Speaker 1: Get in tech with technology with tech Stuff from half
Speaker 1: stuff works dot com. Hey, they're welcome to tech Stuff.
Speaker 1: I'm Jonathan Strickland. I'm an executive producer with how Stuff
Speaker 1: Works and I love all things tech and I'm continuing
Speaker 1: the special series coming to you from Las Vegas, Nevada,
Speaker 1: at the IBM Think Conference and UH. In our last episode,
Speaker 1: I talked about attending the IBM Research Science Slam, in
Speaker 1: which several people who have been doing some incredible research
Speaker 1: into different fields and utilizing technology and interesting ways took
Speaker 1: the stage to talk about their work. And it was
Speaker 1: a fantastic night. I really enjoyed myself. I'm very thankful
Speaker 1: that I got to attend. I also got a really
Speaker 1: comfortable seat right up front because I happened to figure
Speaker 1: out where the main doors were before they opened. That's
Speaker 1: that's something I'm really proud of, even though it was
Speaker 1: really anyone could have done it, but I managed to
Speaker 1: get a nice, big, comfy seat, and I chatted a
Speaker 1: little bit about the first presenters. But we've got a
Speaker 1: couple more to talk about today, so we're going to
Speaker 1: transition into that episode and what else I saw while
Speaker 1: I was at the IBM Research science Slam. Hope you enjoy.
Speaker 1: The next presenter was Tom Zimmerman, who uh MS Garcia
Speaker 1: referred to as mcgever, saying that he could take any
Speaker 1: two objects and turn it into kind of a microprocessor.
Speaker 1: He was credited as a human slash machine devices and
Speaker 1: paradigm scientists, which honestly did not know was a thing
Speaker 1: before last night, so this was a treat for me.
Speaker 1: He was very expressive, very funny, probably the most humorous
Speaker 1: of all the presenters who came up there. And he
Speaker 1: talked about how he made sort of a private project
Speaker 1: for himself and how that turned into something that could
Speaker 1: be much greater. And he did this by taking an
Speaker 1: image sensor, essentially the same sort of sensor that you
Speaker 1: could find in a smartphone for the camera. He took
Speaker 1: the image sensor and a couple of l e ed
Speaker 1: s and he put it into sort of a waterproof
Speaker 1: container and created a basic three D microscope. Um. He
Speaker 1: used Python, the programming language, to create a method to
Speaker 1: plot out where things were within a three D space,
Speaker 1: so not just the x y coordinates, but also the
Speaker 1: z coordinates if you think of the x y z
Speaker 1: ax s. He was able to plot all that out,
Speaker 1: so not just how high up in a picture something
Speaker 1: is or how low down in the picture something is,
Speaker 1: but how close or far away that thing is. And
Speaker 1: he used it to look at the life forms within
Speaker 1: a single drop of water. And he found this to
Speaker 1: be really fascinating watching all these tiny, little microscope opic
Speaker 1: life forms moving around and being able to plot where
Speaker 1: they were and track their progress. And this got him
Speaker 1: interested in the subject of plankton. Now, as Mr Zimmerman
Speaker 1: pointed out, plankton are incredibly important to our our world.
Speaker 1: Without plankton, we would find it very, very difficult to exist.
Speaker 1: Plankton produced two thirds of the oxygen that we breathe.
Speaker 1: So you know, plants take carbon dioxide and then they
Speaker 1: convert that into oxygen, and then we breathe that oxygen,
Speaker 1: we exhale carbon dioxide. We're all part of that cycle. Well,
Speaker 1: plankton are responsible for two thirds of that oxygen. So
Speaker 1: while you might think of all the big forests out
Speaker 1: there is being really important carbon sinks, and they are,
Speaker 1: don't get me wrong, we don't want to cut those down. Uh,
Speaker 1: Plankton are even more important. They are huge carbon sinks.
Speaker 1: They sequester carbon from the ecosystem. Which means that they
Speaker 1: also can counteract that that effect. Obviously, if we we
Speaker 1: keep dumping carbon into the ecosystem that contributes to climate change,
Speaker 1: it contributes to the greenhouse effect, which some people would
Speaker 1: say is all about global warming. As it turns out,
Speaker 1: the climate on Earth is more complicated than warming or cooling.
