Sources and Methods #43: Teaching Programming with Matthias Felleisen

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Show Notes:

Bootstrap World - Teaching Outreach Project

Racket-lang.org - Our  Research Programming Language

Personal webpage

7:00 - I am a transplant from Germany. I came as a Fulbright student when I was 21. Fulbright decided that my major in Germany corresponded to an MBA combined with an engineering degree, which they thought was MIS, or Management Information Systems. So they put me in Tucson, Arizona.

I fell in love with the place. I moved into a house with a bunch of people. One of them was an astrophysics PhD student. And he told me what a PhD was, because I had no clue. And he said ‘A PhD is when they pay you to think.’ I couldn’t believe it. I almost tripped when he said it - they pay you to think? Sign me up.

I switched majors to Computer Science... Went to a PhD program in Indiana. After 3.5 years, it was time to go, and the choices were between Berkeley and Rice, my top two offers. It took me only a few short seconds to decide that Rice was it. Because when I interviewed there, 8 out of 12 people had published in the conference that I consider my home.

At Berkeley, out of 45 people, only 1 person was even in that realm. So I decided to go to Rice.

9:45 (On moving to Northeastern) Northeastern definitely had a plan to turnaround and become a research university. And it’s always fun - just like I had decided to go to Rice, Rice was ranked in the top 20, but Berkeley was probably #1 or 2 in computer science - but it’s always fun that’s at a place that’s moving up. And Northeastern had a plan to move up. And they have risen in Computer Science, it’s on the map. I was able to create a programming language group that is definitely one of the best in the country.

12:20 - Functional programming for the lay person is basically what you learn in Algebra, in middle school or high school. Let me explain it.

Algebra, when you learn it, is the weird idea of maybe getting a word problem and devloping an expression that describes the problem in there and then plugging variables into the expression and calculating out the results. And then nobody looks at it, and throws it away. The teacher throws it away. That’s algebra, and it sounds weird that functional programming is the same as algebra, but it is.

What you write down in a functional program are these functions or variable expressions as some algebra textbooks call them. And the big difference is that in addition to numbers, you have other forms of data. In algebra, you can think of expressions - just about manipulating numbers. Now imagine that in addition to numbers, you also have texts. For example, the symbol +, which means 1 + 1 is 2. When you say hello + a spacebar + world, gets you the text ‘hello world.’

Just like you manipulate numbers in algebraic expressions, you can manipulate images, texts, and other kinds of interesting forms of data that you have about people, about the world, about anything you like. Functional programming is a very enriched form of algebra.

19:00 - I was probably the first academic in computer science to do a broad based outreach program.

And I saw tremendous problems that children had aligning the knowledge that they had from algebra with what they saw in programming. Because imperative program - or what I sometimes call dysfunctional programming - has a cognitive dissonance to algebra, which is the closest thing kids know.

20:28 (On the lessons from watching children trying to code) I came to a conclusion that is to this day not clear to the vast majority of people who teach computer science. The conclusion is that no programming language that is in use by real programmers is suitable for beginners.

Racket Computer Language

29:29 - At this point, we are the largest organized Computer Science outreach program in schools in the United States. But it took this insight that we can’t radically change education processes.

Let’s incrementally change student behavior, not a wholesale change here.

39:44 - I belive that Computer Science is actually the discipline of developing and applying problem solving processes.

45:40 - (On educational outcomes, measuring them, etc) Let me recommend a book: Let’s Kill Dick and Jane.

54:07 - The United States spends more money on education than any other OECD country. So where does this money go? What is this money used for? I don’t believe it arrives in the classroom. If we spend more money than everyone else already, I don’t believe pumping more money in will solve the problem either. It goes to administrative things, overhead. What we’re seeing in higher education now over the last 20 years is that higher education is catching up with K-12. My own college has grown from 4 or 5 administrative assistants that fit into a small dean’s suite on the floor downstairs, to occupying the entire second floor, which is probably 30-40 people. In just 3 years, since the new dean arrived. If you have a bigger staff, than you look more important. If more people report to you, you’re more important. I have routinely refused promotions to administrative positions. Bureaucracies grow much much faster than then quality in the classroom.

