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In what was a turbulent week for machine learning (ML) and artificial intelligence (AI) workers. As part of its 11,000 employee layoffs, Meta axed an entire ML research team that was focused on infrastructure. However, on the opposite side of the industry’s economic spectrum, biotech company, Sanofi this week detailed a $1.2 billion AI drug discovery deal with Insilico Medicine.
The contrasting news demonstrates that although some companies are adjusting for what may be a pending recession, others continue to show signs of growth. That growth was also underscored in a recent State of AI report released by Appen, which detailed the many advancements the sector has made this year, including in ML models. It also shed light on challenges AI and ML professionals continue to grapple with, including data sourcing and preparation related to model testing and deployment.
In data news, the warehouse and lake house giant, Snowflake, this week announced updates to its platform to enhance both collaboration and improve data cloud economics.
Snowflake wasn’t the only company looking to improve enterprise economic tools this week. MachineFi Lab, a decentralized blockchain platform provider, unveiled a new tool dubbed the “W3bstream” — aimed at “disrupting the machine economy,” the company said.
Join us in San Francisco on July 11-12, where top executives will share how they have integrated and optimized AI investments for success and avoided common pitfalls.
Here’s more from our top 5 tech stories of the week:
- Meta layoffs hit an entire ML research team focused on infrastructure
After Wednesday’s Meta layoffs, which cut 11,000 employees, CEO Mark Zuckerberg publicly shared a message to Meta employees that signaled, to some, that those working in AI and ML might be spared the brunt of the cuts.
However, a Meta research scientist who was laid off tweeted that he and the entire research organization called “Probability,” which focused on applying machine learning across the infrastructure stack, was cut.
- Why data remains the greatest challenge for machine learning projects
Quality data is at the heart of the success of enterprise AI. And accordingly, it remains the main source of challenges for companies that want to apply ML in their applications and operations.
The industry has made impressive advances in helping enterprises overcome the barriers to sourcing and preparing their data, according to Appen’s latest State of AI Report. But there is still a lot more to be done at different levels, including organization structure and company policies.
- Snowflake announces plans to improve query performance, collaboration
This week Snowflake, at its annual Snowday event, announced improvements to its platform’s key elements such as the elastic performance engine and Snowgrid technology. The move, it said, will provide enterprises with a faster querying experience for improving overall data cloud economics as well as streamlined collaboration capabilities.
- Why the metaverse will rely on blockchain frameworks to connect to physical devices
This week, MachineFi Lab — the core developer of IoTeX, a decentralized blockchain platform that enables interactions between humans and machines — announced the release of W3bstream.
The new tool, it says, is “a blockchain-agnostic infra with the power to disrupt the machine economy where innovation until now has remained stagnant,” according to the company’s press release.
- Sanofi signs latest billion-dollar AI drug discovery deal
In an era when biopharma research in drug R&D continues to be costly and slow, and AI-based drug discovery platforms are rapidly growing, Paris-based pharmaceutical leader Sanofi announced a $1.2 billion AI drug discovery deal with startup Insilico Medicine.
The research collaboration comes on the heels of several other high-value AI drug discovery partnership announcements from Sanofi, including with Atomwise in August; a partnership expansion with Exscientia last January; and an equity investment in Owkin a year ago.
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