December 2018: 11 Professors Prophecies for AI and IoT2018-12-12T14:53:49+00:00

December 2018:

11 Professors Prophecies for AI and IoT

Many of today’s mainstream media news headlines focus on the downsides of technology and the tech workplace, whether it’s cybercrime, election hacking, or sexual-harassment coverups. While technology’s dark side bears constant scrutiny, we’re here to celebrate the often-overlooked humane, uplifting, and powerful stories of the IoT (Internet of Things) and other emerging technological breakthroughs impacting the good of mankind.

Each year Connected World coins these very unique individuals as Pioneers. They are the disruptors forging new pathways in this ever-emerging digital age. They have a vision for progress that goes way beyond making just predications based on high-growth numbers.

We have all heard the predictions from analysts firms such as IDC forecasting spending on AI (artificial intelligence) and machine learning to grow to almost $58 billion by 2021.

Our soothsayers are looking well-beyond just a few years out. They look well into the future.

That is exactly why our 2019 Pioneers honor the people that are, indeed, making sure the next generation understands the care and thoughtfulness needed to discuss the laws, policies, and regulations that will govern and manage robots and AI, and so much more.

The IoT (Internet of Things) and AI (artificial intelligence) are beginning to provide big value, helping organizations solve business challenges with the help of new, emerging technology.

Each year, the Connected World Awards recognizes a successful company within a vertical market for scaling new heights in emerging tech.

They dedicate their life’s work to educating researchers down the line about how AI fits into the larger IoT and emerging-tech world.

They consider today’s workers, too. One of the Pioneer winners thinks every day about how much more humane fruit picking could be when robots take over the chores that people now endure climbing up and down ladders for 12 hours a day in 110-degree heat, or clipping grapevines in the snow, or being exposed to pesticides through the day and night.

Our Pioneers understand that truly transformative technology isn’t, for example, a human-like hand with fingers, but a combination of control, perception, intelligence, hardware design, and more. They’re expecting that, as these innovations move forward, perhaps human beings can become more transformative, too. That will happen when people take the advice of one of our Pioneers: Listen to others. Think for yourself. Do the right thing. Surround yourself with great people who also listen, think, push, and prod.

Besides developing new techniques to ensure more secure and privacy-preserving AI techniques, our Pioneer award winners seek to make learning more transferable and data-efficient.

They are solving real-world problems, reinventing systems, and creating amazing partnerships. We are starting to see profound changes emerging from these brilliant leaders and motivators that are sparking amazing new ideas in this entrepreneurial landscape.

One example is a human-in-the-loop reinforcement learning process, which is a proselytizer aiming to program to let algorithms “reason” about their own limited performance and reach out to humans for help when they need, for example, to expand their set of possible decisions.

These theorists seek out problems to be solved—not just any problems, but those that people cannot easily or are unwilling to work on, whose solutions often involve AI and IoT. One example of a Pioneer winner’s efforts is an exosuit—a soft wearable robot—that uses textiles to anchor or attach to the wearer’s legs, making it possible for an otherwise wheelchair-bound person to walk. In fact, sensing devices play a big role in this year’s winners’ list.

Another of the Pioneers is working toward a more functional map of the brain—one that looks at how sets of cells are involved in processes—from recalling memories to filtering what we see.

Yet the winners aren’t only researchers. At least one is a funder. He unveiled the AI Fund, a $175 million incubator that backs small teams of experts looking to solve key problems using machine learning.

Alter egos.

So who would Pioneers be without superhero alter-egos—or at least superhero idols? Among these winners, those alter egos would be “Kitchen Man”—a nickname for a winner who was super-efficient in cooking meals for college roommates—or the unassuming man who turned into the raging Incredible Hulk, or a more inclusive version of Jimi Hendrix’s Astro Man.

At the end of the day, Webster defines a Pioneer asa person or group that originates or helps open up a new line of thought or activity or a new method or technical development.” Connected World simply defines this year’s Pioneers as mentors who have a knack for seeing what’s coming.

