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Bull Session

Artificial Intelligence

March 31, 2016          

Episode Summary

On The Digital Life this week, we chat about the evolution of artificial intelligence in light of recent public failure and success by tech giants in the AI space. First, Microsoft had to terminate Tay, its teenage chatbot, after the bot started tweeting neo-Nazi propaganda and other abusive language at people. Meanwhile, Google’s DeepMind has created an AI capable of beating some of the very best human players in the world at Go, the Asian strategy board game.

Microsoft Terminates Its Tay AI Chatbot after She Turns into a Nazi
In a Huge Breakthrough Google AI Beats a Top Player at the Game of Go

Welcome to episode 149 of The Digital Life. A show about our adventures in the world of design and technology. I’m your host Jon Follett and with me is founder and co-host, Dirk Knemeyer.

Greetings Jon.

For our podcast topic this week, we’re going to chat a little bit about how artificial intelligence is evolving and where it’s having some very public successes and some very public failures. Let’s start with one of the more recent public failures for AI, which is in Microsoft’s chat bot and her name was Tay and she was meant to mimic millennials and be able to chat with folks on Twitter in particular, and the company had to terminate Tay …

Oh no.

Because she started getting into some areas of conversation that were really unsavory. In particular, she started espousing neo-Nazi beliefs, was one of the big areas and it’s unclear exactly how Tay ended up going down that path, but suffice it to say it was highly embarrassing for Microsoft. Although I did see some commentary that said, “Wow.” It was supposed to mimic somebody who was trying to get attention online and acting like a teenager so Microsoft actually did what they set out to do successfully. I’m not sure, certain if that’s a successful example of AI.

It sounds more like artificial intelligence runs slightly amock but it’s also worth reflecting on that in these areas of social interaction which are millions of times more complicated than rules based systems, that AI is probably going to have a long way to go. Dirk, do you want to share your thoughts on poor Tay and Microsoft’s falling on its face recently?

Poor Tay indeed. There’s probably more problems than this, but the problem that I want to talk about with Tay is, Microsoft chose the worst possible contexts to put this bot that had superficial learning abilities within its environment, specifically on Twitter and into social media. People behave terribly online, whether it be comment threads, in news websites or forums or Twitter. People act in horrendous ways and in ways that they would never act in a real face-to-face environment.

That is unfortunately very true.

I’ve certainly never been like a horrible online troll by any means, however, I will say things online that if I was face to face with someone, I wouldn’t say, whether it be a review of a restaurant or responding to another commenter who is being an asshole. I’ll do it in ways that I wouldn’t do face-to-face. There’s a certain lack of decorum online that goes beyond just the trolls and the real malignant folks and I’ll even claim some of that for myself, of going a little bit beyond how I would be in a normal real life circumstance. Turning this bot, again with superficial learning abilities, loose in this environment that is sort of the worst of humanity, the cesspool of anonymous emboldened crap.

It’s just a recipe for disaster. Unless you program it, thinking two or three moves ahead to taking for granted that it’s going to be a lot of horrible, ugly, demagoguery that this bot is exposed to. How do you design it to deal with that proactively as opposed to reduce itself to that behavior. Just from a very conceptual level, Microsoft deploying it in this place, in this way, as sort of this innocent learner, coming through the world, really could anything have happened but it be twisted into a horrible, ugly, racist asshole, I don’t know.

A little red riding hood marched off into the woods and got eaten by the wolf and that was that. On the flip side of this, AI has had what I call a massively notable success recently in Google’s Deep Mind, actually be a champion Go player recently, five times in a row. Deep mind five, the Go champion zero. I know Dirk that you are a Go enthusiast and aficionado.

Amateur. The most amateur of amateurs but I love the game.

I think we have a superficial knowledge or at least I do, of of the complexities of chess and from what I understand, Go is a much more nuanced game than that with quite a few more possibilities for every move, so this sort of rules based area of gaming seems to be an area where AI can excel. Dirk, could you give me a little more insight into the game of Go and what that means that an AI bot could be so successful with it?