This existed in K-12 education way before it existed in higher education.

To follow my work:

Bootstrap World - Teaching Outreach Project

Racket-lang.org - Our  Research Programming Language

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?

Sources & Methods #41: Improving Counterterrorism with Stephen Tankel

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Stephen Tankel 101:

stephentankel.com

Twitter: @StephenTankel

Reaching out: tankel@american.edu if people have questions.

Professor at American University -  https://www.american.edu/sis/faculty/tankel.cfm

Senior Editor at War On The Rocks - War on the Rocks

Adjunct Senior Fellow at the Center for New American Security

First book: Storming the World Stage: The Story of Lashkar-e-Taiba
New Book: With Us and Against Us: How America's Partners Help and Hinder the War on Terror

Show Notes:

4:51 - What I was trying to do (with this book) is ask the question: If most of these partners are both helpful and hurtful, what can the United States reasonably expect from them? And then to offer propositions about what it can expect in terms of cooperation, with the understanding that managing expectations here and what to expect from partners here in knowing what to expect from partners is a critical component.

5:50 - The inspiration for the book came from the year I spent working in government as a senior advisor at the Department of Defense, working primarily in south and central asia, and being forced to wrestle from a policy perspective with the tradeoffs involved in dealing with these countries.

10:21 - I think it’s fair to say that the Bush Administration had often prioritized counter terrorism interests above other interest, and the Obama Administration tried to treat them as a separate entity rather than what really needs to happen in either of those administrations - which is integrating counter-terrorism within the broader US foreign policy approach.

11:03 - There’s another threat between short term security interests and long term interests in promoting good governance, rule of law, human rights, stability overall. We see this with a new report from a US Institute of Peace report on Fragile States.

It’s really hard to do, when you’re focused on shorter term objectives like access for forces for security cooperation, training and equipping local CT forces, or for drone strikes.

These tradeoffs are not unique to the United States.

13:12 - This book is primarily for US policy makers and people in those circles, and also intended to get me tenure (laughs).

17:21 - What I really try to do is get at things from the perspective of the partner with whom the United States is partnering. So I spend as much if not more time about the threat perceptions, the politics, the security compulsions of the partner in question than I do the United States.

It’s strategic empathy - I’m trying to get into the shoes of a partner.

18:09 - One of the main recommendations I have at the end is that the United States needs to devote at least as much time to understanding the perspectives and perceptions of its partners as it does its own internal machinations. Within the confines of the book, I’m trying to get at this from other countries’ perspectives.

23:33 - Another point on regionalism (to Matt’s question) - I think you’re talking primarily about regionalism from the perspective of having people with expertise on this regions. I think that is important. But I think there’s another type of regionalism - to create instruments of statecraft, policies, what we would call Congressional Authorities in the US, that are regional rather than nation specific, that encourage being able to work across a region or across part of a region from a policy perspective rather than always working bilaterally.

Bilateral is going to remain the primary mechanism through which any two countries. But at least from a US perspective, I have encouraged the idea of regional authorities for security assistance, cooperation, development, and things like that because I do believe it is helpful to take a regional view to these issues rather than working bilaterally.

26:22 - Quite frankly - standard metrics for me, that’s the brass ring. I would settle for metrics. I would settle for State Department having metrics, DoD having metrics, and NCTC having metrics, I would settle, from a USG perspective, every agency having metrics.

[On why we don’t have standard metrics everyone can look at and figure out where things are] I think it’s human nature, I think it’s bureaucracy, I think it’s those different theories. I would add more. First - it is my sense that practitioners often don’t have an appreciation for spending time and money on measurement because they want to just get out there and do it. And they see spending time and money on measurement as taking away from everything they could be throwing at the problem. I’ve been an evangelist for the idea that metrics will help you get more bang for your buck. I don’t want to spend my time and money measuring, I want to spend it doing. So you need to change the way you think. You need to think about measurement as intrinsic to whatever you’re trying to accomplish.

