Press "Enter" to skip to content

Category: Podcast

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. :)

American startup disrupting Japanese sushi industry

This Week in Startups episode 783: Jason speaks with Finless Foods co-founder and CEO Mike Selden about how his company produces real fish meat from stem cells, the increasing scarcity of healthy and affordable seafood, more

Finless Foods looks very interesting and promising to me, but at the same time the existence of this startup concerns me being a native Japanese loving sushi so much.

This kind of innovation should happen within Japan, a country surrounded by sea and lived with it for long time, not America. Looking at it from different angle, what we are seeing here is a American startup disrupting Japanese sushi industry.

I’m not talking about Japan vs the rest of the world or anything like that. I’m talking about Japanese government and Japanese startups only trying to ‘rescue’ the people in fishing industry either by pouring millions of dollars of grants into the industry or partially automating the process where human used to do with the help of IT and robotics. Needless to say, both are not considered true innovation.

I’m based out of Kyoto, and we have a top-notch stem cell scientists at Kyoto University which is known as the driving force behind a Nobel prize winning iPS (induced pluripotent stem cell) technology.

Why aren’t we seeing a startup like this here? Should I convince someone to do that? Should I make an angel invest into the startup working on a similar topic and turn around? Or, am I the one who is supposed to get it done myself?

Hmm. There are so many things to work on, but I got a single life to spend. Life is too short indeed.

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.