{"url": "https://feeds.soundcloud.com/stream/221288316-samim-ethical-machines-ep2-jack-clark.mp3", "page_url": "https://soundcloud.com/samim/ethical-machines-ep2-jack-clark", "title": "Ethical Machines - Ep2 - Jack Clark", "show_or_channel": "Ethical Machines (samim)", "date": "2015-08-28", "duration_s": 1804, "type": "podcast", "confidence": "high", "notes": "Bloomberg-era; hosts Roelof Pieters & Samim Winiger; Clark as \"world's only neural network reporter\"", "src": "p0", "id": "2015-08-28_ethical-machines-samim-ethical-machines-ep2-jack-clark", "audio_duration_s": 1804.00475, "worker": "jc-w05", "transcribe_s": 20.7, "model": "large-v3", "segments": [{"start": 8.72, "end": 42.75, "text": "Ethical Machines, we are hosts and Samin. Ethical Machines is a series of conversations about humans, machines and ethics. It aims at sparking a deeply better informed debate about the implications of intelligence systems for society and individuals. For the second episode, we invited Jack Clark, the world's first neural network journalist, reporting for Bloomberg to talk with us. Let's dive into the interviews. Ethical Machines. So how long have you been in the US for now?"}, {"start": 42.75, "end": 68.29, "text": "I've been here for about three years now. I moved here to join the register where I wrote about AI and databases and then I got hired by Bloomberg and so now I'm helping to cover AI for us as well as doing more traditional enterprise companies. The way I think of it is neural networks are going to be fundamental to a very large amount of..."}, {"start": 68.29, "end": 98.21, "text": "The AI that we'll see and experience for the next few years. So I figure if I report on any company or individual messing around with this technology then I can get a view of a good section of the AI world as it expands. And from a story point of view it's very fruitful because there's lots of research happening and it's creating some fascinating experiments and products that we can write about. So what do you think is the best approach to explain these complicated topics?"}, {"start": 98.21, "end": 118.59, "text": "I think you have to read everything. I spend about one or two hours a day reading the preprints as they come on archive, read the papers and study that, and then try to turn it into an analogy. Because no one knows what a neural network is outside of academia, but everyone knows that when you're an extremely young child, you're like..."}, {"start": 118.59, "end": 148.37, "text": "Pick up a flower and stare at it for several hours. And it's this early form of learning that children do, but it's analogous to what we're starting to do with some of these systems. You've heard major figures in deep learning and beyond blaming journalists lately for overhyping the issue, for mischaracterizing the issue. You've heard a lot of ugly words being thrown around. How would you confront these very regarded figures criticizing journalism in such a broad way?"}, {"start": 148.37, "end": 171.68, "text": "95% of the people making these criticisms have spent years studying artificial intelligence. They have a technical background. They probably have a PhD. They have years of experience of looking at very complex technology and coming away with objective and sort of applied applications of it. Most journalists don't have PhDs in machine learning."}, {"start": 171.68, "end": 195.26, "text": "From the journalist perspective, you know, I've had to spend several years reading a lot of literature and doing a lot of mathematics and trying to teach myself this stuff, and I've made that investment. It's difficult to find the time. So people like Demis Hassabis or Jan LeCun or Geoffrey Hinton, one of their main responsibilities in their public relations department should be to..."}, {"start": 195.26, "end": 222.4, "text": "Spend a lot of time with journalists and make sure that they just educate the journalists about how this stuff works. It yields a media where you have an understanding of it because they have to be generous with their time just as we do in our writing of their subject. So what are things which kind of happened, be it like unexpected or just interesting to you? Like what are the kind of most exciting things? The two very exciting things to me, one is..."}, {"start": 222.4, "end": 248.98, "text": "Memory systems. You know, we have seen in recent years, everyone has started to look at long, short-term memory again. The appreciation for the role the hippocampus plays in consciousness, that same observation is being applied to AI to give us systems that can do long-term reasoning and multi-part pattern recognition. So the second one is about reinforcement learning being combined with deep learning in robotics. I mean, you saw..."