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You probably know that by 2022 an estimated 5 million jobs worldwide will be lost to AI-enabled automation technologies. You probably also know that Oxford University says that 47 percent of American jobs are at risk of being automated, and you probably know two or three more harrowing statistics along the same lines.

But did you know that AI is able to spot genetic diseases that human doctors can’t detect? Or greatly reduce power consumption using smart energy grids? Or educate children with hyperpersonal teaching techniques?

Scary numbers like those above are used liberally for attention-getting headlines (guilty), but we rarely focus on the huge potential for positive global impact that AI can and will have — if we begin fostering its growth in positive directions through transparency and thoughtful collaboration.

As a mother, I’m nervous about the world my children will grow up in, wary of AI’s impending impact on the business world and job market. But I’m also an entrepreneur and an optimist, and I don’t think AI is all bad news. In fact, I believe the benefits of AI will far outweigh any of the setbacks of its upheaval. But there is a real danger of this potential never being realized if we continue to encourage the culture of fear surrounding AI.


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We can build a brighter future, and AI can help us do it. But major stakeholders — governments, tech companies, researchers, and educators — must work together to develop global solutions that take into account the social impact (both good and bad) that AI will undoubtedly cause.

An end to AI-solation

The AI bandwagon is standing room only. Organizations are scrambling to incorporate machine learning into their business models no matter their industries: 2016 was a record year for AI investment, and by 2019, the market for machine learning applications will reach over $30 billion.

Competition to be “the AI company” is fierce. After 25 years in tech, I’d say the AI industry is the most cutthroat I’ve seen. Google and Uber are still battling over self-driving car trade secrets. Amazon, Microsoft, and Google are racing to produce the best virtual assistant. And companies are paying huge salaries for entry-level AI experts. AI is pitting top tech companies against one another, and the stakes are higher than ever.

So it’s no surprise that tech giants are working in such isolation and secrecy. Apple has been notoriously secretive about its AI advancements, refusing to publish research papers and restricting its staff with strict NDA contracts. Only after Facebook squawked that Apple’s policies were preventing the company from hiring top AI talent did it agree to publish some research publicly. Google holds its Tensor Processing Unit (TPU) — a cutting-edge machine learning and AI lab — tightly to the vest, only occasionally revealing high-level developments at conferences that are inaccessible to the general public.

A consequence of this secrecy-obsessed infrastructure is big resources being dedicated to a small number of projects. Research and development also tends to focus on the flashiest and most consumer-facing initiatives — self-driving cars, chatbots, and virtual assistants — leaving little work dedicated to solutions for health, clean energy, education, and more. Not only do we need to prioritize these more socially impactful technologies, but we need to work together to create them.

A start to an AI-lliance

AI researchers are all working towards solving the fundamental problems with machine learning: how to recognize and process raw text, speech, and images; predict behavior and events; and navigate uncertainty when an algorithm can’t make a high-confidence decision. If key learnings about these problems were openly shared, I believe we could more rapidly develop socially impactful AI technologies.

As head of work and learning at MaRS Discovery District in Toronto, I work with a few companies that set out to achieve these goals. PeopleAnalytics uses AI and language psychology to manage organizational risks, such as workplace violence and fraud; Rank Software uses big data and deep learning to fight cybersecurity risks and protect customers; and Clearfit uses AI technology to ensure organizations get the right people into the right jobs. But these three companies have more in common than developing AI for good — they collaborate to strengthen their technologies.

MaRS is also home to the new Vector Institute for Artificial Intelligence, an independent research institution that brings together the world’s best and brightest minds in AI. Vector houses academic talent, world-renowned researchers, and leading tech companies under one roof, encouraging information sharing and cross-collaboration. It is here that IBM Watson’s researchers can grab coffee with engineers from 3D design software company Autodesk, where machine learning researchers from the University of Toronto can work with companies like Deep Genomics to turn their ideas into reality, and where socially impactful technologies are being built every day.

An AI-cessible future for all

By working communally on issues like recognizing bias in algorithms, optimizing predictive intelligence, and solving other fundamental problems with machine learning, we can apply these learnings to a multitude of industries — from clean energy to health — and create holistic technologies that greatly benefit our society. Collaboration frees up resources and allows us to generate radically new ideas, think outside of our boxes, and offer guidance on key technology questions. I get to see this in action at MaRS.

These technologies, however, are not being built at the pace necessary to balance the impending impact of automation, nor meeting the market on a global scale. Self-driving cars and chatbots alone will not bring the positive change that our communities need. We need more open resources dedicated to AI that can spot our diseases, help educate our children, and build sustainable environmental solutions — but we won’t get there by working in isolation.

We must begin fostering the growth of AI in more productive ways by incentivizing large corporations to be less secretive, building naturally open and collaborative spaces, and recognizing and investing in the positive social impact that technology companies can create.

Krista Jones is the head of work and learning at the MaRS Discovery District

Above: The Machine Intelligence Landscape. This article is part of our Artificial Intelligence series. You can download a high-resolution version of the landscape featuring 288 companies by clicking the image.

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