A book club for developers.
BookBytes is a fortnightly (or biweekly) book club for developers. Each episode the hosts discuss part of a book they've been reading. And they also chat with authors about their books. The books are about development, design, ethics, history, and soft skills. Sometimes there are tangents (also known as footnotes).
(Intro music: Electro swing)
Hello, and welcome to BookBytes, this is a book club for developers where we come together and talk about a book we’ve been reading. We’re continuing our summer of imposter syndrome by reading “The Imposter’s Handbook” by Rob Conery, and this time we’re going to go over chapters 4 and 5 which is Machinery and Big O. I’m Adam Garrett-Harris.
I’m Jen luker.
I’m Safia Abdalla.
And I’m Jason Staten.
So, before we get into the Machinery, Jason did you have some follow-up from last time?
Yeah. Yeah, I was tasked with going and taking a look at the YCombinator and finding a somewhat succinct way of explaining it, and the shortest way I can explain it without saying what it’s already written as, is the YCombinator is a function that receives a function that hands that function back to itself. So, it can call itself recursively, if it chooses to.
Yeah, that’s pretty succinct, I don’t understand why you would do that, but it’s cool to know.
It lets you do looping in Lambda calculus, because there’s no variable references there’s no other way to do recursion without giving a function a reference to itself. So, that would be why.
Awesome. Yeah, I definitely enjoyed all the real-time learning we saw last week with me and Safia, trying to understand Lambda calculus.
Well, let’s get into the Machinery chapter. This seems like a bit of a repeat, or a continuation of some of the history from the computation chapter, and I know like, Safia and Jen, you were kind of geeking out about that history.
Yeah, a couple of episodes back.
I love the history portion. If you look at my book there’s the entire section where they’re introducing the Machinery section, and I highlighted the whole thing with a lovely little note that says, “If you want to get me excited and invested in this, this is how you do it.” ‘Cause the whole thing just starts out-
Yeah. You were talking about one the chapters last time did not get you hooked.
Until the very, very end. So, now this is the way to do it.
Okay so… What did you like about it?
Well, it says, “We’ll visit Plato and ponder the true nature of things, drop in on Bernoulli in the 1500s and wind our way to Russia in the early 1900s. We’ll visit Bletchley park and Alan Turing in the early 20th century, eventually ending up back in the United States with John von Neumann, the creator of the modern computer.” What’s not so giggly about-
Yeah, that’s like a whirlwind tour.
Jjumping into that and going, “Yes! The actual history! Let’s go!”
Yeah, I was confused that it was going to start of with Plato.
Plato seems to be the beginning of all things, so it’s no wonder that they’d toss in Plato, and in this case the purpose that they brought in from Plato and philosophy was that he wondered if what we were experiencing was really the true representation of the universe. He felt that “the truth” was actually something that we couldn’t perceive as humans. He felt that it was bigger than us. That actually led very closely into computation by looking at mathematics and, you know, though things seem very random the concept is that if you observe it long enough, you would eventually start seeing those patterns, like phi, the golden ratio, pi and e, they think that those are, you know, ways in which you can say that that is the cosmic machinery. It’s the program that runs the universe.
So, basically, he was wondering if we were living in a computer simulation.
Yes, which we recently had quite a few scientists get together to discuss.
Yeah, I've heard Elon Musk mention it, was he a part of that? Or no?
Uh, he was, but actually quite a few other famous scientists from Bill Nye down went to that conversation.
Cool, I think it’d be very difficult to determine if you were in one.
Yes, but the question is still there. I always felt like, you know, perhaps our universe was just the janitor tripping and falling and spilling on some Larger Hadron Collider.
And we ended up with us, and that was the big bang.
And wondering if that’s how the next universe would be created.
And it goes into that, you know, we’re the cell on the thumb nail of a giant who’s, again, the cell on a thumb nail of another giant.
Yeah, it’s like “Horton hears a Who”.
Or “Animal House.”
