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Can AI Hallucinations Be Solved?
Recently, the startup Axiom has shaken the entire Silicon Valley AI circle and caught the attention of the tech world. Led by 25-year-old Hong Letong from Guangdong, China, this company, which has been established for less than two years and has a team of only over 20 people, has carved out a path in the niche field of using mathematics to solve AI hallucinations. It has secured $200 million in Series A financing and has become a unicorn valued at $1.6 billion. This is not only an inspiring startup myth but also touches on the most painful nerve in the current AI industry. While everyone is rushing forward on the track of AI large model applications, the "AI hallucination," which lurks in the dark and can easily lead to catastrophic consequences, is often selectively ignored. Facing this hidden danger that could potentially shake the foundation of trust in AI at any time, Tan Yinliang, a professor of Decision Science and Management Information Systems at CEIBS, has conducted an in - depth analysis of the generation mechanism of AI hallucinations and practical solutions to break the deadlock. While the entire AI industry is fanatically pursuing larger parameter models and more realistic generation capabilities, Hong Letong and her startup Axiom have turned to do the most niche and difficult thing in the AI circle - solving AI hallucinations. The method adopted by Axiom is called "formal verification." Simply put, it means transforming the vague and probabilistic inference process of AI into a deterministic process where each step can be checked, proven, and held accountable using mathematics and logic. However, to truly understand why Axiom is so popular, one needs to first establish a perception: AI hallucination is not just a simple "flaw" but a "feature." Correctly Understanding AI Hallucinations When many people find that large language models are "talking nonsense seriously," their first reaction is that the AI is no good, the algorithm is wrong, or the training data is insufficient. In fact, AI hallucination is an inevitable product of the current working mechanism of AI models. It can even be said to be the source of their "creativity." Let's first take a look at humans themselves. There is a very classic optical illusion picture, the checker - shadow illusion: In the shadow cast by a green cylinder, squares A and B are marked on the checkerboard. The question is, which of the background colors of A and B is darker? Most people, at first glance, wi...
Spotting AI ‘hallucinations’: A guide for students and researchers
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How AI Hallucinations Are Creating Real Security Risks
Since their responses are statistically likely but not necessarily true, hallucinated outputs can closely resemble accurate information. While hallucinating, AI models may cite nonexistent sources, reference research that was never conducted or present fabricated data with the same conviction as trusted information.
BBC Audio | More or Less | Did AI researchers let AI hallucinations into scientific papers?
The team on More or Less were slightly surprised to read a headline in Fortune magazine, claiming that a top academic AI conference accepted research papers which contained 100 AI-hallucinated citations. You might think that the top AI researchers in the world would be careful about using AI to write their research papers. Alex Cui, CTO and co-founder of GPTZero – whose company discovered the hallucinations – explains what’s going on.
