> By the end of 2027, models will frequently outperform experts on many tasks.
In passing the quiz-es
> Models will be able to autonomously work for full days (8 working hours) by mid-2026.
Who will carry responsibility for the consequences of these model's errors? What tools will be avaiable to that resposible _person_?
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Tehchno optimists will be optimistic. Techno pessimists will be pessimistic.
Processes we're discussing have their own limiting factors which no one mentiones. Why to mention what exactly makes graph go up and holds it from going exponential? Why to mention or discuss inherit limitations of the LLMs architecture? Or what is legal perspective on AI agency?
Thus we're discussing results of AI models passing tests and people's perception of other people opinions.
You don't actually need to have a "responsible person"; you can just have an AI do stuff. It might make a mistake; the only difference between that and an employee is that you can't punish an AI. If you're any good at management and not a psychopath, the ability to have someone to punish for mistakes isn't actually important
The importance of having a human be responsible is about alignment. We have a fundamental belief that human beings are comprehensible and have goals that are not completely opaque. That is not true of any piece of software. In the case of deterministic software, you can’t argue with a bug. It doesn’t matter how many times you tell it that no, that’s not what either the company or the user intended, the result will be the same.
With an AI, the problem is more subtle. The AI may absolutely be able to understand what you’re saying, and may not care at all, because its goals are not your goals, and you can’t tell what its goals are. Having a human be responsible bypasses that. The point is not to punish the AI, the point is to have a hope to stop it from doing things that are harmful.
In passing the quiz-es
> Models will be able to autonomously work for full days (8 working hours) by mid-2026.
Who will carry responsibility for the consequences of these model's errors? What tools will be avaiable to that resposible _person_?
--
Tehchno optimists will be optimistic. Techno pessimists will be pessimistic.
Processes we're discussing have their own limiting factors which no one mentiones. Why to mention what exactly makes graph go up and holds it from going exponential? Why to mention or discuss inherit limitations of the LLMs architecture? Or what is legal perspective on AI agency?
Thus we're discussing results of AI models passing tests and people's perception of other people opinions.