Artificial Intelligence - General News

AI and Security Management: Challenges and Opportunities Discussed at Cybertech Global Tel Aviv

One key topic was AI’s integration with the cloud, where AI spans the entire product lifecycle. The panelists identified four essential pillars of AI security: infrastructure, models, data, and applications.
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AI is already being leveraged by cybercriminals to create deepfakes and execute highly targeted attacks. Attackers are also automating entire attack workflows, using AI agents to manage cyber intrusions. This technological shift has expanded the attack surface, making large-scale cyberattacks more feasible and overwhelming defenders' ability to respond effectively.
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The panel raised concerns about mass-scale AI-driven attacks, questioning whether cybersecurity teams can keep pace with the rapid advancements in AI-powered threats.
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Some commentary I stumbled across on the latest Chat GPT update.

For much of this week, X has been filled with eerily familiar yet undeniably off-putting images that represent a new frontier in AI’s consumption of human art. Users have discovered that ChatGPT’s latest update allows them to transform any photograph into something resembling a scene from a Studio Ghibli film. Within hours, the internet was awash with these uncanny renderings — everything from 9/11 and the Kennedy assassination to Pennsylvania treasurer R. Budd Dwyer’s televised suicide, all reimagined in the distinctive style of Japanese animator Hayao Miyazaki. This is more than just a passing trend, though. Instead, it’s another milestone in AI’s ongoing appropriation of human artistic expression.
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Studio Ghibli AI memes expose growing artistic crisis
 
 
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Why some LLMs show dramatic improvements in reasoning whilst others plateau.

Cognitive Behaviors that Enable Self-Improving Reasoners, or, Four Habits of Highly Effective STaRs

Test-time inference has emerged as a powerful paradigm for enabling language models to ``think'' longer and more carefully about complex challenges, much like skilled human experts. While reinforcement learning (RL) can drive self-improvement in language models on verifiable tasks, some models exhibit substantial gains while others quickly plateau. For instance, we find that Qwen-2.5-3B far exceeds Llama-3.2-3B under identical RL training for the game of Countdown. This discrepancy raises a critical question: what intrinsic properties enable effective self-improvement? We introduce a framework to investigate this question by analyzing four key cognitive behaviors -- verification, backtracking, subgoal setting, and backward chaining -- that both expert human problem solvers and successful language models employ. Our study reveals that Qwen naturally exhibits these reasoning behaviors, whereas Llama initially lacks them. In systematic experimentation with controlled behavioral datasets, we find that priming Llama with examples containing these reasoning behaviors enables substantial improvements during RL, matching or exceeding Qwen's performance. Importantly, the presence of reasoning behaviors, rather than correctness of answers, proves to be the critical factor -- models primed with incorrect solutions containing proper reasoning patterns achieve comparable performance to those trained on correct solutions. Finally, leveraging continued pretraining with OpenWebMath data, filtered to amplify reasoning behaviors, enables the Llama model to match Qwen's self-improvement trajectory. Our findings establish a fundamental relationship between initial reasoning behaviors and the capacity for improvement, explaining why some language models effectively utilize additional computation while others plateau.
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Cognitive Behaviors that Enable Self-Improving Reasoners, or, Four Habits of Highly Effective STaRs

Test-time inference has emerged as a powerful paradigm for enabling language models to ``think'' longer and more carefully about complex challenges, much like skilled human experts. While reinforcement learning (RL) can drive self-improvement in language models on verifiable tasks, some models...
arxiv.org arxiv.org
 
No regrets - What happens to AI Beyond Generative?

Discussing ideas of what happens after Generative AI plateaus, Dr Jakob Foerster is based at the University of Oxford.
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View: https://youtu.be/fN3gdUMB_Yc?si=zWdkZRKS6cDr5VUg


As the comments point out underneath this idea of simulating RL to progress AI brings its own set of issues to the table. Intresting that he’s writing about cutting edge AI on good old fashioned dot matrix paper. I remember getting swamped in the office with that stuff back in the day.
 
AI will try to cheat & escape:

As Large Language Models improve, the tokens they predict form ever more complicated and nuanced outcomes. Rob Miles and Ryan Greenblatt discuss "Alignment Faking" a paper Ryan's team created - ideas about which Rob made a series of videos on Computerphile in 2017.
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View: https://youtu.be/AqJnK9Dh-eQ?si=h8-bAoC6XTQF__p_
 

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