Two, I don’t think any bureaucratic culture plays to the strengths of monitoring and evaluation. Because monitoring and evaluation is meant to be objective. And objectively speaking, not every program is going to succeed. Simply because your program fails doesn’t mean it’s your fault. But nobody wants to be the person who was running the program that failed. So I do think there is a human nature issue but especially a bureaucratic culture issue that pushes back against monitoring and evaluation because nobody wants to be on the one who runs the program that doesn’t go well.

One of my dream projects that I want to find funding for is to explore ways in which it might be possible to import into a government culture the culture that in some ways, if not favors, at least s applauds failing early in Silicon Valley or business or something.

This idea that effective monitoring and evaluation - it shouldn’t be that you don’t want to fail, it should be that you want to fail early and figure it out so that you can reform. But that requires a big cultural change within government, within UN, within anything about how we think about these issues.

Monitoring and evaluation is is hard. It’s hard to gather data. There are disagreements about how to analyze that data.

Article at War on the Rocks: Doing More With Less: How to Optimize US Counter-terrorism

37:32 - One of the areas where we just don’t have a good sense of how well or poorly we’re doing is the question of resiliency. I’f you’d asked me 5-7 years ago, I would’ve said we’re doing poorly on that. Now, I just don’t know because we haven’t had a major attack. We’ve had some smaller attacks  in the US but we’ve kind of gone about our business. At the end of the day, it may be policy makers who are in some cases - I don’t want to say more seriously than they should, but are inflating it more than it needs to be more than the general public.

39:30 - If one looks at where we ultimately want to go with this - it’s that this becomes for most of these countries a law enforcement problem and not a military problem, and that it is a problem that not just their police are strong enough to deal with, but their judiciaries are strong enough and they have prosecutorial capacity and they have capable judiciaries that are able to prosecute people that are involved in terrorism or terrorism-related offenses. And they have prisons that are capable of holding these people where they will not be radicalized. Those are really big asks.

42:06 - [On training police and justice systems actors vs training military soldiers in foreign countries] The United States, for legal reasons, has a lot of trouble training police. Because it used to be that the secret police were used to terrorize the population, so we have laws on the books going against training police. Those laws need to change.

45:40 - Individually, policy makers are all really smart. Collectively, policy-making does not look that smart.

Even though individuals may not be risk averse, institutions typically are risk averse.  
57:30 - [On Useful Tools for Your Career] Learning Languages - I lived in Egypt for awhile, I studied in Syria, I spent a lot of time for my PhD on the ground in Algeria and Lebanon, using other language. I think it’s not only obviously useful in being able to conduct an interview or read a newspaper, two things I would probably struggle to do nearly as well today because I haven’t used it as much as I should have, but the simple - and it’s hardly simple - the exercise of trying to learn a language in and of itself is helpful in understanding other people, other cultures, what have you. I think there’s a lot of value in having the exercise of trying to learn, even if you’re never going to be that great of a linguist. I often encourage my students who have an interest in another part of the world to go live there. I think living in other places - its a bit trite - but it’s an eye-opening experience. I think it’s very easy to say, much harder to do.

1:00:13 - To say to people coming up - ultimately at the end of the day, there’s no substitute for this other than doing it for awhile, which is something people told me 10 years ago when I was starting out and I found frustrating, but now a decade out, I find useful.

And that was useful for government - I understood it a lot better after being inside it.
1:01:40 - Advice from Vali Nasr - He said, Stephen, if you learn a single thing about South Asia during your year in government, you can no longer call yourself an expert on South Asia. Your job is to be a participant observer. It is to work on whatever your bosses want you to work on. It is to participate in the bureaucracy. Go think big strategic thoughts whenever they want you to, but really learn how everything works so that when you come out you have an understanding of the challenges the average bureaucrat faces. That’s an approach I’ve tried to take with me with everything.