Before I go on, I think it’s best to level set on what constitutes machines. In the context of this article (and probably the much broader global context as well), machines describe computers and computerized equipment, like robots, that have been programmed to learn, sometimes like humans. Occasionally we call this artificial intelligence (AI), other times we call this machine learning, and still other times we call this robotics…or simply bots. And, yes, these are technically different things. But, within the broad discussion related to the future of work, these are totally interrelated. Factory floors deploy robots that are increasingly driven by machine learning algorithms such that they can adjust to people working alongside them. Similarly, AI is being used to turn hand-drawn sketches (done by humans) into digital source code.
AI is not just a hot new buzzword either. In 2016 Tractica, a market research firm released a report that “forecasts that the annual global revenue for artificial intelligence products and services will grow from 643.7 million in 2016 to $36.8 billion by 2025, a 57-fold increase over that time period. As such, it represents the fastest growing segment of any size in the IT sector.”
Similarly, “The Boston Consulting Group estimates that more than $67 billion will be spent worldwide in the robotics sector by 2025, compared to only $11 billion in 2005.”
Companies are clearly developing their AI and robotics expertise with the idea that through these technological innovations they’ll be able to A) cut costs; B) increase efficiencies; C) offer new value propositions; D) execute new business models; or E) all of the above.
Perhaps you’re someone who sees doom residing within these trends. I don’t blame you if you do. However, there are some really exciting examples where the opposite is true. In fact, the signals are all around us that the very same companies that are investing heavily in AI and robotics (and automation using these) are also finding that the best, most efficient, cost-effective solutions include humans and machines working together.
For instance, at Airbnb, the giant startup known for enabling homeowners to rent out their homes (and couches) to travelers, “is currently developing a new AI system that will empower its designers and product engineers to literally take ideas from the drawing board and turn them into actual products almost instantaneously.” If you’re a designer, engineer or some other kind of technologist, this could be a huge breakthrough. As Benjamin Wilkins, an Airbnb design technology lead, put it, “As it stands now, every step in the design process and every artifact produced is a dead end…work stops whenever one discipline finishes a portion of the project and passes responsibility to another discipline.” In this case, human creative endeavors are transformed and enhanced by machines for usability and scale.
Source: The Next Web, ‘Airbnb built an AI that turns design sketches into product source code‘
Of course, it’s not just machines and creatives working together either. In another example, Amazon has employed more than 100,000 robots in its warehouses to efficiently move things around while it has increased its warehouse workforce by more than 80,000. Humans, in Amazon’s case, do the picking and packing of goods (in has more than 480,000,000 items on its “shelves”!) while robots move orders around the giant warehouses, essentially cutting “down on the walking required of workers, making Amazon pickers more efficient and less tired.” Plus, the robots “allow Amazon to pack shelves together like cars in rush-hour traffic because they no longer need aisle space for humans. The greater density of shelf space means more inventory under one roof, which means better selection for customers.”