Speaker 1: Welcome to tex Stuff, a production from I Heart Radio.
Speaker 1: Hey there, and welcome to tex Stuff. I'm your host,
Speaker 1: Jonathan Strickland. I'm an executive producer with I Heart Radio.
Speaker 1: And how the tech are you. Well, it's time for
Speaker 1: a tech stuff tidbit. And in the world of tech,
Speaker 1: we often refer to various laws that aren't actually laws
Speaker 1: at all, either in the legal or the scientific sense.
Speaker 1: So I thought I would dedicate a Tidbits episode to
Speaker 1: some of those laws, not all of them, and talk
Speaker 1: about where they came from and what they mean. So
Speaker 1: keep in mind, these laws are really more observations of
Speaker 1: things like trends in technology, and they have a tendency
Speaker 1: to be relevant, but there's no fundamental aspect of the
Speaker 1: universe that actually forces them to be true. Also, keep
Speaker 1: in mind, again we're just looking at some of the
Speaker 1: observations we referred to as laws in tech. There are
Speaker 1: a lot more of them out there than the ones
Speaker 1: I'm going to cover, and some of those end up
Speaker 1: getting super technical. Um And in fact, we're going to
Speaker 1: take a pretty high level on most of these. But
Speaker 1: the first one we should start off with is of
Speaker 1: course Moore's law. That's perhaps the most frequently referenced law
Speaker 1: quote unquote in tech now these days, we generally interpret
Speaker 1: More's law to mean that every eighteen months to two
Speaker 1: years the computers we produced double in processing power, meaning
Speaker 1: a computer produced today has twice the processing capability of
Speaker 1: a computer that was produced in twenty Assuming that you're
Speaker 1: actually listening to this episode in two that's when it
Speaker 1: originally aired. This is a fairly loose interpretation of the
Speaker 1: observation that Gordon Moore made in his paper Cramming more
Speaker 1: components onto integrated circuits way back in n More observed that,
Speaker 1: due to many different factors, not all of them directly technological,
Speaker 1: there was this trend for semiconductor companies fabricators to double
Speaker 1: the number of transistors on a silicon chip every year
Speaker 1: in the early days, and that held true for about
Speaker 1: a decade. But More would later revise his observation in
Speaker 1: the seventies to say every two years, and that's mostly
Speaker 1: where it's sat ever since then. So, in other words,
Speaker 1: an integrated circuit in nineteen sixty five would have twice
Speaker 1: the number of transistors as one that was produced in
Speaker 1: sixty four, and an integrated circuit in nineteen sixty six
Speaker 1: would have twice as many transistors as the one in
Speaker 1: nineteen sixty five, And you could project this out and
Speaker 1: make predictions and so on, and then eventually we hit
Speaker 1: a point where this slowed down a bit, and it
Speaker 1: took two years to double the number of components rather
Speaker 1: than just one. Now, Moore's observation took into account not
Speaker 1: just the advance in technological capabilities that would be required
Speaker 1: to make this happen, right, we'd actually have to build
Speaker 1: out the systems to make these components smaller and then
Speaker 1: cram them onto a silicon chip, As he would say,
Speaker 1: we also had to take into account the economic drivers
Speaker 1: that would push companies to pursue this trend. See, there
Speaker 1: has to be a reason for the push to cram
Speaker 1: more components on the circuit, because if there's no reason
Speaker 1: to do it, companies wouldn't pour the money into making
Speaker 1: it happen. After all, building out more complex circuits means
Speaker 1: investing a lot of money, time and expertise into finding
Speaker 1: new ways to produce smaller and smaller components and then
Speaker 1: designing a chip architecture that takes advantage of those smaller components.
Speaker 1: So there has to be that economic driver or else,
Speaker 1: the expense of doing this would be prohibitive. You wouldn't
Speaker 1: do it. Now. Lots of folks have predicted Moore's law
Speaker 1: would end as semiconductor fabrication facilities started hitting increasingly challenging obstacles.
