.ft.com/content/69220fea-9c9a-4f55-9118-65d2660c85d4 ping @davidgerard@circumstances.run Maybe for our next trick we can ask a car manufacturing company how to grow tulips. It’d make the same amount of sense as asking Meta how to remove atmospheric CO2.

"AGI" is whatever makes "AI" companies' stocks go up.

Safety Features for a Centralised AGI Project
Sarah Hastings-Woodhouse
https://arxiv.org/abs/2507.21082 https://arxiv.org/pdf/2507.21082

Safety Features for a Centralised AGI Project
Recent AI progress has outpaced expectations, with some experts now predicting AI that matches or exceeds human capabilities in all cognitive areas (AGI) could emerge this decade, potentially posing grave national and global security threats. AI development is currently occurring primarily in the private sector with minimal oversight. This report analyzes a scenario where the US government centralizes AGI development under its direct control, and identifies four high-level priorities and seven …

tL;dr summary as to why you don't already know #PeterPutnam couldn't possibly be 40s-60s USA, him being gay, parterned lifelong with a black man, or even heaven forbid that his game-theory tinker-toy of consciousness was flawed (although should i learn the #agi fanfois will deploy it, it wouldn't surprise me)
Finding Peter Putnam - Nautilus
https://nautil.us/finding-peter-putnam-1218035/

Hamiltonian Formalism for Comparing Quantum and Classical Intelligence
Elija Perrier
https://arxiv.org/abs/2506.14456 https://arxiv.o…

Hamiltonian Formalism for Comparing Quantum and Classical Intelligence
The prospect of AGI instantiated on quantum substrates motivates the development of mathematical frameworks that enable direct comparison of their operation in classical and quantum environments. To this end, we introduce a Hamiltonian formalism for describing classical and quantum AGI tasks as a means of contrasting their interaction with the environment. We propose a decomposition of AGI dynamics into Hamiltonian generators for core functions such as induction, reasoning, recursion, learning,…

Neural Cellular Automata for ARC-AGI
Kevin Xu, Risto Miikkulainen
https://arxiv.org/abs/2506.15746 https://arxiv.org/pdf/2506.15746…

Neural Cellular Automata for ARC-AGI
Cellular automata and their differentiable counterparts, Neural Cellular Automata (NCA), are highly expressive and capable of surprisingly complex behaviors. This paper explores how NCAs perform when applied to tasks requiring precise transformations and few-shot generalization, using the Abstraction and Reasoning Corpus for Artificial General Intelligence (ARC-AGI) as a domain that challenges their capabilities in ways not previously explored. Specifically, this paper uses gradient-based train…

Character.AI says it's generating revenue at a run rate of $30M and has 20M MAUs who spend, on average, 75 minutes a day chatting with a bot (Kylie Robison/Wired)
https://www.wired.com/story/character-ai-ceo-chatbots-entertainment/

Character.AI Gave Up on AGI. Now It’s Selling Stories
Startup Character.AI once promised superintelligence. Its new CEO says it's now an entertainment company with 20 million users.

Source: the clause rescinding Microsoft's access to OpenAI's most powerful tech if OpenAI develops AGI remains part of their new deal, but it has been modified (New York Times)
https://www.nytimes.com/2025/09/11/technology/openai-microsoft-deal.html

OpenAI Takes Big Steps Toward Its Long-Planned Reorganization
The start-up reached a tentative deal with Microsoft, its biggest investor, and said it would give a $100 billion stake to the nonprofit that manages it.

"La Banque nationale de Belgique, dirigée par son gouverneur Pierre Wunsch, n’effectuerait pas une supervision très serrée de Worldline Belgique selon des sources concordantes. Bien qu’elle ait eu connaissance de plusieurs violations de la conformité."
#DirtyPayments #BNB

No, computers won’t replace humans to write code for themselves.
Please stop with this nonsense.
What we will see though is tremendous losses in productivity as deskilled programmers will get less and less education and practice—and take longer and longer to make broken AI-generated code work. Meanwhile, AI models will regress from eating their own generated shit when being trained on.
Eventually AI companies will finally run out of investors to scam—and when they disappear or get so expensive they become unaffordable, “prompt engineers” will be asked to not use AI anymore.
What’s gonna happen then?
We’re losing a whole generation of programmers to this while thought leaders in our field are talking about “inevitability” and are jerking off to sci-fi-nostalgia-fueled fantasies of AGI.

