udging by the books, movies and TED Talks about artificial intelligence (AI) that get the most attention, it’s easy to come away with a binary perspective about the future of technology.
AI seemingly has two futures: a dark one, in which cold, unfeeling technology takes over our world in ways we may not be able to control, or a more hopeful vision, in which AI helps bring out the best in humanity.
If we’re to realize more of the latter than the former vision, we need promising and committed researchers to make this happen. Luckily, there are some out there leading the charge. From projects that will help computers understand how we’re feeling to the use of machine learning to help redress societal imbalances, each of these five researchers is helping move us further toward AI that reflects our better selves and ambitions.
Global Lead for Responsible AI, Accenture
There is beauty in AI, says Rumman Chowdhury. “It’s beautiful to think that there’s order in the world, that there’s a pattern to how tides shift and birds fly,” says Chowdhury, who creates solutions for ethical and transparent AI for Accenture Applied Intelligence. “And I am fascinated by the idea of finding patterns in human behavior using data and algorithms.”
Chowdhury approaches data science from the standpoint of quantitative social science: Her undergraduate degrees from MIT were in political science and management science, while her doctorate from the University of California, San Diego, was in political science. In her work for several technology companies and now Accenture, Chowdhury is most passionate about ensuring that when AI is used to understand or predict human behavior, it’s done fairly and ethically.
She and other Accenture data scientists have led the development of Accenture’s “Fairness Tool,” designed to help companies avoid bias in their data and algorithmic models. “I’m very proud we were first to market with this,” Chowdhury says. The Fairness Tool looks at potential for bias at every stage of a project, and involves multiple stakeholders. “It’s a way to trust what’s being handed to you,” she says, since organizations will ultimately be responsible for the algorithms and data they choose.
Chowdhury is also committed to keeping AI fair even as it’s used to accelerate innovation. “We are educating clients about the concept of ‘ethical debt’—the idea that if you don’t look at fairness from the ground up, you can cause expensive or possibly irreparable harm, monetarily or reputationally,” she says.
One way to avoid accumulating ethical debt is broadening algorithm development teams beyond a narrow group of scientists. “This stuff should be as open source as possible,” Chowdhury says. “Community interaction and multiple viewpoints are incredibly valuable—people want transparency.”
Assistant Professor, Nanyang Technological University
Computers can now interpret natural language, but they’re still terrible at understanding its many nuances. Can we train AI to understand things like irony, sarcasm and earnestness in written language? Erik Cambria aims to bring them up to speed by developing AI applications that can detect and interpret human sentiment.
“I was always enthralled by languages, as in both natural and artificial languages,” says Cambria. “It is interesting to see how different people in different contexts encoded the same meanings differently.”
Traditionally, AI has focused on statistical models to interpret sentiment in phrases. “Statistical reasoning is good for some tasks, but not for everything,” says Cambria. “Take the phrase ‘I love the movie you hate.’ A statistical method would classify this as negative because of the word ‘hate,’” he says, even though it’s not technically negative.
Instead of using one tool to help understand sentiment, Cambria has developed a toolbox that expands beyond statistical models and includes semantic analysis to encode meaning, and deep neural networks to infer syntactic patterns from data.
Cambria recently started a company, Sentic.net, to help bring his ideas to market. The hope is to develop tools that can help brands understand what customers really think of them by analyzing an ocean of social media posts. This would then help companies market their products and position their brands more effectively.
You can read more about Cambria’s research here.
Assistant Professor of Professional Practice in Computing Science, Simon Fraser University
Angelica Lim seemed destined for her current job as an AI roboticist at Simon Fraser University. Her mother was a mathematician and a pioneer in computer science in the Philippines, and there was always technology in the house.
“I loved Rosie the Robot from the ‘Jetsons’ growing up, and having one of those animated Teddy Ruxpin toys probably helped me imagine the idea of a robot companion and friend,” says Lim, adding that she used to wonder whether robots could have feelings.
Lim completed her doctorate in robotics at Kyoto University, where she developed a system to make robots more emotionally intelligent. She followed up with three years at robotics giant SoftBank, developing systems to help its humanlike robots recognize emotions. Now, she heads up SFU’s Rosie Lab, which she named after her early childhood hero. Her team works on AI software to help robots understand what people are thinking and feeling.
Enabling robots to show emotions and to see them in others is a tough challenge. Lim’s projects, which include teaching robots to play music and to speak and gesture with emotion, are her attempt to create a future where technology is warm and engaging—and more human.
“I'm interested in robots that can interact seamlessly with humans. Robots that can understand speech, gesture and expressions,” she says. “Building humanity into our technology means that we can move off the path toward becoming isolated phone drones and back to the social animals we are.”
Ph.D. candidate, Montreal Institute of Learning Algorithms
You can ask Siri to translate miles to kilometers and find the nearest pizza joint, but try explaining how it should draw a technical diagram and she’ll just offer to do a web search for you. If Dzmitry Bahdanau has his way, that will soon change.
The former programmer for Russian search giant Yandex was drawn to AI after he saw how the company used machine learning to rank its results. Now he is researching how AI algorithms can understand more complex instructions, and tackling that challenge by creating simple software-based worlds with just a couple of objects and developing algorithms that let him explain how to move them around.
It’s the first step on a much bigger journey—one that hints at where the future of voice assistants is headed. “We could one day have natural language interfaces for software that we use daily,” he says.
Bahdanau has a track record. On a summer internship to the University of Montreal from his master’s program in Germany, he discovered a technique called the Soft-Attention Mechanism that changed how neural networks translated language.
Neural networks historically looked at phrases or sentences when translating text. “I found that allowing neural networks to focus on the specific word that it’s translating helps it produce better results,” he says.
The resulting paper, which he co-authored, has been cited more than 4,000 times in other academic works and has changed the game for neural network translation.
Bahdanau now wants to build software that could give Alexa or Siri a bigger brain. For example, he would like to use natural language AI to have it interpret verbal instructions to create a complex diagram, rather than fumbling with commands in a menu system and dragging boxes and lines around a screen.
With a record like that, perhaps one day he’ll help make natural language the human-computer interface that we all hope it could be.
To learn more about Bahdanau, check out his website.
Assistant Professor in the Tepper School of Business, Carnegie Mellon University
Computer scientists look at code. Sociologists look at people. Lawyers look at legislation. Zack Lipton explores the gap between all three. The assistant professor at Carnegie Mellon moved into AI after completing a degree in math and economics, with a focus on probability and statistics. “Machine learning is where computational thinking and probability collide,” he explains. It also gave Lipton, formerly a jazz musician in New York, the chance to do what he loves best: studying the gaps between different disciplines.
“A lot of people understand the law, but not the technology. Others are brilliant computer scientists, but are unwilling to explore non-computer science concerns,” he says. Now, he spends his time studying the societal blind spots where AI could have unpredicted effects.
One such concern is an AI-induced feedback loop. If an AI-fueled platform refuses bank loans for the unemployed, it could make them less likely to get jobs, which would make them even less likely to get a loan. Those unforeseen results can have profound effects on society, he warns.
Researchers have spent years developing algorithms that apply AI across a broad range of disciplines, from self-driving cars to approving loans. Those algorithms are likely to have profound social and economic effects over the next decade, but we don’t know what they are yet. Lipton’s research hopes to shed some light on that so policymakers and users of AI have some idea of what to expect.
Learn more about Zack here.