Speaker 1: A one big obstacle is truly a physical one, like
Speaker 1: like physics. It's it's once you get your components down
Speaker 1: to the nanoscale, you know, like a nanometer is a
Speaker 1: billionth of a meter. Once you get down there, you
Speaker 1: start having to account for quantum effects. And these strange
Speaker 1: effects don't happen in the macro scale, so they seem
Speaker 1: almost magical to us. It seems like stuff that would
Speaker 1: be impossible because in our daily experience we don't encounter
Speaker 1: anything like this. So, for example, there is an effect
Speaker 1: called quantum tunneling. So imagine you've got a sub atomic
Speaker 1: particle like an electron, and let's say you've got a channel,
Speaker 1: and this electron can travel down the channel. It's a
Speaker 1: one way channel, so you can just go from one
Speaker 1: end to the other, and you've built it just for
Speaker 1: this purpose. And at the end of the channel you
Speaker 1: have a gate blocking the end of it. But it's
Speaker 1: a very very thin gate, and the electron just moves
Speaker 1: down your channel and then surprise gets close to the gate,
Speaker 1: and at some point it just appears on the other
Speaker 1: side of the gate, and the electron continue is on
Speaker 1: it's very little way. Uh now it's to you. It
Speaker 1: looks like the electron somehow dug a pathway through the
Speaker 1: gate and kept on going. But the electron didn't dig
Speaker 1: a path. It was just on one side of the
Speaker 1: gate at one point and then on the other side
Speaker 1: of the gate. And the reason for this is that
Speaker 1: electrons occupy more of an area than a specific point
Speaker 1: in space, or that they can uh inhabit any point
Speaker 1: within a certain area at any given given times. So
Speaker 1: there's the small region where the electron could possibly occupy
Speaker 1: any of the points within that region. Now, if the
Speaker 1: gate is so thin that this region of possibility overlaps
Speaker 1: the gate to the other side, well that means that
Speaker 1: there is a chance. It might be a very small chance,
Speaker 1: but there's still a chance that the electron could be
Speaker 1: on the other side of the gate without the gate
Speaker 1: ever having opened or the electron physically passing through it.
Speaker 1: And if there is a chance, that means that given
Speaker 1: enough opportunities, it will happen like that's kind of what
Speaker 1: chance means. So this is a bad thing if you
Speaker 1: want an integrated circuit, which you can think of as
Speaker 1: being a very very complicated system of pathways and gates
Speaker 1: for electrons to pass through or to be held back from.
Speaker 1: So for that reason and for lots of other ones
Speaker 1: that we don't really need to get into, semiconductor manufacturers
Speaker 1: don't really reduce all the size of components the way
Speaker 1: they used to back in the sixties and seventies and
Speaker 1: leading up to fairly recent years actually, but they have
Speaker 1: kept the naming convention. That is, you designate the type
Speaker 1: of the semiconductor node, the chip node with a metric
Speaker 1: such as like you might call it a four nanometer chip. Now,
Speaker 1: not that long ago, the metric would actually refer to
Speaker 1: the size of specific components on the chip itself. But
Speaker 1: these days it's really more of a way to say
Speaker 1: this chip performs at a higher level than say, a
Speaker 1: ten nanometer chip would, So it's really more to tell
Speaker 1: you about performance, but it's not necessarily uh in reference
Speaker 1: to the actual size of anything that's located on the
Speaker 1: chip itself. Now, there are a bunch of other laws
Speaker 1: in tech that reference or build off of Moore's laws. So,
Speaker 1: for example, there's Rocks Law, which is also sometimes called
Speaker 1: Moore's second law. This observes that as computational power increases,
Speaker 1: the cost to perpetuate Moore's law also increases, So in
Speaker 1: other words, it gets progressively more expensive to meet the
Speaker 1: fabrication requirements in order to build more powerful processors that
Speaker 1: keep pace with Moore's law. Moore's law is something of
Speaker 1: almost like a self fulfilling prophecy. There are companies that
Speaker 1: push themselves to keep pace with Moore's law, even though
Speaker 1: again there's no like fundamental law of the universe that
Speaker 1: requires them to do this. And this touches on something
Speaker 1: that I talked about in a recent episode about how
Speaker 1: Taiwan play such an important part in the semiconductor industry.
Speaker 1: Taiwan began investing in fabrication facilities in the nineteen seventies
Speaker 1: and built on that considerably in the nineteen eighties. Meanwhile,
Speaker 1: you had companies in the United States that we're really
Speaker 1: starting to shift and focus primarily on chip design, but
Speaker 1: not chip fabrication, because the fabrication cycle required a huge
Speaker 1: recurring investment. You would spend millions of dollars to build
Speaker 1: out all the tools and facilities you would need to
Speaker 1: make a chip with components of a certain size. Meanwhile,
Speaker 1: your designers are coming up with the next generation of
Speaker 1: chips and those are all going to need their own,
Speaker 1: you know, special equipment and facilities. So it's just a
Speaker 1: never ending cycle of having to reinvest in your process.