November 13, 2018

Peggy and Basavaraj Patil, a lead member of technical staff in AT&T’s Internet of Things business unit, discuss the definition of the terms AI, machine learning, and deep learning. He says the term AI was coined back in 1956, but the technology has morphed today. He also explains how the IoT relates to all of it. Today companies are trying to figure out how to use all the data, he says, and AI and machine learning can improve prosperity, react faster to malfunctions, and more. att.com

Ronald Arkin

Regents’ Professor
College of Computing
Georgia Institute of Technology, Atlanta, Ga.

Ronald Arkin has played key leadership roles in making technology more human—and humane.

He has helped develop innovations such as multi-robot teams, human-robot interaction, hybrid robot software architectures, and more recently, robot ethics. His research incorporates ethical reasoning into the context of military and healthcare applications for autonomous robots.

Arkin’s goal: To keep top-of-mind and to mentor others about the care and thoughtfulness needed in discussing law, policies, and regulation governing and managing artificial intelligence.

Toward that end, he works to ensure that his graduate students and the junior faculty understand not only the technical issues but also the socio-political landscape involved in the increasingly pervasive ways that advanced technology affects people’s lives.

He takes pride in seeing his students change the world in ways he hasn’t foreseen, such as in disaster robotics and computational finance. He says his faith and belief in God have served as a cornerstone in his life, and credits his two degrees in chemistry prior to his PhD, in computer science with helping to make him a better scientist.

Indeed, Arkin wants his legacy to include leaving the world a better and safer place for humanity, as a result of introducing cutting-edge technology.

Emma Brunskill

Assistant Professor, Computer Science
Stanford AI for Human Impact Lab
Stanford University, Stanford, Calif.

Emma Brunskill and her research group are working on making computerized teaching more intuitive. It’s a science called reinforcement learning.

The goal is to develop a software-based tutor, for example, that would alter its activities in response to how students perform on tests after using it. One way to accomplish better reinforcement learning might be to gather data from online educational systems and use it to help the software tutor estimate the effectiveness of different teaching approaches, Brunskill wrote in a column for MIT Technology Review.

When a student logs in, should the system provide him or her with a problem to solve? Or would starting with an explanatory video be better? The data can help it decide. But in some cases there’s not enough data, or not the right kind of data, which makes it challenging to develop systems that make good decisions.

It would be nice if we could create a system that didn’t need so much data in the first place. And that’s exactly what Brunskill’s group is working on. The goal is to develop reinforcement-learning algorithms and statistical techniques to let computers develop good suggestions while using less data. Yet computers still need the human touch, she says. So-called “human-in-the-loop” reinforcement learning can accelerate the process, allowing algorithms to “reason” about their own limited performance and reach out to humans for help when they need, for example, to expand the set of possible decisions.

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Joshua Gans

Jeffrey S. Skoll Chair of Technical Innovation and Entrepreneurship
Chief Economist of the Creative Destruction Lab
Professor of Strategic Management
Rotman School of Management
University of Toronto, Toronto, Ont.

Joshua Gans studies the economics of AI, or how technology is likely to impact the economy and business. He does that by looking at AI as a new advance in statistics that allows for better, faster, and cheaper prediction.

Gans leverages economics to research how, when the cost of something—in this case prediction—falls dramatically, it will be used more often and in new applications. He uses such insight to figure out where AI will be useful for business. Gans, who has written a book on parenting, puts a priority on educating future researchers about how AI fits into the larger IoT and emerging tech world.

He wants his and his colleagues’ legacy to be making it easier for businesses to adopt AI. He also works to support entrepreneurial startups, since he strongly believes in their power to bring about change. In fact, he says if he’d have to pick a superhero alter-ego in today’s disruptive times, it would be Dr. Robert Bruce Banner–aka The Incredible Hulk.

Manoj Karkee

Associate Professor
Biological Systems Engineering Dept.
Washington State University, Pullman, Wash.

Manoj Karkee works to create robots that help farmers and the farming industry grow and harvest their fruits and vegetables more safely and efficiently.

His drive comes in part from his belief that, as human beings, we deserve better than clipping grapevines in snow-covered vineyards or climbing up and down ladders for 12 hours a day in 110-degree heat and carrying 40-pound bags of produce throughout the process.

