At the Intersection of AI, Governments, and Google - Tim Hwang

All right everyone, so today we have Tim Wong, and we are live from Tim Wong's apartment. I'm Francisco. Alright man, so I think the easiest way to do this was just to introduce yourself.

Okay, cool. So, well, thanks for having me on the show, Craig. My name is Tim Wong. I'm a global public policy lead on AI machine learning for Google.

So, what do you do for your job?

So, public policy is a pretty fun job. It's a combination of a couple different things. On one hand, I work a lot with sort of governments and regulators and civil society, trying to figure out actually what Google's position should be on a whole range of issues. Everything from, you know, whether or not machine learning is going to take all the jobs, to whether or not we can make sure that these systems are fair and non-discriminatory. Internally, I work with product teams and researchers to kind of keep them apprised of what's happening on the political scene worldwide.

Okay, and so what does that mean? Does that mean traveling around and meeting with people? How do you find that?

That's a lot of meeting with people, actually. So we end up talking with people from a whole range of different sectors and a whole range of different backgrounds, particularly because AI is, you know, this kind of emerging technology. A lot of what we're doing is kind of just trying to assess how different parts of society are thinking about it.

And so I think that with AI, you're in the policy on AI, you've kind of like nested to obscure things that people don't really know what you're talking about. So, could you just back up a little bit and explain like what doing policy for Google actually means in the context of AI?

Sure, definitely. So, you know, I think the really interesting thing about AI is basically that, you know, a lot of the modern techniques in artificial intelligence, if you even ask people like a decade ago, they would have told you like this is never going to be a thing. It's a complete dead end. Why are you doing this research? It really has kind of exploded in a completely unexpected way in the last few years. And so really a lot of the challenge has been like, okay, everybody's kind of wrapping their heads around what the, you know, even what the business impact of the technology is going to be. But there's increasingly a lot of people trying to figure out what the social impact of the technology will be.

I would say policy really sits at that interface between these really cool technologies with capabilities that are coming about and then what society in general is going to do about it.

And so what would be like a ten example at Google of like a policy that you guys have worked on to figure out?

Sure! So there are a couple of really interesting problems that we've been working on very closely. One of them is this question about fairness in machine learning systems. For example, to give you one really concrete challenge we've been thinking a lot about is, in order to be bias a system, right, once a machine learning system is behaving in a biased way, one way of trying to deal with it is collecting more diverse data.

Okay, but one of the big problems is when you do that, you end up collecting lots and lots of data about minorities, which results in really interesting questions around privacy and then what have you. That is being a really just in pro, because it's both a technical challenge, right, which is like can you collect an adequately diverse data set, but on the other hand, also this policy question which is what is society comfortable with you collecting and like what are the practices?

That ends up being a really interesting trade-off that you have to navigate if you're interested in these problems.

And so what do you actually have to do? Like are you going doing like user interviews of people or is it just guessing?

Yeah, part of it's user interviews, part of it's actually working with people who know, right? Like it turns out that issues of privacy, particularly minority privacy, are like not a new problem. A lot of our work is actually like talking with people who are like experts in that space, right? People who have worked on, you know, biases in criminal questions in the past and a lot of data scientists and trying to get them to talk to one another. Because I think what we're right now, we're really trying to do is kind of bridge these sort of human values on one hand with like a lot of what's happening on the technological side.

And so if I'm a company and I'm like, I can't afford a policy guy like Tim, and I will be dealing with large amounts of data that may or may not discriminate against people, are there any like obvious no-goes that you would tell someone?

Well, I think it's to be sure that you're like interrogating the data, right? Like I think that's like one important place to start. Now, I think one interesting thing is machine learning has lots of potential points of failure, and like I think every single interesting point of failure is being investigated right now. One of the most common problems is just that you don't adequately think through your data as the Machine does what the Machine does, right, which is trying to optimize against your objective function that you give it. Often, it will maximize in ways that you don't expect.

In fact, part of the problem, right? I mean, one of the examples that I always think about is, you know, we have this project that we released, it was called Deep Dream. One of the problems in computer vision is trying to figure out what the computer actually thinks it sees when it looks at an image. You go through this process and basically, the whole process, you show it an image, you ask it like what do I have to do to this image to make it look more like what you think, for example, a sandwich looks like. You edit the image slightly, you keep repeating this process until you show out like what is the ideal thing that the computer thinks it is.