Speaker 1: That's why a lot of people now call it climate
Speaker 1: change rather than global warming. But controlling the amount of
Speaker 1: carbon that we introduced into the ecosystem is incredibly important,
Speaker 1: and plankton are really good at soaking up carbon. But
Speaker 1: here's the problem is that we're actually dumping more carbon
Speaker 1: into the environment than the plankton can easily absorb, and
Speaker 1: plankton are dying as a result, So we're killing off
Speaker 1: the life form that is responsible for producing most of
Speaker 1: the oxygen we breathe. Not only that, but plankton are
Speaker 1: also the bottom of the food chain over in or
Speaker 1: very close to the very bottom the food chain over
Speaker 1: in the oceans, so they serve as a food source
Speaker 1: for just about every species of baby fish out there.
Speaker 1: So if the plankton die off, then the food supply
Speaker 1: for these fish die off, then the fish die off,
Speaker 1: then the predators that eat those fish die off and
Speaker 1: you start to see the food chain collapse in on itself. Obviously,
Speaker 1: this is a really bad thing, but it's also uh
Speaker 1: a tricky thing to study plankton because typically the way
Speaker 1: scientists would study plankton is they would go out into
Speaker 1: the field and by the field, I mean the ocean,
Speaker 1: and they take a big net and they would trawl
Speaker 1: the ocean and they would pull up some plankton. They
Speaker 1: would put this in jars with preservatives which would kill
Speaker 1: the plankton, and then they would come back to the
Speaker 1: lab and they would study the plankton under a microscope.
Speaker 1: Zimmerman equated this to someone who is it's their job
Speaker 1: to UH to evaluate to analyze sports, and they are
Speaker 1: following a football team, and the way they figure out
Speaker 1: how well the football team performs is they are allowed
Speaker 1: to go on the football team's bus after a game
Speaker 1: and take pictures of the football players as they're asleep,
Speaker 1: and then try to analyze how well they play the
Speaker 1: game based upon those pictures of sleeping people. He said,
Speaker 1: that's kind of the equivalent of what scientists are having
Speaker 1: to do with plankton. That if you're only able to
Speaker 1: study them after they've been preserved and therefore they're no
Speaker 1: longer alive. You can only gather so much information about them,
Speaker 1: and it's not terribly useful. It would be better if
Speaker 1: you could study plankton within their own ecosystem and not
Speaker 1: brought back to this this microscope he had been playing with,
Speaker 1: this idea he had created, and he said again that
Speaker 1: the basic parts were all pretty easy to use. You
Speaker 1: could have an image sensor from a smartphone, a couple
Speaker 1: of LEDs, and a waterproof container. You could program some software,
Speaker 1: some artificial intelligence software, use chips that were develop for
Speaker 1: smart cameras that were meant to do things like image recognition,
Speaker 1: face recognition, you know, the sort of basic artificial intelligence
Speaker 1: that I talked about in my preview episode. But then
Speaker 1: reprogram it, retrain the neural network to recognize plankton and
Speaker 1: to track their behaviors. By looking at those behaviors, you
Speaker 1: can learn more about that plankton, like how plankton eat
Speaker 1: other things. And then he gave an example of plankton
Speaker 1: that like a particular type of algae and occasionally this
Speaker 1: plankton will eat a different type of algae that is
Speaker 1: capable of creating a toxin, and that toxin UH more
Speaker 1: or less makes the plankton drunk. As Zimmerman explained, the
Speaker 1: plankton on its own would dart all over the place
Speaker 1: and be able to elude predators because it's able to
Speaker 1: to move around quite a bit. But when it gets drunk,
Speaker 1: when it eats this particular type of algae, it just
Speaker 1: will swib in a straight line. It's kind of how
Speaker 1: UH and intoxicated plankton would move around in its environment.
Speaker 1: But if it moves in the straight line, it makes
Speaker 1: it very easy for predators to eat that plankton. Well.
Speaker 1: As predators eat the plankton, then that plankton is less
Speaker 1: capable of eating algae, you know, obviously, because the population
Speaker 1: of the plankton starts to drop, algae has fewer predators
Speaker 1: of its own, and then its population begins to grow,
Speaker 1: and then you get algae blooms. That could be a
Speaker 1: big problem. So it's better if you're able to monitor
Speaker 1: the plankton and monitor what's happening in the ecosystem and
Speaker 1: be able to perhaps intervene if things are not going well.
Speaker 1: But you can only intervene if you have all the information.