Karkee’s work uses sensing, such as machine vision, IoT, AI, parallel computing, and robotic technologies, to develop automatic solutions for field operations. The results range from machine-vision systems that could automate apple-tree pruning and estimating apple-crop load, to robots that could apply pesticides more precisely, keeping workers away from the chemicals.

Karkee believes farming can soon be made “touch-free,” while, at the same time, making it precise—a result that would see laborers working out of air-conditioned offices to supervise and troubleshoot the robots, and farmers producing high-yield and high-quality foods with no waste of water, labor, or chemicals.

Matthew Lange

Food and Health Informatician
Food Science and Technology Dept.
IC-FOODS (The Intl. Center for Food Ontology Operability Data and Semantics)
University of California-Davis, Davis, Calif.

Matthew Lange wants to do more than just innovate. That’s because the key question is, “What’s the most important innovation one can make?” Lange says. In other words, if you fail to ask yourself the best and most fundamental ways you can innovate, you will cease to be relevant.

Even more importantly, he has managed to combine his greatest loves in his quest for the best: His love of cooking and eating better kinds of food, his love of watching things grow, his love of word games, and his love of data—using it to answer questions and visualizing it to tell stories.

These interests propelled Lange to his role leading an initiative at the University of California at Davis called the International Center for Food Ontology Operability Data and Semantics. It’s the only academic center dedicated to building infrastructure for the burgeoning Internet of Food.

Lange’s goal is to develop an internationally recognizable language to describe food, food processes, and health characteristics from nourishing processes.

To do that, the center brings together ontologists with environmental, agricultural, food, and health scientists, technologists, humanitarian scholars, and policy wonks from across industry, government, and academia to develop this language in concert.

The end result is to give consumers clear insights into the food they eat, where it originates, and how it’s been treated. That means building a standardized language and aligning more consumer demands like a desire for increased transparency and traceability of the foods being eaten. Ultimately, Lange says, this will result in new economies, where companies are competing to be more transparent, to be more traceable, and to be more trustworthy.

Matt Mason

Professor
Robotics and Computer Science
Carnegie Mellon University, Pittsburgh, Pa.

Matt Mason believes the challenge of creating a robot that can adapt to new tasks without human intervention is not only possible but can have a positive impact on society.

Mason envisions researchers gaining the ability to test, build, and ponder on a grand scale, ultimately delivering amazing technologies that improve people’s lives and deliver value for businesses. Yet the irony is there’s no silver bullet, he says.

The answer to truly transformative technology isn’t, for example, a human-like hand with lots of fingers, but a combination of control, perception, intelligence, hardware design, and more, Mason says.

Humans have a role in being transformative forces, too. How? Mason’s advice: Listen to others. Think for yourself. Do the right thing. Surround yourself with great people who also listen and think and push and prod.

Meanwhile, Mason now is working with a stealth startup to change the way work gets done in a fundamental way.

Andrew Ng

Cofounder of Coursera
Adjunct Professor
Computer Science
Stanford University, Stanford, Calif.

Andrew Ng, a computer scientist who led Google’s AI division, Google Brain, and formerly served as vice president and chief scientist at Baidu, is reimagining the way that artificial intelligence is used in education, giving universities access to AI-enabled online courses.

In 2011, Ng led the development of Stanford’s main MOOC (Massive Open Online Courses) platform and taught an online machine learning class. This led him to cofound Coursera with Stanford colleague Daphne Koller. Additionally, this year, he unveiled the AI Fund, an incubator that backs startups looking to solve key problems using artificial intelligence.

His work on machine learning focuses on what’s called deep learning. He calls deep learning the new electricity because it will eventually play a role in everything we interact with.

His Google Brain venture resulted in the famous “Google cat” result. In this project, a neural network with 1 billion parameters learned from unlabeled YouTube videos to detect cats. Today, Ng continues to work on deep learning and its applications to computer vision and speech, with a greater focus on such applications as autonomous driving.