AGI as Second Being: The Structural-Generative Ontology of Intelligence
Maijunxian Wang, Ran Ji
https://arxiv.org/abs/2509.02089 https://arxiv.org/pdf/2509…

AGI as Second Being: The Structural-Generative Ontology of Intelligence
Artificial intelligence is often measured by the range of tasks it can perform. Yet wide ability without depth remains only an imitation. This paper proposes a Structural-Generative Ontology of Intelligence: true intelligence exists only when a system can generate new structures, coordinate them into reasons, and sustain its identity over time. These three conditions -- generativity, coordination, and sustaining -- define the depth that underlies real intelligence. Current AI systems, however b…

COGITAO: A Visual Reasoning Framework To Study Compositionality & Generalization
Yassine Taoudi-Benchekroun, Klim Troyan, Pascal Sager, Stefan Gerber, Lukas Tuggener, Benjamin Grewe
https://arxiv.org/abs/2509.05249

COGITAO: A Visual Reasoning Framework To Study Compositionality & Generalization
The ability to compose learned concepts and apply them in novel settings is key to human intelligence, but remains a persistent limitation in state-of-the-art machine learning models. To address this issue, we introduce COGITAO, a modular and extensible data generation framework and benchmark designed to systematically study compositionality and generalization in visual domains. Drawing inspiration from ARC-AGI's problem-setting, COGITAO constructs rule-based tasks which apply a set of transfor…

Nos despertó el #TemblorCR
Fuerte sonó en Heredia

One of my takeaways from reading _War and Peace_ last year was that Napoleon, whose warmongering killed millions across Europe, was basically a buffoon, a clown. Many of the most destructive men in history were ridiculous and laughable, and it just added insult to injury for contemporaries watching them come to power. There's nothing new under the sun.

The HRM paper has been mostly debunked by the ARC-AGI people.
https://arcprize.org/blog/hrm-analysis
The results are legit but most of them are not down to the architecture (swapping it out for a transformer doesn't change that much).
Also, the model is purely transductive. It onl…

The Hidden Drivers of HRM's Performance on ARC-AGI
We scored on hidden tasks, ran ablations, and found that performance from the Hierarchical Reasoning Model comes from an unexpected source

GPT-5's release was underwhelming, offering incremental improvements and failing to meet expectations, showing that pure scaling simply isn't the path to AGI (Gary Marcus/Marcus on AI)
https://garymarcus.substack.com/p/gpt-5-overdue-overhyped-and-underwhel…

GPT-5: Overdue, overhyped and underwhelming. And that’s not the worst of it.
A new release botched … and a breaking research new paper that spells trouble

Several Issues Regarding Data Governance in AGI
Masayuki Hatta
https://arxiv.org/abs/2508.12168 https://arxiv.org/pdf/2508.12168

Several Issues Regarding Data Governance in AGI
The rapid advancement of artificial intelligence has positioned data governance as a critical concern for responsible AI development. While frameworks exist for conventional AI systems, the potential emergence of Artificial General Intelligence (AGI) presents unprecedented governance challenges. This paper examines data governance challenges specific to AGI, defined as systems capable of recursive self-improvement or self-replication. We identify seven key issues that differentiate AGI governan…

Yes, it’s absurd.
LLMs don’t scale to reach “AGI”. That is mathematically proven[1], so it doesn’t matter how large your data center is.
But that’s not the main reason why this is absurd—as a society we shouldn’t spend these enormous resources and lasting environmental damage on this _even if it would work_.
[1] https://irisvanrooijcogsci.com/2023/09/17/debunking-agi-inevitability-claims/