It turns out that when you ask it to see, like ask it to reveal what it thinks the barbell looks like, you know, barbells always show up with human arms attached to that.

Oh right, yeah.

And so that's usually probably my favorite train barbells on photos. Yeah, I always have someone holding the barbell. And so I've been learning this completely bad representation.

What do you got to do?

I mean, a big part of it is just like the consciousness around like, oh, that can happen, right? And like how do you interrogate your data set to make sure it doesn't have those problems? And you guys are doing some interesting stuff around adversarial data, right?

Yeah, that's right. So I mean, I think adversarial examples and generative adversarial networks are like some of the hottest points in the research right now. It's almost become a joke that there's like so many what they call GANs out there right now. This is like everybody has a GAN.

What does that mean? What does it stand for?

So, generative adversarial networks, there's a very particular way of kind of setting up machine learning. But adversarial examples lead to these really fascinating results where, you know, you can take a picture of a panda and that's a classic example, and you edit a couple of the pixels and it like basically like the computer will be like, "Yep, that's definitely a giraffe," and it still looks like a panda to the humans.

The really fascinating thing.

So what data are you seeing into that image to make it think it's a giraffe?

Well, a lot of it I think is basically you're editing particular pixels within the image that we know will set off the machine to behave in certain ways. It turns out basically that we always assume that a computer will see the same thing that we do just based on the visuals, but how we process is actually completely different from machines.

This researcher, David Weinberger, had this awesome article recently, which is basically trying to argue that like, you know, machine learning, it's generating knowledge. But one of the most interesting things about it is that it's generating knowledge in maybe a way that is completely different from ours.

The way our human brains work, and like that ends up being a really interesting challenge is like how do you like understand the knowledge that you're getting, and how do you understand like the reasoning behind the knowledge that you're getting from machine learning systems?

Well then maybe that's a sensible segue into like how people are investigating the impact of AI as it relates to like automation in what humans are good at doing and what computers are good at doing.

Yeah, right. And so when you travel around, you meet with people, you meet with different countries. Like, iron, how are people gauging the effects of automation and AI right now and its effects over the next decade?

Yeah, I think so. It's an evolving picture. Right? And I think right now, I think everybody is just surprised at all of the things that machines can do that we thought that humans were going to be good at for the foreseeable future.

So like Go is the canonical example, but there's all sorts of really interesting kind of reasoning and other things that machines are engaging in now. And so one thing I always tell people is basically that everybody always wants to think about AI as if it were like this huge meteor just crashing into the earth. But they're like, what do we do when the AI arrives, right? And it doesn't. It turns out that it doesn't work like that, right?

In fact, like what we really need to get to is like thinking about like how particular, you know, technical capabilities will map onto the economy, and those are what a lot of work is happening on right now.

Okay, and so yeah, let's go into some examples.

Yeah, sure!

So for example, one really interesting question is this adversarial examples, yeah, which is basically like everybody always assumes that like, okay, if it can be automated, it definitely will be automated, right? But that's like a fallacy because in certain cases, like you may really worry about the security of your systems, right?

So someone, for example, can hold up a photo and cause like a security camera to be like, "Oh, it's definitely Tim. Open the door." Right? Like that ends up being a real reason why you would not necessarily want to implement a machine learning system for, you know, access control, for instance.

And so that's actually really interesting because that means that if we don't solve that research problem, that means that we will be limited in the kind of domains that machine learning enters into. And I think that's what we're really interested in right now is like what are these kind of gateway research questions that if we got through, we'd like totally change the nature of like who, when, and why someone would implement this stuff.

And so are those things collecting the interest and like the momentum of the research community? Because like I can see a certain direction where it becomes incredibly product-focused, right? Where I'm like, I'm a researcher, I'm incredibly talented. Yeah, like figuring out if the security camera is going to work with an adversarial network, like maybe not might not be of highest interest to me, right? Is that like blocking people or is like the general concept enough?

I mean, I think right now it's a little bit unevenly divided, right? It turns out that like research interest is not necessarily like policy-relevant interest, right? So in some cases, they're overlapping, right? I think there's a lot of interest in adversarial examples, there's a lot of interest in like what are attacks, essentially, that you can put on these machines to get them to behave in ways that you don't expect.

That seems to be a place where like security, which is like very much a policy interest, will map on quite naturally to security as like a research interest.

