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How Microsoft plans to address AI and machine learning challenges

Steven Max Patterson | March 30, 2017
Peter Lee, Microsoft’s research vice president, answers hard questions about artificial intelligence and machine learning.

Peter Lee, Microsoft’s research vice president
Credit: MIT Technology Review

Microsoft, Google and IBM face difficult challenges in winning early enterprise adopters of artificial intelligence (AI) and machine learning (ML). Microsoft AI Research Vice President Peter Lee shed some like on how Microsoft will meet those challenges when we met at this week’s MIT Technology Review EmTech Digital conference.

Lee began with the same explanation that he gave to Microsoft CEO Satya Nadella. Nadella, a former research engineer, does not need to have technology spoon fed to him, but he does need to understand the framework used to manage the future of the vertical industries transferred to the stewardship of Microsoft’s AI group. It is a big bet, amounting to 6,000 employees or one-fourth of the company and includes leading industry businesses such as healthcare, education, automotive, finance and retail. A very big bet on the future of AI, indeed.


How Microsoft categorizes AI opportunities

Lee divided ML into three tiers for me, as he did for Nadella:

Tier 0: The customer can use one of Microsoft’s pretrained models, perhaps adding domain-specific categories. This might be as simple as using Microsoft’s machine translation API to translate comments or to add a domain-specific category such as women’s fashion vocabulary to the translation API service—like eBay did to enable shopping across international markets and different languages.

Tier 1: This tier puts tools such as Microsoft’s Cognitive Tool Kit and cloud hardware optimized for ML in the hands of developers who can build a novel and original domain-specific model. This says easy and does hard—in New Jersey parlance—because the task requires experts in ML, computer architecture and system optimization.

Lee cited partner UPMC Enterprises, a for-profit division of non-profit healthcare delivery service University of Pittsburgh Medical Center, as an example. Although Lee cannot talk about the details of the model that UPMC is building yet, he did say it will enable physicians to improve their performance and the care they provide to patients.

UPMC recruited ML expert Adam Berger, Ph.D. to build a team and lead the project. After the model is created, trained and proven, UPMC plans to market the solution to other healthcare delivery services where Microsoft has 168,000 productivity and IT customers in 140 countries. Pittsburgh is situated at the nexus of a large medical community that has leading teaching hospitals and medical research institutions combined with a leading AI and ML community centered around Carnegie Mellon.

Tier 2: Carnegie Mellon University’s Libratus pokerbot that beat poker pros and Google’s AlphaGo-bot that beat Lee Sedol represent the special case of Tier 2, an artificial general intelligence (AGI). AGI is a  hypothetical machine intelligence discussed by researchers and sci-fi writers that will be able to perform any intellectual human task. These gamebots represent a narrow AGI in the field of gaming, but this technology is unique because the bots are self-trained and not limited by human experience.


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