Speaker 1: So you have these chip designers who are creating a
Speaker 1: chip architecture, but then they would outsource the actual manufacturing
Speaker 1: to companies around the world, with Taiwan taking the lead. Also,
Speaker 1: this implies that a lot of chip companies out there
Speaker 1: are just consciously working to keep Moore's law going, even
Speaker 1: if it might not economically make much sense to keep
Speaker 1: up that pace. Um, there's almost like the weight of
Speaker 1: expectation upon it if you're a Luisa out there. Some
Speaker 1: observations playoff Moore's law in other ways or seem to
Speaker 1: evoke More's law. For example, there's Worth's law w I
Speaker 1: R T H that's named after Nicholas Worth, who wrote
Speaker 1: an article that's titled a Plea for Lean Software. Worth
Speaker 1: observation was that as computers get faster, software is getting slower,
Speaker 1: and effect software it gets slower at a rate that's
Speaker 1: greater than hardware's improvement. So hardware is getting faster, but
Speaker 1: software is getting slower faster than hardware is getting faster.
Speaker 1: If you will, so words law explains why you might
Speaker 1: go out and buy a brand new computer and it
Speaker 1: doesn't necessarily feel that much faster than the one you
Speaker 1: had a couple of years ago. Now, it's pretty natural
Speaker 1: for us to think, like, let's say we're using a computer,
Speaker 1: and we might think, oh, if I buy a new
Speaker 1: one in a couple of years, it's gonna leave this
Speaker 1: one in the dust. It's gonna be so much faster.
Speaker 1: But this ignores the fact that within those same two years,
Speaker 1: developers are going to make increasingly complex software that gobbles
Speaker 1: up those precious resources on the new machines. The software
Speaker 1: two years from now would likely require more than what
Speaker 1: your current machine could even handle. So Worth was saying
Speaker 1: to software developers, hey, guys, chill out and uh figure
Speaker 1: out ways to create less demanding software. Don't just pounce
Speaker 1: on computational capabilities just because they're they're this. Don't treat
Speaker 1: it like everest and you're climbing it because it's there.
Speaker 1: And this always makes me think of Triple A video games,
Speaker 1: which frequently on their highest settings anyway, have extremely high
Speaker 1: graphics processing demands, and they often push even the most
Speaker 1: powerful GPUs to their limits. And then you get the
Speaker 1: next entry in the franchise, and it will be even
Speaker 1: more demanding and will push whatever the current bleeding edge
Speaker 1: GPU is to its limits, and so on. It never
Speaker 1: eases off. On a sort of similar note, and one
Speaker 1: that involves not just tech but human nature is Brooks's law,
Speaker 1: and this comes from an observation by Fred Brooks back
Speaker 1: in nineteen He says that under some conditions, adding a
Speaker 1: person to a project, particularly software development, when the project
Speaker 1: is running behind schedule, will push the project to um
Speaker 1: to even further behind. So, in other words, if a
Speaker 1: project is not going to meet deadline, adding another person
Speaker 1: will likely make things worse. And there are lots of
Speaker 1: different reasons for that. I'm sure many of you are
Speaker 1: anticipating some of them. If you've ever gone through any
Speaker 1: sort of onboarding process, either with a company or a team,
Speaker 1: or if, bless your heart, you have been in charge
Speaker 1: of on boarding someone else, you know that it takes
Speaker 1: time for a new team member to get their bearings
Speaker 1: and get understanding of how things are working and find
Speaker 1: ways to contribute to that process. And until they get
Speaker 1: to that point, while the new person is more likely
Speaker 1: to be a drain on resources rather than adding to
Speaker 1: the resources. And it's not it's not their fault. They
Speaker 1: don't magically know where the project is and it's development cycle,
Speaker 1: or what is working or was not working, or how
Speaker 1: to make it better. It's just kind of the way
Speaker 1: things are. Other issues can also contribute to a slowdown.
Speaker 1: For example, as you add more people to a team,
Speaker 1: communication becomes more complicated. And boy, how do you do
Speaker 1: I feel this one? If you've ever used any sort
Speaker 1: of project management or communication platform to communicate with a team,
Speaker 1: you know, things like Slack or base camp or whatever,
Speaker 1: you know that things can get pretty chaotic. As more
Speaker 1: people join into a project. You can get cross talk,
Speaker 1: you can get conversations that maybe should happen within a
Speaker 1: subgroup rather than the whole group. You can have points
Speaker 1: where various subgroups haven't communicated with one another, and so
Speaker 1: things get all jump lee, just a lot of stuff
Speaker 1: that gets harder as more people join in. Uh and uh,
Speaker 1: you know that's something that we're going to touch on again.