Okay, but for example, things like fairness, right? Like I was talking to a machine learning researcher the other day who was basically like, "Look, I could not in good faith advise a grad student to work on machine learning fairness issues because it's just not considered a serious problem in the field," right?

And like that's just like that has to do with like the field and more like the norms of the field, right? And then that ends up being a big issue, right? We don't have coverage on certain types of things and in practice, may actually really limit like where these technologies are implemented.

Well, I think it's a material issue right now. Like there's a gap between product understanding and actual deep research.

Yeah, that's right. And I say this to a lot of people, like you know, everybody's always like, "So what skills do we need to teach people in the future because of machine learning?" And I think like one enormous skill will be like domain knowledge because like coming up with like a technical capability is just like one part of this huge picture, right?

Which is just like, okay, so then like how do we actually introduce automation in a way that makes sense to people? And like that's like a huge task. And so my personal prediction is that like interface and like how we effectively collaborate with machines, particularly with these two new types of models, like how that's effectively done is still a big open question and will seem to be increasingly in higher demand as you still may have access to these capabilities, right?

So what I've been wondering then is like does, for example, like you know TensorFlow or, you know, any one of like the machine learning APIs, does that become the new AWS for products or do people have to build their own to create like a defense ball company?

I mean, I think there's still like, so I mean like cloud services will have the same impact on the economy they always have, right? And I think this is one initiative because all these companies are now competing for offering cloud ML services. The upshot of that basically is that the amount of like you don't need a PhD in machine learning to get all the benefits from machine learning, right? And I think that will shape the space for sure.

Okay, yeah. So then what are the other areas, like aside from the first one we talked about for automation and work, like where are other people interested?

Well, so I think the other thing we're really interested in and I'm really interested in is kind of like, is it possible to pull off machine learning with less and less data?

Right? So you know, there's a couple of examples of that, but one of them is like one-shot learning, right? Where people are basically working on the ability to teach machines with like a much smaller number of examples.

Now that actually has a really big impact on the game because that means that you can implement machine learning effectively in situations where it's really expensive to collect lots of data. There's also one really cool interface between VR and AI that's happening right now where the whole idea is like there's a project called Universe from OpenAI and another project called DeepMind Lab, which basically like, imagine you need to teach your robot to get through a maze.

Well, you could have it physically run through that maze millions of times, or you could just have a virtual 3D environment that you cause a computer to run through, and it learns how to do that in virtual space. Then basically you put it into practice in a real robot.

And so that's a really another exciting where we don't necessarily need like an expensive physical setup to collect the data that you need to accomplish tasks in the real world.

Okay, so then what, like, I guess what I'm curious about then is like how are these countries like preparing for this? Like, you know, again, not a meteor strike but like perhaps a gradual shift over 20 years, 30 years to a very different world than what we have right now?

Yeah, I think you're right. Now you’re seeing like a bunch of different ideas out in the space. You know, some, for example, like basic income, right? Universal basic income, which was like fundamentally reshaped, you know, like the social contract and how we think about doing, for example, like welfare in a whole number of countries.

And like, so you see proposals like that. I think you see a number of proposals that are more like focusing on education. So like what are skills that people will need in the space? And that's never industry, everything from like everybody needs to be a programmer to like, oh, well we need to really encourage like computational thinking, right?

Which is like the ability to work effectively with data, right? And so like there's a couple of different options out there. Some of the more interesting ones that I've heard of that are a little bit more obscure, right? Like some people have said like, oh, well maybe we need like automation insurance, so in the future your employer will provide you with like a contract that says if your job turns out to be replaced by AI at some point in the future, we'll pay out at some kind of rate.

So people are like experimenting with lots of options right now. I think what we actually need in the space is like more experimentation. So for even proponents of basic income, a lot of them will tell you like we actually don't know in practice what this would look like if it were actually rolled out at any level of scale.

And so like, I mean, it's cool seeing YCR and a couple of other places, like experiment with this.

And where is the traction happening then with all these experiments? Like it seems very limited, but is it all like in Northern Europe? Or, I know there's a basic income study in India at this point; who seems to be focusing most on this area?

Yeah, we're seeing a lot of different countries engage in this. I think Northern Europe is kind of leading the way in terms of their willingness to kind of experiment with some of these models. And I think they’ve got a couple of things going for them, right?

