Sources and Methods #45: Rowing with Bruce Smith

 
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Bruce Smith 101:

Company website: Hydrow.com

Show Notes:

2:32 - If you play the piano, which I do, if like math, or you like repetitive motion, there’s  something really really compelling about the rowing motion. 

Within five years of arriving in Chicago, we’d built five boathouses. 

4:00 - [What is the best way to get nothing done?] I have a tremendous nack for tanking things quickly, and it involves me telling them how great my idea is. I’m an expert on that. I still do it, despite having my head beat in a whole bunch of times. So, the best way to not get anything done is to not listen. It took me many, many many iterations to learn that the  best way to get stuff done is to ask people questions about what they think about your idea. One or two sentences, and then ‘what do you think?’ is probably the best thing you can do. 

Presenting the facts and skipping the discussion is probably the worst way to get anything done. 

11:10 - I think that being a founder definitely requires some element of delusion and grandiosity. If you don’t have that, if you don’t believe on some level that you’re right about something,  then you absolutely cannot be a founder. It’s just extremely stressful. You have to be willing to jump off a cliff without any kind of net. If there was a net there, than 18 people would’ve already jumped. So you really have to believe in something. To me, that’s the most valuable thing.  And the people I like being around are people who believe in something to that degree. People that are actually willing to go to the edge. 

There’s an upside and a downside. If you’re crazy enough to believe in something that, that often means that you do not see reality very well. I think that really great founders are people who can survive that cognitive dissonance between believing something that is not there yet and has no evidence, so they have faith in their ability to see  something that other people can’t see. And then also the ability to take in the reality of the situation and understand that there are real gaps that you have to explain to people and walk them through. And be able to see the gaps in your own idea and your own faith. It’s a crazy tension. 

15:52 - Joseph Conrad talked about The Work. Sailing a ship across the ocean is incredibly tedious, and likened it to a sewing machine, just keep working on the machine.

16:46 - That’s one of the things about success - you have the euphoria, you have the terror. But you also have to be able to grind. And rowers grind.

18:56 - Rowing brings people together. There’s really good brain science that shows that people who do things together, like synchronous motion, build trust. 

19:35 - The more time you spend with your screen, the more  time you spend isolated, the worse you feel. What’s the best thing that I could do as a human being to help other human beings feel better about themselves? That was the motivation for the company. If someone could tell me something else that would build more trust. 

21:09 - I think a lot about the model of the tragedy of the commons. How do you get people to make decisions that are not in their personal best interest in the short run, but are in everybody’s - including their own - best interest in the long run? What I see developing in society is this horrible nexus of concerns, where the tragedy of the commons is actually coming to hit us in our daily lives, so we’re not able to make decisions in favor of the environment. We’re not able to make decisions in favor of public education. I think it’s because  people feel more alienated and more separate from their fellow human beings. We have to do something. 

I think it’s not just about the activity of the mind. It’s the mind-body connection. 

25:41 - There are three kinds of competition: positive-sum, equal-sum, and negative-sum competition. In one kind of analysis, you could say that football or hockey or lacrosse are negative-sum competitions. Two teams enter the field - the only way for us to determine a winner is for one team to make the other team lose, and the team that wins has to physically hurt themselves to take that win. 

With a positive-sum sport like rowing,  or track and field, or swimming, you can put as many teams as you want on the field. In rowing, there are six lanes, so in the Olympics six teams go down the field. All six of those crews can have a personal record in the final race. One person still wins, but everyone has come to the table and may have produced their personal record. Everyone leaves with a better record. 

That’s the kind of competition that we want to foster. Where people understand competition not as something that is negative and destructive, that involves taking something away from the other person or group, but something that lifts everybody up. By everybody bringing their best effort to the table, everybody gets better. 

That was the competitive model before two world wars - rowing used to be the most popular sport in the United States. Tens of thousands of people would watch rowing races. 

31:26 - This is really four different companies - the software, the hardware, the content,  and the marketing, all have pretty different agendas and would like to spend our money differently. But it’s also a great moment for creativity. It is unbelievably satisfying to have all these facets of human life reflected in one place. We all come together and argue all day long in order to get to the end goal. 

38:20 - I haven’t raised a single penny from a cold call. And I haven’t hired anybody without being  introduced to them through an acquaintance for a friend. I call it the Quality Mafia. You find one really great person, and hold onto them like Grim Death, and give them whatever they need to come with you on your journey. And once you find that one great person, then they know about 20 or  30 really great people. And so you put out the call to those 20 or 30 people - “You need X? Oh, I know someone who used to do that.” And you keep being honest and open with people.

