Browse entries 1249–1253 of 1253. Return to the first page to search and filter the complete collection.
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AI Engineer session on Buy Now, Maybe Pay Later: Dealing with Prompt-Tax While Staying at the Frontier - Andrew Thomspson. It adds practical context for how teams are building and operating AI systems in production.
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A single spatial biology run can yield two to six terabytes of data, far more than a scientist can eyeball, and Kenny Workman argues that this is the raw material for teaching AI to actually do science.
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RLHF made models that are extraordinary at pleasing the human in the loop, and Diogo Almeida, a GPT-4 co author, argues that is exactly the problem.
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The old story is that a base model is a mirror of the internet, a good model of human web text that everything else gets bolted onto. Varun Singh, who leads pre-training at Arcee AI, argues that story is dead: no modern base model reflects the web the way GPT-3 once did.
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Asked to file an expense, the agent gets signed out mid task, reasons that it can infer the password, guesses twice, and locks the account. In a second run it clicks a sponsored button styled like the real submit button, lands on a different site, and begins typing personal details into it.