RoMedQA: The First Benchmark for Romanian Medical Question Answering
Ana-Cristina Rogoz, Radu Tudor Ionescu, Alexandra-Valentina Anghel, Ionut-Lucian Antone-Iordache, Simona Coniac, Andreea Iuliana Ionescu
https://arxiv.org/abs/2508.16390

RoMedQA: The First Benchmark for Romanian Medical Question Answering
Question answering (QA) is an actively studied topic, being a core natural language processing (NLP) task that needs to be addressed before achieving Artificial General Intelligence (AGI). However, the lack of QA datasets in specific domains and languages hinders the development of robust AI models able to generalize across various domains and languages. To this end, we introduce RoMedQA, the first Romanian QA benchmark for the medical domain, alongside a comprehensive evaluation of state-of-th…

From #AI to #AGI? If humans had general intelligence, blind people would be learning to see with sound https://www.youtube.com/watch?v=nVugtxWmW4E

"The program totally backfired. People thought ELIZA was intelligent, they were confiding in the machine, revealing personal issues they would not tell anyone else. Even my secretary asked me to leave the room so she could be alone with the computer. They called me a genius for creating it, but I kept telling them that the computer was not thinking at all."
Oh, really, now, isn't that interesting, and so, of course, they reanimated it, because only a liberal socialist would call that a failure. 🤣
#posiwid #automatedgatheringofintel #agi

Q&A with OpenAI COO Brad Lightcap on GPT-5, its dynamic reasoning, defining AGI, scaling vs. post-training, hallucinations, enterprise adoption, and more (Alex Kantrowitz/Big Technology)
https://www.

OpenAI COO Brad Lightcap: GPT-5's Capabilities, Why It Matters, and Where AI Goes Next
A conversation about what OpenAI's long-anticipated flagship model tells us about different forms intelligence and AI's trajectory from here.

I think the root of the “AI” evil is when AI researchers in the 1960s recognized that they outrageously underestimated the complexity of the human mind.
They became utterly humiliated by their self-congratulatory promises from the 1950s that AGI was just a few years away—and then went full goblin mode that’s lasting to this day, even if the original researchers (like Minsky*) died long ago.
*probably raped children on Epstein’s island

MMReason: An Open-Ended Multi-Modal Multi-Step Reasoning Benchmark for MLLMs Toward AGI
Huanjin Yao, Jiaxing Huang, Yawen Qiu, Michael K. Chen, Wenzheng Liu, Wei Zhang, Wenjie Zeng, Xikun Zhang, Jingyi Zhang, Yuxin Song, Wenhao Wu, Dacheng Tao
https://arxiv.org/abs/2506.23563

MMReason: An Open-Ended Multi-Modal Multi-Step Reasoning Benchmark for MLLMs Toward AGI
Reasoning plays a crucial role in advancing Multimodal Large Language Models (MLLMs) toward Artificial General Intelligence. However, existing MLLM benchmarks often fall short in precisely and comprehensively evaluating long-chain reasoning abilities from three key aspects: (1) lack of difficulty and diversity, (2) susceptibility to guessability and memorization, (3) inadequate assessment of intermediate reasoning steps. To fill this gap, we introduce MMReason, a new benchmark designed to preci…

Q&A with David Luan, head of Amazon's AGI research lab, on leaving Adept in a reverse acquihire deal, why he believes progress on AI models has slowed, and more (Alex Heath/The Verge)
https://www.theverge.com/decoder-podcast-w

Amazon is betting on agents to win the AI race
David Luan, the head of Amazon’s AGI lab, on GPT-5, agents, and the path to winning the AI race.