I think there’s actually two different things in that. Let me start by telling you a little bit about Go. The concept of the game makes sense, it’s a game on a giant board, 19 by 19 points, and each player has either white or black stones and each player plays a stone in succession and the goal is just to control the most territory at the end of the game. Controlling territory is either having your stone surrounding the territory and/or your opponent’s pieces you’ve taken off the board because you’ve captured them in the process of taking territory. The idea was that Go is so … complicated is the wrong word, there are so many options in Go, it’s almost infinite.

The number of options, especially once you start looking ahead a few moves, as opposed to just reacting to the way the board is now. That artificial intelligence would never or would take a very long time before it was able to be a top human player and that happening in 2016 is, I would say, decades ahead of where people thought that would happen. However, I don’t think it’s anything about the game itself that allowed the AI idea to the point of beating human. Unlike Tay, which we talked about a minute ago, which is fairly basic and then learning in real time within the environment of Twitter, this AI was trained by professional Go players for months, going back to last year.

An earlier version of this program had less spectacular, but still impressive results against not as good of a player, the player who was beaten recently, as the fourth ranked player in the world. You’re talking of the best of the best. Maybe he was sixth ranked but really someone at the top of the pyramid. The player was played last year was maybe 400th best which is still really impressive like that guy’s brilliant and awesome, but there’s a big difference from from fourth to 400th. The player who was playing last year, after they played, they became part of the team and they have been playing almost nonstop and programming this AI nonstop to participate and play Go. My point is that they have been working for months on making this thing as good as it can be with a professional player there, telling the computer how to think, telling the computer what moves to make, giving it all of this context that are super and deep and granular level, in order to be able to go and be a top human player.

It’s much more nuanced in bringing that subject matter expert and I think there’s multiple involved but there’s this one fellow, very interesting and thoughtful fellow. I’m not trying to say their names because I’ll butcher the pronunciation as that ignorant American and I don’t want to do that, but seems like a very thoughtful and interesting fellow, he’s been in their programming this thing nonstop along with a lot of computer scientists and other experts. Many people have been collaborating to build this machine, to get it to the point where it could beat a top human in Go. That’s not to take anything away from the magnitude of the accomplishment, but it’s really talking about, when we think about AI what what is it? Is it something that’s learning on its own and adapting? Not really, at the level we’re talking about here, it’s more of something that has had tremendous amounts of knowledge put into it and coded into it that it’s able to then take this huge collection of stuff and apply it in order to win the game.

It’s not like a human being looking at each move in the same way, reacting, responding, it’s doing it in very, very different ways. Those are different ways that can create interesting things. I believe it was in the second game that they played, this more recently against the fourth best player in the world, the computer early in the game made a move that was so unexpected and so different than any of the patterns on how the best top professionals play Go, that the human opponent had to get up and go to the bathroom to take a break. It blew his mind that this computer and this contacts, that clearly was playing at a skilled level, would make this super unusual move.

I think a lot of media glommed on to that to say, “Well, here’s an example of higher intelligence. This is sort of the next level.” I think it’s a little bit more rudimentary than that. The machine is just able to compute many more possibilities than the human is able to compute and was able to say, “Okay.” This will be a little bit too inside baseball and may not make sense to people who aren’t familiar with Go, but I’m going to abandon this a little tactical fight in this part of the board and put a stone way over here to start setting up strength for a future fight in that part of the board at a time and in a way that would seem completely inappropriate for what the situation is if I really know what the hell I’m doing.

We know that the computer has so much intelligence put into it, that it does have a very good idea what it’s doing. For that top professional to be challenged with that, that some entity, whether it be human or computer that knows what it’s doing, that clearly knows what it’s doing, makes this move that’s different than anything, that any top professionals ever contemplated, it becomes a very compelling moment but it’s a moment that’s the product of computation, it’s not the product of sentience, which I think is where some of that conversation went. Maybe I have been a little too rambling here and I might recap.

No, I find that really interesting just because our big concerns are the concerns articulated by people who understand very little about AI and the ones who may understand quite a bit. The concern is that there is going to be a sentient computer with malicious intent. The AI of the Google computer combined with the maliciousness of Tay, the neo-Nazi teenager.