Speaker 1: You can only make a meaningful and helpful act. If
Speaker 1: you know what's going on, without that information, you may
Speaker 1: do more harm than good. So the Zimmerman's point was
Speaker 1: that we now have the capability of making these tools
Speaker 1: to gather the information we need to make more responsible choices.
Speaker 1: And it was a really fascinating way of putting technology
Speaker 1: in the role of an effective tool for a really
Speaker 1: difficult problem. I have more to say about the science
Speaker 1: slam over at the THINK two thousand eighteen conference, but
Speaker 1: before I jump into the next little segment, I'd like
Speaker 1: to take a quick break to thank our sponsor. The
Speaker 1: next person to take the stage was Francesco Rossi. Francesca
Speaker 1: Rossi's area of expertise was something that I thought was
Speaker 1: truly interesting, artificial intelligence ethics. She talked about AI in
Speaker 1: a way that a lot of people at IBM like
Speaker 1: to talk about AI. They don't necessarily talk about artificial intelligence.
Speaker 1: They talk about augmented intelligence. In other words, these are
Speaker 1: the devices and the programs, the software of the firmware
Speaker 1: that help us make decisions. They don't necessarily make all
Speaker 1: the di visions for us. They aren't thinking, they aren't
Speaker 1: having communications with us. They are guiding us as we
Speaker 1: try to make decisions, and then we use that information
Speaker 1: as a tool to help us in our tasks. So,
Speaker 1: how do we build machines to help people make smarter
Speaker 1: and more grounded positions and decisions? Uh, Artificial intelligence can
Speaker 1: help solve some of the world's most difficult problems. And
Speaker 1: Rossie talked about how she's been working in the AI
Speaker 1: field for decades and that the conversation has gradually shifted
Speaker 1: during her time and studies of AI. She said that
Speaker 1: early when she was studying AI, the conversations were all
Speaker 1: about how can we make it smarter? How can we
Speaker 1: make these artificially intelligent programs faster, more capable, Uh, make
Speaker 1: decisions more reliably? How do we do that? And so
Speaker 1: the focus was just on performance. It had nothing to
Speaker 1: do with the quality of those decisions, or maybe the
Speaker 1: impact those decisions might have on other people, but rather, uh,
Speaker 1: just can we make a machine that's able to to
Speaker 1: do this task better than the ones we have right now?
Speaker 1: These days, she said, you know, and back then it
Speaker 1: was just computer scientists who are having this conversation. These days,
Speaker 1: she said, there's a huge number of disciplines that all
Speaker 1: get together to talk about these sort of things that
Speaker 1: not only include computer scientists, but also philosophers, lawyers, economists, policymakers,
Speaker 1: people who have recognized that machines not only have the
Speaker 1: capability of making decisions quickly, but that those decisions can
Speaker 1: have a real effect, positive or negative, on actual human
Speaker 1: beings in the real world, and that there has to
Speaker 1: be some sort of ethical approach to the development of
Speaker 1: AI if we want AI to actually benefit humanity. One
Speaker 1: of the big problems, or several of them, actually, she mentioned,
Speaker 1: transparency is a huge issue. How do you know how
Speaker 1: the AI arrived at its decision? You want a transparent
Speaker 1: way of communicating that. Without that, then you just have
Speaker 1: a black box. You have something that has taken data
Speaker 1: and produced a result and you have no idea how
Speaker 1: it went from A to b uh, And without knowing,
Speaker 1: you don't know if the decision is a good one. Right, So,
Speaker 1: as AI gets more complex and starts to make more
Speaker 1: complicated decisions, if you don't have transparency, it's it's like
Speaker 1: you're consulting a mysterious oracle and you don't really know
Speaker 1: if the oracle has his or her act together or
Speaker 1: is just making stuff up. So transparency is very important.
Speaker 1: Explainability also very important. Can you explain how the machine
Speaker 1: came to its conclusions, not just the pathway it took,
Speaker 1: but how it decided one set of factors was more
Speaker 1: important than another set of factors. And she also talked
Speaker 1: about bias, and in fact, most of her conversation was
Speaker 1: about bias. Bias is prejudice. It could be positive or
Speaker 1: negative in regards to any particular set of data points.
Speaker 1: So bias is something that we humans have. Now, it's
Speaker 1: something that it's a quality we possess. We do get
Speaker 1: a bias four different things. Uh, we could have a
Speaker 1: positive experience with a particular thing. Let's let's take Let's
Speaker 1: take roller coasters for example. Let's say that you ride
Speaker 1: your very first roller coaster when you're a little kid,
Speaker 1: and it's a wonderful trip, it's a wonderful ride, you
Speaker 1: love it, and then you have sort of a bias
Speaker 1: toward roller coasters because you love that feeling you had.