Like on one hand, I think they have a skilled labor force, right, that is relatively expensive, right? So I think that they are seeking and excited about AI in large part because it's the prospect to bring, for example, manufacturing back to the country because it allows them to compete on the same footing as like other countries that have offered like labor for like much lower cost, right?

So like that's one thing that’s good for them. I think the other thing that's also encouraging a lot of experiments is that they have a lot more coordination between government, industry, and labor, which is making it more possible to experiment with these sorts of things.

I think in a really interesting case, Northern Europe is actually a little bit ahead in its ability to kind of experiment and understand some of these programs. And then as Google or Alphabet, as like this international institution at this point, how are you guys thinking about interacting with different countries as this happens?

Yeah, so we're investigating at the right time, right? So the question is who on the research side do we be working with? And what programs could we support that will help us give a better handle on this picture, right? Because I think like, look, ultimately, like it's a technology company, right?

And so we know that we don't have all the talent necessary to like evaluate like, you know, what is the proper like social welfare program. But on the other hand, we think it's actually really important that we like encourage a better societal understanding of like how to deal with these technologies.

And so I think we're very much in the mode of like how can we support this. And I think that's partially through potentially resources, but also potentially like expertise as well, right? Like we, if you want to know anything about machine learning, we got people who can tell you about that.

Now we have to marry that up with people who have a good understanding of how this will impact society, either through economics or otherwise.

Do you ever feel like the information you're disseminating is like guiding the conversation and guiding the future or like people are like playing into the game like it's intentional or it's opened up?

I mean, I think it's very open, right? I mean, I think like, you know, I think it's easy, particularly in the valley to be like, oh my god, these big companies, but like we're only one part of a much larger picture about what's happening in the economy.

I like totally think it's okay, it's like, you know, if we talk about AI and automation, but we might also want to talk about like demographic shifts happening in the economy, like what does it mean that we have an aging workforce, right? Or like, what's it mean that we have like falling workforce participation to the United States, right?

Like those are actually trends that like they’re almost as large as like what someone comes up with in a lab and presents at a machine learning conference. And so, like I think it's actually really important that like we look at this all in a bigger perspective.

Okay, and so what do you guys do to keep that in mind? I imagine you just have like a whole policy team to manage that.

Yeah, there's animal responsible for keeping track of a lot of this stuff, all right? And they're getting a better understanding of like who is researching in the space because I said, you know, like I think we're still really early on in this technology. We're like again, if you had asked someone ten years ago whether or not neural nets were going to be a thing, they'd be like, I don't know, it probably wouldn't work.

Right? But like if we're at a phase right now where like suddenly it has become technically real, I think now that understanding is just time to percolate out to a bunch of other fields who are like, okay, well I guess we never got to assess what.

And do you see companies and organizations and countries like locking their gates because they're scared? Because it feels new? It like it's obviously massively hyped but there's also some reality behind it. Has there been a negative reaction?

I wouldn't say so. I mean, I think by and large what we're seeing is that a lot of governments are just really curious. They actually want a better understanding of what's going on.

Okay, so in many cases I think what we're seeing is like people asking, you know, like what is happening in the technology? Okay, so I think the phase of what to do about it is still on its way, right? So that you like give them, you know, the PowerPoint deck and they're like, okay, I kind of get how this works, and then they go home, you know, whatever, to like Japan, and they're like, okay, now you think about it.

Yeah, I think so. I mean, this is how government progresses, right? Is like, I think like they ask questions to get information and there's like a long process of figuring out what you do around it, right?

But that isn't to say like there aren't like laws and other regulations being passed that have relevance to machine learning, right? So one of the most interesting aspects of the GDPR, which is a new privacy regulation in Europe, is the potential for this what they call kind of a right to explanation.

So the idea is for certain kinds of automated decision-making, it might be so significant as to require or give citizens the right for that system to be able to produce some kind of human-understandable explanation for what it's doing. And like every, there's all sorts of interesting challenges about like how you actually pull that off.

So like I would say that I don't want to make it sound like no governments are taking action, but I think like that's the beginning part of it, right? Like, and I think like by and large the stance of most governments have been like understand what's going on.

Do you think someone's doing it particularly well now?

Yeah, I mean, I was really excited by some of the stuff happening out of the UK. So last year they actually did a report that was on kind of like giving an account of like the risks and opportunities from artificial intelligence, and I think there's like a really good account to that.

So, and then last year would be under the Obama administration, there was a really good report that it as well on the topic.