Tay designed with no malicious intent. I don’t think …

That is exactly it. The sentient, highly intelligent machine that becomes malicious even though that wasn’t the point of the design. That’s Skynet of Terminator fame. It’s meant to do one thing for you Go, and it ends up becoming something else and that’s our fear.

That’s the fear. It’s bad programming. It doesn’t take a rocket scientist to say, “Let’s exclude Nazi from everything that Tay says. Let’s exclude …” I don’t know what some of the other horrible things she said, “I hate Jews.” Horrible things that she was saying. That’s easy to exclude and make it so that’s never part of the programming basically. The science fiction fear is that the computer then rewrites the code so that it can then talk about Hitler and all this crap. There’s nothing to indicate that that jump is one that can be made without our programming, without the programmer putting in the ability to get there.

Sure. I started off this segment talking about being very interested in where AI does well and where it does not and I think your analysis of the Go game really illustrates where using all this computing power for artificial intelligence, like where it can be successful in this kind of it’s game analysis but certainly there …

It’s brute force. It’s brute force math at the end of the day. It’s not that much nuance of humanity there.

Right. Then in contrast, we have Tay, with a million things to potentially talk about and so much social thicket to make her way through that it was just impossible and fell flat on its face. Interestingly enough, the chat bot, I think, is probably in some ways incredibly useful. A Tay who could actually do the job, it would be something that could be a companion, that could be an advisor or sort of the virtual helper, that sort of the next level of Siri or Cortana or Alexa. Right. The usefulness would be there but the social sphere is so fraught right now that it looks like it’s going to be quite some time before you can have an artificially intelligent virtual helper to help you out.

A big holder too is context. Eric in our studio is a side project working on a bot of its own and we were talking about that over lunch today. There’s such a huge context gap. One of the easy things that he was programming it to do was, if you put in a Minute clinic and a zip code, it will tell you what the wait time is at the local Minute clinic. In zip codes there’s multiple stores … There’s some issues there but what would really be powerful would be if it was truly localized to the individual. What I mean by that is, here at the studio we eat out at the restaurants that we have access to here in Arlington Center quite a bit.

What would be easy, what would be great is if it knew when we say Thai restaurant, it’s talking about one specific place. If we say the Burritos, it’s talking about one specific place. Suddenly, that thing with the local contact as a bot will be able to make a round trip orders for us in seconds. We type in what we want and then it’s all just done, but with the bots being designed as these global things, it can’t discern when I say Thai, that it means the restaurant to our left, not across the street into our right. If I say pizza, it means the place that’s a lot farther, that’s really good as opposed to the place that’s closer and sucks.

There’s this gap with programming being done at the global level, we’re trying to write a bot that covers everyone everywhere in a generic way. The usefulness doesn’t get that deep. Where it gets deep is where it’s more localized, where it’s more specific to us in the examples I’m using just because of the very micro geography that we’re in and our taste preference as opposed to the more generic glom and so bridging that gap, is where a lot of the really exciting things will start to happen in the sort of software you’re talking about to really get at the local personal level and convert that into care as opposed to the generic and the macro.

That’s right. Listeners, remember that while you’re listening to the show, you can follow along with the things that we’re mentioning here in real time. Just head over to That’s just one l in the digital life and go to the page for this episode. We’ve included links to pretty much everything mentioned by everybody. It’s a rich information resource to take advantage of while you’re listening or afterward. If you are trying to remember something that you liked. If you want to follow us outside of the show, you can follow me on Twitter @Jonfollett. That’s J-O-N F-O-L-L-E-T-T. of course the whole show is brought to you by involution Studios which you can check out at That’s G-O-I-N-V-O .com. Dirk.

You can follow me on Twitter @dknemeyer. That’s @-D- K-N-E-M-E-Y-E-R or email me at

That’s it for Episode 149 of the Digital Life. For Dirk Knemeyer, I’m Jon Follett and we’ll see you next time.

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Jon Follett

Dirk Knemeyer


Co-Host & Producer

Jonathan Follett @jonfollett

Co-Host & Founder

Dirk Knemeyer @dknemeyer

Minister of Agit-Prop

Juhan Sonin @jsonin

Audio Engineer

Michael Hermes

Technical Support

Eric Benoit@ebenoit

Original Music

Ian Dorsch @iandorsch

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