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Janelle Shane

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33

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2018-05-02

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2018-05-02

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1

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1. “I think one of the big risks that we run as a machine learning community is the incredible amount of hyperbole that's going on right now, where it's like, we're going to have general intelligence right around the corner. We're not. Okay, we really aren't as you start your own company. Do not overpromise. It's much better to under promise and over-deliver than the other way around.”

   2018-05-02 · a16z Podcast · a16z Podcast: Breaking Into Bio · IDENTIFIED FROM THE TRANSCRIPT · source

2. “And so it's very instructive to realize that there was that big cliff and a desert. And right now in the middle of this, we can't imagine how could that have been and how could we have another one of those. But there was incredible overpromising and still things didn't catch up. Lack of data, lack of integrated data could be one of those that could slow things down a lot. And when Daphne says being creative, I'm going to take that further, you have to be creative to convince people with data to share and collect and aggregate.”

   2018-05-02 · a16z Podcast · a16z Podcast: Breaking Into Bio · IDENTIFIED FROM THE TRANSCRIPT · source

3. “So I'll go even further for the distant past. There was AI before machine learning, right? I mean, so Stanford, Ted Shortliv, early 1970s came up with Mysen, which was a recommender system. This was all rules-based stuff. There were literally New England Journal of Medicine perspectives in the mid-1970s saying computers and AI were going to just really revolutionize medicine. And replace doctors.”

   2018-05-02 · a16z Podcast · a16z Podcast: Breaking Into Bio · IDENTIFIED FROM THE TRANSCRIPT · source

4. “But we're still not in the large, large data regime where blind architectures that don't exploit structure of the problem can just work out of the box. So you really have to understand your problem domain and figure out how to exploit the structure that you have in these biological or medical data sets to eke out those percentage points of improvements that's going to make your system stand out relative to everybody else's.”

   2018-05-02 · a16z Podcast · a16z Podcast: Breaking Into Bio · IDENTIFIED FROM THE TRANSCRIPT · source

5. “It's a lot harder to deal with less data. So, this is a more challenging space, you all. You're going to have to be more creative in some ways than a lot of the people who are lucky enough to be working in recommender systems for advertising where there's Unbelievable amounts of data. And so this is an interesting point for us. There is enough data that machine learning models can really start differentiating and some are going to do much better than others.”

   2018-05-02 · a16z Podcast · a16z Podcast: Breaking Into Bio · IDENTIFIED FROM THE TRANSCRIPT · source

6. “Different models to now start differentiating from each other. Now you can have a model that is really much better than anything else because if you think about how to capture the right structure in it all of a sudden you have enough data to really refine that. So I think that's a really important thing and we are hitting that era in biology and health. We're not there yet. Our data sets are still way smaller than the ones that you see in images and text and so on.”

   2018-05-02 · a16z Podcast · a16z Podcast: Breaking Into Bio · IDENTIFIED FROM THE TRANSCRIPT · source

7. “Back in 2011-2012, a big data set was a couple hundred samples that was really big. Now we're in a world where big data and biology is actually a reality, data that is being collected in large human cohorts, as well as data that one can produce in laboratory settings that allow us to, we can now engineer, perturb, and measure model systems in an amazing range of different ways that really allow us to uncover new science using machine learning in ways that we just couldn't do before, because you could generate millions of samples in a matter of a few weeks as opposed to a matter of years. And what has happened in so many of those fields where machine learning is now transforming entire sectors, images, text, speech, video, is that the amount of data is now almost limitless. And that allows”

   2018-05-02 · a16z Podcast · a16z Podcast: Breaking Into Bio · IDENTIFIED FROM THE TRANSCRIPT · source

8. “Because to do anything. And I think, you know, to some extent that was true. It was sort of, if you had to take something out of the box and not think about it very hard and not really engineer your model, then you could throw in your own network at it and do decently well. Over time, we got to the point that lots of other things would do equally decently well, like kernel machines and random forests and so on and so forth would probably do about the same. And then we hit a saturation point. And the reason we hit a saturation point in terms of how well machine learning models were doing wasn't because there weren't smart people around thinking about new innovative things was really because we hit a plateau in terms of the amount of available data. And there's only so much you can do if you have a thousand samples. You can innovate and innovate, but there's only so much performance that you can eke out of that. When I started Coursera,”