42:10 - Fast, Cheap, and Good. Pick two. We chose Fast and Good. 

42:58 [On Workflow] - I use email. I star emails that need responses, and my goal is to keep the starred list around 10. 

I have a huge amount of  respect for work that happens face to face. If you’re working face to face with your direct reports, things go a lot better. I don’t know how that scales, but I think we  can handle it for this type of company, we don’t anticipate growing beyond a few hundred people.

We use Slack internally, because that’s fun. I hate powerpoints, we only use them when absolutely necessary. The power of a clear, well-constructed sentence clarifies everyone’s thinking and ensures communication is rock solid. 

I draw clear distinctions between kinds  of meetings. There are Decision Meetings, and those should never take more than half an hour. If it’s more than half an hour, then you missed the point of the meeting. There’s not enough information, you’re chasing your tail, and you should not have that meeting. 

If you don’t know what kind of decision you’re trying to make, you need a different kind of meeting. I call those Making Meetings. It’s not my idea. Basically, an hour is a minimum, and 2.5 to 3 hours if you’re mapping stuff out. Those are different things. If you confuse these two kinds  of meetings, you waste everyone’s time. 

I’m very skeptical of making very good decisions. Trying to make a decision is better than trying to make a really good decision. 

48:54 [Advice on starting a company] Start early, do it often. It’s really really fun. Don’t try to make money, do something you believe in. The money part  of it is so beyond irrelevant if you’re trying to effect some kind of positive change in the world. Then, once you get that  straight, money will flow from an idea. If you don’t have a good idea, you won’t get any money, so don’t worry. Put emphasis on values and live those values. 

That said, I love making money. Money is time, and money is freedom. It’s not like it’s not a goal, it’s a secondary goal. First goal: value. Second goal: money. 

52:15 - I think a lot about Dostoevsky, and the University of Chicago, and people who were suffering after the war. Suffering a lot. If you work at the University of Chicago, you are surrounded by this violent,  poor neighborhood. And yet, they produce the greatest number of Nobel Prize winners. You’re Fyodor Dostoevsky, and you can’t write what you want, but you create the greatest novels of all time, because you’re under this  extreme pressure. Those are just two anecdotes and I have no idea if they hold over a broader spectrum. But it seems to me that creativity comes out of some level of discomfort. Cognitive dissonance, pain, and something that happens in peoples’ lives that produces creativity. 

55:15 [On living a full life] - I will be completely didactic on this. If you want to be a complete human being, there are two things that you have to do. You have to read John Milton’s Paradise Lost, and a short  biography of John Milton. 

Then, you must read Anna Karenina. I read it every year. It is a complete compendium to all intellectual responses, human responses, emotional responses, to the existential challenges that we face as human beings. It’s a bit like the Bible - it is a complete story. It catalogues how you can respond to life. 

Books Discussed in the show: 

Sources and Methods #44: Deep Learning with fast.ai's Jeremy Howard

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Jeremy Howard 101:

Jeremy on Twitter: JeremyPHoward

Free online programme / MOOC (“Practical Deep Learning for Coders”) at: fast.ai

“The wonderful and terrifying implications of computers that can learn” (YouTube)

Show Notes:

5:55 - My entire education is one degree in philosophy. 

7:30 - Joined McKinsey at 18 with extremely basic knowledge.

12:19 - At Fast.ai our target audience really is people who have interesting and useful problems, and have a feeling that using AI might be a useful way to do that, that maybe don’t have a background in machine learning. It’s the people I came across in my career who were working in extremely diverse industries and roles and geographies, who are smart and passionate and working on interesting and important problems but don’t have any particular background in computer science or math. There’s a snobbish-ness in machine learning, that most people in it have extremely homogeneous backgrounds, young, white, male, who have studied computer science at a handful of universities in America or Europe. 

David Perkins at Harvard, and his learning theory of the ‘Whole Game.’ 

18:10 - For some reason, the STEM field on the whole have gotten away with shoddy, slack teaching methods, where we expect the students to do the work of sticking with it for 10 years and putting it all together. 

20:02 - We’ve discovered that the most practical component in AI is transfer learning. Taking a model that someone else has created and fine tuning it for your task. It turns out that this is the most important thing by far for actually getting AI to work in the real world. Apply and transfer learning effectively. 

I think many people teach a list or a menu of things that they know, rather than really getting to student learning. 

22:41 - Each year, we try to get to a point where the course covers twice as much as the previous year, with half as much code, with twice the accuracy at twice the speed. So far, we’ve been successful at doing that three years running. 