}, {"start": 248.98, "end": 276.32, "text": "Just this week, Fanuc invested a stake in Preferred Networks. Preferred Networks have been doing reinforcement learning and deep learning applied to robotics platforms. They read the Neural Turing Machine paper. They read the Q-Learning paper. They've also read work out of Berkeley's lab from Peter Abiel and Sergei Levine on end-to-end visuomotor policy training. So that already has created a situation where Fanuc has invested money"}, {"start": 276.32, "end": 302.08, "text": "ABB has put money into Vicarious. And we have some startups I've just heard about who are all doing this. So this is exciting. Especially after we saw the DARPA Grand Challenge, those robots looked so kind of drunk. They were falling all over the place. They were stupid. They were very slow. And I spoke to Dr. Jill Pratt, who ran that, and he's of the opinion as well that we're going to get a..."}, {"start": 302.08, "end": 331.42, "text": "Huge increase in robotics capability from the application of sensing systems from neural networks. But connected to that, what would you recommend as a graduate student in machine learning? Stay in academia? Join one of the established corporate research labs? Or start your own startup? I think that we're going to get deep learning artists at some point. Now that is the worst idea in the world if you would like to have money or a career."}, {"start": 331.42, "end": 353.18, "text": "But it is something that will happen. We're going to get some artists who are using this generative stuff. I've seen work that you've done. I've seen a lot of it. I think that the field of new aesthetics we're seeing from this could become very, very interesting once we have a better understanding on how to use the models. To answer your actual question, though, there are two areas of possibility. One is..."}, {"start": 353.18, "end": 381.44, "text": "Custom accelerators. If you can figure out how to do good FPGAs to do learning, then you can start to put classifiers on low-cost drones and things like that. That would be the area I'd recommend, but FPGAs are incredibly difficult. So, you know, good luck. So recently we've seen lots of friends of us that are working on deep learning libraries or key pieces of the puzzle getting hired immediately by Facebook, Google, etc."}, {"start": 381.44, "end": 396.34, "text": "And so I guess as a student especially, you're confronted with this reality right now. You can either get immediately hired by one of the big players or go and do a PhD by an institution that is sponsored by one of the big players or start a startup."}, {"start": 396.34, "end": 425.1, "text": "To reformulate that question, which one of these is the less evil poison, in a sense? Well, here is the problem. This is a poison question, right? Because from a society view, everyone should stay in academia because it begets the largest quantity of open research and the best teaching for the next generation. I don't know about Europe so much, but in America, with the way funding is and competitive tenure situations and just the misery of being a postdoc at, say,"}, {"start": 425.1, "end": 453.58, "text": "If you're not a top tier institution, that's such a hard life that the rational decision for many is going to be to go and work at Google or Facebook because you will get excellent training, you will get some of the best data you can access in the world, and you will be paid giant amounts of money. So go for the company, but keep in mind that everyone in this community has a responsibility."}, {"start": 453.58, "end": 482.56, "text": "To do the best research they can in the most open way. Yeah, connected to this is the question of biopolitics, you know, in a post-structuralist set, where it's about control of the landscape, where you have all these people, you know, in their free time creating libraries, but then getting hired by one of the big companies. It's a land grab for talent, that's clear. But at the same time, it's also a land grab for control of the resources on the software level."}, {"start": 482.56, "end": 506.11, "text": "So that's a trend at least. Do you see this trend as well? Yes, definitely. Partly it's that AI is a relatively small community. You know, Jan LeCun and his friends at NYU all work on Torch, whereas DeepMind has done work on other libraries, even the languages. You know, some people really like Lua, others are more doing stuff in Python. Some people who are..."}, {"start": 506.11, "end": 527.55, "text": "Very intelligent to just writing things in C, but I am afraid of those people because I don't know how you can do that. There's a diversity happening here. I don't know how it gets fixed. Someone needs to grow up and commit the community to one or two of them. If we look at the history of software, that's not going to happen for a few years."}, {"start": 527.55, "end": 554.74, "text": "Pivoting from the corporate discussion, in the previous episode where we had Mark Riedel, we had a long chat about generative tech in story generation, that kind of thing. And we touched briefly on generative journalism. And he mentioned that that was one of the few areas where generative text creation really is being deployed in industry in a large way, apparently. Which was, to me, eye-opening. And I knew at the borders of this what's happening."