Speaker 1: A little bit later in this episode, when we talk
Speaker 1: about processors adding people or resources to some jobs sometimes
Speaker 1: makes sense if those jobs can be divided up into
Speaker 1: individual tasks, but it makes less sense if the job
Speaker 1: isn't divisible, And we'll come back to that idea in
Speaker 1: just a moment. First, before we get into more loss stuff,
Speaker 1: let's take a quick break. We're back e Room's Law.
Speaker 1: Here's another one that's all play on Moore's law, because
Speaker 1: the room is actually more spelled backwards. It's e R
Speaker 1: O O M. It describes the process of developing new
Speaker 1: drugs like pharmaceuticals, and that developing new drugs gets progressively
Speaker 1: harder over time, and that means it takes longer and
Speaker 1: costs more money to develop new drugs as time goes on.
Speaker 1: Despite the fact that you've got scientists and engineers and
Speaker 1: chemists who have developed some really super cool high tech
Speaker 1: equipment specifically for the purposes of drug development, that even
Speaker 1: though our capabilities grow, the difficulty still grows. And generally,
Speaker 1: the Rooms Law says that the cost of developing a
Speaker 1: new drug doubles pretty much every nine years. Okay, now,
Speaker 1: way back in three, which was more than a decade
Speaker 1: before Gordon Moore's article about cramming components on integrated circuits
Speaker 1: would come out, there was a person named Herb Gross
Speaker 1: who observed that computer performance increases as the square of
Speaker 1: the cost. So, in other words, as a computer's price doubles,
Speaker 1: its computer processing power should be four times as great.
Speaker 1: So if you're looking at two computers and the first
Speaker 1: one is five dollars and the second one is one
Speaker 1: thousand dollars, the second computer should be four times as
Speaker 1: powerful as the first computer. Uh. This is one of
Speaker 1: those laws that at various times wasn't you know, totally accurate.
Speaker 1: But generally it's saying that as computers get more expensive,
Speaker 1: the price of computational performance, if you were able to
Speaker 1: like divide that up on a per unit basis, is
Speaker 1: actually coming down. Then there's Kumi's law, which describes the
Speaker 1: power efficiency of hardware over time. Essentially, Kumi said that
Speaker 1: every one point five seven years, the amount of battery
Speaker 1: you would need to handle a fixed computing load would
Speaker 1: fall by a factor of two, meaning that if you
Speaker 1: were to perform the exact same computational load on computers
Speaker 1: that were made essentially a year and a half apart
Speaker 1: from each other, the more recent computer would do it
Speaker 1: without consuming nearly as much power. After two thousand, Kumi
Speaker 1: adjusted this to say the trend would actually follow every
Speaker 1: two point six year instead of one point five seven.
Speaker 1: And you might wonder, well, why doesn't this mean that
Speaker 1: we see laptop computers that have batteries that have you know,
Speaker 1: an all day operation, Like why don't they last many, many,
Speaker 1: many more hours than old laptops? After all, if we're
Speaker 1: talking about it, the battery load decreasing by a factor
Speaker 1: of two every even every two point six years, shouldn't
Speaker 1: that be the case. Well, then we have to remember
Speaker 1: Worth's law and the fact that software bloats at are
Speaker 1: really fast rate, so we're not running the exact same
Speaker 1: computational loads as time goes on. The computational loads are
Speaker 1: getting bigger as time goes on, so it all kind
Speaker 1: of negates each other, all right. Then we have Gustafson's law,
Speaker 1: which gets back to that issue I was talking about
Speaker 1: with Brooks law. You know, that was the one that
Speaker 1: says adding more people to a project doesn't necessarily speed
Speaker 1: things up. Gustupson's law covers how much faster a computer
Speaker 1: with parallel processors will complete given task compared to a
Speaker 1: computer with a single core processor. All right, now, when
Speaker 1: it comes to parallel processing, I use this analogy pretty
Speaker 1: much every single time to describe single core versus multi
Speaker 1: core processors. But let's do it again. Okay, Let's say
Speaker 1: you got yourself a math class and there are six
Speaker 1: students in the math class. One student, and we're gonna
Speaker 1: call her Annie, is like super good at math. She
Speaker 1: is a genius. Now, the other five students in the
Speaker 1: class are really good at math, but they're not at
Speaker 1: Annie's level. One day, the teacher comes in and proposes
Speaker 1: a contest. She's going to hand out a pop quiz
Speaker 1: with five math problems on it. Annie will have to
Speaker 1: complete all five problems, but the other five students will
Speaker 1: each only have to solve one of the five problems.
