Cybersecurity researchers have flagged a new technique that cybercriminals have adopted to bypass social media platform X’s malvertising protections and propagate malicious links using its artificial intelligence (AI) assistant Grok.
The findings were highlighted by Nati Tal, head of Guardio Labs, in a series of posts on X. The technique has been codenamed Grokking.
The approach is designed to get around restrictions imposed by X in Promoted Ads that allow users to only include text, images, or videos, and subsequently amplify them to a broader audience, attracting hundreds of thousands of impressions through paid promotion.
WARNING: AI could end humanity, and we’re completely unprepared. Dr. Roman Yampolskiy reveals how AI will take 99% of jobs, why Sam Altman is ignoring safety, and how we’re heading toward global collapse…or even World War III.
Dr. Roman Yampolskiy is a leading voice in AI safety and a Professor of Computer Science and Engineering. He coined the term “AI safety” in 2010 and has published over 100 papers on the dangers of AI. He is also the author of books such as, ‘Considerations on the AI Endgame: Ethics, Risks and Computational Frameworks’
He explains: ⬛How AI could release a deadly virus. ⬛Why these 5 jobs might be the only ones left. ⬛How superintelligence will dominate humans. ⬛Why ‘superintelligence’ could trigger a global collapse by 2027 ⬛How AI could be worse than nuclear weapons. ⬛Why we’re almost certainly living in a simulation.
00:00 Intro. 02:28 How to Stop AI From Killing Everyone. 04:35 What’s the Probability Something Goes Wrong? 04:57 How Long Have You Been Working on AI Safety? 08:15 What Is AI? 09:54 Prediction for 2027 11:38 What Jobs Will Actually Exist? 14:27 Can AI Really Take All Jobs? 18:49 What Happens When All Jobs Are Taken? 20:32 Is There a Good Argument Against AI Replacing Humans? 22:04 Prediction for 2030 23:58 What Happens by 2045? 25:37 Will We Just Find New Careers and Ways to Live? 28:51 Is Anything More Important Than AI Safety Right Now? 30:07 Can’t We Just Unplug It? 31:32 Do We Just Go With It? 37:20 What Is Most Likely to Cause Human Extinction? 39:45 No One Knows What’s Going On Inside AI 41:30 Ads. 42:32 Thoughts on OpenAI and Sam Altman. 46:24 What Will the World Look Like in 2100? 46:56 What Can Be Done About the AI Doom Narrative? 53:55 Should People Be Protesting? 56:10 Are We Living in a Simulation? 1:01:45 How Certain Are You We’re in a Simulation? 1:07:45 Can We Live Forever? 1:12:20 Bitcoin. 1:14:03 What Should I Do Differently After This Conversation? 1:15:07 Are You Religious? 1:17:11 Do These Conversations Make People Feel Good? 1:20:10 What Do Your Strongest Critics Say? 1:21:36 Closing Statements. 1:22:08 If You Had One Button, What Would You Pick? 1:23:36 Are We Moving Toward Mass Unemployment? 1:24:37 Most Important Characteristics.
In a major leap for artificial intelligence (AI) and photonics, researchers at the University of California, Los Angeles (UCLA) have created optical generative models capable of producing novel images using the physics of light instead of conventional electronic computation.
Published in Nature, the work presents a new paradigm for generative AI that could dramatically reduce energy use while enabling scalable, high-performance content creation.
Generative models, including diffusion models and large language models, form the backbone of today’s AI revolution. These systems can create realistic images, videos, and human-like text, but their rapid growth comes at a steep cost: escalating power demands, large carbon footprints, and increasingly complex hardware requirements. Running such models requires massive computational infrastructure, raising concerns about their long-term sustainability.
A research team has developed a novel direct sampling method based on deep generative models. Their method enables efficient sampling of the Boltzmann distribution across a continuous temperature range. The findings have been published in Physical Review Letters. The team was led by Prof. Pan Ding, Associate Professor from the Departments of Physics and Chemistry, and Dr. Li Shuo-Hui, Research Assistant Professor from the Department of Physics at the Hong Kong University of Science and Technology (HKUST).
Neural networks are computing systems designed to mimic both the structure and function of the human brain. Caltech researchers have been developing a neural network made out of strands of DNA instead of electronic parts that carries out computation through chemical reactions rather than digital signals.
An important property of any neural network is the ability to learn by taking in information and retaining it for future decisions. Now, researchers in the laboratory of Lulu Qian, professor of bioengineering, have created a DNA-based neural network that can learn. The work represents a first step toward demonstrating more complex learning behaviors in chemical systems.
A paper describing the research appears in the journal Nature on September 3. Kevin Cherry, Ph.D., is the study’s first author.
For all their technological brilliance, from navigating distant planets to performing complex surgery, robots still struggle with a few basic human tasks. One of the most significant challenges is dexterity, which refers to the ability to grasp, hold and manipulate objects. Until now, that is. Scientists from the Toyota Research Institute in Massachusetts have trained a robot to use its entire body to handle large objects, much like humans do.