28:48 - I think that will be one of the two most important skills over the next decade or two - the idea of how to work as a domain expert to provide appropriate data to a machine learning system and to interpret the results of those things in a way appropriate to your work. If you don’t know how to do it, you’re going to be totally obsolete. 

31:09 - Back in the early days of the commercial internet, being an internet expert was extremely useful and you could have a job as an internet expert and be in a company of internet experts, and sell yourself as an internet expert company. Today, very few people do that, because on the whole the internet is what it is, and there’s a relatively few number of people who need such a level of expertise that they can go in and change the way your router operates and such. I think we’re going to see the same thing with AI. 

39:08 - I started learning Chinese not because I had any interest in Chinese, but because I was such a bad language learner in highschool. I did six months of French, I got 28% and I quit. When I wanted to dig into machine learning, I thought one of the things that might be better to understand was human learning, so I used myself as a subject. A hopeless subject. If I can come up with a way that even I can learn a language, that would be great. And to make sure that was challenging enough, I tried to pick the hardest language I could. So according to according to CIA guidelines, Arabic and Chinese are the hardest languages for people to pick up. Then I spent three months studying learning theory, and language learning theory, and then software to help me with that process. 

It turns out that even I can learn Chinese. After a year of this - by no means a full time thing, an hour or two a day - I went to China to a top language learning program and based on the results of my exam got placed with all these language PhDs, and I thought wow. Studying smart is important. It’s all about how you do it. 

Spaced repetition is such an easy thing that anyone can do, for free, you can start using it. 

[Jeremy’s amazing Anki talk]

If you’re not using Anki, you’re many orders of magnitude less likely to remember a piece of vocab. So you come away like I did, thinking you can’t learn a language. But once you learn vocab, the rest is really not that hard. Don’t try to learn grammar, just spend all your time reading. 

45:04 - If you’re not spending a significant portion of your early learning, learning how to learn, then you’re going to be at a disadvantage to those that did for that entire learning journey. Spending 12 years at school learning things, but nobody ever thought you how to learn, is the dumbest things I’ve ever heard. 

Coursera’s most popular course is Learning How To Learn

Exercise is the other most important thing. 

49:03 - My third superpower is taking notes. Exceptional people take a lot of notes. Less exceptional people assume they’re going to remember. 

50:19 - Taking notes in class is kind of a waste of time. I don’t really see the point of going to class most of the time honestly, it’s probably being videotaped. 

52:54 - Learn Python if you’re interested in data science, deep learning. 

54:22 - I think there are two critical skills going forward, pick one. One is knowing how to use machine learning. And the other is knowing how to interact with and care for human beings. Because the latter one can’t be replaced by AI. The former one will gradually replace everything.

Sources and Methods #42: Parsing Complexity with Zavain Dar

 
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Zavain Dar 101:

Zavain on Twitter: https://twitter.com/zavaindar

Email: zavain.dar@luxcapital.com

Blog: http://beardedbrownman.com/

Show Notes:

3:00 - The firm created a name for ourselves as one of the first funds specifically focused on deep tech, or emerging tech. Over the years, that’s really encompassed anything from nanotech to metamaterials, spaceships, crypto, satellites, biotech, AI, blockchain, nextgen manufacturing, autonomous cars. All sorts of weird and wonky ideas that are out there.

You’re not only an allocator of capital, but it does feel like you’re pulling the metaphysical strong forward that connects the future or science fiction to the present.

I focus on complex software systems that may or may not be coupled to the real world.

5:21 - How do we not fund the next Theranos? It’s a great question. I’m lucky to be part of a team that’s not scared of primary literature. All of us taken pride in having the ability to scour and read and understand from a first principles basis, a lot of the technologies and engineering systems we invest in.

I wouldn’t say we’re only bottoms up. A lot of what we talk about internally is ‘If this works, then what?’ If this technology is actually able to get off the ground, are there real, applicable strong market forces that this dictates that this captures value, that it’s great for the entrepreneurs, for investors, and for our investors as well. Candidly, that can be the harder part to asses.

12:16 [On advice / lessons from his first startup] Trust your instincts. Be intellectually disciplined to think through all of your decisions without relying on high level proxies, like what’s on TechCrunch or what else in the ecosystem is getting funded, what’s hot or what’s not. Those things are fads and often times its layered iterative processes of others peoples proxies for what other people are thinking over and over again, which ends up being decoupled from reality. If there’s one thing in my career I’ve looked back on, and wished my former or past self had done more of, it would be that I wish my younger Zavain had listened to his instincts with greater enthusiasm or confidence.

The other is to surround yourself with phenomenally intelligent people.