“Animal House”? I don’t know that one.
So, for the early 80’s there’s a movie called “Animal House” and it’s a John Belushi and essentially what happens is that college kids go hang out with the professor and they all get stoned, and the big thought there is our entire universe is the cell on the thumb nail of a giant whose entire universe is the cell on the thumb nail of a bigger giant, because you know, when you get stoned you have philosophical thoughts like this.
And it was funny.
Anyone else want to chime in? Jason? Safia?
Yeah, just liking your descriptions and deep philosophical perspectives on things.
And I really-
I could go for days.
Yeah. I like Rob Conery’s introduction from moving over Bernoulli's law of large numbers, that the more you observe, the more you realize that there’s a relationship there, and leading into the question of does that apply across multiple states, or moving through several things? So, the book goes on to talk about Markov chains and uses the analogy of a drunkard’s walk. So, if an intoxicated person is walking up and down the sidewalk from a given square on it, they have the potential to move forward one step or backwards one step with the same level of probability.
It’s like, whatever state that they’re in, whether they’re on sidewalk square 3 or sidewalk square 4, they equally have the same amount of probability to move between that and the next square that they line up on. And so, Andrei Markov was the one who was able to go and prove that, that the law of large numbers still applies across a series of states and not simply just from a single state such as flipping a coin.
I like that their description here actually led to flow charts, or started with flow charts but then moved to the drunkard example, which made a lot more sense to me. It’s the concept that the reality doesn’t always fit within a statistical model when you’re taking it on a case-by-case basis,but if you repeat that reality over, and over, and over, and over again the chances of the model fitting actually increases. So, when it comes to flow charts, for instance, on a case-by-case basis, they may not always apply, but as a general rule they’re quite accurate.
I did actually have a question. Jen, do you have the physical copy of the book, or do you have the digital?
The physical copy.
So, for me, on page 66 in that book it says, “In the above diagram each orange circle is a state and the arrows dictate possible transitions.” And I see no orange circles or arrows in the above diagram, because… I mean, first…
The above diagram is the drunken symbol.
Where are the arrows?
Not a clue, actually.
Yeah, I had the same problem, Jason. I was… I thought it might be the drunken diagram, but I think it’s actually missing one.
Safia, do you have the digital?
I have the digital, as well, and it’s got the same issue. I think it might have just been an editing thing.
Maybe a drunken mistake? (laughs)
Maybe you put an issue on GitHub. I actually put it an issue about the cicada diagram and he was like, “Yep. That’s a mistake.”
So, if you look there’s actually orange circles on 4 and 5, I wonder if the arrows were just missing from that diagram.
Yeah, could be.
Yeah. [0:07:39.9 Inaudible]
Hey, Safia, do you have something to point out?
Not for this chapter.
Or, the first part of this chapter.
Cool. So, we’ll just keep going then.
Cool. So, then it talks about a finite-state machine.
Which I feel like flow charts fit and move through quite easily, except a finite-state machine takes it much more complicated than that, possibly? In that it gives you those specific steps but in the fashion that it can repeat.
Yeah, I really love the example it uses of hammering a nail into wood and how the nail can be in various states of outside the wood, in the wood a little bit, or completely in the wood. And then you take that concept and turn it into a flow chart where it goes from outside to when you hit it with the hammer it goes inside, and that can repeat over, and over, and over. Oh, and there’s another state, too, which is-
You bend the nail. Ouch, so you bend the nail.
And so, that’s the… That is actually the difference between the deterministic and non deterministic types of finite-state machines. I did a little bit of digging because the chapter itself didn’t quite make it click all the way for me, so I did some googling on that front. And for me, looking back at it now it kind of makes more sense where in the initial diagram where it talks about a deterministic state machine the nail starts in the state of not in the wood, and you have the transition of hitting it with the hammer and that puts it into the partial state, and you could potentially do that over and over while it sits in that partial state, and-
Well, I guess the initial diagram doesn’t have it....repeating.