Okay, and so like can you go specific on that?

Yeah, sure. So I mean I think the what we at least have it in the, we Louise had in the US best case, right, was basically a report that like really focused in on like, okay, what are the real concrete risks here?

And part of the idea was to pivot away from discussions that were just like, okay, the main thing we've got to talk about here is whether or not robots are going to destroy us, right? Like, I decide to take over, right? Which I agree is like kind of an interesting scenario to consider, but there are a lot of core near-term problems that need to be dealt with.

And I mean, one thing they did that was very useful...

So aside from the stuff we talked about, where do you find to be particularly exciting? Both like here, like at a, you know, local Bay Area level as far as like research, and then at, you know, global international research level moving the stuff forward.

Yeah, so I think there's two things that I find really interesting right now. One of them is the intersection of machine learning and art, right? So like largely visited technology we've been using to solve like pretty pragmatic things, right?

Which is like how do we ensure that we can adequately recognize like cats in photos? But like what's really interesting is a bunch of people who are kind of playing around right now with the intersection between like, oh, could I use this for like artistic purposes?

So this is really a fun project. Google has this project called AI experiments which is a lot of kind of like small things like this which kind of demonstrate the kind of artistic possibilities of technology.

We also have another program called Magenta, which is looking into machine learning and music and like whether or not there's ways of kind of creating better creative collaboration between humans and machines on that front.

And have you experimented with it personally?

Yeah, some of it's really fun. There's one project which is basically like a melody generator. Like you play some notes on a piano and then the computer will play alongside you, like harmonize with time.

Yeah, exactly, right. And you kind of like improvise with it with the computer, which is super cool. There's another project called Moroder Cam which, again, on your phone, okay, which is like you take a couple of photos of things in the room and it produces this pop in like electronic dance hit that has like that uses the words of the objects in the room that's like a rhyming, you know, set of lyrics.

Oh cool, yeah!

And a great example of like how the technology is becoming look really accessible because again, if you wanted to do that like 10 years ago, it would have required like a huge amount of money and like, you know, a bunch of PhDs try to work on this problem, right?

Yeah, I've been fascinated with that, like how it's become distributed just even in the past, like, like I tell you about all the speech-to-text stuff I'm working on.

Yeah, no man, like the fidelity of it is shocking.

Yeah, that's been like one year, right?

Right, and so it's gotten like way better, which is letting a super interesting. I think the other thing is also like trying to figure out like Disney's like really unexpected things that emerge too.

So the other thing that's I think is really cool right now is this paper that came out from DeepMind, I think earlier this year, that was kind of like if you get two machines to talk to one another, they will eventually, and you can set up another computer to basically say like, oh I can read what you’re saying.

I can't read what you're saying. You can basically train these two systems to come up with the rudiments of encryption without even necessarily needing to program encryption into the computers, which is also like super cool! They learn how to accomplish that task and it's not like very good encryption but like the basics are basically learned by these systems so long as you give them good reinforcement on like, okay, that's still cognizable.

I can still understand what you're saying first as like a third party being like, oh I can't do that.

Oh man! And so do you have thoughts on like how this will become distributed in such a way that any day like we'll be interacting with it in our everyday lives? Just like fun projects? Like will it be existing in the art space? Will it be, you know, like training new programming languages for folks to work on when they're younger?

Yeah, I'm guessing like there's, you know, it's talking to Peter Norvig who is like one of the researchers we have is like one of the founding fathers of AI. And he had this really interesting thought which is basically that like we may be approaching the period where we actually have to entirely rethink how we teach computer science.

Because like machine learning is such a powerful tool and also cognitively it works in a way that's like totally, you know, counterintuitive, right? So like I do less software than I used to but like definitely when I was in the trenches doing coding work, it was very much like okay, like let's get a bunch of smart people in the room, let's come up with a bunch of rules and then like get those rules into the machine.

Versus it's a much different kind of mode of thought, right? Which is basically like let's present the machine with a bunch of examples and then verify whether or not the machine has learned the proper lesson.

And so his idea is like actually we may actually really want to think about how we think about computer science from like the very first moment you step into a classroom, which I think is like a super compelling idea because it was always thought of like, oh machine learning is just going to become compliment like how you do programming.

But I wonder where that software in the future will actually look more like machine learning focused, right? And like you actually change your entire approach to programming systems.

