Max Sklar: Chapter 3: The possibility of an AI winter and the challenges of multi-language NLP

M-A-C-H-I-N-E-Y-E-S-I-S-T.

It's Machine Learning from Assist. Another week where we continue on this adventure where marketers, brands, and entrepreneurs get to have a place to think, dream, and ask questions about the future of AI, the talking internet, and how we're reshaping our culture. This week is a rocket, and the rocket has a name: Max Sklar. Max is a machine learning engineer at Foursquare and is a fountain of smart, concise thinking on privacy, social media, and whether we're looking at an imminent AI winter. In this chapter from the longer podcast, Shane Mac from Assist got so much goodness from his fellow podcaster. We think this is so packed with goodness, you want to give it a few listens and definitely share it with friends. Then make sure you check out Max's Pod, The Local Maximum. It's awesome.

With your work at Foursquare, the amount of real-time location tracking has so many privacy implications.

Yeah.

Give me your personal privacy manifesto, like your baseline. What is it?

Well, in my personal internet usage, I don't focus on my personal privacy too much. Maybe I should a little bit more, but I'm on all of the social media, you know, I'm pretty open about which apps I give location to. So our location technology is kind of licensed out to other consumer apps, and I like the idea that each of those apps has a specific reason for having that location technology. For example, we're working with a jukebox app, so it knows when you're in a specific location with the jukebox, then you can kind of play with it, or a coupon app that reminds you to use the coupon at the right time. And so I like that because there's a reason. I think the negative example would be if some app wasn't using your location for any other purpose other than gathering information on you for ads or malware, that kind of thing. You know, I feel like there needs to have some benefit on the consumer side. Also, I appreciate when companies take take their security very seriously because there's a lot of— and I'm not an expert in security, but there's always people trying to hack or, you know, trying to get access, particularly when you have a very valuable database.

And you said social. So social is getting hammered right now. There's talk of an AI winner, research and investment waxes and wanes. AI, machine learning, conversational commerce. This is really hot right now. Do you think an AI winner is coming?

You know, it's interesting. I spoke about that a few episodes ago on The Local Maximum, and I named the episode Enjoying the AI Summer because I feel like AI is very big right now. It's like a hot topic, right? Valuations are high. I guess kind of the idea of AI as a chatbot maybe comes and goes a little bit, but Machine learning engineers, very high demand. And so there hasn't really been the fall and the winter yet. But yeah, I mean, this thing always comes in cycles. Ultimately, we are in the information age. Information is very valuable. Information processing is very valuable. So even if there's a down cycle, I'm pretty confident it'll come back.

In the area of AI, where do you think would be the first to experience an AI winner? Where, where is the hype right now?

Hmm. I think that there was a lot of hype in terms of consumer applications. So if, if you talk about consumer chatbots or consumer applications for image recognition, like, let, let me give you an example. I, I, I was thinking the other day, if Google Photos, they, they have a system where they can automatically caption a photo. Like if you take a photo of a dog, you know, chasing after a ball, then it will caption your photo, "A dog chasing after a ball." Now, if Google Photos said, "Hey, we just upgraded Google Photos and now we've captioned, automatically captioned all your photos," I would be like, "Awesome, that's really cool." But if they wanted me to pay a significant amount, or if another service came along and said, "Hey, just pay like, you know, $20 a month and your photos will be captions, I don't think a lot of people would be that interested in it. They might be like, oh, let me just caption it myself. Or, you know, maybe if the price point is really low, but that's not— it might not be terribly profitable. So I think that the hype might be on the consumer side because that's sort of what's interesting to people from like a media perspective. You immediately understand applications if they're relevant to you personally, like, oh, my photos look like captions. The things that don't get as much hype, that are not relevant unless you're in a specific industry, is like industrial applications or agricultural applications, or just sort of general business-to-business type applications, which are sort of— don't always make as good of a news article, but are probably where the biggest opportunity lies.

Can you give me an example of a really great use of AI that no one's ever heard of?

Hmm. You know, I think that's something that we've talked about, but is sort of very buried at Foursquare, buried in terms of like what our recommendation system does in the popular imagination, is how we built up this system of tastes where we have all this natural language processing that identifies all of the noun phrases that people are talking about in Foursquare. So that's like a lot of the menu items, a lot of the things that you do at places, and that gives us the kind of context, the kind of understanding to be able to tell people what exactly to do at a specific place. And so that use of ML was really interesting from the recommendation standpoint. In terms of our core tech, a lot of machine learning has gone into understanding where people are, and you might not necessarily realize it. You might think, "Oh, it's just venue shapes," right? Like, you get someone's latitude and longitude, and that says that they're at this Starbucks coffee shop. Like, why would that be so hard? But it turns out that the phone signals are actually really complicated. They jitter around a lot depending upon where you are, what time of day it is. So there's all sorts of signals that go into that designation to try to figure out exactly, you know, whether you're in this shop or whether you're in the shop next door. And I don't think people realize that we can do that, but they also realize that it's a hard kind of machine learning problem.

The use case for Foursquare is so insane, like so many people, locations, pronunciation variations, correcting the errors is a huge part of the job. What have been your biggest surprises with programming for natural language applications.

NLP is like— I think one of the things he said is that kind of sums it up when I did an episode in NLP, and it was like, with NLP, there's always something. I don't know how to speak any languages other than English, but I got to learn a lot about lots of different languages, and it turns out that anything you think you know about languages, there's always some exception. One example that I had was like with Russian, like it turned out that you can't just get singular or plural. You have to know exactly how many you're talking about or what the ones digit is in some cases. And the frustrating—

Is that true? Yeah. The Russian language, every single quantity amount changes the pronunciation?

Well, yeah. I mean, it's based on like the ones digit, you know, there's, it's not like there's an infinite number of pronunciations, but there's some languages that are just one and many like English. There's some that are one, two, and many. And then there are some where, you know, you have to gender it, and there's some where you have to know what the ones digit is. There's all sorts of crazy stuff out there. And so with internationalization and NLP, language is such a hard nut to crack because there's so many exceptions to every rule, and it's amazing how the human brain just kind of gets it.

What needs to be cracked then for, like, the next big leaps forward? Where's the breakthrough going to happen if the complexity is so high?

I don't know if the goal is necessarily to build a machine that can hold a conversation with a human as a human just yet. I think that from what I've read, the natural language understanding of a document, like summarizing a document and getting information from it, it's still very early in terms of being able to do that as well as a human can. Like, yes, a machine can, you know, scan the entire internet, all of Wikipedia, and get all sorts of information, but being able to add to its database with like every sentence, what exactly that sentence means, there's a whole lot of improvement on that, which I'm sure the big tech companies like Google are working on.

Thank you for your time today.

Yeah, thanks a lot. It's really great to finally be on Machine Learning and join your podcast. And all these discussions are always really fascinating because I feel like I'm not telling the audience This is what the answer is. It's like we kind of figure out some ideas and it's like, oh, now I got to go back and kind of maybe try to put some of these into action and see how it looks.

Totally.

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