Salesforce’s AI Economist research wants to explore the equilibrium between equality and productivity
Economic theory is known to be constrained by a number of inefficiencies in its modeling. Salesforce researchers claim AI can help address that, leading to more robust economic policies.
2016 was a pivotal year for Salesforce. That was when the company acquired MetaMind, “an enterprise AI platform that worked in medical imaging and eCommerce images and NLP and a bunch of other things, a horizontal platform play as a machine learning tool for developers,” as founder Richard Socher described it.
If that sounds interesting today, it was probably ahead of its time then. The acquisition propelled Socher to Chief Data Scientist at Salesforce, leading more than 100 researchers and many hundreds of engineers working on applications that were deployed at Salesforce scale and impact. AI became an integral part of Salesforce’s efforts, mainly via Salesforce Einstein, a wide-ranging initiative to inject AI capabilities into Salesforce’s platform.
Besides market-oriented efforts, Salesforce also sponsors “AI for good” initiatives. This includes what Salesforce frames as a moonshot: building an AI social planner that learns optimal economic policies for the real world. The project going under the name “AI Economist” has recently published some new results. Stephan Zheng, Salesforce Lead Research Scientist, Senior Manager, AI Economist Team, shared more on the project background, results and roadmap.
Zheng was working towards his PhD in physics around the time that deep learning exploded — 2013. The motivation he cited for his work at Salesforce is twofold: “to push the boundaries of machine learning to discover the principles of general intelligence, but also to do social good”.