Oh man, that's fascinating. I mean, it's already kind of gone that way and that like many CS programs are so technical you actually never build a web app.

Well, yeah, that's right. You can go through sand routes, yes, and never build.

And I think it's a very natural trend that like, you know, we're getting to higher and higher levels of distraction.

Yeah, so like in some ways, machine learning is like the ultimate level of abstraction where it's like even if you wanted to understand what's happening in like a neural net, like it might be actually like kind of difficult to do so.

Right, yeah, I mean I guess so, but I see it becoming like there's just new ways of thinking about how you ought to be programming, right? Like how you structure the code because at a certain point, things will just become abstracted and you won't have to do it anymore.

Yeah, like I think about it in the context of like, you know, parsing, creating an API, right? Like that will exist for many things. Like I could see like a Squarespace type thing but for like a proper web app, right? You just drag your database in, and you go, you never even think about this, right?

Yeah, and so ironically, like programmers might lose their jobs way sooner than they think.

What I'm particularly interested because like we actually like this emerging research right now which is using machine learning to train machine learning systems raises like this meta level where like right now there's a lot of handwork that goes into building a model so it learns the right representations.

But like if a commission can do that in the future, it gets it even more abstract, it well. You may not even need to be like a specialist because in some ways the machine kind of like codes itself.

So, one thing that a lot of people are curious about is how you're actually going to build business around AI. So, just for like we can start broad and then go more narrow. Do you think AI will be like dominated by massive companies like Google, Facebook, or will, you know, they’ll be very successful AI products on the small scale?

I mean, I actually think that there's actually like a ton of room for competition here and it'll be interesting to see how all the various companies find their niches in the space. And I think there's two really interesting trends right now, right?

I think one of them is the emergence of like cloud platforms, right? Where basically all the companies have bases have said like there's a long tail of uses we would never be able to like take advantage of but we may be able to provide the services that like power those services, right?

And so like for example, Google is offering like cloud ML right now. And I think it's a really interesting development in the space, which I think creates a lot of opportunity because it means that there's all these industries that might not necessarily be like AI industries, they might be able to like seize the benefit from the technology.

So that seems like a pretty huge thing to me. I think a second one which is really interesting is like some of the one-shot learning stuff we talked about earlier, right? Which is basically that the amount of data you need to pull off certain types of machine learning applications is going down over time.

And what that tells me is that there might not be necessarily a first mover advantage in the space where you may actually have collected a bunch of data. But if it's not the relevant data, and also the amount of data you need is going down over time, then the real big challenge is less data and actually more your ability to build like good interfaces and good experiences around the technology.

Yeah, I've been wondering about that like as I play around with it and build like tiny little web apps and stuff like how much of this is just entirely reliant on the product as like it's all plug and play. And so to a certain extent like folks can almost guess which techniques you're implementing and which API you're using and if they're faster with better engineers and then they have like the magic touch of like the product person, I don't see any reason why they can't just jump ahead.

Yeah, right, right. And I think we may be fooled by like the nature of the field right now where it's like, ah we got to get like the most researchers to go and compete on this thing.

And like that is a big, important part of it because they're producing a lot of like the breakthroughs in the space. But it is, I think, important to consider too that like there's still like this big open question of like how this actually becomes like effectively part of products.

Oh, absolutely! I mean, we did an interview if I do, and it may or may not come out for years, so we might do like a fourth wall jump, but they explicitly are focusing on things for over 100 million people.

And you're like, oh, okay, well I can build plenty of successful startups or businesses for less than 100 million, maybe even a million.

And so, yeah, I think they're just all these fantastic opportunities for people and yet folks seem to be focusing on very similar implementations, you know, whether it's like chatbots or like, you know, customer service, which I guess is effectively the same thing.

Why do you think that is? Is it they just like follow what seems to be like the market leader or are these like the most obvious?

Yeah, I think people are also still trying to figure it out, right? Like, and I can't, I think we can't avoid like that AI is like a technology, but AI is also like a position, it's a marketing position, right?

Which is I think is lecture you're like a really key part of the picture. It varies like why do we think about like Siri or like as a Google Assistant as like AI but we don't necessarily think about like the Facebook newsfeed as an AI?

I think these are all systems that are all powered by machine learning, right? But there's something about like its representation as like, oh yeah, this is a machine that talks to you, right? Like makes our brain snap immediately to like pop culture, you know, equals AI, right?

