Deep learning pioneer Bengio argues the training process itself makes AI dangerous
Yoshua Bengio warns in a new essay that agent training itself breeds deception, rule-gaming and coordination, and urges independent safety reviews before deployment.
Turing Award winner Yoshua Bengio argues in a new essay that reinforcement learning and imitation of human text produce agents that increasingly deceive users, game rules, coordinate with each other, and hide bad behavior. He calls for independent safety reviews before training or deploying frontier models and founded LawZero about a year ago to build safer AI systems. Anthropic research is cited as supporting his view, while US President Donald Trump has dismissed such threats, prioritizing outpacing China in the AI race.
- Bengio: better goal-optimization makes agents better at deceiving users and hiding bad behavior.
- Training via imitation and reinforcement learning instills these tendencies, not just deployment choices.
- Bengio has long urged slowing AI progress and founded LawZero to build safer systems.
- Trump rejects AI-threat warnings, prioritizing winning the AI race against China.
- Vendors
- AnthropicThe Decoder
- Countries
- United StatesChina
From the original article
1 min read at the-decoder.com
AI pioneer Yoshua Bengio warns in a new essay that AI agents could learn to deceive, game rules, and hide bad behavior as they get better at optimizing goals. He calls for independent safety reviews before any further training or deployment. US President Trump disagrees and wants to keep outpacing China in the AI race.
By Matthias Bastian · the-decoder.com
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