Keeping up with an industry as fast-moving as AI is a tall order. So until an AI can do it for you, here’s a handy roundup of recent stories in the world of machine learning, along with notable research and experiments we didn’t cover on their own. This week, it was impossible to tune out — for this reporter included, much to my sleep-deprived brain’s dismay — the leadership controversy surrounding AI startup OpenAI. The board ousted Sam Altman, CEO and a co-founder, allegedly over what they saw as misplaced priorities on his part: commercializing AI at the expense of safety. Altman was — in large part thanks to the efforts of Microsoft, a major OpenAI backer — reinstated as CEO and most of the original board replaced. But the saga illustrates the perils of AI companies, even those as large and influential as OpenAI, as the temptation to tap into… monetization-oriented sources of funding grows ever-stronger. It’s not that AI labs necessarily want to become enmeshed with commercially-aligned, hungry-for-returns venture firms and tech giants. It’s that the sky-high costs of training and developing AI models makes it nigh impossible to avoid this fate. According to CNBC, the process of training a large language model such as GPT-3, the predecessor to OpenAI’s flagship text-generating AI model, GPT-4, could cost over $4 million. That estimate doesn’t factor in the cost of hiring data scientists, AI experts and software engineers — all of whom command high salaries. It’s no accident that many large AI labs… Click below to read the full story from TechCrunch
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