Speaker 1: So student one gets problem one, Student two gets problem
Speaker 1: to et cetera, And then the teacher starts the clock
Speaker 1: and lo and behold the group of five students finish.
Speaker 1: There's collectively first, Annie is faster at answering individual questions
Speaker 1: than her counterparts are, so she can answer question one
Speaker 1: before student one can do the same. But keep in mind,
Speaker 1: students two through five are still working on those other
Speaker 1: problems while Annie is still finishing up question one, so
Speaker 1: she is not able to finish her quiz faster than
Speaker 1: the collective classmates for that particular pop quiz. This is
Speaker 1: kind of like parallel processing. Parallel processors are great for
Speaker 1: certain types of computational problems, namely problems that can be
Speaker 1: solved in parts. The various cores can tackle the different
Speaker 1: parts of the problem and then together they all arrive
Speaker 1: at the solution, and potentially they can do this much
Speaker 1: faster than a more powerful single core processor could. However,
Speaker 1: not all problems are parallel. Some problems require a serial
Speaker 1: approach s e. R I. A L not captain crunch,
Speaker 1: and a serial approach means that you can't just divide
Speaker 1: the problem up into parts. You have to go from
Speaker 1: beginning through all the way to the end. And in
Speaker 1: those cases, the more powerful single core processor is going
Speaker 1: to solve the problem faster than the parallel processor where
Speaker 1: each core is not running at the same you know,
Speaker 1: high level clock speed. So Gustopherson's law takes all this
Speaker 1: into account and gives a mathematical expression to estimate how
Speaker 1: much faster a parallel processor can solve a given type
Speaker 1: of problem if you happen to know how much of
Speaker 1: that problem is serial rather than parallel. Alternatively, you could
Speaker 1: use this to estimate how slowly a single core processor
Speaker 1: would solve a parallel problem. All right, Well, what if
Speaker 1: you were to look at Moore's law and say, hey,
Speaker 1: that's nice and all, but it's way too slow. Well,
Speaker 1: then you could take a gander at Nevin's law. This
Speaker 1: is named after Hartmut Nevin, who works in discipline is
Speaker 1: like quantum computing in robotics, and his law proposes that
Speaker 1: quantum computers increase in power at a doubly exponential rate,
Speaker 1: which is quick and crazy fast. And we can sort
Speaker 1: of see why this is too so. For classical computers,
Speaker 1: the basic unit of information is the bit, and a
Speaker 1: bit is a binary digit, so we designated as either
Speaker 1: a zero or a one, which you can think of
Speaker 1: like a switch being turned off or on. Using lots
Speaker 1: of bits, we can tell machines to do all sorts
Speaker 1: of stuff based on specific input. This is the basis
Speaker 1: of computer science. But quantum computing has a different basic unit.
Speaker 1: It is called the cubit or quantum bit, and a
Speaker 1: bit can either be a zero or a one, right,
Speaker 1: it has to be one or the other. It is binary,
Speaker 1: But a cubit, thanks to stuff like superposition, can effectively
Speaker 1: inhabit both the zero state and the one states simultaneously,
Speaker 1: as well as technically all states in between. So as
Speaker 1: you add in the ability to handle more cubits, as
Speaker 1: you build quantum systems that have more cubits incorporated into them,
Speaker 1: you vastly expand what the computer is capable of doing.
Speaker 1: Your collection of cubits can, in a sense, perform drastically
Speaker 1: larger numbers of simultaneous processes to solve a specific subset
Speaker 1: of computational problems. That sounds like word sellad, but it
Speaker 1: does make sense if you start to break it down,
Speaker 1: And it doesn't mean that a quantum computer is good
Speaker 1: for every kind of computational problem, just like a parallel
Speaker 1: processor is not going to be good for are not
Speaker 1: going to be better at at a serial level kind
Speaker 1: of computational problem than a single core processor could be.
