It’s always a great pleasure to catch up with David Li, founder of the XinCheJian (新车间) Hacker Space, whenever I’m in Shanghai. He has a truly unique ability to identify and synthesize technology innovation trends far in advance of others and predict their potential impact in China and the rest of the world.
When I first met David a TEDx Shanghai event a few years ago, he was already talking about the rise of the shanzhai (山寨) ecosystem in Shenzhen long before it became a global smart phone and tablet manufacturing powerhouse and – most recently – the self-proclaimed “City of Makers”.
So what’s next on the horizon, I wondered, as we sat down for lunch after David had finished taking part in a panel discussion at the 2015 Shanghai Design Week. “Machine learning” was the simple and direct answer. “Think of the possibilities of connecting multiple cameras to low-cost computing modules and processing all the visual data with algorithms in the cloud. Equipped with these capabilities, factory robots could learn on the job rather than having to be programmed with highly-sophisticated code for each new task.”
“As computing power continues to increase at an exponential rate, the cost of such systems will go down dramatically.” David continued. “A lot of efforts are being made to teach young people coding, but we should be focusing on teaching them to understand how to manage machine learning systems.”
Unlike many experts who believe that robotics and industrial automation systems will lead a massive decline in manufacturing jobs, David believes that their impact will be most strongly felt in service jobs that involve the massive processing of data in, for example, the legal and medical arenas. “People will still need to make stuff,” he shrugged. “But machines can already handle routine data capture and handling tasks such as transcription and document searches almost as accurately as people. As Moore’s Law really kicks in they will be able to do much more quickly and cheaper.”
As to what kind of stuff people will make in the future, David talks passionately about the potential of what he calls “emotional objects”. “Think of a coffee-making machine with the persona of your favorite comedian,” David laughs. “By processing thousands of hours of the relevant video and audio data using deep learning algorithms, it would be possible for the machine not just to speak in the same way as the comedian but also to crack a few jokes to make you laugh when prepare your first cup of Joe in the morning. Fans would pay a premium for that.”
Assuming the technology challenges can be overcome, David argues that a change in industry thinking and business models also will be required for emotional objects to be successful. “These won’t be mass-market devices,” he asserts. “The value won’t so much be in their features but in the experience they deliver to the consumer. Makers of them will have to use their creativity and imagination like traditional artists and craftsmen. Technology will just provide the basic building blocks.”
On that note, David had to rush off to another meeting – leaving me with a lot to ponder over. Even though the concept of emotional objects may sound fanciful to some, with the computing, cloud, and connectivity pieces rapidly converging into the IoT I personally don’t think it’s that far off.
As David pointed out, the final major challenge is driving a fundamental shift in the industry mindset away from mass production towards mass-customization. This will take time, but with the rapid growth of the maker movement, the widespread availability of low cost computing platforms and sensing components, and the rise of a supporting manufacturing ecosystem in Shenzhen, the signs are already there that it is already beginning to take place.