}, {"start": 554.74, "end": 581.26, "text": "To hear it firsthand was very interesting. I mean, what do you think? Is it going to be a radically new journalism quite soon, or what do you think about that? Yes. Right. So, number one, I work at Bloomberg. We obviously do very competitive stories. When you do an earnings story, we try and have a first version out within three or four minutes. We do that by writing..."}, {"start": 581.26, "end": 608.69, "text": "We have incredibly detailed templates. We talk to people in the days running up. We have a whole team of people standing around with numbers, checking everyone, and then you push it live. Obviously this is something that is going to be increasingly automated because this is a job where I am trying to be like a computer. And whenever you're doing that job, you realize at some point a computer will do this. So the Associated Press already uses technology from narrative sciences."}, {"start": 608.69, "end": 637.15, "text": "To generate earnings reports for companies that they don't cover. The only problem which these tools have is that they can't do context. So it will look at all of the indicators in the earnings release. It will look at the analyst's recommendations. And it will make a sentiment decision based on whether the company beat or didn't beat. The problem is that the market isn't rational. So sometimes a company can beat on all of the analyst estimates."}, {"start": 637.15, "end": 665.52, "text": "But go way down because buried somewhere in the release is a reference to how they're changing accounting or they're writing down something or whatever. Generative journalism will be a reality. We're not quite there yet, but it's very clear to me that it's going to foster a sort of winner-take-all situation where if we at Bloomberg develop some smart tools, I will be able to do stories much more efficiently and that leaves more time for investigation."}, {"start": 665.55, "end": 695.14, "text": "The New York Times, in a prototype version of their new CMS, they're using recurrent neural networks to suggest tags based on the story. So they're already kind of augmenting articles with some of this machine intelligence, which is a great idea. Well, and the other way around. There's this recent work on question answering systems where some newspapers were very specific in their style by using both the summary and the actual article."}, {"start": 695.14, "end": 724.14, "text": "You may have been aware DeepMind did some of the work there and they took in all of the Daily Mail, which is a large tabloid website. And what they found is that if you put the phrase into their learn system, you know, does coffee cause dot dot dot,"}, {"start": 724.14, "end": 753.82, "text": "Or does eating lobster cause dot, dot, dot? Every time the dot, dot, dot would be cancer because the Daily Mail loves writing articles about how everything is going to give you cancer. So that shows you how even with a very large data set, there can be some problems that are very unpredictable. Yeah, I mean, this is in my experience as well, working with generative systems, you really have to rethink the design process of choosing inputs and outputs."}, {"start": 753.82, "end": 782.43, "text": "Choosing the Daily Mail seems like an exercise in computational comedy more than anything else. It's funny, but one of the things that Google has been talking about a lot is that, and you may know more about this, the European Commission publishes huge documents and it publishes them concurrently in 27 different languages. So there's an idea that not only can we use that as a very good store of text to learn concepts,"}, {"start": 782.43, "end": 801.3, "text": "But we can learn concepts as they cross from one language to another because we have that mapping. And because it's not just a French-German dictionary, it's French, German, Italian, English all on one thing, you can learn a very complex, rich representation across the different cultures. So that seems fruitful to me."}, {"start": 802.19, "end": 831.2, "text": "It just brings back a discussion I had two or three days ago with Kyle Costa, one of the developers in deep learning, and he brought up this interesting idea that the trained weights of these nets can be set in connection with what previously was the database. So the next oracle, in a sense, will be the one that is wielding the most pre-trained weights, you know, and that there will be a whole set of law cases unfolding soon where people will get sued for..."}, {"start": 831.2, "end": 860.99, "text": "Specialized training on top of pre-trained nets and things like that. Do you find it interesting? It just came to mind when you were talking. Well, if you think about it, what we're doing is we're turning very high-dimensional mathematic representations of a sort of large knowledge space into intellectual property, which should be the most frightening idea in the world to anyone. This is the most abstract thing you could possibly try and turn into a capitalist object, and we're heading down that direction."