Speaker 1: Or let's say the opposite happened. You ride your first
Speaker 1: roller coaster and it it rattles you around a lot,
Speaker 1: makes you feel sick, and you get off that ride
Speaker 1: and your decision making process tells you, hey, this is
Speaker 1: not for me. Roller coasters are bad. They are not
Speaker 1: well designed rides. They hurt, they make me feel sick.
Speaker 1: I don't like them. They scare me. I'm never writing
Speaker 1: a roller coaster again. You've created a bias based on
Speaker 1: that experience, and may very well be the your bias
Speaker 1: plays out properly. Uh. It could be that that experience
Speaker 1: just tells you that this is how you're going to
Speaker 1: react every single time you can move forward with this
Speaker 1: particular thing. In some cases, that's not a bad thing,
Speaker 1: and she actually Rosie talks about that about how bias
Speaker 1: is not inherently bad. But when it comes to things
Speaker 1: like judging people, then obviously that's much more problematic. If
Speaker 1: you go to a different culture and you encounter something
Speaker 1: that upsets you, you might end up developing a bias
Speaker 1: against anyone who comes from that culture. And that's not
Speaker 1: necessarily representative, it's not fair. It can mean that you
Speaker 1: then treat an entire group of people unfairly based upon
Speaker 1: this bias. And that's where the scary part comes in
Speaker 1: with AI, because while AI is going to follow very
Speaker 1: specific rules that are set out based upon the AIS programming,
Speaker 1: and AI can still be biased. Now, that doesn't mean
Speaker 1: the AI is developing opinions of its own about people.
Speaker 1: It means that the AI is referencing the data sets
Speaker 1: that were fed to it, and data sets are created
Speaker 1: by human beings. If the human beings who create the
Speaker 1: data sets failed to include enough diversity, enough representation in
Speaker 1: that data set, then the people who are not represented
Speaker 1: can be affected negatively. And there are great examples of
Speaker 1: this out in the world that you can actually see
Speaker 1: things that have shown that there are problematic implementations of
Speaker 1: artificial intelligence that do in fact indicate a bias is present. Uh.
Speaker 1: There were stories about facial recognition technology that worked fine
Speaker 1: if you happen to be a white person, but if
Speaker 1: you were of any other race, particularly if you were uh,
Speaker 1: if you were black, then it wasn't working properly. It
Speaker 1: wasn't detecting people properly. Well. That indicates that perhaps the
Speaker 1: data set that was used to train that artificial intelligence
Speaker 1: at a lack of representation of people of different races
Speaker 1: than than just white people. And it's not necessarily that
Speaker 1: it was uh planned that way, or that the people
Speaker 1: who were designing it were specifically excluding an entire group.
Speaker 1: There might have been no malicious intent whatsoever. But that
Speaker 1: doesn't really matter if there was malicious intent from the
Speaker 1: beginning or not. The effect is the same whether it
Speaker 1: was intended to exclude a group or just accidentally excluded
Speaker 1: a group because the person who is designing the system
Speaker 1: didn't belong to that group. That lack of diversity creates
Speaker 1: a bias, and that bias has the potential to negatively
Speaker 1: impact an entire population of people as a result, this
Speaker 1: is not a good thing. You want to have AI
Speaker 1: that is as unbiased as you can possibly be. Now,
Speaker 1: Rosie argues that in the long run, over years and
Speaker 1: years and years, we will have an explosion in AI
Speaker 1: over multiple disciplines, multiple industries, and I completely agree that
Speaker 1: is exactly what we're already seeing it. We're seeing AI
Speaker 1: being developed in all sorts of different ways. And she
Speaker 1: also argues that the ones that will stick around, the
Speaker 1: ones we will rely upon, will ultimately be the ones
Speaker 1: that do not have bias. We will realize that those
Speaker 1: are the ones that are valuable, and we will abandon
Speaker 1: all the AI constructs that contain by us. But that's
Speaker 1: the long run. In the mid term, we're going to
Speaker 1: have problems. We're going to have a I that because
Speaker 1: of a lack of diversity in their data sets, are
Speaker 1: not going to be able to handle real world situations
Speaker 1: that are gonna have real world impact on people. So
Speaker 1: she said, it's absolutely imperative that we have these ethical
Speaker 1: discussions now and start consciously developing AI with an attempt
Speaker 1: to avoid introducing bias. In order to do that, you
Speaker 1: have to create multidisciplinary, multi under multi stakeholder, multicultural teams
Speaker 1: to develop that artificial intelligence. You have to have this
Speaker 1: representation and this diversity from the ground level and then
Speaker 1: build up as you are creating this AI, and only
Speaker 1: then can you be reasonably certain that you have the
Speaker 1: representation you need to avoid bias. At that point, you
Speaker 1: would have an AI that, no matter what it was
Speaker 1: it was intended to do, will be considered much more
Speaker 1: trustworthy and beneficial, not just smart, not just efficient. And
Speaker 1: so I found this to be really a fascinating UH
Speaker 1: presentation again to to think about how our way of
Speaker 1: thinking about AI has changed so dramatically over the last
Speaker 1: couple of decades, and that we've shifted from how can
Speaker 1: we make this machine think too? How can we make
Speaker 1: sure that this machine is performing in a way that
Speaker 1: is not inherently unfair to any particular group of people.
