Given the misunderstandings and explanation of how they struggled with a long-solved ml problem, I believe this article was likely written by someone without much formal experience in AI.
This is probably a case where some educational training could have saved the engineer(s) involved a lot of frustration.
Both “Principle 1” and “Principle 2” in the article are essentially LLM-focused details of basic principles in ML that have been known since before I (and probably you, if you’re still working age) were born.
Fair criticism. I was going for the colloquial usage of "you get one shot" but yeah I did read that Google paper the other day referring to these as zero-shot.
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What is a “one-shot” AI Agent? A one-shot AI agent enables automated execution of a complex task without a human in the loop.
Not at all what one-shot means in the field. Zero-shot, one-shot and many-shot means how many examples at inference time are needed to perform a task
Zero shot: "convert these files from csv to json"
One shot: "convert from csv to json, like "id,name,age/n1,john,20" to {id:"1",name:"tom",age:"20"}
Given the misunderstandings and explanation of how they struggled with a long-solved ml problem, I believe this article was likely written by someone without much formal experience in AI.
This is probably a case where some educational training could have saved the engineer(s) involved a lot of frustration.
As a casual ML non-practicioner, what was the long-solved ML problem they ran up against?
Both “Principle 1” and “Principle 2” in the article are essentially LLM-focused details of basic principles in ML that have been known since before I (and probably you, if you’re still working age) were born.
Fair criticism. I was going for the colloquial usage of "you get one shot" but yeah I did read that Google paper the other day referring to these as zero-shot.
fully-autonomous makes more sense in the agentic vicab imho
at the end its fine if the agent self corrects amongst many shots too