What Leading AI, Machine Learning And Robotics Scientists Say About The Future

Author

Jason Lim

October 14, 2016

Every year there is a new hot topic in tech. Today, it’s all about artificial intelligence, machine learning, virtual reality and autonomous vehicles. The difference between now and the past is that everything is becoming interconnected at a faster rate.

We are entering an extremely critical time in history where society will change dramatically – how we work, live and play. Science fiction is morphing into reality. Flying cars exist, cars that drive themselves are on the road, and artificial intelligence that automates our lives is here.

To make all of this amazing science and technology happen, it takes some extremely intelligent and curious people. In many ways, scientists are still at the helm of discovering breakthroughs through research. Increasingly, tech companies are accelerating innovation by applying it to the real world by leveraging their data and distribution networks. For such innovative companies and pioneering startups to exist, investors provide the necessary capital.

Leading scientists meet at G-Summit

It is unusual for these different yet linked groups to come together, even in Silicon Valley. But in a rare event, some of the world’s leading scientists, tech execs and investors convened in San Francisco at the first G-Summit, organized by the GWC, an organization from China that connects tech innovators around the world. From September 27-28, the exclusive group shared breakthrough research and discussed both good and challenging implications.

Beyond bringing a multi-faceted group of people together, what made G-Summit extra special was its focus on making it interdisciplinary. The diversity of leading experts from the fields of AI, computer vision, robotics, neurology and materials science amplified learning and sparked interesting conversations. Leaders from Stanford, MIT, Carnegie Mellon, Facebook, Google, Pinterest, Alibaba and many more were all in the same room.

Now is our opportunity to make choices about AI

Although there were many fascinating topics explored at G-Summit, I will highlight AI and machine learning.

Will the future of intelligent machines and robots be more Terminator or more like Baymax from Big Hero 6? Will they help or hurt us? This question has been at the heart of sci-fi films for generations. But now we will really have to deal with them and find out. Recently a fatal Tesla Model S self-driving car crash brought to light the risks of fully trusting technology. In a Senate inquiry, Tesla said the automatic braking system was faulty. Another Tesla Model S autopilot crash case has been taken to court in China and is still under investigation.

Kai-Fu Lee, chairman and CEO of Innovation Works delivered the opening keynote of G-Summit. Lee speaks with authority as a pioneer in speech-recognition. He developed the world’s first speaker-independent, continuous speech recognition system and later established Google China. ”The future of jobs will change and reinvent every imaginable part of work (from AI),” said Lee. This is a statement we all comprehend at a basic level but is hard for many of us to grapple with at a deep level. Ultimately he means that artificial intelligence will augment humans and make our lives easier, no matter what industry we’re in. For example, robo-traders make financial trades smarter and faster than humans, robots save lives by replacing humans in life-threatening rescue operations and in an ironic way, they even take over some of the work of the people that made them – engineers.

 

Kai-Fu Lee, Chairman & CEO, Innovation Works

 

Kai-Fu Lee, Chairman & CEO, Innovation Works

He also made an insightful comment saying, “Traditional data from hospitals and banks provides a rich source of data to mine.” This point is important because technology has the biggest opportunity to impact some of the least tech-savvy industries. Machine learning and natural language processing algorithms feed off data and that is what hospitals and banks have a lot of.

But what about the legitimate concern for many that AI will displace people’s jobs en masse? It’s not just the low-skilled labor people that are worried. Highly-educated professionals are also at risk. In a conversation with Fei-Fei Li, director of Stanford AI Lab, she’s more optimistic. “I’m flattered about how much confidence people have in AI, but there is really a long way to go. We’re at the historical moment where we still have a choice to either replace or augment humans. I believe they should augment. AI has to have cognition in context and knowledge. It’s not just about playing GO.”

 

Fei-Fei Li, Director of Stanford A.I. Lab Stanford University

 

Fei-Fei Li, Director of Stanford AI Lab, Stanford University

So there is a choice. It’s not that AI and robots can’t replace things, but a matter of what and how much they should replace. It’s also an ethical question. But according to Li, for machines to really take over, they need a high degree of critical thinking and some kind of conscience.

