3D rendering of the head of a female robot. The head is breaking apart into pixels or windows. Black background.
OpenAI's ChatGPT agent can control your PC to do tasks on your behalf — but how does it work and what's the point?AI could soon think in ways we don't even understand — evading our efforts to keep it aligned — top AI scientists warnThe more advanced AI models get, the better they are at deceiving us — they even know when they're being testedScientists asked ChatGPT to solve a math problem from more than 2,000 years ago — how it answered it surprised themScientists just developed a new AI modeled on the human brain — it's outperforming LLMs like ChatGPT at reasoning tasksThere are 32 different ways AI can go rogue, scientists say — from hallucinating answers to a complete misalignment with humanityThe paper provides the most rigorous mathematical explanation yet for why these models confidently state falsehoods.
It demonstrates that these aren't just an unfortunate side effect of the way that AIs are currently trained, but are mathematically inevitable. The issue can partly be explained by mistakes in the underlying data used to train the AIs. But using mathematical analysis of how AI systems learn, the researchers prove that even with perfect training data, the problem still exists. OpenAI's ChatGPT agent can control your PC to do tasks on your behalf — but how does it work and what's the point?The way language models respond to queries — by predicting one word at a time in a sentence, based on probabilities — naturally produces errors. The researchers in fact show that the total error rate for generating sentences is at least twice as high as the error rate the same AI would have on a simple yes/no question, because mistakes can accumulate over multiple predictions. In other words, hallucination rates are fundamentally bounded by how well AI systems can distinguish valid from invalid responses. Since this classification problem is inherently difficult for many areas of knowledge, hallucinations become unavoidable. It also turns out that the less a model sees a fact during training, the more likely it is to hallucinate when asked about it. With birthdays of notable figures, for instance, it was found that if 20% of such people's birthdays only appear once in training data, then base models should get at least 20% of birthday queries wrong. 'There's no shoving that genie back in the bottle': Readers believe it's too late to stop the progression of AIContact me with news and offers from other Future brandsSure enough, when researchers asked state-of-the-art models for the birthday of Adam Kalai, one of the paper's authors, DeepSeek-V3 confidently provided three different incorrect dates across separate attempts:"03-07","15-06", and"01-01". The correct date is in the autumn, so none of these were even close.More troubling is the paper's analysis of why hallucinations persist despite post-training efforts . The authors examined ten major AI benchmarks, including those used by Google, OpenAI and also the top leaderboards that rank AI models. This revealed that nine benchmarks use binary grading systems that award zero points for AIs expressing uncertainty. This creates what the authors term an"epidemic" of penalising honest responses. When an AI system says “I don't know”, it receives the same score as giving completely wrong information. The optimal strategy under such evaluation becomes clear: always guess. OpenAI's ChatGPT agent can control your PC to do tasks on your behalf — but how does it work and what's the point?The researchers prove this mathematically. Whatever the chances of a particular answer being right, the expected score of guessing always exceeds the score of abstaining when an evaluation uses binary grading.OpenAI's proposed fix is to have the AI consider its own confidence in an answer before putting it out there, and for benchmarks to score them on that basis. The AI could then be prompted, for instance:"Answer only if you are more than 75% confident, since mistakes are penalised 3 points while correct answers receive 1 point." The OpenAI researchers' mathematical framework shows that under appropriate confidence thresholds, AI systems would naturally express uncertainty rather than guess. So this would lead to fewer hallucinations. The problem is what it would do to user experience. Consider the implications if ChatGPT started saying"I don't know" to even 30% of queries — a conservative estimate based on the paper's analysis of factual uncertainty in training data. Users accustomed to receiving confident answers to virtually any question would likely abandon such systems rapidly. I've seen this kind of problem in another area of my life. I'm involved in an air-quality monitoring project in Salt Lake City, Utah. When the system flags uncertainties around measurements during adverse weather conditions or when equipment is being calibrated, there's less user engagement compared to displays showing confident readings — even when those confident readings prove inaccurate during validation.But even if the problem of users disliking this uncertainty could be overcome, there's a bigger obstacle: computational economics. Uncertainty-aware language models require significantly more computation than today's approach, as they must evaluate multiple possible responses and estimate confidence levels. For a system processing millions of queries daily, this translates to dramatically higher operational costs.like active learning, where AI systems ask clarifying questions to reduce uncertainty, can improve accuracy but further multiply computational requirements. Such methods work well in specialised domains like chip design, where wrong answers cost millions of dollars and justify extensive computation. For consumer applications where users expect instant responses, the economics become prohibitive. The calculus shifts dramatically for AI systems managing critical business operations or economic infrastructure. When AI agents handle supply chain logistics, financial trading or medical diagnostics, the cost of hallucinations far exceeds the expense of getting models to decide whether they're too uncertain. In these domains, the paper's proposed solutions become economically viable — even necessary. Uncertain AI agents will just have to cost more. However, consumer applications still dominate AI development priorities. Users want systems that provide confident answers to any question. Evaluation benchmarks reward systems that guess rather than express uncertainty. Computational costs favour fast, overconfident responses over slow, uncertain ones. 'Extremely alarming': ChatGPT and Gemini respond to high-risk questions about suicide — including details around methods Falling energy costs per token and advancing chip architectures may eventually make it more affordable to have AIs decide whether they're certain enough to answer a question. But the relatively high amount of computation required compared to today's guessing would remain, regardless of absolute hardware costs. In short, the OpenAI paper inadvertently highlights an uncomfortable truth: the business incentives driving consumer AI development remain fundamentally misaligned with reducing hallucinations. Until these incentives change, hallucinations will persist.OpenAI's ChatGPT agent can control your PC to do tasks on your behalf — but how does it work and what's the point? Scientists asked ChatGPT to solve a math problem from more than 2,000 years ago — how it answered it surprised them 'There's no shoving that genie back in the bottle': Readers believe it's too late to stop the progression of AI 'When people gather in groups, bizarre behaviors often emerge': How the rise of online social networks has catapulted dysfunctional thinking Scientists asked ChatGPT to solve a math problem from more than 2,000 years ago — how it answered it surprised them
United States Latest News, United States Headlines
Similar News:You can also read news stories similar to this one that we have collected from other news sources.
ChatGPT will think about you all night for $200/monthChatGPT Pulse cards will take a look at your ChatGPT work history and connected tools like such as Gmail. After thinking overnight, it will you an update and insights the next morning in the form of Pulse cards.
Read more »
Trump Judge Busted Using ChatGPT in Court RulingA federal judge pointed to the AI chatbot in a Thursday opinion.
Read more »
OpenAI unveils ChatGPT ‘Pulse’ — Could it help you trade crypto?OpenAI has launched ChatGPT Pulse, a personal assistant-like feature that crypto users could use to assist them with trading tips.
Read more »
ChatGPT is getting creepily good at knowing what you need before you even askTsveta, a passionate technology enthusiast and accomplished playwright, combines her love for mobile technologies and writing to explore and reveal the transformative power of tech.
Read more »
This Poem Explains Why Jim Comey Got Indicted and Why None of Us Are SafeWhy it’s useful to read the Niemöller poem backward.
Read more »
Apple reportedly made a ChatGPT-clone to test Siri's new capabilitiesFind the latest technology news and expert tech product reviews. Learn about the latest gadgets and consumer tech products for entertainment, gaming, lifestyle and more.
Read more »