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What AI practitioners could learn from Tesla

This is the second blog about Tesla, please also read the blog of The Rise and Fall of a Great Inventor if you are interested to learn more about Tesla’s life.

Tesla is one of the key figures in the early evolution of the electrical industry. Tesla has good showmanship and is very good at attracting public attention through jaw-dropping demos.  In one such public demo, Tesla ignited light bulbs using his body. Those demos helped Tesla raise funding for his Alternating-Current motors, which greatly extended the applications of electricity.

0_tc81proTFGVIpWqC.pngTesla’s Magnifying transmitter 

In Tesla’s later years, his focus shifted to wireless energy transmission. Tesla planned to set up a set of energy transmission towers in the world, and any person could receive energy through a hand-held device. It was a grand project. Tesla raised some initial funding from J.P. Morgan to implement a prototype. Unfortunately, an Italian physicist and radio pioneer Marconi finished the wireless telegraph across the Atlantic Ocean in 1901, which attracted most of the public attention and overshadowed Tesla’s work. What’s worse, Tesla spent all the funding to build a huge tower in Wardenclyffe but failed to deliver a workable solution. He was turned down when trying to request more funding from J.P. Morgan. He was never able to fulfill this dream for the rest of his life.

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1904 Image of Wardenclyffe Tower.

Although it happened one century ago, Tesla’s story is still very relevant in the contemporary world in which AI is the new electricity. As an AI practitioner, I think there are several lessons we could learn from Tesla’s experience.

First, even for a super ambitious project, it is still important to make sure there are reasonable deliverables in the process. An ambitious vision may be crucial to get the initial resources. But in order to keep the marathon running, it is always good to plan a sequence of deliverables throughout the journey. The anti-pattern of promising too much while delivering too little needs to be avoided. Tesla was a visionary Inventor, but he lacked the practical mindset to manage the expectation of investors and showing deliverables.

Second, it is super important to be mindful of the relevant opportunities and be flexible for the plan. The development of technology is never a linear process. Tesla’s technology was very similar to what Marconi used for telegraph across the Atlantic and Tesla had much more experience than Marconi. Why didn’t he become the inventor of the telegraph? He failed to realize another important application of his technology — information transmission — and went straight to the grand goal of wireless energy transmission. Had he realize that achieving wireless communication was equally important and may be helpful for his final goal, he probably would invest more in the direction. 

For AI, the 2016 game between AlphaGo and Lee Sedol played a similar role as Tesla’s public demonstrations. The game attracted huge public attention and made many people realize the potential of AI. Under this hype, a lot of companies were founded with super ambitious goals that require decades to fulfill. And a lot of investors invested without a good understanding of this. What’s worse, a lot of the companies didn’t set up reasonable deliverables in a typical cycle of an investment fund. When those investors realized this gap, they may pull back investments, which will make the industry enter another winter.

It doesn’t mean that we shouldn’t work on moonshot AI projects. On the one hand, a lot of advanced AI projects will and should take place in universities under public support. On the other hand, for AI moonshots that are done in companies, we need to balance the grand vision with concrete milestones that are associated with the company’s core business. For example, one of the fields that AI works very well so far is the recommender system (e.g. the algorithm behind and Youtube or Instagram Feeds). The main reason for this is that its deliverables are very quantifiable (e.g., improve the daily activities users by x percentage) and directly contribute to the core business of the company, which is crucial to ensure continual support. I hope other fields could also find a similar positive feedback loop. It won’t be an easy path, but it is something that industrial AI  practitioners need to figure out.

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