Speaker 1: A quantum computer is not going to be great at
Speaker 1: every single type of computational load, but for a subset
Speaker 1: of them, they could be phenomenal as long as computer
Speaker 1: scientists develop effective algorithms to lever the quantum computing UH,
Speaker 1: and these are problems that would take a traditional computer
Speaker 1: decades or maybe centuries or thousands of years to complete,
Speaker 1: depending upon the complexity. So one example of of the
Speaker 1: type of problem that quantum computers might be really good
Speaker 1: at tackling is UH is known as the traveling salesman problem.
Speaker 1: And here's a version of this problem. Let's say you've
Speaker 1: got a salesman whose region includes you know, ten different cities,
Speaker 1: and the salesman's trying to figure out what is the
Speaker 1: most efficient route that will allow the salesman to visit
Speaker 1: every city at least once with the least amount of
Speaker 1: travel time. And in a classical computer system, a computer
Speaker 1: would have to go through every single possible variation of
Speaker 1: every route between every city and record the results. And
Speaker 1: then once it had run every single variation, it could
Speaker 1: then compare all the results against each other UH and
Speaker 1: then determine which route would be the fastest, and that,
Speaker 1: depending again on the complexity of the problem, could take
Speaker 1: hundreds of years. A quantum computer with a sufficient number
Speaker 1: of cubits along with the appropriate algorithm, could potentially solve
Speaker 1: this problem much faster. Also, interestingly, the solutions that quantum computers.
Speaker 1: Generator actually listed in probabilities, not certainties, so you would
Speaker 1: get an answer that what might have like a threshold
Speaker 1: of certainty saying that this is the right answer, which
Speaker 1: means it might not be, but it probably is. Now
Speaker 1: I'm being very fast and loose and very very high
Speaker 1: level with this description. It gets so much more complicated
Speaker 1: and technical than what I'm I'm saying. But this is
Speaker 1: just to give you an idea of what quantum computers
Speaker 1: will be used for. They won't be used for everything,
Speaker 1: but for the applications that they can be used for,
Speaker 1: they can potentially be far more powerful than any classical system.
Speaker 1: All Right, We've got a few more laws to cover
Speaker 1: before we wrap this up, but let's take another quick break. Okay,
Speaker 1: let's let's talk about a few more laws. Robert Metcalf
Speaker 1: made an observation that we now call Metcalf's law, and
Speaker 1: this has to do with the value of a telecommunications network.
Speaker 1: And by value, we're kind of talking about the number
Speaker 1: of possible connections that can exist within a network. And
Speaker 1: the mathematical expression of this law is N times and
Speaker 1: minus one all divided by two, So N represents the
Speaker 1: number of nodes in a network. All right, Let's let's
Speaker 1: start using actual examples to kind of explain what this means.
Speaker 1: So let's say we have the simplest network imaginable. Let's
Speaker 1: say we've got a couple of kids and they've got
Speaker 1: to tin cans and a string connecting to them, so
Speaker 1: the two kids can talk to one another. But that's it, right,
Speaker 1: you can only have two people using that network, and
Speaker 1: so that means our n in this case, the number
Speaker 1: of nodes is too. We have two people, two nodes.
Speaker 1: So then we take that mathematical expression you know, in
Speaker 1: times in minus one divided by two, So that means
Speaker 1: it's two times two minus one divided by two, and
Speaker 1: two minus one is one, so then you have two
Speaker 1: times one. That means you've got to you're divided by two,
Speaker 1: you're left with one. So one is the number of
Speaker 1: connections we can make with this network. We have two nodes,
Speaker 1: only one connection can happen. What happens if we add
Speaker 1: a third person in there. Let's say we've graduated from
Speaker 1: tin cans and string to actual like um telephone system. Well,
Speaker 1: now our n is three, so it's three times three
Speaker 1: minus one divided by two three minus one is two
Speaker 1: two times three six six divided by two is three.
Speaker 1: We went from one connection to three potential connection connections
Speaker 1: just by adding one person. Now let's say we go
Speaker 1: up to twenty. We go through that same mathematical expression,
Speaker 1: which I'm not going to walk you through because you've
Speaker 1: heard it several times, but it ends up with one.
Speaker 1: So again, with two people, you have a maximum number
Speaker 1: of connections set at one. With twenty you have one
Speaker 1: possible different connected pairs. So the more nodes you have
Speaker 1: in the communications network, the higher the value of the network.
Speaker 1: On a similar note, David P. Read R E. D.
