Hello and welcome to Eye on AI…In this edition…Meta snags a top AI researcher from Apple…an energy executive warns that AI data centers could destabilize electrical grids…and AI companies go art hunting.
Last week, I promised to bring you additional insights from the “Future of Professionals” roundtable I attended at the Oxford University Said School of Business last week. One of the most interesting discussions was about the performance criteria companies use when deciding whether to deploy AI.
The majority of companies use existing human performance as the benchmark by which AI is judged. But beyond that, decisions get complicated and nuanced.
Simon Robinson, executive editor at the news agency Reuters, which has begun using AI in a variety of ways in its newsroom, said that his company had made a commitment to not deploying any AI tool in the production of news unless its average error rate was better than for humans doing the same task. So, for example, the company has now begun to deploy AI to automatically translate news stories into foreign languages because on average AI software can now do this with fewer errors than human translators.
This is the standard most companies use—better than humans on average. But in many cases, this might not be appropriate. Utham Ali, the global responsible AI officer at BP, said that the oil giant wanted to see if a large language model (LLM) could act as a decision-support system, advising its human safety and reliability engineers. One experiment it conducted was to see if the LLM could pass the safety engineering exam that BP requires all its safety engineers to take. The LLM—Ali didn’t say which AI model it was—did well, scoring 92%, which is well above the pass mark and better than the average grade for humans taking the test.
