Could you describe how familiar you are with AI/ML in general?
However, supposing the answer is “very little,” then the first simplified point I’ll highlight is that ML language models already seek goals, at least during training: the neural networks adjust to perform better at the language task they’ve been given (to put it simplistically).
If your question is “how do they start to take actions aside from ‘output optimal text prediction’”, then the answer is more complicated.
As a starting point for further research, have you watched Rob Miles videos about AI and/or read Superintelligence, Human Compatible, or The Alignment Problem?
I have read the alignment problem, the first few chapters of Superintelligence, seen one or two Rob Miles videos. My question is more the second one; I agree that technically GPT-3 already has a goal / utility function (to find the most highly predicted token, roughly), but it’s not an ‘interesting’ goal in that it doesn’t imply doing anything in the world.
Could you describe how familiar you are with AI/ML in general?
However, supposing the answer is “very little,” then the first simplified point I’ll highlight is that ML language models already seek goals, at least during training: the neural networks adjust to perform better at the language task they’ve been given (to put it simplistically).
If your question is “how do they start to take actions aside from ‘output optimal text prediction’”, then the answer is more complicated.
As a starting point for further research, have you watched Rob Miles videos about AI and/or read Superintelligence, Human Compatible, or The Alignment Problem?
I have read the alignment problem, the first few chapters of Superintelligence, seen one or two Rob Miles videos. My question is more the second one; I agree that technically GPT-3 already has a goal / utility function (to find the most highly predicted token, roughly), but it’s not an ‘interesting’ goal in that it doesn’t imply doing anything in the world.