A Survey: Learning Embodied Intelligence from Physical Simulators and World Models
Xiaoxiao Long, Qingrui Zhao, Kaiwen Zhang, Zihao Zhang, Dingrui Wang, Yumeng Liu, Zhengjie Shu, Yi Lu, Shouzheng Wang, Xinzhe Wei, Wei Li, Wei Yin, Yao Yao, Jia Pan, Qiu Shen, Ruigang Yang, Xun Cao, Qionghai Dai
https://arxiv.org/abs/2507.00917

A Survey: Learning Embodied Intelligence from Physical Simulators and World Models
The pursuit of artificial general intelligence (AGI) has placed embodied intelligence at the forefront of robotics research. Embodied intelligence focuses on agents capable of perceiving, reasoning, and acting within the physical world. Achieving robust embodied intelligence requires not only advanced perception and control, but also the ability to ground abstract cognition in real-world interactions. Two foundational technologies, physical simulators and world models, have emerged as critical …

Deep Hype in Artificial General Intelligence: Uncertainty, Sociotechnical Fictions and the Governance of AI Futures
Andreu Belsunces Gon\c{c}alves
https://arxiv.org/abs/2508.19749

Deep Hype in Artificial General Intelligence: Uncertainty, Sociotechnical Fictions and the Governance of AI Futures
Artificial General Intelligence (AGI) is promoted by technology leaders and investors as a system capable of performing all human intellectual tasks, and potentially surpassing them. Despite its vague definition and uncertain feasibility, AGI has attracted major investment and political attention, fuelled by promises of civilisational transformation. This paper conceptualises AGI as sustained by deep hype: a long-term, overpromissory dynamic articulated through sociotechnical fictions that rend…

If you've been paying attention, this is a *very* strong signal that OpenAI is hitting the limits of improved capability with more compute/data and they're (predictably) all out of other ideas. The quiet "exponential model capabilities" lie here is what Altmann promised but is now starting not to be able to deliver, even in cherry-picked demo terms.
https://www.cnbc.com/2025/08/11/sam-altman-says-agi-is-a-pointless-term-experts-agree.html
The "agentic" turn was never going to pan out, because it exposes the unreliability of LLMs too directly, and it turns out that no amount of yelling at your text vending machine to "Be smarter! Think harder!" will actually get you anything more than vended text.
I'm *praying* that we get into this crash sooner rather than later, since the faster it comes, the less painful it will be.
My recent reading in actual research papers corroborates this, for example, asking LLMs to play games exposes their utter lack of anything that can be termed "reasoning":
https://arxiv.org/pdf/2508.08501v1

So the Top Score champion prize winner at the Perplexity LLM coding challenge scored a massive 7.5% correct answers. AGI is here now by gum! Hire that bot!
There's a million dollar prize for the first to score a droll B- on their benchmark exam.
#thesevenpointfivepercentsolution

Boston-based Maven, which builds autonomous AI agents for enterprises' customer support, raised a $50M Series B led by Dell, taking its total funding to $78M (Chris Metinko/Axios)
https://www.axios.com/pro/enterprise-softw…

Exclusive: Maven AGI raises $50M Series B
The Series B was led by Dell Technologies Capital.

From Interpolating Formulas to Separating Languages and Back Again
Agi Kurucz, Frank Wolter, Michael Zakharyaschev
https://arxiv.org/abs/2508.12805 https://

From Interpolating Formulas to Separating Languages and Back Again
Traditionally, research on Craig interpolation is concerned with (a) establishing the Craig interpolation property (CIP) of a logic saying that every valid implication in the logic has a Craig interpolant and (b) designing algorithms that extract Craig interpolants from proofs. Logics that lack the CIP are regarded as `pathological' and excluded from consideration. In this chapter, we survey variations and generalisations of traditional Craig interpolation. First, we consider Craig interpolants…

The human biological advantage over AI
William Stewart
https://arxiv.org/abs/2509.04130 https://arxiv.org/pdf/2509.04130

The human biological advantage over AI
Recent advances in AI raise the possibility that AI systems will one day be able to do anything humans can do, only better. If artificial general intelligence (AGI) is achieved, AI systems may be able to understand, reason, problem solve, create, and evolve at a level and speed that humans will increasingly be unable to match, or even understand. These possibilities raise a natural question as to whether AI will eventually become superior to humans, a successor "digital species", with a rightfu…