}, {"start": 860.99, "end": 890.66, "text": "I don't think that can work. I think that if you look at the way that you encode information from a trained net, the legal cases will be hugely complex. But Google has been acquiring many patents, and so has IBM, and so has Microsoft, so we might get a Cold War scenario where there is no lawsuit because all of them have enough patents to threaten each other with a nuclear bomb lawsuit. Who knows? I mean, it's a horrible scenario. Nobody wants to see that because..."}, {"start": 890.66, "end": 913.41, "text": "I suppose you would stifle innovation really. How do we avoid it? What are things that you guys think could be done to stop this happening? I think step one is to support the current openness because we see this marketing money rushing in and obviously they're smart marketing people. They try to set a cultural sentiment."}, {"start": 913.41, "end": 938.43, "text": "And I think that's one dial that as a society we can start to turn in the other direction, upgrading the now somewhat old-sounding notion of the public domain. Especially in the US, it sounds completely out of date, but it could quite easily actually be dialed back into fashion. So that's one approach, I suppose. It's a really hard problem, isn't it?"}, {"start": 938.43, "end": 967.44, "text": "There is Creative Commons licenses, right? I mean, why does Google, for instance, bring out a patent for all these different algorithms they've given? Why not launch it with a Creative Commons license with maybe attribution class? Okay, you have to attribute it to Google. So Google would still protect you in the attribution principle, but it still would be actually open. Well, it really is, unfortunately, this horrible sort of mutually assured destruction game theory scenario, you know, where..."}, {"start": 967.44, "end": 996.27, "text": "Google may not have wanted to patent this, but what it may have done, which companies do regularly, is looked at all of the patents IBM has on AI and said, like, holy moly, if we don't have some AI patents, we can be sort of in a legally weak position with respect to IBM should there be a lawsuit. So it creates the scenario where even if it's not a good idea, you're going to amass these patents because..."}, {"start": 996.27, "end": 1022.08, "text": "As a corporation, you have to do rational things for your investors. And any investor would kind of rightly say, hey, Google, by not patenting any of this stuff, you're putting yourself at a disadvantage to your competitors in the marketplace who are amassing the tools necessary to mount a legal attack. It's a bit depressing, but I think that is a rational corporate response."}, {"start": 1022.08, "end": 1049.18, "text": "One of the big stories is also the more future concerns about the development of AI leading to mass employment or to terminators or etc. But let's stick to the mass employment scenario for now. This is then being argued if there is mass employment there needs to be a solution for the people who are unemployed which might be something like a minimum income. So where do you stand on this issue? It's a huge problem. I've read a lot of research by..."}, {"start": 1049.18, "end": 1073.15, "text": "David Autor, who is a great economist, I believe at MIT, has written a lot about this. His analysis is that we don't have the data to be able to project that AI could lead to large-scale unemployment. But we also don't have the data to say that that won't happen. And then if you look at people like Andrew Ning, who does AI at Baidu, he said to me,"}, {"start": 1073.15, "end": 1102.77, "text": "When the US went from 50% of people working in farming to 2% over 50 years, that was fine because the farmer knew that their son or daughter should go to college because farming would be mechanized and there wouldn't be a job. The speed of today's economy means that this same transition is happening within a single generation. And that's where the problems come in. We have no system in society for retraining people."}, {"start": 1102.77, "end": 1130.61, "text": "When they're midway through their lives to take on a new type of employment or job. And that will be the issue that AI brings to the table. Because if you're a truck driver, if you're a lawyer working in e-discovery or data stuff, if you're a journalist doing a lot of journalism that just requires sort of analysis of numbers which are out there, there is huge evidence that AI is coming to you and is moving very rapidly."}, {"start": 1130.61, "end": 1159.38, "text": "And just from another slightly more basic economic point of view, the thing that AI does is it makes your existing capital expenditures, your warehouses, your factories, it increases the efficiency of them and it lowers the depreciation of them. So as a business operator like Amazon, you have the hugest incentive in the world to roll out Kiva Systems robots to as many of your warehouses as fast as you can."}, {"start": 1159.38, "end": 1189.17, "text": "Because whenever you look at the numbers, the efficiency is so much greater than with a staffed model. This is going to be a defining issue. Maybe I expect within the next five to ten years we see the big effects. If self-driving cars come on schedule and receive the kind of uptake that people at J.P. Morgan, people at the big banks are projecting, it's coming. It's super interesting. It gets to the question of"}, {"start": 1189.17, "end": 1205.79, "text": "How society should frame optimization, etc. In the West, especially, self-worth and these more philosophical constructs are really based on full employment and so forth, right? I mean, the whole psychic in the West is built on these notions."