Speaker 1: Um and obviously in today's environment, as we get more
Speaker 1: and more sensitive to this sort of thing, I mean,
Speaker 1: we have whole sections of the world where people are
Speaker 1: becoming more xenophobic and they're becoming more isolationist. They they
Speaker 1: are are banding together with people they identify, with the
Speaker 1: people that they feel represent who they are, and they
Speaker 1: are more readily excluding people who don't fit that group.
Speaker 1: That's a dangerous way of thinking, as a dangerous approach.
Speaker 1: In some cases, it's necessary if you are part of
Speaker 1: a very small population, if you are a minority within
Speaker 1: a population that is the far outnumbers you, then you
Speaker 1: might be banding together with other people of your identity,
Speaker 1: you know that that share these cultural or or ethnic
Speaker 1: identities that you have in a way of protecting yourself,
Speaker 1: which is completely understandable if you are vastly outnumbered by
Speaker 1: other is that's a self preservation technique. But then you
Speaker 1: also have the flip side of it, where you have
Speaker 1: the majority. If they're doing it, then they are more
Speaker 1: likely to create situations that are disadvantageous or oppressive to
Speaker 1: those minorities. And so it's it's really important moving forward,
Speaker 1: that we try to break through that that we try
Speaker 1: to embrace this sort of multicultural, diverse, representative approach so
Speaker 1: that we don't create inherently unfair divisive scenarios, whether it's
Speaker 1: with technology or anything else. Honestly, so, uh, I really
Speaker 1: like this presentation. I have a feeling that not everybody
Speaker 1: would because some people feel very strongly about this sort
Speaker 1: of xenophobic kind of philosophy. They probably don't even think
Speaker 1: of themselves as xenophobic. Um. In fact, I would be
Speaker 1: shocked if they did. But yeah, I thought that this
Speaker 1: was a really valuable talk. We're in the home stretch,
Speaker 1: we're all molost done, but there's still some more to
Speaker 1: talk about that I saw over at this Science Slam,
Speaker 1: this incredible evening of science and technology and just geeking
Speaker 1: out like crazy. Before we conclude, let's take another quick
Speaker 1: break to thank our sponsor. The last person to get
Speaker 1: up and speak was Talia Gershon. She got up to
Speaker 1: talk about quantum computing and AI challenges. So again there's
Speaker 1: some overlap here with some of the previous discussions. She
Speaker 1: was talking specifically about that example I gave earlier with
Speaker 1: miss Garcia, the the polymer chemist and talked about how
Speaker 1: using a computer to accurately simulate the bonding of large
Speaker 1: molecules does grow exponentially as you grow the size of
Speaker 1: the molecule itself. So if you add more atoms to
Speaker 1: a molecule, then the amount of computing power it takes
Speaker 1: to simulate and model that molecule grows dramatically to point
Speaker 1: where a even the most powerful supercomputer would find the
Speaker 1: problem so difficult that it would take ages to create
Speaker 1: a simulation. So even if you were creating a simulation
Speaker 1: that was supposed to represent a micro second of time,
Speaker 1: it might take days or longer weeks to create that simulation.