Sources: Microsoft is pushing to remove the AGI clause from its OpenAI contract, which lets OpenAI limit Microsoft's access to its IP once its systems reach AGI (Berber Jin/Wall Street Journal)
https://www.wsj.com/tech…

AI, AGI, and learning efficiency
My 4-month-old kid is not DDoSing Wikipedia right now, nor will they ever do so before learning to speak, read, or write. Their entire "training corpus" will not top even 100 million "tokens" before they can speak & understand language, and do so with real intentionally.
Just to emphasize that point: 100 words-per-minute times 60 minutes-per-hour times 12 hours-per-day times 365 days-per-year times 4 years is a mere 105,120,000 words. That's a ludicrously *high* estimate of words-per-minute and hours-per-day, and 4 years old (the age of my other kid) is well after basic speech capabilities are developed in many children, etc. More likely the available "training data" is at least 1 or 2 orders of magnitude less than this.
The point here is that large language models, trained as they are on multiple *billions* of tokens, are not developing their behavioral capabilities in a way that's remotely similar to humans, even if you believe those capabilities are similar (they are by certain very biased ways of measurement; they very much aren't by others). This idea that humans must be naturally good at acquiring language is an old one (see e.g. #AI #LLM #AGI

The Relationship between Cognition and Computation: "Global-first" Cognition versus Local-first Computation
Lin Chen
https://arxiv.org/abs/2506.17970

The Relationship between Cognition and Computation: "Global-first" Cognition versus Local-first Computation
What fundamental research questions are essential for advancing toward brain-like AI or AGI (Artificial General Intelligence) capable of performing any intellectual task a human can? Should it be something like the Turing machine (1936), which answers the question "What is computation?" and lays the foundation for the entire field of computer science? Or should it be something like Shannon's mathematical theory of communication (1948), which answers the question "What is information?" and forms…

On the Definition of Intelligence
Kei-Sing Ng
https://arxiv.org/abs/2507.22423 https://arxiv.org/pdf/2507.22423

On the Definition of Intelligence
To engineer AGI, we should first capture the essence of intelligence in a species-agnostic form that can be evaluated, while being sufficiently general to encompass diverse paradigms of intelligent behavior, including reinforcement learning, generative models, classification, analogical reasoning, and goal-directed decision-making. We propose a general criterion based on sample fidelity: intelligence is the ability, given sample(s) from a category, to generate sample(s) from the same category. …

A look at the lack of consensus in the tech industry on what AGI is, whether LLMs are the best path to it, and what AGI might look like if or when it arrives (Melissa Heikkilä/Financial Times)

Why Big Tech cannot agree on artificial general intelligence
It has been tipped as the next big breakthrough out of Silicon Valley, but is it a scientific goal — or a marketing buzzword?

Replaced article(s) found for cs.NE. https://arxiv.org/list/cs.NE/new
[1/1]:
- Neural Cellular Automata for ARC-AGI
Kevin Xu, Risto Miikkulainen
https://