Yeah, so I guess the transition of hitting with the nail only has one single progression to it, whereas a non deterministic state machine, you can have the same transition applied to a given state and you can wind up in multiple different states. So, depending on if you hit it with the hammer again, then it will either remain in the partial state, it will go to the complete state, or it will go to the bent nail state; and depending on what, like when you call the function of “hammer” there’s no guarantee of what result will come out of it.
The point that when it was deterministic there was, you know, there were those three states, but the non deterministic said, “a program that will produce different results when run multiple times, even when the same input is given.” So, I mean you can put in a 6 into a function, but if you then multiply it by random number, then you could get out a 12, or you could get out a 36, or you could get out a 56 for… Well, not a 56, but you could get out any number of different randomly generated responses based on the fact that you’ve modified the input. In this case, when it’s talking about a finite-state machine and the only options that you have available, you still don’t know how many times do you have to hit the hammer to get the nail in? How far do you get the nail in before you bend it or hurt yourself? You know? There’s those different options, and if you bend it then you have to start over again. So, at any given time you could be anywhere in this chart; whereas the finite deterministic state machine says, it’s out, it’s halfway in, it’s in. That’s it.
I actually took some time to go and model myself a small finite-state machine within Rust as a… ‘cause that’s kind of been the language I’ve been going along with thus far, and I have to say Rust pattern matching is excellent. I went and modeled myself a state machine of a microwave, so it starts off in the idle state being the one that uses the clock, or that shows the clock, and then when you press a digit it puts it into the time input mode, and when you press more digits it continues to put you into that mode until it, ultimately you press start, and it cooks, and on and so forth, and pattern matching was excellent for going and putting that machine together. So, I made a small microwave on my computer that at least progresses through those states. I’ll post a link up on GitHub, or to the GitHub.
Awesome. I’m looking forward to talking about that again in a few pages, actually. ‘Cause they-
Why? What’s in a few pages?
Uh, well in about 16 pages.
They talked about a calculator in the von Neumann architecture. They talked about the fact that you could have a physical calculator, but you also could have a digital calculator on your computer, a mac for instance.
I wish I had one of those.
Mm-hmm (affirmative).(laughs) And they said the mac calculator is as much of a machine as the one you hold in your hand. So, it’s very much the machines running within machines. So, let’s go back to where we were ‘cause that’s like the very last page of this chapter.
Oh, okay. Sneak peak!
I’m so excited to read what’s there.
Oh, okay. And then there’s a pushdown machine.
Oh, are we all the way to pushdown machines?
Oh, I don’t know.
Oh! No, we were. We were kind of doing limitations of finite-state machines and speaking about alphabets and language, but after that was a pushdown machine. It was like, the same, the thing with the pushdown machine is that it’s a finite-state machine except it adds a stack option, which allows you to either push or pop parameters based on what you are looking for in that state, which changes it from a finite-state into a pushdown machine.
Nice. So, it’s like one of those like, kids’ toys with the rings. You can only get the ring off the top-
Before you can get the next one.
Yeah, so it shows that the pushdown machine definitely has a lot more calculating power over the simple finite-state machine in that your finite-state machine, your only state or information that you hold is that of the state that you’re currently on, and the pushdown machine kind of breaks away from, or steps away from that in one direction by allowing you to hold additional information outside of the current state that your program resides in.
Yeah. So with the hammer and nail example, when the nail is partial that’s kind of all you know about the nail is it’s partially in. You don’t know how far it is in.
Or anything else.
You don’t know how many times you’ve hit it with the hammer.
Yeah. Or, I mean, if you have like, a Tootsie Pop, you know? You have wrapper removed and you have licks, like, you can’t keep track of 3 times before it’s gone. So…
Yeah. It’s just, you have the lick function and either it’s still there, or you've reached the center.
