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Category: AI

AI in Retail

This is my response to the latest TechCrunch article: Walmart unveils an AI-powered store of the future, now open to the public.

Every time I see this kind of AI-in-retail application, I somehow feel sad. It’s because of the introduction of retail AI is often designed to one particular goal, which is to encourage consumers to buy more foods and goods by analyzing the buying behavior and removing whatever obstacles between the seller and buyer.

To me, this is totally against a food crisis which we will face in the near future. Even worse, more food and good consumption lead to more logistics that will cause more gas consumption.  Electric trucks are coming, but it will take decades for every logistic company to adopt them.

In the ideal world, all foods and goods will be produced nearby a consumer neighborhood. Thanks to the innovation happening in plat factory and 3D printing, we are seeing some of them becoming reality. I’m a big fan of startups that are tackling a non-animal based protein problem.

AI should be used to foster the latter, not to drive the current consumption heavy economy. In the long run, the companies that use AI to create a sustainable economy will gain trust from consumers and eventually win.

What’s Wrong With Japanese AI Talent Education?

The Japanese government finally announced its policy for AI talent education. To me, this is just another instant reaction without thinking too much that we’ve seen many times in the past.

In 1987, the government said we need to educate software engineers because there will be a shortage of 40,000 system engineers and programmers by 2000. In order to solve this problem, the government launched a plan to train 16,000 teachers who would be responsible for teaching programming at junior high school.

Did Japan become a top country in this domain? Nope. India, China, Vietnam, and the Philippines are doing a much better job right now.

In 2016, the government said we need to educate security experts who can prevent the country from getting cyber attacks because there will be a shortage of 200,000 security experts. Again, Israel and Chine are doing a much better job. Do you see the pattern here?

When the government says ‘educate’, it means they are trying to create more users, not inventors. Creating more AI users, particularly people who can use deep leaning, do not make Japan the leading country in AI. In fact, it’s forcing people to become consumers of AI, not producers. There is a huge gap between the two.

Look at Canada. Why does this country host so many scientists who contributed to fundamental research in AI? To name a few, Yoshua Bengio, Geoffrey Hinton, Yann LeCun, and Robert Tibshirani (a core contributor of LASSO which my startup uses it a lot), they are either born, lived, studied or worked in Canada.

If Japan were serious about being the leading country in AI, then all investments should be made toward creating more researchers in statistics, mathematics, machine learning, and AI fields. I strongly feel sorry for our children who are obligated to go through this ridiculous education policy driven by the government.

We must do something about it.

Power of GPU: 20 Years Ago and Now

When I got my first job at Sony, I was assigned to work on the graphics library development of PlayStation2 gaming system.

My job at that time consisted of writing code with assembly-like programming language called microcode, packing that code together with polygon and texture data, sending them to Vector Units (aka GPU) via DMA, and letting GPU do the rest of work while minimizing involvement of CPU.

I had to adopt every technical tweak in order to get the best performance and achieve the highest frame rate out of that gaming machine and GPU. That’s simply because the library was intended to be used by so many developers that make blockbuster game titles and they relied on it.

Time passes and now GPU is used for more generic purposes including computation of neural network. If I were a software engineer who just graduated from university today, I would jump straight into the world of neural-network-on-chip bandwagon without thinking much.

I didn’t realize that my knowledge and experience of GPU programming had to do anything with AI back then. I believe NVIDIA didn’t realize it neither.

To me, it’s quite interesting to see young software engineers learning how to write code against GPU without CUDA these days, especially ones trying to use Raspberry Pi as a deep learning accelerator. Special thanks to Broadcom for making its VideoCore specification public.

At the same time, it’s sad to see that Japan is a bit behind of this movement. The country used to host many GPU engineers. We had Sony, Sega and Nintendo. Every gaming system had such a sophisticated graphic library that pulled nearly 100% performance from its GPU.

I cannot stop wondering what if the engineers behind these gaming machines were given a chance to work on today’s neural network chip development. Maybe Japan could be in different position in AI industry by now.

a16z Podcast: Taking the Pulse on Bio

As a CEO of digital health startup, I could not miss this one.

Bioengineering can also benefit from unsupervised learning approach as discussed in another a16z podcast by letting AI show us the unique features that lead to a theory (or CRISPER) rather than having a human ‘guess’ it. There is no doubt that quantum computing will play an important role here in the near future.

I’m 100% certain that new type of entrepreneur who understands both bioengineering and computer science (perhaps, quantum computing) will attract so much attention from VCs as a bio industry needs this kind of talent.

Well, I gotta go back to college and study bioengineering again. :)

a16z Podcast: AI, from ‘Toy’ Problems to Production

I like this “end of theory” approach. Some businesses might not need a theory about what’s correct outcome will look like. In other words, AI deployment in the future will always start with unsupervised learning, let the AI tell us unique features, and then define a problem to solve from what the algorithm told us.

Plus, it’s no more about how to implement AI or what technique to use given that the technologies like TensorFlow are available for everyone today. As the podcast says, a couple of data scientists can start a real AI business within few days using these ready-to-use technologies.

It’s about how you pick which business problem to solve in terms of ROI. If you apply a cutting-edge technique to a problem that does not have a meaningful business impact at the company you are talking to, then the whole project is considered as ‘failure.’ You are simply solving a wrong problem.

So my conclusion is that as AI technologies are commoditized the startups whose strength is only technology will eventually go out of business. Instead, the startups that can tell a customer which problem to solve and contribute to the customer’s ROI will stay in business. Sometimes it results in denying what the customer wants to do with AI.

To do that, what you need is not a data scientist or software developer. You need a new type of business person who can take advantage of unsupervised learning and lead a customer in the right direction.

I also agree that a key differentiator will be domain expertise and not AI technology itself, meaning that a startup with the specific vertical focus such as medical imaging will stand out from others. I think this is true for both supervised and unsupervised learning.