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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.

Most ML models are trained by scraping human intelligence from photos or other corpora, such as Google’s language translation that was trained by consuming about a third of the public internet. If Libratus was a more traditional type of intelligence, it would have first observed a million poker players playing a billion hands of poker, then applied the observation to train a model based on human behavior. Instead, it learned the rules for winning at poker by experimenting with every possible poker hand.

AlphaGo was similarly trained. It startled Sedol with its own invented style of play that was not limited by human intelligence. Lee used Libratus and AlphaGo to explain to Nadella that in narrowly defined cases, AGI is possible.


The hard question facing every company delivering ML to the enterprise

The supply chain for delivering the benefits of ML to the enterprise has a problem. Lee says machine learning models such as language translation are created by privileged teams of experts. They’re privileged in the sense that they are very skilled in machine learning, linear algebra and probability, and often they have Ph.Ds from top universities. It is a finite expert labor pool concentrated at just a few companies, such as Facebook, Google, IBM and Microsoft.

The question to Lee is how to navigate this labor shortage and deliver AI to the enterprise. Hard to build ML models are first proven by advanced researchers, then the models are optimized by ML and computer systems experts to run on today’s underpowered hardware. In the last step, very specialized developers rewrite resource-consuming components with fast native code and optimize them to run cost effectively. ML development tools are complex and methods are immature, which prevents lesser-skilled developers from building models. 


How Microsoft plans to deliver the benefits of AI

Tier 0 works today. Microsoft has few thousand customers that use its cloud portfolio of pretrained models, adding features such as natural language processing, text analytics and language translation to existing applications with Microsoft Cognitive Services APIs.

Tier 1 is a not as easy because expanding the expert labor pool is not an option, and a mature tool chain enabling less-skilled developers to build models has not yet emerged. Lee says Microsoft will leverage the two decades of Microsoft’s AI research to help partners build original ML models to solve new problems. Lee says this tier is best delivered by partners—partners with domain expertise and ML expertise that can create a new and original model to solve a problem that once proven, could be marketed to an entire industry. This is similar to how the UPMC plans to sell its model to the healthcare industry.


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