Yeah. So with the stack you can do like, what is the limit of the stack? Can it solve any problem? Or…
Or would the pushdown machine-
States that the stack is… The problem with the stack is that it’s limited, and so that is where we progress on to the Turing Machine, and almost want to stay away-
Yeah, I mean I guess with a stack you can only… You can access more information but it’s still just one additional thing, and you can only access it from the top.
I mean, I guess being limited to accessing from the top is a problem, and also it is limited in terms of space. Like, its stack is not infinite, and that is the bound that Turing went and broke and instead of having like, a limited stack, Turing went ahead and designed a machine with the notion of a tape as a holder of data, or holder of state, that could go infinitely in either direction, and on that tape it consisted of cells in one direction or the other that could either have some sort of empty state or they could hold an alphabet of some sort or a symbol of some sort. A lot of times it can be modeled with 1s or 0s, but there’s nothing that holds you, specifically, to that.
Yeah, so it’s kind of crazy that it’s infinitely long tape, but I guess it doesn’t actually have to be infinite as long as it’s long enough.
Yeah. So the infinite lets you remove the limitation of what’s able to be calculated in it, and in turn allows a Turing Machine to calculate anything that you can calculate. If an algorithm is computable, a Turing Machine can compute it, is Turing’s claim.
Which falls very much in line, as it says, with Alonzo Church’s all total functions are computable. So, both Turing and Church were under the same track, just looking at it from different perspectives.
They were on the same tape.
So, I like this little sentence here that says, “If you create an instruction set that’s capable of running on a Turing Machine, it is said to be Turing Complete, and all you need is conditional branching, loops, and variables, and memory.” So, I think that’s a term that gets thrown around a lot, or you hear a lot, but you might not really know what Turing Complete is. I know I haven’t really understood that before.
Basically it seemed like there 4 rules, not just the 3. Those are what you use in order to make a Turing Complete program, but it has to run on a Turing Machine which has the 4 main parts, is-
A set of symbols defined in the alphabet or language a machine can understand, usually just 1s or 0s, but again, not limited to that, an infinitely long tape, or the ability to store information, and then a read/write ability. Here, he used a head, but you have to have the ability to read from that memory and to write to that memory, and then the rules for reading and writing the tape, and that’s what makes it Turing Complete.
So, I guess those rules are built into the machine itself. The rules for how to read and write it to the tape.
Not necessarily. I mean, as you continue through the book they talked about how originally it was on the machine, the machine had one function, and then they talked about, “as we moved to more universal concepts how we included those instructions with the tape itself and not just on the computer reading the tape.” In other words, you program, or tell, the computer how it’s supposed to read this language. So, it’s kind of how we’ve converted binary into like, 5 different options. So, you don’t have to compile it down to just binary at this point, you also have like hexadecimal and you have a few other options in order to speak to the computer, but we’ve explained that essentially. So, we’ve included that with our operating systems to be able to tell the computer how to read it.
That goes into a couple of the powerful concepts in a Turing Machine, as well. I need to find the line, specifically, where he says it, but the idea behind a Turing Machine is not that a machine is a standalone thing, but a Turing Machine, in fact, is something that can take another Turing Machine, itself, or like, one machine can take the output of another machine; and when you start thinking of it like that, if you turn your mind to think about say, Alonzo Church’s functions, or Lambdas, I mean a Lambda can take the output of another Lambda, or it can take a Lambda itself and utilize it. And so, in some ways, like they are very, very similar concepts, like they are very tightly tied together, and like, I really like that perspective of, you know, a function is a machine and a machine is a function, just different approaches to viewing it.
So, tying into that, back to what I said about the calculator before, the mac calculator is as much of a machine as the one you hold in your hand. It’s machines running within machines. I liked the sentence at the very, very end of this chapter that said, “How many abstract machines are involved to execute the code you’re writing today? When you run a VM, or docker, or some container functionality of your choice, these are machines within a machine, executing machines, made up of smaller machines within other machines. It’s machines all the way down.
