Knowledge catalogue
Gary Marcus critiques the AI industry's financial model, noting that companies with high burn rates continue to see rising market valuations despite unprofitable operations. The post sarcastically que
Happy to see that The White House has finally realized that the Andreessen-Sacks zero regulation approach to AI was going to blow up in the their face. Also glad to see them finally consider the kind
Holy AI “fact checking” disaster We are in deep, deep trouble. A reader wrote in to me this week saying that they wouldn't read my Trump corruption story because ChatGPT 'fact-checked the piece' and i
I warned you early that covid-19 could get really serious. Hantavirus is not as easily transmitted; not likely to be the same level of threat. Nearly 40 cruise passengers exposed to a deadly virus may
i wish i could fast forward five years to see how badly this all turned out. it’s painful to watch. “Combined free cash flow across Microsoft, Alphabet, Amazon, Meta, and Oracle is projected to FALL m
Jan Leike expresses gratitude for collaborators he has worked with on AI alignment research throughout his career. The post, shared on X (Twitter), reflects on the privilege of working with talented i
It’s honestly painful to root for Elon in the trial, given the kind of person he has become. Elon Musk’s DOGE “blatantly used” race, gender and other protected characteristics to execute the largest m
A judge ruled that the Department of Government Efficiency (DOGE) violated regulations by using ChatGPT to process grant applications in an unauthorized manner, determining the practice was both incom
Morgan Stanley sees capital spending by the five largest AI hyperscalers topping 800 billion this year and 1.1 trillion next year. At 3.3% of gross domestic product, next year’s figure would exceed pr
My view on Mythos was and is somewhere in between. It’s real, it’s a wakeup call, and it’s not quite what some of the media coverage suggested. • Mythos is not going to allow an 8 year old to accident
Next week at The Trial could be wild. Here’s why: Sam Altman will be on the stand. He is one of the world’s most convincing (though not always truthful) talkers (as I saw up close when we testified si
Professions with guilds or membership associations are going to get different AI policy reactions than those without The Bar & the AMA will ensure that human doctors or lawyers are legally required fo
Remember that MIT study that showed that the ROI for generative AI wasn’t really there for most businesses? Or any of the six or seven studies from other teams that followed, showing basically the sam
Current critic-less RLHF methods aggregate multi-objective rewards via an arithmetic mean, leaving them vulnerable to constraint neglect: high-magnitude success in one objective can numerically offset
Sadly I am forced to agree. @karaswisher shilled for her friend Sam (who interviewed her for her book tour!), and ran me down for seeking (and stating) the truth. And never apologized, even after my t
This appears to be a post by cognitive scientist and AI researcher Gary Marcus on X (formerly Twitter) expressing skepticism or disagreement about a claim or development, likely related to AI, machine
SCOOP: VP JD Vance held a secret call with CEOs of giant AI companies including Elon Musk, Dario Amodei and Sam Altman. Vance expressed concern with the potential of AI systems and impact they could h
Gary Marcus discusses the prospect of writing an updated edition of a work that has entered the public domain, suggesting he may revise and republish it. The post reflects on opportunities created by
Gary Marcus shared appreciation for a humorous or witty piece of content from the Financial Times (FT), praising its deadpan British humor style. The post suggests the FT occasionally publishes unexpe
Jan Leike announced he is beginning a new research project at Anthropic focused on contributing to safe and beneficial AGI development. The post expresses enthusiasm about the initiative and indicates
Take Mythos seriously, but don’t panic. Here are the facts: • Mythos is a real threat. And it can really help with finding bugs (as @mozilla’s new report documents well). • It’s not unique; similar vu
The human brain🧠 is incredibly efficient because it only activates the specific neurons needed for a thought. Modern LLMs naturally try to do this too (> 95% of neurons in feedforward layers stay sile
🚨The slow decline of OpenAI has begun. 🚨 These two hesitations from SoftBank and Broadcom, back to back in my feed from @HedgieMarkets and @anissagardizy8, are major signs. The money question of 2026:
We also had three third-party AI safety organizations provide feedback on our analysis: @redwood_ai, @apolloaievals, @METR_Evals. You can find @redwood_ai's report here: https://blog.redwoodresearch.o
We’ve spent a lot of time on the framework underneath Codex, so it can move quickly on routine work while stopping for review when the risk changes. Here’s how we use sandboxing, approvals, network po
Gary Marcus argues that despite 3.5 years of rapid AI development and deployment, there is minimal empirical evidence that AI has significantly impacted overall economic productivity metrics. The post
arXiv:2605.05182v1 Announce Type: new Abstract: We present a dual-barrier control barrier function (CBF) safety filter for real-time, safety-critical velocity control of holonomic robots operating in
arXiv:2605.04439v1 Announce Type: new Abstract: Deep neural networks enriched with structural information have been widely employed for facial expression recognition tasks. However, these methods ofte
arXiv:2605.03537v1 Announce Type: cross Abstract: This paper presents a modular AI agentic skill pipeline for automating subject indexing with Library of Congress Subject Headings (LCSH). Subject inde
arXiv:2605.04066v1 Announce Type: new Abstract: Reinforcement Learning with Verifiable Rewards (RLVR) is an essential paradigm that enhances the reasoning capabilities of Large Language Models (LLMs).