   2018-05-02 · a16z Podcast · a16z Podcast: Breaking Into Bio · IDENTIFIED FROM THE TRANSCRIPT · source

9. “No, I mean, yes, I was doing machine learning back in the mid-90s before there was this thing called deep learning. I mean, there were neural networks at the time that I was teaching machine learning back in those days. We used to say that neural networks are the second best way to do just about anything.”

   2018-05-02 · a16z Podcast · a16z Podcast: Breaking Into Bio · IDENTIFIED FROM THE TRANSCRIPT · source

10. “Definitely, it's a point which I commonly tell people in career advice is that I even see in my own career said I feel like my biggest mistakes were not trying for more. And like 80%, 50% of amazing is much better than 99% of good. Fantastic. So let's talk a little bit about machine learning in biology, something where I think a lot of us share interests. And, you know, I heard this rumor that apparently there was machine learning before deep learning. I don't know. I mean, Daphne, have you heard this rumor?”

    2018-05-02 · a16z Podcast · a16z Podcast: Breaking Into Bio · IDENTIFIED FROM THE TRANSCRIPT · source

11. “That you could have spent maybe doing something that had more remuneration, more success, but I think the opportunity cost of not trying to do something that you think is really meaningful is much, much larger.”

    2018-05-02 · a16z Podcast · a16z Podcast: Breaking Into Bio · IDENTIFIED FROM THE TRANSCRIPT · source

12. “Was asked today what advice would you give to your 21 year old self who started a PhD at Stanford? And the advice that I would give is really this is a time and a place of amazing opportunities. The opportunities are boundless. Think big, be willing to do something really significant, really impactful. Because at the end of your life, when you look back, that's the thing. I don't know very many people who regret having tried for something big even if it failed, but I know a lot of people who regret never having tried. And so I would go ahead and do it. And honestly, I'm going to disagree here. Your risk is not that high. As in, you have an amazing skill set of machine learning today. That skill set's not going to go obsolete anytime soon. So if you go and do the startup thing and three years later the startup fails, sure there was an opportunity cost for those three years.”

    2018-05-02 · a16z Podcast · a16z Podcast: Breaking Into Bio · IDENTIFIED FROM THE TRANSCRIPT · source

13. “Me, if I had to pick a company, it would be one that goes after an important problem. I think there are many important problems that need solving in the world. I think that's probably a high criteria. And then that if this company solves it, it's going to be super significant for the world, right? That's the kind of amplifying effect. When you're in the beginning of a company, it's pretty clear you're at the beginning. They don't even have a table or desk or nothing for you. So I think that in some ways, a lot of that comes down to your own risk tolerance. Not everyone has the same level of risk tolerance. And we shouldn't all think everything is equal there. And don't feel pressured or threatened or anything that you should be doing something that someone else is doing because everyone's risk tolerance is different.”

    2018-05-02 · a16z Podcast · a16z Podcast: Breaking Into Bio · IDENTIFIED FROM THE TRANSCRIPT · source

14. “Do I really want to spend time working with the founder or founders of this company because you're going to be spending a lot of time with this person or these people? And it's going to be tough times because this is not Google. Most startups are not money printing machines. And you will have times when you think you're about to hit bottom and go deeper than bottom. And you need to be willing to stick with that person and trust them that they will be able to get you out of whatever message it is that the company has dug itself into. And I guarantee you this will happen at every startup. There will be moments when you think you're hitting bottom. So you have to go in with that realization and you have to go in with the trust that the person or people who are leading this company are there for the long haul, are going to be willing to do hard things to get the company on the right.”

