The artificial intelligence field has evolved dramatically since the deep learning revolution kicked off in 2012, and Richard Socher has been around for all of it. He earned his PhD from Stanford working on NLP (natural language processing) before co-founding an AI startup called MetaMind in 2013. He then spent several years leading the AI team at Salesforce (after it acquired MetaMind) before tackling the search space with his new startup, you.com, in 2021.

In this interview, Socher discusses a number of topics, including: how things have changed for AI startups in the last decade; the differences between doing AI in startups, enterprises, and academia; and how new machine learning techniques, such as transformer models, empower companies to build advanced products with a fraction of the resources they would have needed in the past.

FUTURE: It seems like a common move is for AI researchers – students and professors – to move from academia into startups, like you did. What are some key differences between those two worlds today?

RICHARD SOCHER: In academia, people still push forward to try to make progress toward new areas where AI can have an impact, and some of them hope to make progress toward AGI (artificial general intelligence). I think two exciting examples of novel, high-impact areas are in the protein space – sequences of proteins or amino acids – and in economics. The latter is so important for the world, but really hasn’t seen as much impact from AI as I think it should.

At the same time, for startups, if you have a lot of data and you have a process that is mostly dependent on the data that you’re already seeing, you basically can just say, “We know how it works.” You have a radiology image, and you try to identify, ‘“Is this bone broken or not?” Or you have a head CT scan and you try to identify, “Is there intracranial hemorrhage or brain bleeds?” Or you’re classifying different kinds of cancer from pathology images. All of these applications are essentially taking a relatively well-established sequence of identifying a problem and collecting data for it; training a large neural network on it; and then optimizing and automating parts or the entirety of that process.

And with that well-proven approach, you can actually have a lot of impact. It’s similar to what we’ve seen with electricity: Once we had the basics of electricity figured out, you could have a lot of impact by just giving it to a town that had only oil lamps and fire before.

This is possible in part because a lot of interesting and important ideas have been developed over the last 10 years. Things that would have been impossible  – like having an AI write a reasonably long text – are now possible. One major change is that not just images, but all data, is essentially vectors. Everything is a list of numbers, and then that list of numbers can be given as an input to a large neural network that can really train anything you want on top of it. There are lots of interesting and important algorithmic improvements, too – not to mention more data and more computer power – but that main idea of end-to-end learning was the big one that changed a lot of things.

What about the transition from startups to large enterprises? It seems like a double-edged sword, with probably more budget but also more restrictions …

The two are different along so many dimensions. I’ll just mention two examples.

AI-tooling startups are successful in the B2B space if they find one part of the process that every other company might have to spend one or two developers on, and they build a product around that process that costs, say, a quarter of a developer. So, a lot of startups now in the AI tooling space are taking the less pleasant, less fun bits, and helping developers do those things.

The best way to do this is probably developing an experience where the companies using the product can still feel like they are building and controlling the AI, but really they found a partner to label their data. They also found partners to look through the bias of the data; collect the data in the first place; implement the model via Huggingface; scale the model analytics as they train it via Weights and Biases; and deploy the model via ZenML.

In the end, they’re dependent on 10 to 15 external systems, but they were able to train AI much more quickly, much more scalably, and much more accurately than if they had to try to reinvent 95 percent of the tooling around a particular AI model. It’s been really interesting for startups to identify these various things that exist already, but they don’t exist in a super-professional way where a strong team is focused on that particular aspect.

At a larger enterprise company like Salesforce, you’re mostly thinking about what really moves the needle for a lot of different customers. How can you help those customers with their datasets that are already in your system, in a way where they still feel like – and actually do – have the control? That’s non-trivial to do because at Salesforce, for example, trust was our No. 1 value. You couldn’t just take everyone’s data and train something on it, because they own their data and they’re paying for the storage. And, so, you need to also work together with customers to try to get their AI projects off the ground.

Once we had the basics of electricity figured out, you could have a lot of impact by just giving it to a town that had only oil lamps and fire before.

So for an enterprise software vendor, the concerns are that customers are paying a lot of money, and you can’t throw a wrench in the works in the name of experimenting with a new feature?

That’s part of it. But maybe more importantly, you have to make sure that it’s trusted,