Dr. Been Kim wants to rip open the black box of deep learning.
A senior researcher at Google Brain, Kim specializes in a sort of AI psychology. Like cognitive psychologists before her, she develops various ways to probe the alien minds of artificial neural networks (ANNs), digging into their gory details to better understand the models and their responses to inputs.
The more interpretable ANNs are, the reasoning goes, the easier it is to reveal potential flaws in their reasoning. And if we understand when or why our systems choke, we’ll know when not to use them—a foundation for building responsible AI.
Yoshua Bengio, Geoffrey Hinton, and Yann LeCun — sometimes called the ‘godfathers of AI’ — have been recognized with the $1 million annual prize for their work developing the AI subfield of deep learning. The techniques the trio developed in the 1990s and 2000s enabled huge breakthroughs in tasks like computer vision and speech recognition. Their work underpins the current proliferation of AI technologies, from self-driving cars to automated medical diagnoses.
Breakthroughs in deep learning are inspiring the evolution of AI. But will this “deep learning + X” model drive AI to its fullest potential? While problem solving with more data and deeper models has a place in AI’s evolution, not every problem can be solved by this model.
It was a way for machines to see the world around them, recognize sounds and even understand natural language. But scientists had spent more than 50 years working on the concept of neural networks, and machines couldn’t really do any of that.
Backed by the Canadian government, Dr. Hinton, a computer science professor at the University of Toronto, organized a new research community with several academics who also tackled the concept. They included Yann LeCun, a professor at New York University, and Yoshua Bengio at the University of Montreal.
On Wednesday, the Association for Computing Machinery, the world’s largest society of computing professionals, announced that Drs. Hinton, LeCun and Bengio had won this year’s Turing Award for their work on neural networks. The Turing Award, which was introduced in 1966, is often called the Nobel Prize of computing, and it includes a $1 million prize, which the three scientists will share.
It is our common responsibility and interest to disseminate openly and honestly not only our success but also our failures. Together, we can realize our dreams for numerous robotic applications and devise a realistic plan to develop them.
The problem, as Giulio Sandini put it, occurs when one sells (or buys) intentions as results. Overselling is a dangerous strategy that can be counterproductive, even for the whole robotics community. Both companies and researchers publish videos of robots doing tasks, but sometimes they fail to point out the limitations of the technology or that those results were achieved in lab conditions. This makes it much more difficult to explain to non-roboticist industry executives the difference between creating a one-off demo and creating a real product that works reliably.
Deep learning, for example, is at the forefront of the AI revolution, but it is too often viewed as the magic train carrying us into the world of technological wonders. AI researchers are warning about overexcitement and that the next AI winter is coming.
Deep Science AI made its debut on stage at Disrupt NY 2017, showing in a live demo how its computer vision system could spot a gun or mask in CCTV footage, potentially alerting a store or security provider to an imminent crime. The company has now been acquired in a friendly merger with Defendry, which is looking to deploy the tech more widely.
It’s a great example of a tech-focused company looking to get into the market, and a market-focused company looking for the right tech.
The idea was that if you have a chain of 20 stores, and 3 cameras at each store, and people can only reliably keep an eye on 8–10 feeds at a time, you’re looking at a significant personnel investment just to make sure those cameras aren’t pointless. If instead you used Deep Science AI’s middle layer that highlighted shady situations like guns drawn, one person could conceivably keep an eye on hundreds of feeds. It was a good pitch, though they didn’t take the cup that year.
Canada’s billionaire power couple, Gerald Schwartz and Heather Reisman, have donated C$100 million ($75 million) to the University of Toronto for an artificial intelligence complex.
The gift, from the duo behind private-equity firm Onex Corp. and Indigo Books & Music Inc., is the largest in the university’s history and biggest for Canada’s technology sector, said the academic institution. It will be used to build the Schwartz Reisman Innovation Centre starting this year.
“You’ve seen companies like Uber and Google and so many others starting to make real commitments to Toronto, and this is drawing people from around the world to come to Toronto to be part of this,” Schwartz, Onex’s founder and chief executive officer, said in an interview at Indigo’s headquarters in the city. “That legacy is going to last for a long time.”
Can human roles in retail survive the relentless march of the robots? Much of the current debate on automation focuses on the possible demise of existing jobs and the spread of automation into service and white-collar sectors – and retail is certainly one industry poised to follow this automation path in pursuit of the next driver of profits. From the advent of the steam engine and mechanisation of farming, through to the introduction of personal computing — jobs have always been automated through the use of technology. However, as new technologies have come to market, human ingenuity and the ability to create new products and services have increased the scope for employment and fulfilment. Retail has enjoyed enormous benefits from technology tools, but has the time come when automation poses a threat to jobs? Here we present two possible scenarios for retail 2020–2025: one where automation eliminates the majority of retail jobs and a second which sees the emergence of new paid roles in retail.
Scenario One: Robo-Retail Rules
By 2020, in-store robots walk the aisles to guide customers, help order from another branch, and bring goods to the checkout, or your car. Artificial intelligence (AI) personal assistants like Siri and Alexa have become personal shoppers, with perfect knowledge of customers’ tastes and preferences. This allows for development of retail algorithms to recommend the perfect item before shoppers even know they want it. The algorithms offer recommendations drawing on databases of consumer preferences (i.e. Amazon recommendations), social media, friends’ recent purchases, and analysis of emerging trends – with our AI assistants providing our profiles to help filter and select the appropriate offers.