}, {"start": 1205.79, "end": 1224.32, "text": "And so in a sense, we are saying it's all going to collapse sooner or later if it's already now in the election cycle topic. Yeah, it's going to be challenging. I have a friend who's actually also English. They work in New York in finance, you know, so they're aware of technology and what happens. I speak to them about this issue."}, {"start": 1224.32, "end": 1251.68, "text": "And they say to me, well, but Jack, what will people do if they don't have to work? People have to work. It's natural. And I talk to a lot of people who have that view. So as you say, we have such a deep-set psychological association between work and self-worth that watching that change is going to be difficult. Maybe this is an area where the Europeans can take leadership because we've always had an appreciation for holiday and not working. Maybe that will help us do that."}, {"start": 1251.68, "end": 1280.34, "text": "Maybe you could argue that Google and Facebook and the Bibles, it would be in their interest to push this new philosophy of there will be people unemployed and it's fine in the sense that they will have the best people working against this trend. They manage to convince political leadership of this kind of trend. And this is a good thing. It can be framed in the narrative of this is good for business, good for the world. And they are, at least on the corporate level, the ones who are most to gain from this, right?"}, {"start": 1280.34, "end": 1305.55, "text": "Yeah, and from a public relations standpoint, as a company, you never want to be associated with the destruction of jobs and increasing inequality. And unfortunately for these AI companies like Facebook and Google, they're already being tagged with that because they have a very competitive market and they give engineers free food and massages and buses."}, {"start": 1305.55, "end": 1333.86, "text": "We're in San Francisco where you have a huge homeless problem and huge inequality as well. So this is an issue that they're going to need to take a leadership role on because otherwise they'll risk discontent from society becoming directed at them because they become a symbol. Right, right. So that actually ties it really beautifully back to the beginning of the discussion of the greater need of explaining this really complex..."}, {"start": 1333.86, "end": 1363.23, "text": "Why can't I pay for Facebook? Why can't I pay for Twitter? Why can't I do some situation where either I pay them and they don't get my data, or they pay me a very small amount of money and I give them my data?"}, {"start": 1363.23, "end": 1392.11, "text": "Because if you taught people that their data has some value, or that they have the option of capitalizing on that value by buying a service instead of getting it for free, they would understand what AI means. Because all AI is, in the way that it will affect a lot of society, is the outcome of us transferring loads of very well annotated, clean data to corporations. And that issue will have to become one, because if you're Google and you say, well,"}, {"start": 1392.11, "end": 1420.67, "text": "You don't pay for Gmail because we subsidize it with adverts, and you get a lot of value from it even though we take your data. That is a very reasonable argument, but at some point people are going to ask, well, why can't I have the other option? And then the companies have to say, well, actually, your data is so valuable when we combine it with everyone else's, but we have no incentive to do this from an AI development standpoint. I believe that would start a conversation among people around this."}, {"start": 1420.67, "end": 1450.26, "text": "I mean, through the generative work I've been doing, I've been involved with copyright a bit. I mean, what happens is if you train a net with copyrighted images and you generate outputs with that, you get into a really interesting situation with copyright law very quickly, really. And that's just the beginning. I mean, I can think of really puzzling scenarios. Like, if I train a generative music system based on a CD I buy of the New York Philharmonic playing Bach,"}, {"start": 1450.26, "end": 1477.14, "text": "At what point am I still using copyrighted performances from the New York Philharmonic versus at what point is it just bark played by a generative system? It's very, very hard to discern that borderline because of what we know what happens as you do this generative stuff is you distort the underlying material to the point that maybe it is fair use. Maybe it isn't the original IP anymore."