He says AI will create a lot of growth opportunities for new startups and large companies. In the end, it’s really about a world where everyone has better access to these many new offerings and education.

Joshua Peschel

Assistant Professor of Agricultural and Biosystems Engineering
The Black and Veatch Faculty Fellow
Iowa State University, Ames, Iowa

Joshua Peschel’s research in what he calls human-infrastructure interaction focuses on the design, understanding, and evaluation of coevolving smart infrastructure systems. That means working to create new data sets, technologies, and computational models for infrastructure such as agricultural, natural, and urban environments.

The idea of new visual sensemaking approaches involves leveraging cutting-edge advances in computer vision and machine learning to advance smart agricultural systems at price points that will make a real difference.

Peschel’s vision is for the continued digital transformation of society—to engineer and apply AI like we have done with other powerful resources such as water and energy, which have brought profound changes and assistive benefits to society. He hopes his legacy will be making science, engineering, and technology accessible to more people to do good and productive things that none of us have even thought of yet.

Terrence Sejnowski

Professor, Laboratory Head, and Francis Crick Chair
Computational Neurobiology Laboratory
Salk Institute for Biological Studies, La Jolla, Calif.

Terrence Sejnowski is working toward understanding the brain in a way that could help researchers understand how memory is affected in disorders such as Alzheimer’s disease.

He and his colleagues took the first step by developing a new model for how memories are consolidated—or stored in the brain—during sleep.

Indeed, Sejnowski uses computer modeling techniques to try to get the gist of what we know about the brain and to test hypotheses on how brain cells process, sort, and store information. While other scientists have focused on tracing which cells connect to which, he is working toward a more functional map of the brain—one that looks at how sets of cells are involved in processes—from recalling memories to filtering what we see, according to the profile.

He gets a better understanding of the kinds of information that different cell types encode by recording the electrical activity of certain cell sets. Sejnowski also discovered that a type of brain cell called astrocytes plays a role in producing unique brain waves that let mice recognize an object as new.

And he is making progress toward recognizing symptoms of multiple sclerosis. His computer model picked up an unexpected finding that determines whether neurons can fire properly.

Dawn Song

Professor
Electrical Engineering and Computer Science
University of California at Berkeley, Berkeley, Calif.

Dawn Song, a self-described explorer, is investigating rarified territory: The intersection of AI (artificial intelligence) and security. Very few people work in both. Song realizes her opportunity to make novel contributions to new problems and domains that have rarely been investigated.

Researchers still have a ways to go to discover what She calls the missing pieces to make learning more transferable and data efficient. One of the missing pieces will require developing new techniques to ensure more secure and privacy-preserving AI techniques.

Another is to make the learning more transferable and data-efficient, Song says. And finally, AI needs to be made more resilient against attacks.

Song appreciates the opportunity to not only design and develop new techniques to improve security and privacy protection, but also to identify new security and privacy issues. No one can say Song isn’t motivated. To celebrate earning tenure at UC-Berkeley, she treated herself by attending summer school in computational neuroscience.

Conor Walsh

Associate Professor of Engineering and Applied Sciences
Faculty Member at the Wyss Institute for Biologically Inspired Engineering
Founder of the Harvard Biodesign Lab
Harvard University, Cambridge, Mass.

Conor Walsh’s research into wearable technology with robotic components is showing promise in helping people who’ve had strokes walk again. The new technology from Walsh’s lab is an exosuit—a soft wearable robot—that uses textiles to anchor or attach to the wearer’s legs. It includes motors that pull on cables attached to the wearer’s ankle joint, as well as sensors that monitor the wearer’s movement.

Based on the sensors’ information, a microprocessor decides when to send power to the motors. The power boost lets the wearer move exactly when he needs to take each step.

The vision is for the next generation of wearable technology to think and act, not just sense, Walsh says. It will do so by using AI (artificial intelligence) and machine learning to figure out when to help the wearer.

Data from the systems can be shared to the cloud, with analytics providing insight on how the system is helping the wearer. The human aspects are just as important, he says, including how a wearable robot fits, how the wearer responds, and what data can be sensed.

Source: Rolex Awards

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