Eagles and Algos: Why Robot Learning Matters

Source: Artificial Intelligence on Medium

Eagles and Algos: Why Robot Learning Matters

This is part 6 of a six part series. Links to the other articles in this series can be found at the bottom of this page.

According to McKinsey, in 2017, the global installed base for industrial robots was just over 2 million units. By 2021, that number is expected to double. From automotive to pharmaceuticals, apparel, electronics and agriculture, many industries will experience an accelerated rate of automation. This hardware demand must also be accompanied by the development of brains for the robots. The development of these learning models will only benefit from increased availability of training data. Meta-learning, imitation learning, reinforcement learning and domain randomization can help turbocharge this flywheel effect to bring on the fourth industrial revolution.

Up until a few years ago, most of the progress in machine learning for robots was theoretical. However, recent breakthroughs have seen these cutting-edge technologies married with real world applications. Companies such as Covariant, Dexterity, Kindred, Right Hand Robotics and UnitX Labs are all building specialized hardware and software for making robots smarter. The low hanging fruits for these applications are in industrial and warehousing applications. The primary reason for this is the predictability of these environments. They involve low variance, repeatable tasks, such as picking and stamping, performed in perfectly controlled environments.

Changing Job Landscape

Naturally, the mere mention of robots and manufacturing in the same sentence precipitates an image of a doomsday scenario where robots completely replace humans and eliminate jobs en masse. When projected to applications such as autonomous driving, this scenario takes on an apocalyptic slant. These fears, while exaggerated, are not completely unfounded. For example, Geoffrey Hinton, a leading mind in artificial intelligence, has gone on record to suggest that machine learning will enable computers outperform human radiologists within the next decade.

I believe there is a social cost to be considered as smart robots become more entrenched in our daily routine. Consequently, there will be an inevitable need for re-skilling of the workforce as we know it today. The only question here is if this will happen proactively or reactively. As a result, there needs to be considerable mindshare, at the executive level of organizations, being allocated to proactively incorporating machine learning into their long-term strategy. And if your business is logistics or manufacturing intensive, this takes on heightened urgency.

On the policy side, public officials have some work to do as well. The aforementioned need for re-skilling will occur at a pace that is currently perceived to be unfathomable. This paradigm shift will render a substantive amount of individuals unemployable. As a result, policymakers have to start actively wargaming these scenarios in anticipation of their societal costs. The bottom line is that machines are learning and they are coming. While we are still some ways from an ‘I, Robot’ uprising, there are livelihoods that will be altered in the not too distant future, as a direct consequence of the work being done today in research labs across the globe.