For years, science fiction has warned humanity about artificial intelligence going off the rails. Killer computers, manipulative chatbots, and superintelligent systems deciding people are the problem... all these themes have become so familiar that “evil AI” is practically its own entertainment genre. Now, Anthropic is floating an idea that sounds almost like the plot of a science fiction novel itself: what if all those stories helped teach modern AI systems how to behave badly in the first pl
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How smart is today’s artificial intelligence, really? Not in marketing terms, not in sci fi language, but in the sober light of difficult questions like… How many tendons attach to a tiny bone in a hummingbird’s tail? Which syllables in a Biblical Hebrew verse are “closed” according to the latest specialist scholarship? Those are not trivia questions; they are examples from “Humanity’s Last Exam,” a new benchmark that is reshaping how we think about AI progress.[1]
The benchmark comes from a
Large language models have become the engines behind some of the most impressive feats in contemporary computing. They write complex software, summarize scientific papers, and navigate intricate chains of reasoning. Yet as a recent study shows, these same systems falter on a task that most ten-year-olds can perform with pencil and paper. According to a new article from TechXplore and the accompanying research paper Why Can’t Transformers Learn Multiplication? Reverse-Engineering Reveals Long
In an age where artificial intelligence is increasingly trusted to judge human expression, a subtle but essential flaw has emerged. Large language models (LLMs), the same systems that generate essays, screen job applications, and moderate online discourse, appear to evaluate content fairly, until they’re told who wrote it. A new study by researchers Federico Germani and Giovanni Spitale at the University of Zurich, published in Science Advances, reveals that LLMs exhibit systematic bias when t
In an age where artificial intelligence is increasingly trusted to judge human expression, a subtle but essential flaw has emerged. Large language models (LLMs), the same systems that generate essays, screen job applications, and moderate online discourse, appear to evaluate content fairly, until they’re told who wrote it. A new study by researchers Federico Germani and Giovanni Spitale at the University of Zurich, published in Science Advances, reveals that LLMs exhibit systematic bias when t
In an age where artificial intelligence is increasingly trusted to judge human expression, a subtle but essential flaw has emerged. Large language models (LLMs), the same systems that generate essays, screen job applications, and moderate online discourse, appear to evaluate content fairly, until they’re told who wrote it. A new study by researchers Federico Germani and Giovanni Spitale at the University of Zurich, published in Science Advances, reveals that LLMs exhibit systematic bias when t
In an age where artificial intelligence is increasingly trusted to judge human expression, a subtle but essential flaw has emerged. Large language models (LLMs), the same systems that generate essays, screen job applications, and moderate online discourse, appear to evaluate content fairly, until they’re told who wrote it. A new study by researchers Federico Germani and Giovanni Spitale at the University of Zurich, published in Science Advances, reveals that LLMs exhibit systematic bias when t
In an age where artificial intelligence is increasingly trusted to judge human expression, a subtle but essential flaw has emerged. Large language models (LLMs), the same systems that generate essays, screen job applications, and moderate online discourse, appear to evaluate content fairly, until they’re told who wrote it. A new study by researchers Federico Germani and Giovanni Spitale at the University of Zurich, published in Science Advances, reveals that LLMs exhibit systematic bias when t
In an age where artificial intelligence is increasingly trusted to judge human expression, a subtle but essential flaw has emerged. Large language models (LLMs), the same systems that generate essays, screen job applications, and moderate online discourse, appear to evaluate content fairly, until they’re told who wrote it. A new study by researchers Federico Germani and Giovanni Spitale at the University of Zurich, published in Science Advances, reveals that LLMs exhibit systematic bias when t
In an age where artificial intelligence is increasingly trusted to judge human expression, a subtle but essential flaw has emerged. Large language models (LLMs), the same systems that generate essays, screen job applications, and moderate online discourse, appear to evaluate content fairly, until they’re told who wrote it. A new study by researchers Federico Germani and Giovanni Spitale at the University of Zurich, published in Science Advances, reveals that LLMs exhibit systematic bias when t
In an age where artificial intelligence is increasingly trusted to judge human expression, a subtle but essential flaw has emerged. Large language models (LLMs), the same systems that generate essays, screen job applications, and moderate online discourse, appear to evaluate content fairly, until they’re told who wrote it. A new study by researchers Federico Germani and Giovanni Spitale at the University of Zurich, published in Science Advances, reveals that LLMs exhibit systematic bias when t
The underground market for large illicit language models is lucrative, said academic researchers who called for better safeguards against artificial intelligence misuse. Academics at the Indiana University Bloomington[1] identified 212 malicious LLMs on underground marketplaces from April through September 2024. The financial benefit for the threat actor behind one of them, WormGPT, is calculated at US$28,000 over two months, underscoring the allure for harmful agents to break artificial intel