arXiv:2605.03423v1 Announce Type: new Abstract: This paper proposes a novel adaptive dual-path framework for covert semantic communication (SemCom), which integrates covert information transmission wi
arXiv:2605.05123v1 Announce Type: new Abstract: In offline-to-online reinforcement learning (O2O-RL), policies are first safely trained offline using previously collected datasets and then further fin
arXiv:2602.13670v2 Announce Type: replace Abstract: Class-incremental learning (CIL) with pre-trained models (PTMs) faces a critical trade-off between efficient adaptation and long-term stability. Whi
arXiv:2605.03648v1 Announce Type: new Abstract: To understand complex system dynamics in dairy farming, it is essential to use modeling tools that capture farm heterogeneity, social interactions, and
arXiv:2605.04231v1 Announce Type: new Abstract: Mirroring its ubiquity in popular media and all human activities, the use of deep learning (DL) is rapidly growing in scientific imaging modalities. How
arXiv:2605.04875v1 Announce Type: new Abstract: Forecasting innovation, intended as the emergence of new technological combinations, is a fundamental challenge for science and policy. We show that for
arXiv:2507.12768v2 Announce Type: replace Abstract: Learning generalizable manipulation policies hinges on data, yet robot manipulation data is scarce and often entangled with specific embodiments, ma
Gary Marcus corrected a previous statement about power consumption, clarifying that the figure was 300 megawatts rather than 30 megawatts. This appears to be a technical correction posted on X regardi
arXiv:2605.04077v1 Announce Type: cross Abstract: Reinforcement learning with verifiable rewards (RLVR) has become a central paradigm for improving reasoning and code generation in large language mode
arXiv:2605.04299v1 Announce Type: new Abstract: Scene understanding is a vital part of autonomous driving systems, which requires the use of deep learning models. Deep learning methods are intrinsical
arXiv:2505.00020v2 Announce Type: replace Abstract: Using a legally obtained dataset of 34 copyrighted O'Reilly Media books, we apply the DE-COP membership inference attack method to investigate wheth
arXiv:2605.03512v1 Announce Type: cross Abstract: The scope of AI safety and alignment work in generative artificial intelligence (GenAI) has so far mostly been limited to harms related to: (a) discri
arXiv:2605.04381v1 Announce Type: cross Abstract: Causal discovery methods such as LiNGAM identify causal structure from observational data by assuming mutually independent disturbances. This assumpti
OpenAI is launching an optional safety feature for ChatGPT that allows adult users to assign an emergency contact for mental health and safety concerns. Friends, family members, or caregivers designat
arXiv:2605.04652v1 Announce Type: new Abstract: Temporal knowledge graph (TKG) reasoning aims to predict future events from historical facts. A key challenge lies in jointly capturing two sources of p
arXiv:2605.04366v1 Announce Type: cross Abstract: Safety-critical scenarios are essential for the development of autonomous vehicles (AVs) but are rare in real-world driving data. While simulation off
arXiv:2605.04073v1 Announce Type: new Abstract: Bail decisions present a fundamental challenge for data-driven decision support systems. When bail is denied, the counterfactual outcome of whether the
arXiv:2605.04080v1 Announce Type: new Abstract: This research investigated how online criminal activities can be better understood and connected using data-driven machine learning methods. Many illega
contest time: First person to update this chart with no mistakes using an AI system and a general prompt (rather than one than that handholds the system every step of the win) wins a signed book! Plea
arXiv:2605.03278v2 Announce Type: cross Abstract: Doubly Robust (DR) estimation of treatment effect relies on an untestable assumption that is the absence of unobserved confounding. This assumption is
arXiv:2605.04555v1 Announce Type: new Abstract: Model-based reinforcement learning (MBRL) offers a promising approach for data-efficient energy management in buildings, combining the strengths of pred
arXiv:2605.04470v1 Announce Type: new Abstract: Open-loop imitation learning has advanced modern autonomous driving policy architectures, but closed-loop deployment remains vulnerable to policy-induce
arXiv:2605.05204v1 Announce Type: new Abstract: The landscape of high-performance image generation models is currently shifting from the inefficient multi-step ones to the efficient few-step counterpa
arXiv:2605.04477v1 Announce Type: new Abstract: Online reinforcement learning from human feedback (RLHF) has emerged as a promising paradigm for aligning large language models (LLMs) by continuously c
arXiv:2605.02970v1 Announce Type: cross Abstract: Network traffic anomaly detection represents a critical cybersecurity task, yet widespread encryption makes this task increasingly challenging. In res
arXiv:2602.05890v2 Announce Type: replace-cross Abstract: Training reinforcement learning (RL) systems in real-world environments remains challenging due to noisy supervision and poor out-of-domain (O
arXiv:2605.05054v1 Announce Type: new Abstract: Recent flow matching (FM) methods improve the few-shot adaptation of vision-language models, by modeling cross-modal alignment as a continuous multi-ste
arXiv:2605.04460v1 Announce Type: new Abstract: Transportation surveys are widely used to understand travel preferences and adoption barriers, yet most survey-based analyses remain descriptive or pred
arXiv:2605.04507v1 Announce Type: new Abstract: Negotiation agents must infer what their counterpart values, update those beliefs over dialogue turns, and choose actions under uncertainty. End-to-end
arXiv:2605.04568v1 Announce Type: new Abstract: State-of-the-art model-based Reinforcement Learning (RL) approaches either use gradient-free, population-based methods for planning, learned policy netw