}, {"start": 1477.14, "end": 1507.04, "text": "There's no way to answer this stuff simply. It's going to be a very horribly complicated time, I think. You can imagine you train a movie system on every single action movie in history, and then you create a generative system which will go from frame to frame or scene to scene and sort of interpolate a new movie out of this. At what point are you infringing copyright? Like, how do you even judge that anymore? It's crazy. And as I said earlier,"}, {"start": 1507.04, "end": 1531.5, "text": "We're going to get artists who do this. We are going to get people who want to contribute to the cultural discussion and the aesthetic discussion, but do this stuff. And the legal system and rights holders will have no clear path for how to react. It's new territory. Well, it's more problematic once you start charging money for the generated material or you want to copyright that."}, {"start": 1531.5, "end": 1558.59, "text": "I mean, it becomes interesting for all those copyright holders from the original input because they might want to also cash out on that. You know, do you end up licensing the object itself, right? So say the value of a photograph of my customized vehicle is quite low. Would it be greater for me to share a data set which is a set of several hundred photos of the car from every single angle?"}, {"start": 1558.59, "end": 1585.14, "text": "So you can train a system to have a representation of it. And should I charge for my data on the richness of the AI representation you can derive from it? Again, I don't know, but this feels like conversations which creative people are going to have to start having. And then you get to the really outlandish scenarios when you start to combine all of this which we're talking about with a distributed trust-based blockchain system for the running of code and validation of it."}, {"start": 1585.14, "end": 1611.71, "text": "Again, you start to get autonomous programs that will mine the internet for content and then will sell generative art for Bitcoin anonymously. Well, what do we do when that has happened in the world? Well, there's a precedent in Holland of someone creating language with natural language processing. I think it was not a neural network, but I mean machine learning in any case, which was argued that it was hate speech or threatening speech."}, {"start": 1611.71, "end": 1639.74, "text": "And he got his door kicked in by the police. And in the end, the question was, who was responsible for this content on Twitter? Was it, you know, the machine learning algorithm or the person behind it who argued that was... Google server in prison. Yeah. But at least according to the Dutch legislation, in the end, it was the person who created the algorithm who was held responsible for it. You know, Google, when they launched Google Photos, a huge problem, which was that the system..."}, {"start": 1639.74, "end": 1664.56, "text": "Was identifying people of color as gorillas, which is literally the most offensive thing your system could do, pretty much. Again, is the Google person responsible for not testing all of the corner cases? Is Google as a corporation responsible for not doing QA? Valid and complicated questions, because it certainly offended and hurt some people."}, {"start": 1664.56, "end": 1688.86, "text": "But then they weren't hurt by a person. They were hurt by generative decision of an algorithm that emerged out of a data set whose provenance we as the public aren't told because it comes from a private company. Where is accountability in this universe? It's tricky. I mean, I suppose on the one hand, yeah, sure, generative systems do manifest a lot of autonomy in a sense."}, {"start": 1688.86, "end": 1715.17, "text": "On the other hand, it's the perfect black box to hide nefarious human action behind. And, you know, you just stand there and you raise your hands and you say, well, you know, it was the black box. Excuse my, or its behavior. Don't sue me. I mean, it's a bit of both, really, isn't it? I had a conversation with a hedge fund recently who I can't name, but it was about what they thought of deep learning and deep learning systems and how it applies to trading."}, {"start": 1715.17, "end": 1744.02, "text": "These people are very concerned by deep learning because we can't inspect the models very easily. We can't get very good telemetry. And the whole concept of taking an emergent generative system and plugging it into a trading environment gives these people nightmares. It is like the worst thing they could imagine. And yet, there is going to be a huge incentive to use deep learning to pick up on some signals which are not yet being processed."}, {"start": 1744.02, "end": 1764.22, "text": "By technical trading firms. So we're going to see some very interesting arms race there. You know, we already see it with satellite imagery being run through deep learning systems to look at the height of oil and gas towers to infer supply. As we get the rollout of low-cost drones, making it possible to surveil."}, {"start": 1764.22, "end": 1794.32, "text": "If you made it this far, thanks for listening. And also, we would really love to hear your comments and any kind of feedback. So drop us a line at info at ethicalmachines.com. See you next time. Adios. Bye bye."}, {"start": 1796.34, "end": 1797.33, "text": "Thank you."}]}