Speaker 1: So you're taking weeks of real time to simulate a
Speaker 1: micro second of simulated time. Obviously, this is not an
Speaker 1: efficient way to go about things. So quantum computing, she argued,
Speaker 1: could help solve this. And she asked the audience, who
Speaker 1: here has heard buzz about quantum computing? And you know,
Speaker 1: about two thirds of the hands went up in the audience,
Speaker 1: and then she said, who here feels like they have
Speaker 1: a really strong grip on what quantum computing is, And
Speaker 1: then there were maybe a dozen hands still up in
Speaker 1: the audience. I wrote, how quantum computing works, and I
Speaker 1: did not raise my hand because while I did a
Speaker 1: lot of research into quantum computing, I felt like my
Speaker 1: understanding of quantum computing is still in the very, very
Speaker 1: basic level, largely because there comes a point with quantum
Speaker 1: mechanics and quantum computers where my understanding hits a wall,
Speaker 1: and rather than feeling like I really have a grip
Speaker 1: on what is happening, I'm just communicating what smarter people
Speaker 1: are telling me quantum computing is all about. But I don't.
Speaker 1: I feel like I don't have a real grasp of it. However,
Speaker 1: I say that I also remember distinctly when I first
Speaker 1: started studying quantum computing, I was also looking into string theory,
Speaker 1: and I watched a documentary in which a leading physicist,
Speaker 1: a leading expert on string theory, said, I I sometimes
Speaker 1: get asked at the end of the day when it
Speaker 1: all boils down, do I really really understand the science
Speaker 1: I'm talking about? And my answer has to be not really.
Speaker 1: There gets to a point where mathematically I can see
Speaker 1: what's supposed to be happening, but there's a barrier between
Speaker 1: the mathematics and my actual human understanding. And I've found
Speaker 1: some relief in that. Talia Gershawn got up and talked
Speaker 1: about this sort of thing. She's talked about how quantum
Speaker 1: computers encode information into complex quantum states and then they
Speaker 1: run uh quantumized processes on these quantum states. They use
Speaker 1: a method to measure the final state that results as
Speaker 1: a part of these quantized calculations, and then they record
Speaker 1: a result which doesn't necessarily clear it up very much
Speaker 1: for for us. And in fact, she was doing this
Speaker 1: to comedic effects, saying that's if you wanted to say
Speaker 1: it in the basic level, and that's still really complicated.
Speaker 1: She argues that quantum computing is an interdisciplinary problem, that
Speaker 1: it requires lots of people working in lots of fields
Speaker 1: in a very specialized way to make quantum computing possible.
Speaker 1: Because you quantum physicists who are experts on quantum mechanics
Speaker 1: to talk about that aspect of quantum computing and quantum
Speaker 1: information and they use the language of linear algebra to
Speaker 1: write out there their work. But then you would need
Speaker 1: computer scientists to take that linear algebra and translate that
Speaker 1: into a language that computers can use to actually run processes.
Speaker 1: So you have to take sort of the formulas created
Speaker 1: by scientists, quantum scientists give it to computer scientists who
Speaker 1: then can transform that into information that computers can actually use.
Speaker 1: Then you would also need people who are material science
Speaker 1: experts to actually create the physical quantum computer. You would
Speaker 1: have to have device manufacturing experts to UH to take
Speaker 1: the designs that the material scientists had created and make
Speaker 1: it a real thing. You have to have physicists who
Speaker 1: would be testing all of this to make sure that
Speaker 1: it was actually working within the realm of quantum mechanics.
Speaker 1: You'd have to have electrical controls expertise to be able
Speaker 1: to create the quantum circuitry. You'd have to have advanced
Speaker 1: cryogenics to keep the quantum computer cold enough to operate.
