What if the next breakthrough in AI doesn’t depend on massive computing power?

The 3rd wave of AI will be driven by new data structures

Paul Lee, M.D., CEO and founder of Mind AI, speaks at the blockchain & AI summit AiDecentralized.

By Paul Lee, M.D., CEO and founder of Mind AI

Last week, the blockchain universe lit up with conversations about the Bitmain IPO. The Chinese cryptocurrency mining equipment company released documents detailing its incredible growth over the last three years, and the question on everyone’s mind is — can they keep it up? So far, their growth has been driven entirely by cryptocurrency miners. With the market currently in a slump, analysts are questioning Bitmain’s future prospects.

I’m intrigued by the Bitmain IPO, but for a different reason than most. While everyone else is looking at their mining rigs and their sales projections, I’m thinking about an interview Jihan Wu, co-CEO of Bitman, did with Bloomberg back in May. In that interview, Wu shared a glimpse of his expectations for the future of the company — a future that could one day be as tied to the state of artificial intelligence as it is to the state of the crypto markets today.

Bitmain released its first AI chips in 2017, focusing on chips that specialize in object, image and facial recognition algorithms. Wu told Bloomberg that he estimates within five years, AI chips could make up 40% of Bitmain’s revenue.

But this estimate hinges on a key assumption, which Wu also shared with Bloomberg.

“Artificial intelligence requires lots of computations,” he said.

Most AI developers take this statement as a given. And looking at the vast majority of AI applications in use today, it appears to be true. Today’s AI is computationally intense, and most of the next-generation projects in the works will be even more so. Other chip companies like NVIDIA and Qualcomm are also doubling down on designing chips capable of crunching more and more data while taking up less space. They’re all banking on the assumption that the future of AI will require exponentially more computational power than what is available today.

But what if that assumption is false?

What if the next breakthrough in AI doesn’t depend on harnessing more and more computational power?

The goal of AI research is to create computers that enhance human lives by using the power of reasoning that humans take for granted. But mainstream AI development involves computationally-intense algorithms that look nothing like human thought patterns.

I’m betting my future on a daring idea: the third wave of AI will not require massive data sets and heavy computing power to train its systems. Instead, third-wave AI applications will be based on new data structures.

There are many reasons to look for a new data structure. Computationally intense AI is…

Wasteful : AI systems that require massive data centers to process their repetitive calculations are a drain on the world’s energy supply with their massive electricity needs.

: AI systems that require massive data centers to process their repetitive calculations are a drain on the world’s energy supply with their massive electricity needs. Expensive : Electricity costs aside, the AI chips themselves are expensive to own or rent.

: Electricity costs aside, the AI chips themselves are expensive to own or rent. Inelegant : An “intelligent” being should not need to view 10,000 images of a dog in order to recognize a dog.

: An “intelligent” being should not need to view 10,000 images of a dog in order to recognize a dog. Invasive : Machine learning algorithms rely on massive data sets to recognize patterns. This need for data is driving irresponsible behavior in the tech sector, with companies extracting as much personal data out of their users as possible, expecting that future AI applications will need it.

: Machine learning algorithms rely on massive data sets to recognize patterns. This need for data is driving irresponsible behavior in the tech sector, with companies extracting as much personal data out of their users as possible, expecting that future AI applications will need it. Inaccessible: AI will empower humans with superhuman capabilities. If those capabilities are based on computationally intense AI, they will be unaffordable, and they will only widen the gap between the wealthy and the poor, the healthy and the unhealthy, the privileged and the disadvantaged.

When I met John Doe, now the chief scientist for Mind AI, I was thrilled to find a talented engineer who shared my frustration with the mainstream approach to AI. Showing incredible foresight, John had begun developing a new data structure, called a canonical, in 2006. Like me, John was looking for an approach to AI that actually modeled human thinking and reasoning.

Realizing our alignment on the future of AI, Doe joined forces with me and my co-founder Joshua Hong in 2016 to found Mind AI, an artificial intelligence engine that’s changing the narrative around AI.

What do you think? Is the future of AI just a computational arms race, where whoever has the most computing power wins? Or is Bitmain making a long bet on fading tech?

Share your thoughts on our Telegram channel, or Tweet at us, or email us directly at info@mind.ai.

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