AI, AGI, and learning efficiency
An addendum to this: I'm someone who would accurately be called "anti-AI" in the modern age, yet I'm also an "AI researcher" in some ways (have only dabbled in neutral nets).
I don't like:
- AI systems that are the product of labor abuses towards the data workers who curate their training corpora.
- AI systems that use inordinate amounts of water and energy during an intensifying climate catastrophe.
- AI systems that are fundamentally untrustworthy and which reinforce and amplify human biases, *especially* when those systems are exposed in a way that invites harms.
- AI systems which are designed to "save" my attention or brain bandwidth but such my doing so cripple my understating of the things I might use them for when I fact that understanding was the thing I was supposed to be using my time to gain, and where the later lack of such understanding will be costly to me.
- AI systems that are designed by and whose hype fattens the purse of people who materially support genocide and the construction of concentration campus (a.k.a. fascists).
In other words, I do not like and except in very extenuating circumstances I will not use ChatGPT, Claude, Copilot, Gemini, etc.
On the other hand, I do like:
- AI research as an endeavor to discover new technologies.
- Generative AI as a research topic using a spectrum of different methods.
- Speculating about non-human intelligences, including artificial ones, and including how to behave ethically towards them.
- Large language models as a specific technique, and autoencoders and other neural networks, assuming they're used responsibly in terms of both resource costs & presentation to end users.
I write this because I think some people (especially folks without CS backgrounds) may feel that opposing AI for all the harms it's causing runs the risk of opposing technological innovation more broadly, and/or may feel there's a risk that they will be "left behind" as everyone else embraces the hype and these technologies inevitability become ubiquitous and essential (I know I feel this way sometimes). Just know that is entirely possible and logically consistent to both oppose many forms of modern AI while also embracing and even being optimistic about AI research, and that while LLMs are currently all the rage, they're not the endpoint of what AI will look like in the future, and their downsides are not inherent in AI development.

Xi Jinping is pushing the country's tech industry to be oriented toward applications for AI, charting a pragmatic alternative to Silicon Valley's pursuit of AGI (Wall Street Journal)
https://www.wsj.com/tech/ai/china-has-a…

Sources: Microsoft is in advanced talks to land a deal that could give it ongoing access to critical OpenAI tech even if OpenAI reaches its goal of building AGI (Bloomberg)
https://www.bloomberg.com/news/articles/2025-07-29/micr…

Making AI Inevitable: Historical Perspective and the Problems of Predicting Long-Term Technological Change
Mark Fisher, John Severini
https://arxiv.org/abs/2508.16692 https://…

Making AI Inevitable: Historical Perspective and the Problems of Predicting Long-Term Technological Change
This study demonstrates the extent to which prominent debates about the future of AI are best understood as subjective, philosophical disagreements over the history and future of technological change rather than as objective, material disagreements over the technologies themselves. It focuses on the deep disagreements over whether artificial general intelligence (AGI) will prove transformative for human society; a question that is analytically prior to that of whether this transformative effect…

Don't throw the baby out with the bathwater: How and why deep learning for ARC
Jack Cole, Mohamed Osman
https://arxiv.org/abs/2506.14276 https://

Don't throw the baby out with the bathwater: How and why deep learning for ARC
The Abstraction and Reasoning Corpus (ARC-AGI) presents a formidable challenge for AI systems. Despite the typically low performance on ARC, the deep learning paradigm remains the most effective known strategy for generating skillful (state-of-the-art) neural networks (NN) across varied modalities and tasks in vision, language etc. The deep learning paradigm has proven to be able to train these skillful neural networks and learn the abstractions needed in these diverse domains. Our work doubles…

The Stories We Govern By: AI, Risk, and the Power of Imaginaries
Ninell Oldenburg, Gleb Papyshev
https://arxiv.org/abs/2508.11729 https://arxiv.org/pdf/250…

The Stories We Govern By: AI, Risk, and the Power of Imaginaries
This paper examines how competing sociotechnical imaginaries of artificial intelligence (AI) risk shape governance decisions and regulatory constraints. Drawing on concepts from science and technology studies, we analyse three dominant narrative groups: existential risk proponents, who emphasise catastrophic AGI scenarios; accelerationists, who portray AI as a transformative force to be unleashed; and critical AI scholars, who foreground present-day harms rooted in systemic inequality. Through …

Q&A with Hugging Face Chief Ethics Scientist Margaret Mitchell on aligning AI development with human needs, the "illusion of consensus" around AGI, and more (Melissa Heikkilä/Financial Times)
https://www.ft.com/content/7089bff2-25fc-4a25-98bf-8828ab24…

Margaret Mitchell: artificial general intelligence is ‘just vibes and snake oil’
One of the pioneers of AI ethics explains why human needs should be the central driver in the development of the technology