During the conference, an ethical question arose around what should a self-driving car do if it had to make a split-second decision to either hit an old woman or a child (if it had to hit one to save the other). Think about all the trade-offs the car has to compute in that moment. Is the person inside the car responsible or the car operator or the maker? How will self-driving cars affect car insurance?

Where will AI go next?

Commercially we interact with AI at a relatively basic level today. It’s already a newly-trained behavior for us to ask our digital assistants like Apple’s Siri, Google Now, Microsoft Cortana or Facebook’s M questions like what the weather is today or to call an Uber ride. But what is next?

Inspired by watching children, Fei-Fei Li believes that the next step is to teach machines contextual sentences like how children are taught. When children learn about the world around them, they match what they hear to what they see and form pattern recognition. Likewise, computers at Stanford’s AI Lab are being trained to learn what is inside a picture or even videos. Li demonstrated machine learning capabilities of detecting who, when and how NBA players made three-pointers. Imagine what that could mean for sports betting.

Luminary Stanford Professor of Chemical Engineering, Zhenan Bao also revealed some fascinating research she has been working on. We know that robots can move but will they be able to feel? Her team has been able to create artificial skin that uses pressure pads, which can convert pressure changes to electronic signals. In the future, this could be used for people with prosthetic limbs to regain a sense of touch. This is indeed life-changing science.

 

Zhenan Bao, Professor of Chemical Engineering Stanford University

 

Zhenan Bao, Professor of Chemical Engineering, Stanford University

Computers are incredibly powerful. But they are becoming a lot more intelligent, too. Today, an engineer or developer programs software. The program executes based on what that programmer intended. The beauty and power of machine learning is that the software is dynamic and changes over time. It’s no longer static or reliant on programmers to make software upgrades.

Tom Mitchell, professor and founding head of the machine learning department at Carnegie Mellon University, the leading faculty in the field, believes that the future of machine learning is by human instruction. Well isn’t written code human instruction? The distinction is that a person (any person), can speak and train a computer what to do in a particular circumstance. Like a quick-learning child, the computer will ask questions. You answer and train it what to do next time.

 

Tom Mitchell, Professor and Founding Head of the Machine Learning Department Carnegie Mellon University

 

Tom Mitchell, Professor and Founding Head of the Machine Learning Department, Carnegie Mellon University

Silicon Valley is at the forefront of technology and innovation. But research and talent is by no means exclusive to it. China’s biggest tech companies like Alibaba are dedicating enormous energy and resources to AI and machine learning. Ming Zeng, Chief Strategy Officer of Alibaba shared how the company uses it to predict what shoppers will buy and therefore what to present to them on the first page. Face++, a Chinese facial recognition technology startup valued at $1 billion powers Alipay’s smile-to-pay capability — just smile to authenticate payment. Google-backed Chinese startup Mobvoi, has announced a new product called Ticmirror, a car rearview mirror with voice control functions with an accompanying autonomous driving system.

In the spirit of keeping G-Summit diverse and inspiring, the last speaker to close out the event was Richard Saul Wurman, creator of TED. After cultivating what has become one of the most powerful platforms for people to share bold ideas, Wurman has learned a lot. “I sell my ignorance and curiosity,” Wurman said. It is his privilege to surround himself with intelligent people. Although the room was full of highly-acclaimed scientists and business people, Wurman encouraged the room to also solve the world’s simpler problems. “The question is more important than the answer,” Wurman ended.

 

This article was written by Jason Lim from Forbes and was legally licensed through the NewsCred publisher network.

There is 1 comment

  • What Leading AI, Machine Learning And Robotics ... - 10/18/2016 06:49
    […] Every year there is a new hot topic in tech. Today, it’s all about artificial intelligence, machine learning, virtual reality and autonomous vehicles  […]

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