Speaker 1: Observed that the usefulness of large networks, specifically social networks,
Speaker 1: scales exponentially with the size of the network. So even
Speaker 1: adding just a few people to a social network creates
Speaker 1: exponential growth in that network's utility. And read described this
Speaker 1: in terms of subgroups, that the more people join a network,
Speaker 1: the more subgroups can exist within that network. And again
Speaker 1: you have a mathematical expression you can used to describe this.
Speaker 1: In this case, you would say that the the number
Speaker 1: of subgroups is you take the number of people in
Speaker 1: your network, or a number of components in your network.
Speaker 1: This is your n and you take two to the
Speaker 1: power of n, then you subtract in from that number,
Speaker 1: and then you subtract one from that number. So let's
Speaker 1: say we've got eight people in this social network. Two
Speaker 1: to the power of eight is two hundred fifty six.
Speaker 1: We substract, subtract eight from that, and we get two
Speaker 1: forty eight. We subtract one from that, and we get
Speaker 1: to forty seven. So with eight people, you could potentially
Speaker 1: have as many as two hundred forty seven subgroups. And
Speaker 1: obviously it just gets crazier from there as you start
Speaker 1: to add people. So this points out how social networks
Speaker 1: can very quickly grow and become more important. Though it's
Speaker 1: obviously not a guarantee because obviously those those eight people
Speaker 1: could have potentially as many as two forty seven subgroups,
Speaker 1: But that doesn't mean you would actually see every single
Speaker 1: variation of a group play out in real life, Like
Speaker 1: maybe any inner mathematics classmates are all part of those
Speaker 1: eight people, and any refuses to be in any subgroup
Speaker 1: with our five classmates, Well, that would eliminate a ton
Speaker 1: of options there. But you get the idea. And then
Speaker 1: we have a couple of dark laws in tech. Laws
Speaker 1: of observations that show some of them not so pleasant
Speaker 1: sides of technology. One of those is called Zimmerman's law,
Speaker 1: which states that the capability for computational devices to track
Speaker 1: what we're doing doubles every eighteen months. And when you
Speaker 1: think about stuff like social networks, location tracking, targeted advertising,
Speaker 1: all this kind of thing, you start to see what
Speaker 1: Zimmerman was saying with this. And some of this is
Speaker 1: based off the capabilities of the technology, how the technology
Speaker 1: is getting better at doing this stuff, but some of
Speaker 1: it also has to do with how we choose to
Speaker 1: interact with tech. Like obviously, if we didn't, if we
Speaker 1: weren't part participants in this, or at least we weren't
Speaker 1: eager participants in this, it wouldn't have quite the same
Speaker 1: level of effectiveness. So if more and more people were
Speaker 1: to say, you know what, I'm not going to do
Speaker 1: social network stuff anymore, maybe they don't even use a
Speaker 1: smartphone or anything like that, it would cut way back
Speaker 1: on this. But most of us are willing participants to
Speaker 1: some degree or another in this system, and that just
Speaker 1: makes it more effective. And then we have Godwin's law.
Speaker 1: Anyone who's been part of any online community is likely
Speaker 1: to be aware of Godwin's law. It's named after Mike Godwin,
Speaker 1: and this law essentially says that the longer any online
Speaker 1: discussion goes, the more likely someone will bring up a
Speaker 1: comparison that involves Nazis or Hitler, and that should a
Speaker 1: conversation go on long enough, the probability becomes a certainty.
Speaker 1: And God would observe this way back in the youth
Speaker 1: net news group days, essentially, people would get into conversations,
Speaker 1: someone would disagree with someone, and else things would get heated,
Speaker 1: and ultimately someone would compare either a person or an
Speaker 1: idea to uh, something that the Nazis would have celebrated
Speaker 1: or something that Hitler would have proposed, and then Godwin's
Speaker 1: law would be complete. Like you would have said, yes,
Speaker 1: this is Godwin's law. This conversation has reached the point
Speaker 1: where someone made that comparison, and essentially they were saying
Speaker 1: that if a conversation goes long enough, the probability that
Speaker 1: would happen ends up being a certainty. Now, some folks
Speaker 1: say that if someone invokes a comparison to Nazis or
Speaker 1: Hitler in a conversation, that person automatically loses whatever the
Speaker 1: argument was about. Essentially, the idea is that you were
Speaker 1: unable to defend your position, so you ended up resorting
Speaker 1: to this comparison. This, this emotionally charged comparison. Therefore, your
Speaker 1: position is indefensible and you lose, You get nothing. As
Speaker 1: Willy Wonka would say, Uh, Now, I do not want
Speaker 1: to end on that note. Obviously it's really a bummer
Speaker 1: of a note. So we're gonna throw in the bonus here.