14:02 - That company was acquired by Twitter, and Given my own disposition against social media, or at least working at a social media company, I obviously left. That was really the catalyst towards my future in venture capital.

16:20 - Todd Davies at Stanford first gave me that quote, that capitalism is a phenomenal tool but not a great ideology, it’s not a dogma. I often think in the Valley and in the US at large, we confuse the two. That the laissez-faire capitalist outcome is the moral or ethical outcome. While it’s true you can point to capitalism and say wow, it’s phenomenal for its ability to drive distributed decentralized innovation across various groups - and I think inarguably is one of the most impressive systems to do exactly that, and we have empirical data for that - it doesn’t equate the end outcomes as necessarily the just outcomes.

17:30 - If you walk around San Francisco, there’s a very clear separation between the Haves and the Haves Not. Generally, the Haves are the folks in Tech and the Have Nots are everyone else. For a region with the ability to create so much value and capture such a large portion of that value, it’s frankly disappointing. I think it’s a failure that we have such a large number of people on the streets. That’s not necessarily something that capitalism points at as a problem to solve.

There’s more capital and more upside in optimizing e-commerce on Instagram. I don’t say that in a pejorative way I just say that that’s actually the case. So we need to be honest with ourselves about what capitalism is actually geared towards. If at all moments in time all firms are geared towards increasing profits or increasing revenue or margins, at what moment in time do we actually solve issues in society for classes that are most vulnerable?

21:52 - The advancement of technology - it’s an awesome tool and an awesome outcome. But we should sit there and really think about how it affects society at large.

29:09 - Some truths are simply out of the realm of complexity that potentially a human brain can actually access. Two examples here:

  1. AlphaGo - We saw a computer Go player start to access strategy that not even the best of the best of the best of the best experts of Go in real life could understand. It might be the case that one day some genius Go player will look back at those games and understand exactly the strategies that AlphaGo was employing. But it also may not be the case. It might just be beyond the level of cognitive ability of humans.

  2. I’m an investor in a company called RecursionPharma. They took pictures of human cells and they track how - based on various genetic changes to the human cell - how those genetic changes manifest morphologically or structurally in the pictures of the cells. Often times, what you get is images of 10,000 cells, all with 5,000 features in each cell, all with highly complex, highly non-linear relationships between the features and the cell. And there’s absolutely no way even the most expertly trained pathologist could look at these 10,000 cells and finds all the correlations. It’s not feasible. If you allow a computer to do that, it can find  interesting, highly complex formulas that split apart perfectly the diseased cells from the non-diseased cells. It’s really interesting, and feels like we are in fact coming to something that is scientifically valid and scientifically true even if it’s maybe beyond the capacity of a human to understand. Candidly, I think most of biology fits in that realm.

32:07 - So for the majority of human history, that’s what we’ve had to rely on as true - the the metaphysical, the language, the epistemological. And what we’re starting to see now with advancement in AI, Machine Learning and Data Science is that you can one by one mix all three of those assumptions.

34:28 - [On investing time to learn about these changes in technology] My own suspicion is that technology is only increasing in its power to rapidly drive change and command attention. Such that if you have the time and the resources to invest in learning about it, it’s absolutely worth learning about it. That’s everything from learning about how networks emerge, what network effects are, to really thinking through and trying to understanding how emergence and connectivity of data will affect the types of problems we can solve. And also of course how that too gives rise to all sorts of social, political and anthropological effects.

37:41 - I look back on my training in philosophy and theoretical computer science as the most impactful for the ability to do my job day to day.

45:24 - Mehran Sahami’s inspirational speech on Computer Science

47:40 - [On his work with the Philadelphia 76ers] - The work there was around understanding this new modality of information coming into the league. If you think about the history of most sports, most sports data is recorded in what we refer to as box scores. If you read a newspaper the day after a game, you’ll get these box scores - who the players are, what their numbers are, maybe how many shots they took, how many shots they made, etc.

At this point now, we’re tracking players at the specificity of where every player is on the court at every moment in time. So you end up with a very big, unstructured data set, where at each moment in time - for basketball, you’re getting 11 geo-coordinates. Where are each of the 5 players on each team, and where’s the ball. And the question was - how do we actually manage this?

There are two problems we want to solve:

  1. One is portfolio management. What players are undervalued, who are overvalued, who should we get off our team, who should we draft.

  2. And the other is game ops. You are the Warriors and you’re playing LeBron James and - at this point - the Lakers tomorrow. What’s the best defensive matchup you can have based on how he’s trending over the last 10 games and how your defense has been playing in some prior window in the past.

So the question was - how do we move towards a radical empiricism in sports?