Why Meta and Twitter’s AI and ML layoffs matter | The Rhythm of AI

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Ten days ago, as part of Twitter’s mass layoffs, the company’s entire ethics artificial intelligence (AI) The team – which worked to make Twitter’s algorithms more transparent and fair – was fired. The team, called ML Ethics, Transparency, and Accountability, was led by Rumman Chowdhury, well known for his leadership in applied algorithmic ethics.

Meanwhile, Meta’s layoffs last week of 11,000 employees, or 13% of the company’s workforce, included a total of 50 people. Research Team focused on machine learning (ML) infrastructure, called probability. The probability team consisted of 19 people doing Bayesian modeling, 9 people doing ranking and recommendations, 5 people doing ML efficiency, 17 people doing AI for chip design and compilers, and managers, according to a researcher on the team.

Both sets of layoffs are significant, experts say, as they signal a shift in the very landscape of the most sought-after talent in AI and ML, as well as consideration for Big Tech and enterprise in terms of reaction. against their own Responsible AI efforts.

Georgios Gousios, head of research at software company Endor Labs and associate professor at Delft University of Technology in the Netherlands, told VentureBeat via email that Meta’s Probability team is “the equivalent of an elite tactical squad of the army”.

Gousios, who worked on the Probability team from October 2020 to February 2022, said that while Facebook had a lot of developers working on various parts of the tech and business stack, Probability was doing work “that is orthogonal to daily software production, aiming to invent and apply new tools/methods that would make other teams more efficient in their daily work.

This included, he explained, probabilistic programming (writing programs where variables are represented by distributions, rather than single values), differentiable programming (making neural networks more efficient), and applications for software engineering, such as tools that use ML to help engineers both write code faster with fewer bugs, and debug unavoidable problems faster.

“The quality of the squad was extremely high,” he said. “Many of us (including me) came from years of academic research; many had decades of industrial research experience at places like Microsoft Research or Bell Labs. I think over 60% had a doctorate”

Many in the AI ​​and ML space expressed surprise at the layoffs, given the high value of the Probability team.

According to Nantas Nardelli, a senior researcher at climate artificial intelligence company Carbon Re, these were some of the best in the field, but not as well known as other researchers.

“They tend to produce work that maybe less flashy, but could end up becoming the backbone of ML products in 5-10 years,” he told VentureBeat in a LinkedIn post.

Their ML work, he explained, is “well applicable” to problems with a low or medium amount of data, high domain knowledge, and where it is important to estimate uncertainty. “That expertise is usually hard to come by, and fewer and fewer people are specializing in it these days,” he said.

Twitter’s ethical AI firings offer lessons for companies

Triveni GandhiHead of AI at Data Science and Platform ML Dataiku, said she was not surprised by the ethical AI layoffs at Twitter.

“My knee-jerk reaction was, of course, they’re the first to be fired, because of how Twitter’s current management has indicated how they feel about issues of ethics, trust, and safety,” he said. she told VentureBeat.

But as a responsible AI manager, she added, she also began to think about what the news meant for her enterprise clients: “Are they also going to start thinking, well, we don’t don’t we need that kind of stuff?”

However, she said she realized the public reaction to the layoffs was indicative of the importance and respect for ethical AI.

“I think other companies are seeing this very public reduction of this specific team, and they’re thinking, I don’t want to go down the same path,” she said. “I don’t want to create a sense of mistrust among consumers of my AI products.”

Among her clients, she added, she sees a “sense of resolve” emerging from the news of Twitter’s AI ethical firings. “They say, ‘we can be better than this, we’ll allow [responsible AI teams] and start getting them to put things into practice,” she said. “Like, let’s get away from the thought leadership stuff on this and put rubber on the road.”

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