Speaker 1: So she was arguing that all of these things require
Speaker 1: people with very deep expertise and very specific fields, which
Speaker 1: makes quantum computing particularly difficult. You can't just have, you know,
Speaker 1: a small team of experts work together. It used to
Speaker 1: be way back in the day when you talk about
Speaker 1: things like the dawn of personal computers, like in the
Speaker 1: nineteen seventies, you could have a person or a couple
Speaker 1: of people put together all the different components and make
Speaker 1: a computer. UH to to design and produce a computer
Speaker 1: like think about Apple computers with jobs in Wozniak working
Speaker 1: out of a garage and actually designing and building the
Speaker 1: first Apple computer. That was possible back then. But with
Speaker 1: quantum paters, you're talking about elements that require such deep
Speaker 1: knowledge that you have to have an entire fleet of
Speaker 1: experts across multiple disciplines in order to build an effective
Speaker 1: quantum computer. To take that and then to build it
Speaker 1: into a scalable technology is going to require a lot
Speaker 1: of breakthroughs. Obviously, you can't just ramp that up. You
Speaker 1: can't just have well, now we've designed this quantum computer,
Speaker 1: let's create an assembly line and churn them out and
Speaker 1: sell them. Uh. It's it's a huge undertaking. And she
Speaker 1: talked about a phrase that one of her colleagues would
Speaker 1: use consistently whenever anyone was working on a quantum computer design,
Speaker 1: which was you're thinking too classically. You're limiting yourself to
Speaker 1: thinking in the old classical physics and classical computer approach
Speaker 1: to the way we do things, which works fine if
Speaker 1: you're working on classical systems, but quantum systems require thinking
Speaker 1: outside of that. It require there's a stretch you have
Speaker 1: to actually go beyond what we typically think about as
Speaker 1: human beings, because the quantum world is not something that
Speaker 1: we can observe in our day to day lives. You know,
Speaker 1: we observe the classical universe. That's what were that's what
Speaker 1: our senses are capable of picking up. When you're getting
Speaker 1: to things that belong to the quantum realm, they don't
Speaker 1: make sense to us, largely because we can't observe them,
Speaker 1: and because we can't observe them, they don't they don't
Speaker 1: seem to be part of our realities. So things that
Speaker 1: we understand, like, for instance, if I walk up to
Speaker 1: a wall and I keep walking, I'm gonna slam into
Speaker 1: that wall. I'm not just gonna pass through that wall.
Speaker 1: I'm gonna hit it. It's gonna hurt, it's gonna stop me.
Speaker 1: But in the quantum world, you can have a field,
Speaker 1: and anywhere within that field you could potentially exist. Right,
Speaker 1: So imagine instead of having a physical location that you
Speaker 1: could identify with like GPS coordinates or something, it's more
Speaker 1: like you have a big, sort of nebulous circle. Within
Speaker 1: that circle, you could be at any of those points
Speaker 1: at any given moment, and if you were to take
Speaker 1: a snapshot of a moment, then yes, you would appear
Speaker 1: at a very specific point within that circle. But if
Speaker 1: you took a different snapshot at a different moment, you
Speaker 1: would be in a totally different part of that circle. Now,
Speaker 1: in this world, if I were to approach a wall,
Speaker 1: sometimes within those snapshots, I would once my circle overlaps
Speaker 1: the wall and goes on to the other side. So
Speaker 1: part of my circle is still on the side of
Speaker 1: the wall that I was on originally. Part of my
Speaker 1: circle now overlaps the other side of the wall. You
Speaker 1: take a snapshot, sometimes that snapshot is gonna show me
Speaker 1: on the other side of that wall, even though I
Speaker 1: didn't actually pass through it. I didn't walk through the wall.
Speaker 1: I just appeared on the other side of the wall
Speaker 1: because my circle overlapped it. That circle represents the probabilities
Speaker 1: that I could be in any of those points at
Speaker 1: any given time. As long as there is probability, that
Speaker 1: means that at some point points I will be in
Speaker 1: that part of the circle. Now, this actually exists in
Speaker 1: the quantum world. It exists in our our microprocessors. It's
Speaker 1: called electron tunneling or quantum tunneling, and this is where
Speaker 1: you have these gates, these logic gates that, because of
Speaker 1: the materials that were used and because of their thinness,
Speaker 1: are so thin that when an electron comes up to
Speaker 1: the gate, there's the possibility that the electron will actually
Speaker 1: be on the opposite side of the gate, not on
Speaker 1: the side that's supposed to be on, and that creates
Speaker 1: electron leakage. This is a bad thing for electronics because
Speaker 1: electronics is all about the controlled pathway of electrons, and
Speaker 1: if electrons can sometimes bypass a gate without the gate
Speaker 1: uh allowing for this like it it's supposed to stop
Speaker 1: the electron. Instead the electron just passes right through because
Speaker 1: it's electron field overlaps where the gate is. Then you
Speaker 1: get errors, you get mistakes. So that is a real
Speaker 1: world example of how these qual tum effects can create problems.