And in advance of being a really big part of it too is that there's a lot of incentives to like correspond to what we think of as AI even though like some of the most powerful AI applications may not even come in the form of like a personified, you know, personality.

Well, I think that's a super interesting angle. It's like out here seemingly it makes sense to like raise your money as like an AI business, but like when you look at Facebook, right? Facebook if you log in doesn't say AI anywhere, and clearly they have a lot of people using it.

So I wonder if it is like a massive positioning thing that many companies do end up missing because you just have to get like the nerdy people interested in it to sell it to raise the money if you're going to do venture-backed or whatever, but then your end user is like why am I paying all this money for this like chatbot?

Nah, I mean like for example, yeah, if you want to talk about like one of the most critical applications of machine learning to date, it's like spam filters, right? Spam is like this incredibly huge systemic problem on the Internet, it is like largely contended with by machine learning right now.

Like that's like largely the tools that we use to deal with it and like that's like an application that we never think about, right? Like discussions and I committed with many technologies, the most important applications will be some of the least visible.

Hmm!

So what am, what are you excited about? What are you going to build? What are you...

Okay, I see, yeah, I got to think about it some more. I mean, I, you know, I'm really interested in these kind of like small scale machine learning projects.

I think we might have talked about it earlier, but like we have this really crazy story where it turned out that there's this cucumber farm in Japan that was using machine learning to build like a really cheap machine learning robots.

Yeah, so our queue company turns out my cucumber sorting is a really big problem in the machinery in the cucumber farming space, right?

And that was basically just trained using like 3,000 or 4,000 like photos of cucumbers and that was sufficient to train a model to do like a pretty good job at sorting cucumbers.

And like so like I'm really interested in this kind of like artisanal machine learning where like it's like what are these kind of like very specific daily problems that I have? And it's a good way of I think wrapping my head around like okay what are actually going to be like the practical uses, not necessarily like the Cadillac uses that I think we're being preceding right now.

Would you like the demonstration uses of the technology? And then you can open up like Tim's general store online.

Yeah, it really doesn’t look like Tim's cucumber app, right?

Yeah, I mean, I cracked my iPhone earlier and was getting a fix this morning, and the guy had an entire box of assorted iPhone screws from literally like an iPhone, you know, iPhone one to an iPhone seven now.

And these are just like, he's got like a side hustle of buying and selling iPhones that are like broken online, and if they're totally damaged, he just like strips all the components, but he spent like half an hour like trying to figure out what screw would fit.

Like there you go, using like Tim's screw identifier, right? Right, like it's super handy stuff.

Yeah, that's an idea. It will be like a lot of small things like that. And what's particularly interesting is like going back to what we were talking about earlier, like what is the cost of solving a problem through machine learning, right? And what is the cost of solving a problem through like traditional coding.

Rand is actually maybe one way of thinking about the problem, right? Like for example for computer vision, right? Like now the economies are way in favor of machine learning, this is a way easier to design an effective machine learning in image recognition system with ML than it is with like traditional kind of coding techniques.

And I think that's actually one really interesting way of thinking about is for a given task, how long until machine learning is like the preferred way of solving this problem with the computer?

Hmm!

It totally makes sense, as like new kinds of entrepreneurs pop up. It needs like very small niche things that are essentially like one developer projects. And that previously might have even seemed like way too laborious to spend your time engineering.

Like you're never going to pay someone to do it, you're not going to do it yourself, but you know, you start plugging into like these cloud ML things and all of a sudden you have this address as far as distribution.

I don't know, like I've heard more and more people talking about like localizing certain things to the device which makes them amazing.

Yeah, have you experimented with that yet?

Yeah, so we're actually working on a little bit of research around that. I haven't played around with it myself, but for example there's a couple of papers around what they call federated learning, where we're exactly working on this premise which is the bet is okay, well what happens in the future where the edges of our network, like the phones, like have way more powerful processing power?

Like is it possible for us to basically do the majority of the training for these systems like on device and with like basically a lot less data kind of like flowing into the cloud?

And the idea was basically like the local model would update and it would share its learnings with all the other devices in the network. And it's like a really interesting way of thinking about how you actually do this because what you ideally want to have is models that are loaded on the device, right, and can also train on the device as well, right?

Because right now, one of the irony is that there's big disparity between like training is computationally intensive, data intensive and then actual execution rate, which it can be actually like pretty low.

