The Salesforce Research mission is to advance AI techniques that pave the path for new products, applications, and research directions, and is an outgrowth of Salesforce CEO Mark Benioff’s commitment to AI as a revenue driver. In 2016, when Salesforce first announced Einstein, Benioff characterized AI as “the next platform” on which he predicted companies’ future applications and capabilities will be built. The next year, Salesforce released research suggesting that AI’s impact through customer relationship management software alone will add over $1 trillion to gross domestic products around the globe and create 800,000 new jobs.
Today, Salesforce Research’s work spans a number of domains including computer vision, deep learning, speech, natural language processing, and reinforcement learning. Far from exclusively commercial in nature, the division’s projects run the gamut from drones that use AI to spot great white sharks to a system that’s able to identify signs of breast cancer from images of tissue. Work continues even as the pandemic forces Salesforce’s scientists out of the office for the foreseeable future. Just this past year, Salesforce Research released an environment, the AI Economist for understanding how AI could improve economic design, a tool for testing natural language model robustness, and a framework spelling out the uses, risks, and biases of AI models.
According to Einstein GM Marco Casalaina, the bulk of Salesforce Research’s work falls into one of two categories: pure research or applied research. Pure research includes things like the AI Economist, which isn’t immediately relevant to tasks that Salesforce or its customers do today. Applied research, on the other hand, has a clear business motivation and use case.
One particularly active subfield of applied research at Salesforce Research is speech. Last spring, as customer service representatives were increasingly ordered to work from home, some companies began to turn to AI to bridge the resulting gaps in service. The technology identifies moments that were good or bad but that were coachable in some fashion. Salesforce is currently working on a number of capabilities like auto escalations and wrap-up, as well as using the contents of calls to prefill fields for you and make your life a little bit easier.
AI with health care applications is another research pillar at Salesforce.To develop, train, and benchmark predictive health care models, Salesforce Research draws from a proprietary database comprising tens of terabytes of data collected from clinics, hospitals, and other points of care in the U.S. Salesforce is committed to adopting privacy-preserving techniques like federated learning that ensure patients a level of anonymity.
Salesforce Research’s ethical AI work straddles applied and pure research. In January, Salesforce researchers released Robustness Gym, which aims to unify a patchwork of libraries to bolster natural language model testing strategies. Robustness Gym provides guidance on how certain variables can help prioritize what evaluations to run. Specifically, it describes the influence of a task via a structure and known prior evaluations, as well as needs such as testing generalization, fairness, or security; and constraints like expertise, compute access, and human resources.
Salesforce aims to remove the groundwork for people. A lot of focus is put on the model and the goodness of the model. But that’s only 20% of the equation. The 80% part of it is how humans use it.
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