    2018-05-02 · a16z Podcast · a16z Podcast: Breaking Into Bio · IDENTIFIED FROM THE TRANSCRIPT · source

15. “If you're new into this space and you've never been at a company before, the amount that you have to learn both about running your own company and about go to market strategy and all those challenges that we talked about earlier, it's really hard to do it at the very start. And so a path to founding your own startup, even if that's where you're headed, could well be first spending a few years at somebody else's. And so I think that's just something to think about, even if you're really entrepreneurial in nature. Do you really want to do it right now, right out of school, or do you want to do it in five years? And when will you be most successful now or later? In terms of picking a startup, I would say pick the one that you wish you had thought about. Pick the one that you were like, wow, this is such a cool idea and I'm so excited to be part of that. And then at the same time, think of this as”

    2018-05-02 · a16z Podcast · a16z Podcast: Breaking Into Bio · IDENTIFIED FROM THE TRANSCRIPT · source

16. “Yes, yeah, yeah, yeah. Not immediately be a founder. That's actually an excellent point. But they may want to immediately go into joining a startup. Joining a startup is still a very sort of opaque process. How do you find the right startup? How do you network? So just to start off with, how do you pick the company? Like, how do you develop a criterion to understand this is the company you want to join? Especially when so early, it's not like joining Facebook or Google or something like that where there's an obvious track record.”

    2018-05-02 · a16z Podcast · a16z Podcast: Breaking Into Bio · IDENTIFIED FROM THE TRANSCRIPT · source

17. “I think a lot of people make a big deal over shares and options and things like that. And I think it's important to pay attention to some of those things. But if it's successful, it's going to be successful. If it's not, it's not. I think in general, what I've seen is if you're going all out to be involved with the founding team of one company, you're probably going to do more than one. And I think having one under your belt, the second one is the one that people always kind of glorify in some weird way, right? I don't know how to explain that well, but get a base hit for the first one. You'll get a home run on the next one, right?”

    2018-05-02 · a16z Podcast · a16z Podcast: Breaking Into Bio · IDENTIFIED FROM THE TRANSCRIPT · source

18. “Co founders, your early employees, or investors who've seen dozens of these companies, those are people who can really save you from making very bad mistakes and can tell you about those unknown, unknowns.”

    2018-05-02 · a16z Podcast · a16z Podcast: Breaking Into Bio · IDENTIFIED FROM THE TRANSCRIPT · source

19. “As you think about founding a company, one of the questions is where to seek funding, a lot of people select funders based on who's going to give them the most money at the highest valuation because they think about their percentage of the pie. What you really want to think about is not your percentage of the pie, but the total value of what you get, and I think to me even more importantly, of what you build. So I would much rather have a smaller piece of a larger pie that even if it's financially neutral, you've built something greater, you've impacted more people, you've changed more lives. So as you think about who you bring on board and the dilution you take from that, who you get funding from and the dilution you take from that, think about who's going to help you make your pie the biggest and most successful that it is. And they are the ones that will often tell you the unknown unknowns, whether it's your...”

    2018-05-02 · a16z Podcast · a16z Podcast: Breaking Into Bio · IDENTIFIED FROM THE TRANSCRIPT · source

20. “Most frustrating one is really competition. We surround ourselves with a kind of bubble filter here in the Bay Area. We think if we don't see someone on our campus that doesn't have the company like ours, we're free and clear, you know, freedom to operate, whatever competition will come from anywhere and everywhere. There are smart people in many campuses, in many countries now. And it's super frustrating and you will just get beaten down when you see the competitor getting 10 times the funding that you thought you were going to try to get. And they just closed on around. And then you're kind of narrowing and narrowing what you do. Learn what others are doing not to kind of steer around, but just be aware like what is the pace you're going to have to keep up with, what are the milestones you're going to have to get to, because it is getting, especially in the computer and AI and machine learning world, there are many people getting trained now and they all have ideas of companies they want to start and they're going to be collisions of these.”

    2018-05-02 · a16z Podcast · a16z Podcast: Breaking Into Bio · IDENTIFIED FROM THE TRANSCRIPT · source

21. “I think some of the hardest things are the unknowns, which you don't even know to ask. You know, what are the counterintuitive things or unknowns that you don't think we got to that you think people in the room should know about if they want to be a founder of a company?”

