What’s Next for the Future of AI and Machine Learning?

What’s Next for the Future of AI and Machine Learning
A woman engages in a fluid, back-and-forth dialogue with an intelligent assistant that helps her juggle upcoming appointments on her calendar. The assistant uses machine learning technology pioneered by Semantic Machines, which Microsoft acquired in May 2018 and will incorporate into all of its conversational AI products and tools. Photo by Microsoft.


Microsoft has some new ideas for the future of AI and its uses. “You have to poke around for magic combinations of words to get various things to happen, and you find out that a lot of the functions that you expect the thing to do, it actually just can’t handle,” said Dan Roth, corporate vice president and former CEO of Semantic Machines, which Microsoft acquired in May 2018, in a press release.

Microsoft wrote in a release that it hopes the “next generation of intelligent assistant technologies” will be able to engage in a coherent conversation with its users and differentiate “contextual information from one skill to assist you in making decisions.”

Microsoft is looking for ways to teach context and concepts in its new AI functions and have the AI understand through a natural language interface.

“Being able to express ourselves in the way we have evolved to communicate and to be able to tie that into all of these really complicated systems without having to know how they work is the promise and vision of natural language interfaces,” said Roth in a press release.

In some ways the next generation of AI will be able to understand, hopefully. According to Microsoft, Semantic Machines technology “extends machine learning beyond its intents all the way through to enabling what the system does.” So in a sense, the machine will start to learn itself from collected data instead of having a programmer enter lines of codes for the machine to learn a new skill.

“That’s missing in today’s intelligent assistants, which are programmed to do a list of isolated things that a programmer anticipated,” said Microsoft technical fellow Dan Klein in a press release. “The machine learning in these systems primarily focuses on words that trigger a skill.”

Klein is a recognized leader in the field of natural language processing and a professor of computer science at the University of California at Berkeley.

“They aren’t focused on learning how to do new things, or mixing and matching the things they already know in order to support new contexts,” said Klein in a press release, who was also a co-founder and chief scientist at Semantic Machines. Once something like machine learning is incorporated into Microsoft’s products, the AI system will hopefully have a more “fluid, natural and powerful experience” for users.

Microsoft acquired Semantic Machines in May 2018 in the efforts to improve it’s products AI programming.

Source: blogs.microsoft.com


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