How Salesforce Research Uses Generative AI as a Force for Good
Quick Take:GenerativeAI has most recently captured the public’s attention for its applications in the workplace. Salesforce’s AI Research team, however, is also exploring how it can be applied to solve problems in other fields — and in society. This story details how Salesforce partnered with an academic institution and a biomedical company to apply an AI language model to protein design with fascinating results.
“AI – especially generative AI – is having such a moment right now,” Salesforce Director of AI Research Nikhil Naik recently said during a Blazing Trails podcast interview.
The “moment” is thanks to headline-making products like ChatGPT, which have generated thousands of headlines — along with questions and ethical concerns — over the past few months. And while using this type of technology to write articles, essays, and not-so-great songs is all the rage right now, the work that Naik and others have been doing for the past five years within Salesforce’s AI Research program has some bigger applications.
For example, they’ve been able to train generative AI on conversational language which is then turned into development code through a large-scale language model called CodeGen. It’s an exciting application of technology for the workplace, but its impact can go even further.
Through their AI for Society initiative, the team is also applying AI research to some of society’s biggest challenges. So far, Salesforce Research work has ranged from implementing AI for more equitable and balanced economic policies, to using computer vision AI for tracking great white sharks, all the way to determining optimal treatment paths for breast cancer patients via artificial intelligence.
It’s mind-bending stuff, but the work is actually built around a simple strategy. “We identify AI techniques that we are very good at, and then identify problems where the AI could be applied,” said Naik.
This approach recently led to the development of ProGen, a Salesforce AI language model trained on the world’s largest protein database.
Yes, you read that right — Salesforce trained its AI models on proteins.
While that might sound like a stretch – taking technology largely used today to develop chatbots and automated user flows to design proteins – the team had uncovered a commonality between the two use cases that made a lot of sense.
“AI models ingest a large amount of text and they learn to predict the next word that might come after a given word,” said Naik. “And just by training using this pretty simple method, you can train an AI algorithm to generate very realistic language about any topic that you might be interested in. And what we realized is that the same technology can be applied to generating proteins.”
And if you can develop novel proteins, the Salesforce Research team believed, that could eventually open the door to new medicines, vaccines, or sustainability innovations — to name a few.
So, in 2020, Naik and his team set out to apply generative AI, especially large language models and their associated techniques, to the problem of protein design. Why protein design? It’s an area where creation and research can be done at exponential speed, meaning “we can accelerate the discovery of novel drugs and useful industrial chemicals,” according to Naik.
