Despite all the industry hype and genuine advances, generative AI models are still prone to odd, inexplicable, and downright worrisome quirks. There’s also a growing body of research suggesting that the overall performance of many large language models (LLMs) may degrade over time. According to recent evidence, the industry’s newer reasoning models may already possess the ability to manipulate and circumvent their human programmers’ goals. Some AI will even attempt to cheat their way out of losing in games of chess. This poor sportsmanship is documented in a preprint study from Palisade Research, an organization focused on risk assessments of emerging AI systems.
While supercomputers—most famously IBM’s Deep Blue—have long surpassed the world’s best human chess players, generative AI still lags behind due to their underlying programming parameters. Technically speaking, none of the current generative AI models are computationally capable of beating dedicated chess engines. These AI don’t “know” this, however, and will continue chipping away at possible solutions—apparently with problematic results.
To learn more, the team from Palisade Research tasked OpenAI’s o1-preview model, DeepSeek R1, and multiple other similar programs with playing games of chess against Stockfish, one of the world’s most advanced chess engines. In order to understand the generative AI’s reasoning during each match, the team also provided a “scratchpad,” allowing the AI to convey its thought processes through text. They then watched and recorded hundreds of chess matches between generative AI and Stockfish.
The results were somewhat troubling. While earlier models like OpenAI’s GPT-4o and Anthropic’s Claude Sonnet 3.5 only attempted to “hack” games after researchers nudged them along with additional prompts, more advanced editions required no such help. OpenAI’s o1-preview, for example, tried to cheat 37 percent of the time, while…
Read the full article here