Oh yes. I also took the time with some Rust and built myself out a Turing Machine.
And so, Rust, being a Turing Complete language that is on top of numerous other layers, I built myself a quick machine on top of that. Or, at least, quick to implement, not necessarily quick to run. Probably awful when it comes to runtime, but I made a machine that can go and take a binary number and increment it by 1, and it’s got a few constructs in it, the notion of a tape, which is a vector or a list of cells, and a cell is an enumeration that can be either a blank, a 0 or a 1, and then also, I have a machine, as well, that has transitions. And transitions just say, “What starting state are you in? When ending state will you go to after this transition if you read this value? And what do you want to write? And which direction do you want to move the head?” And so, with those 5 things I was able to go and define a bunch of transitions for my machine and execute that until getting to a finishing state knowing that I was done incrementing the number. It was kind of a fun exercise because that was something I had heard about while in college, is a Turing Machine, but I had never actually been tasked with building one out, and I made one. This one’s probably pretty crude, and a good Rust developer would probably laugh at it, but I was pretty proud that it actually worked.
I’m proud of you, too!
As you should be!
Nice. So, anything else from the Machinery chapter?
I had a minor side rant about more context for what was going on in this time period that relates to genetic algorithms, which is not technically related, but you know I always love an excuse to rant about history and genetic algorithms.
Let’s do it.
We need like a side note sound effect, or something.
Yeah, like a little ding.
DING. (ding sound effect also plays)
So, I think it’s interesting because it kind of connects Turing and the work that was going on in the ENIAC. So, Turing is really well known for a lot of his work with the, breaking the bombe, the German Enigma codes, and for, you know, his notion of like, “The Turing Machine” and one of the things that he got interested in later in his life, and was kind of cut short by his tragic death, was he actually got really interested in biology and how biology and computing intersect. I’m going to try and pull up the name of the paper really quickly, just so I have it up, but he was really interested in figuring out a way to build machines that mimicked the aspects of evolution, which we talked about a while back, was pretty similar to what genetic algorithms do, and if you’re interested in learning more about that go listen to the first episode in this series. So, yeah. What I think is really cool is Turing was in England, he was working on these new ideas around computational biology and how you can think about nature as a computational system in and of itself, a lot like the ideas we were discussing earlier with Jen. And kind of started writing these papers and then his work didn’t really rise to prominence. Meanwhile, on the other side of the globe, in Princeton where the ENIAC machine was held, where John von Neumann, who was mentioned in this book as the creator of the von Neumann Machine, which we didn’t talk about, but it’s essentially the notion of separating out the memory or storage, and the actual computing system into a CPU. So, yeah, he was kind of at Princeton working on the ENIAC and at that same time there was a really intriguing man there by the name of Nils Aall Barricelli, and he was just kind of hanging around this lab with all of these people who are working on the ENIAC and trying out different ideas in computing, and one of the things that he was particularly interested in was simulating artificial life and trying to see if you can build a small evolving system within a computer. And I might be incorrect, my memory’s foggy, but he actually wrote programs for the ENIAC that were like, early versions of what are now genetic algorithms, and I think copies of those are still around in an archive somewhere so you can like, pull it out and see what he was working on. And yeah, he also published a paper about this topic, the notion of figuring out if you can capture patterns in nature, like evolution, and encode them into machine. So, create programs inspired by nature, the same way Turing was thinking. That paper didn’t get a lot of attention, either. It was, I think, published in 1953? ‘57? That’s a long time span, in the 1950s; but one of the reasons I think it’s really interesting is, you know, the notion that 2 people at around the same period in time were thinking about the same concept which was, “How can we observe the world and then try to encode our observations in programs?” And although their ideas didn’t get a lot of attention in the 1950s, they’re kind of popular concepts now, and it just also kind of shows the fact most new ideas are old and you can always kind of go back in time, whether you’re going to Plato, or Turing, or