Speaker 1: But we don't observe these directly because it's on a level,
Speaker 1: it's on a scale that's far too small for us
Speaker 1: to to see. So I found it really interesting to
Speaker 1: think about that as well, about how this the strange
Speaker 1: world that doesn't seem to behave according to physics that
Speaker 1: we have observed, can still have real impacts on us. Obviously,
Speaker 1: using this sort of world to create electronics that we
Speaker 1: can then use to do all sorts of stuff is
Speaker 1: pretty complicated. So how do we fix this? Tellia Gershon
Speaker 1: said that one thing we need to do is start
Speaker 1: in the classroom. We need to teach people how to
Speaker 1: think outside the classical system um. I certainly would have
Speaker 1: benefited from this. When I was a kid, I didn't
Speaker 1: have a whole lot of exposure to quantum physics and
Speaker 1: quantum mechanics. When I was going to school, I had
Speaker 1: a little bit, but just enough to really confuse me,
Speaker 1: not enough to get kind of a bay sick understanding
Speaker 1: and a field for things. She said that within five years,
Speaker 1: you're going to see physics departments and computer science departments,
Speaker 1: and electrical engineering departments and mechanical engineering departments all talking
Speaker 1: about uh, quantum effects and quantum mechanics, quantum computing, quantum states,
Speaker 1: and that there will actually be classes on things like
Speaker 1: designing quantum circuitry and quantum programming. And when we see that,
Speaker 1: we're gonna see a huge development in this space because
Speaker 1: people who otherwise kind of had to forge a pathway
Speaker 1: towards quantum computing will now have the torch lifted up
Speaker 1: by people who were being trained on this from the
Speaker 1: get go, and therefore will end up having a benefit
Speaker 1: of the previous pioneers knowledge and be able to carry
Speaker 1: it much further and develop the technology to a point
Speaker 1: that is really really useful for all of us. And
Speaker 1: that was the final presenter that night, and one of
Speaker 1: the last messages that IBM Research gave that evening was
Speaker 1: that effective science communication is more critical today than ever before.
Speaker 1: That science communication is a tricky, difficult thing. We're talking
Speaker 1: about very hard concepts for some people to understand because
Speaker 1: they've had limited exposure to those ideas and their counterintuitive
Speaker 1: in many cases. So you have to be really good
Speaker 1: at communicating this to people so they understand not just
Speaker 1: what is going on, but why it's important. And that
Speaker 1: another really critical element is public engagement in science to
Speaker 1: create a conversation in science, to to not just educate
Speaker 1: the public, but then to invite the public to take
Speaker 1: part in these conversations because you'll get more representation that way,
Speaker 1: and more uh ideas and more challenges to your notions
Speaker 1: which are equally important, and that way you can take
Speaker 1: part in making the this visions that will create these
Speaker 1: technologies and ultimately help us move forward. I found that
Speaker 1: to be really inspiring as well. Well. That wraps up
Speaker 1: the Science Slam at the IBM research session that happened
Speaker 1: on March nineteenth, two thousand eighteen. UM. I look forward
Speaker 1: to attending the conference. Today. We're getting close to six am,
Speaker 1: which means pretty soon I'm gonna go hit the gym
Speaker 1: and then I'll go to the conference and see what
Speaker 1: else I can find and who I can talk to.
Speaker 1: And I hope to record a whole bunch more episodes
Speaker 1: special episodes for you in this mini series about Think
Speaker 1: two thousand eighteen, talking about cutting edge technologies, getting insight
Speaker 1: into some of the most complicated and fascinating aspects of
Speaker 1: science and technology, and where these might be taking us.
Speaker 1: I hope you're enjoying the mini series so far, and
Speaker 1: I look forward to including more of these. If you
Speaker 1: guys have suggestions for future episodes of tech Stuff, I
Speaker 1: highly recommend that you can in touch with me and
Speaker 1: let me know. My email address is tech stuff at
Speaker 1: how stuff works dot com, or you can drop me
Speaker 1: a line on Facebook or Twitter. The handle for both
Speaker 1: of those is tech Stuff hs W. You can follow
Speaker 1: us on Instagram see lots of behind the scenes good
Speaker 1: ease that way, and uh make sure you tune into
Speaker 1: twitch dot tv slash tech stuff on a typical week
Speaker 1: on Wednesdays and Friday's, I record live. I live stream
Speaker 1: tech stuff and you can check it out. Just go
Speaker 1: to twitch dot tv slash tech Stuff. There's a chat
Speaker 1: room there. You can participate and say hi to me.
Speaker 1: I always love seeing people there and I'll talk to
Speaker 1: you again really soon for more on this and bathos
Speaker 1: of other topics because it how staff works dot com