    2018-05-02 · a16z Podcast · a16z Podcast: Breaking Into Bio · IDENTIFIED FROM THE TRANSCRIPT · source

22. “At least he got perceived that, right? That they're willing to go all the way with you because everything in a medical world, we talk about survivor bias, right? So we write stories and glorify all the survivors of cancer, right? We certainly have a survivor bias with companies in the Bay Area, right? You see, every day all the survivors. We never hear about all the ones that don't work, right? And that's the majority still, right? And so it is hard. And just, you know, a Bayesian prior would tell you you're going to fail. So you had to beat those odds here. And that takes a lot of luck, skill determination, and patience, especially in biomed.”

    2018-05-02 · a16z Podcast · a16z Podcast: Breaking Into Bio · IDENTIFIED FROM THE TRANSCRIPT · source

23. “Put it in terms of kind of a formulaic kind of way to describe it. The co founder has got to have skills you don't have, right? A major skill could be going after funding. They know how to make a story and make a pitch, especially if you don't have that skill. Their determination should be greater than or equal to your determination. If it's less, there could be issues down the road, I think. And risk tolerance should be greater than or equal to your risk tolerance. But what if they're using that criteria with you?”

    2018-05-02 · a16z Podcast · a16z Podcast: Breaking Into Bio · IDENTIFIED FROM THE TRANSCRIPT · source

24. “So, first of all, as it relates to, in general, picking co-founders as well as picking some of your earliest employees, which is the next step beyond that, you need to be really, really honest, brutally honest with yourself about what you do not know, about the parts of this world that you're completely unfamiliar with, and then be willing to go out and get people who complement you. And that, by the way, is not just technical knowledge. So, for instance, if you're more on the medical side and only lightweight and machine learning, you want to get a machine learning person and vice versa, but it's also on all those other things like the go-to-market, understanding the space, and then even beyond that, the management skills, all of these things are things you can learn, but it's a lot easier if there's someone at your company who's been there, done that. These things, none of them is rocket science, but figuring out all of them at the very beginning while you're also trying to get your business model.”

    2018-05-02 · a16z Podcast · a16z Podcast: Breaking Into Bio · IDENTIFIED FROM THE TRANSCRIPT · source

25. “One way to handle the crazy go to market that is healthcare is to get a co founder. You know, when you're thinking about sort of starting a company and you think you have the pieces that sort of reach that level, how do you pick the co-founder? To me, picking the co-founder is probably one of the most important things that you do. It's a thing where changing co-founders later is probably one of the most painful things you do. And so how do you find that magic? How do you find the right person?”

    2018-05-02 · a16z Podcast · a16z Podcast: Breaking Into Bio · IDENTIFIED FROM THE TRANSCRIPT · source

26. “Really need to either spend serious time in either a hospital or a company, an existing company that actually has that as a market, or you get a co-founder who's had that. Because for someone who comes in from a technology background, you have this cool, shiny new technology that you want to apply but you don't understand the problems and you don't understand the path forward, I think it's a very, very tough trajectory to follow.”

    2018-05-02 · a16z Podcast · a16z Podcast: Breaking Into Bio · IDENTIFIED FROM THE TRANSCRIPT · source

27. “Yeah, it's also a very conservative space, right? When we graduate, we were citing oaths that are 1,000 plus years old that include phrases like do no harm, right? And then you're asking us to try something new, right? So it's a very conservative space. And you have to learn what a BAA is, what an IRB is, what HIPAA is. You cannot just walk in and not know what those are. One thing I tell a lot of folks, how to get started with the startup. So here's the problem. You're surrounded by Stanford and BC stuff, which are like the best of the best here, right? So you're thinking this will be an easy sale, but you're talking to two of the best in the country. It's a tough first customer to have, right? There are other hospitals around you, medical systems and practices that are not Stanford and UCF. And that's the first account you should try to get, not Stanford UCSF. I always tend to El Camino Hospital in Mountain. Let's create a little hospital. They're partnered with neither of the two, but they're a tech-oriented hospital. But if you could get something worked.”