Speaker 1: Of the laws of robotics as originally proposed by science
Speaker 1: fiction author Isaac Asimov, and originally there were just three
Speaker 1: laws of robotics. The first law states that a robot
Speaker 1: may not cause harm to a human being or through inaction,
Speaker 1: allow a human to come to harm. Uh. The second
Speaker 1: law is that a robot must obey any and all
Speaker 1: orders given to it by a human, except if doing
Speaker 1: so would violate the first law. So I couldn't tell
Speaker 1: a robot to um to grab a specific chair just
Speaker 1: as you're trying to sit in that chair, because if
Speaker 1: the robot did that, you would miss the chair and
Speaker 1: you would sit down and hit the floor and possibly
Speaker 1: hurt yourself. And so the robot would not be able
Speaker 1: to do that, even though it is otherwise compelled to
Speaker 1: obey every command given to it by a human. The
Speaker 1: third law is that a robot must protect itself unless
Speaker 1: by doing so it would come into conflict with the
Speaker 1: first or second law. So if the robot protecting itself
Speaker 1: would allow a human to come to harm, well, then
Speaker 1: the robot will take whatever action is necessary, including actions
Speaker 1: that would harm itself. Now later, as Amov would add
Speaker 1: the zero law, this states that a robot may not
Speaker 1: harm humanity or through an action, allow humanity to come
Speaker 1: to harm. So not just a human, but humanity in general. Uh,
Speaker 1: this is the law that really helps you get around
Speaker 1: that science fiction trope in which engineers create a super
Speaker 1: powerful artificial intelligence like superhuman AI, and then you know,
Speaker 1: they use the AI as a decision making engine and
Speaker 1: they ask for it to bring about world peace, like
Speaker 1: this is a problem so big that humans can't solve it,
Speaker 1: but you are smarter than humans, make world peace, And
Speaker 1: the AI ends up saying, well, the only guarantee for
Speaker 1: world peace is if I wipe out all the humans,
Speaker 1: because then they can't fight each other, and that's the
Speaker 1: only guarantee for world peace. So I guess a better
Speaker 1: get to it launch nuclear weapons now, the zero law
Speaker 1: would presumably tell the AI, Nope, that's off the table,
Speaker 1: try again, and then the AI would probably dissolve into
Speaker 1: goo because the problem we gave it was way too hard. Anyway,
Speaker 1: those are the basic laws of robotics. Obviously, science fiction authors,
Speaker 1: including Isaac Asimov have played with those in various ways
Speaker 1: to create scenarios in which robots would behave in in
Speaker 1: what you might consider an unpredictable manner, because it was
Speaker 1: the robots method of attempting to complete a task while
Speaker 1: also trying to obey the laws of robotics. As the
Speaker 1: science fiction stories show, these basic ideas, while it seems
Speaker 1: like it's like covering all your bases, doesn't necessarily result
Speaker 1: in that when you put it into practice, uh, which, again,
Speaker 1: like good science fiction should be something that that teaches
Speaker 1: us something either about ourselves or about the potential for
Speaker 1: us to have blinders on when we make certain decisions
Speaker 1: and not foresee the consequences of our actions, so that
Speaker 1: maybe we spend a little more time considering things before
Speaker 1: we act on them. Anyway, I hope you enjoyed this
Speaker 1: Tex stuff. Tidbits ended up being now almost the length
Speaker 1: of a regular episode, so I'm again really bad at
Speaker 1: this whole tidbit thing. But yes, that's just a selection
Speaker 1: of some of the quote unquote laws in technology. Maybe
Speaker 1: sometime I'll do a follow up, or I'll cover some
Speaker 1: of the more specific laws and talk about, you know,
Speaker 1: how those came to be? Uh they there, I would argue,
Speaker 1: more obscure except for specific sectors of the tech industry,
Speaker 1: and thus you're less likely to come across them. But
Speaker 1: we might do a follow up episode in the future.
Speaker 1: If you have suggestions for topics I should cover in
Speaker 1: episodes of tech Stuff, feel free to reach out to me.
Speaker 1: The best way to do that is over on Twitter,
Speaker 1: and a handle for the show is tech Stuff h
Speaker 1: s W and I'll talk to you again, really sis y.
Speaker 1: Tech Stuff is an I Heart Radio production. For more
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