Why Meta and Twitter AI and ML Layoffs Matter | The pace of AI

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

Meanwhile, Meta’s layoffs last week of 11,000 employees, or 13% of the company’s workforce, included an entire 50-person research team focused on machine learning (ML) infrastructure, called Probability. The Probability team consisted of 19 people doing Bayesian modeling, 9 people doing classification and recommendations, 5 people doing ML efficiency, 17 people doing AI for chip design and compilers, as well as managers, according to a researcher at the equipment.

Both sets of layoffs are important, experts say, because they signal a changing landscape for even the most sought-after AI and ML talent, as well as a reckoning for Big Tech and the enterprise business in terms of how they respond to their own responsibilities. AI efforts.

Georgios Gousios, head of research at software company Endor Labs and an associate professor at Delft University of Technology in the Netherlands, told VentureBeat via email that the Meta probability team was the “equivalent of a military tactical squad.” of Elite”.

Gousios, who worked on the Probability team from October 2020 to February 2022, said that while Facebook had many developers working on various parts of the technology and business, Probability was doing work “that is orthogonal to the production of everyday software, with the aim of inventing and applying 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 unique 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, as well as debug unavoidable problems more quickly.

“The quality of the team was extremely high,” he said. “Many of us (including myself) 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 Ph.Ds.”

Many in the AI ​​and ML field expressed surprise at the layoffs, given how highly regarded the Probability team was.

According to Nantas Nardelli, a senior research scientist at climate technology AI 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 is perhaps less flashy, but could end up becoming the backbone of ML products in 5-10 years,” he told VentureBeat in a LinkedIn message.

His ML work, he explained, is “well applicable” to problems with low to medium amounts of data, high domain knowledge, and where estimating uncertainty is important. “This expertise is generally hard to come by, and fewer and fewer people are specializing in it today,” she said.

Twitter’s AI Ethical Firings Offer Lessons for Businesses

Triveni Gandhi, the lead responsible for AI at ML and data science platform Dataiku, said she was not surprised by the ethical dismissals of AI on Twitter.

“My knee-jerk reaction was of course they’re the first to get fired, because of the way Twitter’s current leaders have indicated what they think about issues related to ethics, trust and safety,” he told VentureBeat. .

But as a responsible AI leader, he added, he also started thinking about what the news meant for his enterprise customers: “Are they also going to start thinking, well, we don’t need this stuff?”

However, he said he realized the public reaction to the firings was indicative of how important and respected ethical AI has become.

“I think other companies see this very public downsizing of that specific team, and think, I don’t want to go down the same path,” he said. “I don’t want to create a sense of mistrust among consumers of my AI products.”

Among his clients, he added, he’s seeing a “sense of resolve” emerging from news of Twitter’s artificial intelligence ethical firings. “They’re saying, ‘we can be better than that, we’re going to enable [responsible AI teams] and start getting them to put things into practice,’” he said. “Like, let’s get away from the thought leadership stuff on this and get going.”

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