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Artificial Intelligence showed some dramatic leaps in 2016, with robotics and autonomous devices gaining ground and popularity like never before. We’ve gone from the Roomba automated vacuum cleaner to self-driving cars in just a few years.
At that rate of growth it becomes simultaneously easy and hard to predict the future of AI and robotics. Easy because you can see what’s coming, hard because changes come out of left field with surprising speed. For that reason, we asked a group of AI experts where they think AI and robotics, which are first cousins, after all, are headed this year and beyond.
Helping us Work
One of the major fears of AI and robotics is that it will take away jobs from humans. Given the losses in manufacturing to automation, this is not an irrational fear. But the experts think AI will augment workers, not displace them.
“In the next decade, AI’s main contribution will be to automate complex and repetitive tasks and help find new insights in data,” said Matt Jones, data scientist with analytics consultancy Tessella. “This may sound less exciting than killer robots, but dig into what that means, and it is genuinely world-changing. Some of the most exciting work is happening in healthcare. IBM’s Watson has shown it can diagnose diseases more accurately than many doctors – which has huge implications for people’s health all over the world, as well as for the economics of overburdened health services.”
It’s also helping optimize the power utility grid. “In 2017, we can expect to see AI solve more complex problems,” said Ganesh Bell, chief digital officer of GE Power. “Whether applied to data from wind turbines as a way of automatically increasing renewable energy output, or used to pilot autonomous drones inspecting power infrastructure in remote areas, the further application of AI has the potential to improve the energy industry at a global scale.”
Michael Schmidt, CTO and founder of Nutonian, developer of predictive analytics software, sees a new scale of AI applications will be available to every business user throughout an organization. “AI and data modelling will no longer only inform data scientists and business leadership, but now operations and decision makers at every level. Companies will be able to optimize detailed business operations at unprecedented granularity, precision, and impact,” he said.
A De-Emphasis on Speed
Several we spoke to think the need for speed will decrease as the desire for accuracy increases as a trade-off.
“The future of enterprise infrastructure is not speeds and feeds, but intelligence and self-management, and, in 2017, that differentiator will separate those storage companies that thrive from those that fade away,” predicts Rod Bagg, vice president of analytics and customer support at Nimble Storage.
Predictive analytics and artificial intelligence enable companies to sharply reduce downtime and ensure optimal application performance, essentially switching from “firefighter mode” to a more proactive IT strategy, he added. “This year, it will become painfully obvious which industry players are holding on to the “speeds and feeds” model, because they will be the ones falling behind,” he said.
“In 2017, intelligence will triumph over measuring speed in flops. Over the last several decades, we have competed upon speed with the intent to build the world’s fastest supercomputer. Supercomputers moving forward will measured not by their computational peaks, but their artificial intelligence capability. AI is the new computational tool for supercomputing,” said Ian Buck, vice president of accelerated computing at NVIDIA.
AI on AI
There have already been attempts to make AI engage in creative efforts, such as artwork and music composition. John Koetsier, mobile researcher and analyst with TUNE, believes AI will attempt to create other AIs next, but not succeed.
“In 2017 we’ll see see more and more artificial intelligences designing artificial intelligences, resulting in many mistakes, plenty of dead ends, and some astonishing successes. Google has tried this already, and AI researchers that I’ve talked to are increasingly turning to this method of iterating intelligence faster and faster. The result will inevitably be intelligences that surpass our own, not just in one narrow niche like chess or Go, but increasingly across categories and perhaps, eventually, a fully general purpose AI,” he said.
“We’ve seen this movie before. Ten years ago, engineers were doing repetitive tasks with JavaScript. The solution wasn’t more JavaScript engineers, but a framework to automate repetitive tasks and make JavaScript easier to develop. The same thing happened with mobile apps. Today there are thousands of tools to make an app developer’s life easier. The same will happen for AI: an industry of software companies will grow to support AI,” said Saket Saurabh, CEO Nexla, a provider of automation tools for data operations.
Humanizing the AI
AI will grow beyond a “tool” to fill the role of “coworker.” Most AI software is too hidden technologically to significantly change the daily experience for the average American worker. They exist only in a backend with little interface with humans. But several AI companies combine advanced AI with automation and intelligent interfaces that drastically alter the day to day workflow for workers such as marketing AI company, Cortex. More than just your normal dashboard, companies combining AI and automation will be relied upon more and more in 2017 like a trusted colleague would.
“AI will be seen as solving the workforce crisis, not creating it,” said Abdul Razack, senior vice president and head of platforms at IT consultancy Infosys. “As the Baby Boomer generation retires, enterprises are on the brink of losing significant institutional mindshare and knowledge. With the astronomical price tag of losing these workers, enterprises are turning to knowledge management and machine learning to train AI to capture institutional knowledge and act on our behalf. In the coming year and beyond, we will see AI adoption not only come from technological need, but also from the need to capture current employee insights and know-how.”
AI will also grow friendlier in an attempt to assuage people’s fears of it. Anton Popov, a senior researcher of Ciklum’s R&D engineering team, said “Hundreds of talented R&D teams worldwide work on the AI and machine learning algorithms. Therefore, there will be created more and more friendly and smart machines aimed at making humans healthy and happy. One of the main AI trends for 2017 and beyond will be Human State detection – prediction and prevention based on the analysis of multiple vital signs using Artificial Intelligence.”
Finally, Gary Saarenvirta, CEO of Daisy Intelligence, believes over the next few years, you will see more business processes and decision making integrate AI into core operations. The companies that lead the revolution will outpace their competitors who don’t invest in AI technologies.
“In retail, you will see AI-driven marketing and merchandise planning drive down pricing putting retailers who can’t follow the price drops under extreme pressure. We expect significant disruption in the retail industry where early adopters of AI technology gain strategic advantage to grow market share,” he said.
Emphasis on Verticals
Mark Hammond, co-founder and CEO of Bonsai, a developer platform for AI apps, is one of a few who thinks AI will shift to specific, vertical markets. “2017 will bring about meaningful advances in the deployment of AI-enabled robotics systems across industrial verticals including manufacturing, mining and utilities. Complimenting this trend, I expect we will see the emergence of simulations as an increasingly viable option for training these intelligent systems.”
Matt Gould, AI expert and co-founder of Arria NLG, which translates data into language, said AI will also bring disruption to the financial space. “Financial services that run on numbers and data are most likely to be overwhelmed by the sheer amount of data available, and the financial tools we use today can only convey data in charts or snapshot visuals. New automated platforms, such as natural language generation, are helping to translate those data into human language-mining for real insights and presenting findings in reports that could have been generated by humans,” he said.
