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AI predictions for 2019 from Yann LeCun, Hilary Mason, Andrew Ng, and Rumman Chowdhury

To cut through the noise and hype, VentureBeat spoke with luminaries whose views on the right way to do AI have been informed by years of working with some of the biggest tech and industry companies on the planet.

beyond fears of a global takeover by intelligent machines as in The Terminator. “With that comes increasing awareness and questions about privacy and security, and the role AI may play in shaping us and future generations,”

Public awareness of AI still isn’t where she thinks it needs to be, however, and in the year ahead Chowdhury hopes to see more people take advantage of educational resources to understand AI systems and be able to intelligently question AI decisions.

The biggest untapped opportunities in AI lie beyond the software industry, he said, citing use cases from a McKinsey report that found that AI will generate $13 trillion in GDP by 2030.

I think the next massive wave of value creation will be when you can get a manufacturing company or agriculture devices company or a health care company to develop dozens of AI solutions to help their businesses.”

Like Chowdhury, Ng was surprised by growth in understanding in what AI can and cannot do in 2018, and pleased that conversations can take place without focusing on the killer robot scenario or fear of artificial general intelligence.

“But if you take your trained model and you apply it to an X-ray taken from a lower-end X-ray machine or taken from a different hospital, where the images are a bit blurrier and maybe the X-ray technician has the patient slightly turned to their right so the angle’s a little bit off, it turns out that human radiologists are much better at generalizing to this new context than today’s learning algorithms.

LeCun believes the open source policy FAIR adopts for its research and tools has helped nudge other large tech companies to do the same, something he believes has moved the AI field forward as a whole.

LeCun spoke with VentureBeat last month ahead of the NeurIPS conference and the fifth anniversary of FAIR, an organization he describes as interested in the “technical, mathematical underbelly of machine learning that makes it all work.”

“Different people give it different names, but essentially human babies and animals learn how the world works by observing and figure out this huge amount of background information about it, and we don’t know how to do this with machines yet, but that’s one of the big challenges,”

“The prize for that is essentially making real progress in AI, as well as machines, to have a bit of common sense and virtual assistants that are not frustrating to talk to and have a wider range of topics and discussions.”

“On the way to solving that problem, we’re hoping to find ways to reduce the amount of data that’s necessary for any particular task like machine translation or image recognition or things like this, and we’re already making progress in that direction;

“You know if you are in a room and a table is in front of you and there is an object on top of it like a water bottle, you know you can push the water bottle and it’s going to move, but you can’t move the table because it’s big and heavy —

After Cloudera acquired Fast Forward Labs in 2017, Hilary Mason became Cloudera’s general manager of machine learning. Fast Forward Labs, while absorbed into Cloudera, is still in operation, producing applied machine learning reports and advising customers to help them see six months to two years into the future.

One advancement in AI that surprised Mason in 2018 was related to multitask learning, which can train a single neural network to apply multiple kinds of labels when inferring, for example, objects seen in an image.

we’ve been writing about ethics in every report but this year [2018] people have really started to pick up and pay attention, and I think next year we’ll start to see the consequences or some accountability in the space for companies and for people who pay no attention to this,”

“What I’m not saying very clearly is that I hope that the practice of data science and AI evolve as such that it becomes the default expectation that both technical folks and business leaders creating products with AI will be accounting for ethics and issues of bias and the development of those products, whereas today it is not the default that anyone thinks about those things.”

As more AI systems become part of business operations in the year ahead, Mason expects that product managers and product leaders will begin to make more contributions on the AI front because they’re in the best position to do so.

“I think it’s clearly the people who have the idea of the whole product in mind and understand the business understand what would be valuable and not valuable, who are in the best position to make these decisions about where they should invest,”

“So if you want my prediction, I think in the same way we expect all of those people to be minimally competent using something like spreadsheets to do simple modeling, we will soon expect them to be minimally competent in recognizing where AI opportunities in their own products are.”

The democratization of AI, or expansion to corners of a company beyond data science teams, is something that several companies have emphasized, including Google Cloud AI products like Kubeflow Pipelines and AI Hub as well as advice from the CI&T consultancy to ensure AI systems are actually utilized within a company.

Like an analogy sometimes used to describe challenges faced by people working in DevOps, Mason said, managing a single system can be done with hand-deployed custom scripts, and cron jobs can manage a few dozen.

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