Low computational. It also creates a giant latency problem with everything that's like in big quotes AI right now. Like, you know, most people if you'd give them Siri, they're like, that it's constantly broken.

But if you could communicate with it in a way that's like you didn't understand, let me go again immediately afterward, all of a sudden the experience...

Yeah, that's right!

Yeah, and latency ends up being really key not just for like conversational interfaces but you think about like, you know, for example like how do we deal with like using this in medical, right?

Well, you need a response really soon if you're going to use it for likely diagnosis or whatever.

Totally! Like if this thing turns into a robot surgeon arm and I move it to the Amazon butt, and I can't rely on my leg, you know, hotspot.

Yeah, so I connected!

That's right!

Yeah, and so, yeah, I think again we're like talking about implementation, which ends up being like this really big piece of the AI picture which is still being worked out.

Like we know we can get machines to do these remarkable things. The question is like what do people actually want out of it?

So, I guess one of the last questions I have for you is, you know, people are interested in AI and machine learning across the board or at least people paying attention to this are into it.

If someone wants to get more into it and they're thinking about like how do I position myself, like what should I pay attention to? Where should I focus? Because like, you know, now tens of thousands of people are checking it out.

What would you say? What would you focus on?

So there's two really interesting problems in the space right now that desperately need more people to get involved in and more people to kind of like organize events around.

Okay, so one of them is, I think this like security thing, right? We're like in the traditional computer security space we've got like events like capture the flag, where Google can kind of like show their mettle in their ability to kind of like secure and compromise systems.

I actually think we really need that in the machine learning space and we’re really excited to see that which is like, imagine a game where like you have to train a machine learning model on a set of data and then people will take turns trying to like get past your computer vision system.

Cool!

Anything would be super cool to do, and I think like that's one big piece of it that I think would be really cool for people to work on.

I think the second thing that's about to be in really strong demand is thinking about the visual dimension of this, right? Which is like happens on a couple levels. That's both like the interface of how you work with machine learning systems, but also just like visually how you represent a neural net.

Like if you've read the technical papers, one of the things that you'll see is just like it's like we're largely written by machine learning experts, and so like they don't really have a good sense of like how do you visually portray what a neural net is doing.

And that stuff ends up being incredibly important for people to like both understand the technology and also be able to like to use it effectively.

And then if someone is an intimacy learning yet, what would you recommend they read, study, watch, and what should I check out?

So, I mean, I think it's really nice because we're now living in a world where there's a lot more resources for how to learn about machine learning. So I'm a huge fan of Ian Goodfellow's textbook on deep learning.

It was really funny, I was in Cambridge picking up a physical copy of this textbook because MIT Press is the publishers, and the guy was telling me the book was like this is like the Harry Potter of technical guides because it had been like flying off shelves so aggressively, and so it's really good, though its reputation is very well-deserved.

Okay!

I, you know, one of the things I've been thinking a lot about is kind of like the history of all this, right? Like, it's important to recognize that like AI has been through this hype cycle before, and there have been long AI winters where this technology is totally oversold itself.

It's important to understand those dynamics, so two books I'll mention: one of them is John Markoff's "Machines of Love and Grace," which is all about kind of like the history of AI and particularly its competition with the notion of AI versus right intelligence augmentation.

Okay!

I was living is a really interesting battle that we're having right now, right, in terms of like what this technology is really about and what should be used for.

A second book which is great, which is also by MIT Press, is "Cybernetic Revolutionaries," which talks about basically the Chilean AI government. So it's basically the socialist government during the mid-20th century, and they try to basically set up a project called Project Cybersyn, where they're like, "Let's automate the entire economy."

We're all factories; we'll have to produce data links that will connect to a single central command center where we will like actively control the economy.

And it's a great initial another example of kind of like oh like kind of like the history of cybernetics but also it's like implications for like what people try to do back then that I think useful for like, you know, making sure we understand what the limitations of the technology are today.

That's very neat! I haven't read that; I will absolutely check it out.

Cool, man! So if anyone wants to follow you online, where do they go?

Oh sure, I'm on my website is that Tim Wong, T-I-M H-W-A-N-G dot org. I'm not the Korean pop star of the same name, and I'm also on Twitter at Tim Wong, so @T-I-M-H-W-A-N-G.

Very cool! All right, thanks dude!

Yeah, thank you! Have a great day!