How Language Led To The Artificial Intelligence Revolution
In 2013 I had a long interview with Peter Lee, corporate vice president of Microsoft Research, about advances in machine learning and neural networks and how language would be the focal point of artificial intelligence in the coming years.
At the time the notion of artificial intelligence and machine learning seemed like a “blue sky” researcher’s fantasy. Artificial intelligence was something coming down the road … but not soon.
I wish I had taken the talk more seriously.
Language is, and will continue to be, the most important tool for the advancement of artificial intelligence. In 2017, natural language understanding engines are what drive the advancement of bots and voice-activated personal assistants like Microsoft’s Cortana, Google Assistant, Amazon’s Alexa and Apple’s Siri. Language was the starting point and the locus of all new machine learning capabilities that have come out in recent years.
Language—both text and spoken—is what is giving rise to a whole new era of human-computer interaction. When people had trouble imagining what could possibly come after smartphone apps as the pinnacle of user experience, researchers were building the tools for a whole new generation of interface based on language.
“We believe that over the years if you build software, everything will want to learn language,” said Lili Cheng, general manager of Microsoft’s FUSE Labs, in a briefing with reporters in Seattle ahead of Microsoft Build 2017. “I think over the year we have seen so much happen over conversational AI and bots.”
One of the reasons that Lee and Microsoft Research focused on language when developing machine learning was because it fitted into several different kinds of buckets of artificial intelligence research. Language could function as a way for researchers to perform theoretical, open field experiments with no intention for practical deployment other than creating knowledge for the sake of knowledge. Language, as we have seen since, also presented the opportunity for distinct commercial applications.
Lee said at the time:
One is that there has been, there is right now for us, a resurgence of hope and optimism in being able to solve some of the longest standing problems in core artificial intelligence. To get machines that see and hear and understand reason at levels that understand or match human capabilities. I think we are seeing that first in dealing with language. I think language is coming first because it is a little bit of a simpler problem but one that has commercial implications. So, that is moving really fast. The application of those ideas to computer vision, to finding patterns and signals on things you wear all day. From looking at all the instrumentation and logging out of factories. From looking at all the electronic health records that hospitals are working with. The applications for deep learning from all of that are pretty impressive.
The focus on language has given us the first commercial taste of artificial intelligence in the real world. In 2011, Microsoft added translation to Skype. Virtual assistants like Cortana, Siri, Google Assistant and Alexa are creating new avenues of human-computer interaction.
But, more importantly, the focus on language (and images) have given rise to the deployment of neural networks, the engines behind machine and deep learning and the harbinger of artificial intelligence.
The concept of neural networks is not new.
The idea has been around for more than 70 years. Some of the first attempts to build computers were modeled after human brains. But logic engines proved to be much more efficient, creating the binary machine code that we use in all of our software today. The idea of neural networks resurfaced in the 1980s when researchers made breakthroughs in decision-making algorithms that veered away from the string logic engines.
