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AI startup Snorkel preps a new kind of expert for enterprise AI

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简介Snorkel's Data-centric Foundation Model Development suite will let a non-programmer use a text promp...

Snorkel's Data-centric Foundation Model Development suite will let a non-programmer use a text prompt with natural language queries to get a neural net to automatically label data. The output labels become a means to supervise the training of a downstream machine learning program, which might be a simple classifier.

Snorkel

In the last big upsurge in artificial intelligence, in the late '70s and '80s, a popular approach took hold known as expert systems, programs that contained rules for tasks based on human knowledge typed into the computer.

Expert systems ultimately failed because they both proved too hard to codify -- what can an expert truly articulate about what they know? -- and also too arduous to build and maintain. They didn't scale, in other words.

In an intriguing twist on that earlier approach, Snorkel, a three-year-old AI startup based in San Francisco, Wednesday unveiled tools to, as they put it, bring the human domain expert back into the driver's seat when it comes to developing neural networks.

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Snorkel's Data-centric Foundation Model Development, as the offering is called, is an enhancement to the startup's flagship Snorkel Flow program. The new features let companies write functions that automatically create labeled training data by using what are called foundation models, the largest neural nets that exist, such as OpenAI's GPT-3.

The new functions in Snorkel Flow let a person who is a domain expert but not a programmer create a workflow that will then automatically generate labeled data sets that can be used to train the foundation programs for specific tasks.

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Drybell, he said, was "one of several examples that showed what the potential for impact in the real world, but also how much engineering and prioritization work had to had to bridge that gap, which was a big motivation for finally spinning out the company in 2019."

There might need to be a commercial package to help companies use the approach.

Ratner, and his thesis advisor, Christopher Re, and three other colleagues went on to form the company in 2019 with venture capital from prominent venture firms such as Greylock Partners and In-Q-Tel.

Re is also the founder, along with another advisor to Ratner, Stanford computer science professor Kunle Olukotun, of AI hardware startup SambaNov Systems.

With the new Model Development capability, Snorkel takes the problem of very data-hungry programs and turns it on its head. Rather than come up with training data for such massive programs as GPT-3, the generative function of those models can be used to produce training data for a smaller neural network, or even for forms of AI that have nothing to do with neural nets.

"We started experimenting," said Ratner, "with this idea of rather than trying to find a foundation model and plug it into production, use the foundation model to auto-label data to accelerate the data-centric development loop, and then look for a different deployable model."