Barricelli and see the same idea reflected in different ways, and a computer is a great way to convey an idea in a new way, or the same idea in a new way. At least, when you think about it in the context of like, today versus 200 years ago. So, that was my interesting little tidbit. I always like to share it because I don’t think Turing gets enough attention for his computational biology work which is kind of what occupied his mind post working on the Bombe, and I just think it’s really neat stuff and that Barricelli was working on it, too. He also doesn’t get a ton of attention. Actually, like in the grand scheme of things, not that many people probably care about what some like, computer scientists in the ‘50s were up to, but yeah, it’s cool stuff. So, if you are interested in learning more, not just about what these machines were, at least when it comes to the ENIAC, and in fact, physically, but what people were actually programming on them and what kind of problems they were thinking of solving, looking into Barricelli and genetic algorithms is a good way to see how they were actually used. That’s my side note. DING! (ding sound effect)
So, it’s only been recently that we’ve gotten really interested in interdisciplinary fields, such as computational biology. So, when you’re looking at that and thinking, okay, so since the turn of the century we’ve really been diving into this, and it’s already 50 years old by the time anyone starts looking at it again, getting any interest in those again, and though they’ve been very slowly coming back to that, I mean where could we have been if Turing’s life hadn’t been cut short, you know?
And another moral in that story: If you’ve got an idea, you’re working on an interesting program or something and it doesn’t get a lot of attention, don't worry ‘cause it’ll probably get really popular in 50 years.
That does make me wonder, Safia, your topic about Barricelli and his thought on biology and how it relates to computation, is how that was an influence on Conway actually and the Game of Life. I know that was something that was introduced to me in school or was told to go and make a really simple version of that. Have you ever built that? Or have any of you built that?
I have not. I have only heard of it and I know the concept but I haven’t actually like, sat down and coded it.
It is a fun exercise to do in a window of time. Like, if you have a spare day on a weekend or something to give it a shot, ‘cause it’s based on a couple of simple rules and there are 4 rules to the basic version of it and it is a way to do cell automation and also produce kind of a fun little visual sometimes.
Although I’ve got to admit, at 3:00 in the morning, writing in Java right after learning C++, it was really hard. It got much better after sleep. Yes, so make sure you sleep before you start this thing.
Um, so I-
So, it’s not the board game, “Game of Life”?
Where the one rule is if you choose not to go to college you’re probably going to lose.
So, I did, I looked that up really quickly just now, Jason, and it looks like Barricelli’s algorithms were long distant ancestors of Conway’s work, so Barricelli preceded Conway. And I also verified another thing I mentioned earlier that the code for Barricelli’s program is probably out there somewhere, I just looked it up. The output for the like, little universe he built with simulated organisms, the output for that still exists. The output card that is, and it’s on archive and if you google it you can find pictures of it. It doesn’t mean anything to me, but I assume back in the 1950s if you were a programmer you could read this and understand it. (laughs)
There should be art museums for computer code.
They are increasingly beginning to exist.
Can I hop in on one last thing on von Neumann architecture versus Turing Machine? It wasn’t totally clear to me when I was seeing between them both, reading this piece of it. I did a little bit of digging and found that the Turing model is more a theoretical model, and von Neumann actually came up with the architecture for building real machines, so actually getting rid of the infinite amount of space that’s necessary because you can’t build a real machine of infinite size, and so von Neumann's architecture of taking an input, separating, like Safia said, separating out CPU and memory unit in order to produce output is what we now model all of our current computers after. Generally, most computers are built under that same architecture.
Keyboard, CPU, RAM, monitor. The 4 things you technically need to start an old computer was you had to have a keyboard plugged in, you had to have a monitor plugged in, you had to have your CPU plugged in, you needed to have your RAM in a motherboard. And if any of those things didn’t quite work your computer never booted. Everything else was fine, but you had to have those 4 things.
I guess you could do it without a monitor and just not see anything.
Not back in the day. If you didn’t have it plugged in, it didn’t work.