Artificial intelligence is getting personal. Advances in massive data gathering and processing, coupled with wholesale leaps in machine processing power are setting the stage for a world where AI could closely simulate the human brain and become ingrained in many aspects of daily life.
How soon that becomes reality could depend a lot on the humans who control it, because the ability to run basic AI initiatives has evolved to a point where generating a machine-learning model is as easy as planting roses in the backyard.
“People like Google and Facebook have made it so easy for the average person to actually do an AI project,” said Bob Friday (pictured, second from right), vice president and chief technology officer of Mist, a Juniper company. “Anyone in your audience could actually train a machine-learning model over the weekend. You personally could become a data scientist over the weekend.”
Friday spoke with John Furrier (@furrier), host of theCUBE, at SiliconANGLE Media’s livestreaming studio in Palo Alto, California, as part of an “Around theCUBE — Unpacking AI” panel discussion. Friday was joined by Eugene Santos Jr. (pictured, second from left), Ph.D, professor of engineering at Dartmouth College, and Ed Henry (pictured, right), senior scientist and distinguished member of the technical staff — machine learning — at Dell EMC. They discussed the impact of AI on daily life, continued dependence of the technology on basic math, how machine intelligence could mimic the human brain, and the future influence of quantum computing. (* Disclosure below.)
AI becomes real
What is paving the way for AI to become an integral part of daily life is its presence in a host of interactions many people have come to accept as routine. People who use smart assistants on mobile or home devices are using AI. Social media feeds are heavily impacted by AI tools, as are music, streaming services, and most of the advertising served on digital platforms.
“A good chunk of AI is real,” Santos said. “It depends on what you apply it to. If it’s making some sort of decisions for you, that’s AI coming into play.”
Despite advances that AI and machine learning have made, the actual field is still very much in its earliest stages. As with much of computing, the mechanics behind AI are still dependent on basic math, which instructs machines how to generate results, and programs must rely on the speed of computer processors or clock cycles.
“Right now, it’s all math,” Henry explained. “Computers don’t work like brains; our brains don’t have a clock. There’s no state that’s kept between different clock cycles.”
Mimic the human brain
AI and machine learning are powered by training models that require vast amounts of data, while the human brain can intuitively learn new tasks with only a minimal amount of instruction. Could AI reach a place where it can match the reasoning of human brains?
A new scientific paper on artificial neural networks raises the possibility that the next generation of intelligent algorithms could enable machines to generalize learning in much the same way as the human brain.
“Are we going to take our human biases and train them into the next generation of AI devices?” Friday asked. “From my point of view, it’s inevitable that we will build something as complex as the brain eventually.”
Influence of quantum computing
One of the potential game changers for advancing AI is quantum computing, the ability to leverage mechanical phenomena and manipulate bits of information at a speed and scale much greater than what is commonly available today.
For example, Google Sycamore, a 53-qubit computer, can reportedly solve a problem in 200 seconds that would have taken a supercomputer 10,000 years.
“It will fundamentally change the way we approach these problems,” Henry said. “Quantum computing allows you to evaluate the entire search space at once. The technology, once we crack that quantum nut, will not look anything like what we have today.”
The development of AI has been in process for many decades, and reaching a point where the technology can truly emulate human intelligence remains the ultimate prize.
“Back in the early 1980s, AI was what people were trying to call artificial general intelligence,” Santos said. “It’s all the things that us humans can do, us humans can reason about, all of the decision sequences that we make. That’s the part that we haven’t quite gotten to yet.”
Watch the complete video interview below, and be sure to check out more of SiliconANGLE’s and theCUBE’s CUBE Conversations. (* Disclosure: Juniper Networks Inc. sponsored this segment of theCUBE. Neither Juniper nor other sponsors have editorial control over content on theCUBE or SiliconANGLE.)
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.(tagsToTranslate)Mark Albertson(t)SiliconANGLE(t)AI for all: Panel assesses impact of new machine-learning tools and rise of quantum computing
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