    2018-05-02 · a16z Podcast · a16z Podcast: Breaking Into Bio · IDENTIFIED FROM THE TRANSCRIPT · source

28. “I think it's really important to realize that this is not your typical, you have three people sitting in a garage writing a web app in the cloud. It goes on everyone's phone and goes viral. That is not this space. This space is you have to deal with a lot of different stakeholders. They have a lot of history, a lot of entrenched interests, and you're not going to be able to just break in by having an app that goes viral.”

    2018-05-02 · a16z Podcast · a16z Podcast: Breaking Into Bio · IDENTIFIED FROM THE TRANSCRIPT · source

29. “Crowded too, right? So a lot of us have passed around this CB Insights logo, like the tree 108 companies with deep learning machine learning. And that's already like three years old, right? I mean, it's probably like five times that number now. It takes time. It takes patience. It takes experience to go deeper to get to more interesting problems. I think there are other kind of silly things that companies do sometimes that are unnecessary. Like say things like physicians are going away, right? With AI and deep learning. Yeah, the greatest way to make us not want to accept your product. That kind of happens within microseconds. And that one is both naive and just plain stupid. All right. Yeah, exactly.”

    2018-05-02 · a16z Podcast · a16z Podcast: Breaking Into Bio · IDENTIFIED FROM THE TRANSCRIPT · source

30. “And so the challenge then becomes if you're sort of coming out of academic lab, how do you even just know what the problems are? I mean, you may have guesses, but unless you spent time in a hospital system or payers or providers and you know the go-to market for healthcare in general is probably the hardest go to market. So how does one even gain that knowledge?”

    2018-05-02 · a16z Podcast · a16z Podcast: Breaking Into Bio · IDENTIFIED FROM THE TRANSCRIPT · source

31. “Complex. So, you really need to take time to understand not only what your technology can do, but also how it fits into the larger ecosystem and to what extent you can circumvent the other obstacles to adoption. It's not really about the machine learning inside the box. It's about how do you get it so that the physician doesn't even have to think about how to use your system. It just happens naturally as they're doing their rounds. They write on little sheets of paper. And that's the problem. Not the machine learning.”

    2018-05-02 · a16z Podcast · a16z Podcast: Breaking Into Bio · IDENTIFIED FROM THE TRANSCRIPT · source

32. “One of the things that you really need to do in this space is connect with your target customers and figure out what problem it is that they really need help with. And I think it's easy to underestimate in this field the complexities and the barriers that prevent adoption, even for something that seems like it's a no-brainer. It obviously makes things better, but there's so many obstacles in terms of the bureaucracies and the approvals, not only government regulations and so on, which are much more significant in the space than in many other applications of tech. But also just in terms of we've always done things this way, we really always have. And it turns out that in many of those cases, even really simple methods actually work much better than the current standard of care, state of the art. And the barrier to adopting those things hasn't been that people haven't recognized that those are better methods. It's been that the system is really”

    2018-05-02 · a16z Podcast · a16z Podcast: Breaking Into Bio · IDENTIFIED FROM THE TRANSCRIPT · source

33. “How do you So it's tempting to start companies. The thing is, though, that especially in bio and medicine, we see so many superficial companies. One of the harder things compared to just generic machine learning AI types of platform companies in this field, you have to go deeper. And it means learning vocabulary. It means really spending time with the folks to truly learn what their pain points are, right? You think you've solved a problem that's a pain point, but you don't really know what the pain point is. So for example, we have a lot of apps that you can take pictures of skin and do something with. Deep learning of moles, cancer, because that seems like a very intuitive kind of problem, but that's an easy one. We have many, many harder problems in biology medicine, but they take time to learn. So you have to be patient. Some of that time is spent in coursework and hanging on labs and rotations and PhDs and stuff like that. But it's worth it because without that right pain point, it's tough to really start an effective company that's really going to solve something that people”

    2018-05-02 · a16z Podcast · a16z Podcast: Breaking Into Bio · IDENTIFIED FROM THE TRANSCRIPT · source