You Ask, I Answer: Most Exciting 2020 Marketing Trend?

In today's episode, Laura asks, what is the industry trend you're most excited about in 2020? Well, the continued growth and the practicality of what's happening in artificial intelligence and machine learning and in marketing data science, of course. There's three specific trends that I think are so exciting and have been exciting for quite some time. Number one, continued growth in natural language processing. 2018 was sort of a watershed year for a lot of natural language processing, and 2019 brought some of the largest models available that we can use to process text to understand it better, to be able to uh in some cases generate it, and that is stuff that I expect to see continue in 2020, not just at the cutting edges of research, but at the ability for the average machine learning practitioner to access it and deploy it.

So by that I mean it's it's relatively friendly, relatively easy to use. It is not uh the uh incredibly challenging, you know, super high-tech stuff, although that that stuff is exciting too. Um but for the average well-versed practitioner in data science and machine learning to be able to pick up something and uh in NLP and use it. Uh, number two, there are some really amazing things happening in the world of audio uh with uh WaveNet a few years back, and now Melnet and uh a bunch of other uh technologies that are making it easier for machines to understand audio, particularly the spoken word, and then able to replicate it and uh synthesize it. And this is some amazing stuff.

Again, there's if you look at the Melnet demo, you can listen to a machine synthetically generate uh voices, uh, synthetically generate music. Uh, I think music composition is has come a long way and continues to accelerate. It's not gonna replace humans anytime soon. Um, but it's getting there, and it it I would say it's at the point now where AI can generate like elevator music, right? It's stuff if you put it on in the elevator, like no one would be offended.

Um, but certainly it's not gonna win any Grammys. Um as with all things in in machine learning, uh the technology improves, the models get better, and whereas two years ago it was it sounded like something like your three-year-old would be you know hammering pots and pans with spoons. Uh now it sounds like elevator music. So expect in time for it to become a credible alternative for people who want access to music composition and don't have the skills themselves, can't play an instrument, but can direct or conduct an orchestra of machines. Uh, number three, and this is a bit more esoteric, but there will be more and better pre-trained models in 2020.

We saw with in the natural language processing field, uh, OpenAI's GPT 2 released its mega model, the 1.5 billion hyperparameter model in 2019, and that was a really big deal for that company because that was the what the model they were so concerned about uh falling into the wrong hands, and they said they didn't see any evidence of its uh its use, and partly because it's such a beast to try and get to use. Um but one of the major overall meta-trends in AI in the last couple of years has been uh instead of generate your own models, pick up a pre-trained model and then fine-tune it and expect to see more of that happen in in music, image recognition, video, uh, natural language processing, good old-fashioned statistical computing, all these things we're seeing more and better models for that are just you you pick it up and you and you work with it. Now, there are some challenges with that, and the chat one of the big challenges, of course, is that you are relying on a pre-trained model and you you don't know for sure how that model was trained. Uh, you have to take on faith to some degree the model was trained well, so that's gonna be a uh a consideration. Which brings me to three trends I'm concerned about.

Number one is interpretability and explainability, the ability for machine learning and AI practitioners to explain what it is their models are doing, uh, which is really important and something that the industry is struggling with and will struggle with for some time, um, partly because interpretability is so much more expensive than explainability, but is the gold standard for being able to say watch and see what the machine is doing at each step of the process. Number two, the things I'm worried about is is biased. Uh, and this goes hand in hand with interpretability. If we don't know what the machine is doing, we don't know if it's doing something wrong. There have been many, many, many examples of machines making decisions that on the surface seem okay, but when you get inside the model or when you see the post hoc results, you're like, uh that's not right.

So bias is a major concern. Uh, and it's one that the industry is making strides on, but the industry as a whole is not going fast enough. Not going fast enough to allay some of the fears that people have, and uh and set aside misconception. That's important. And number three, and by far the one that I'm most concerned about in 2020, because it is an election year, is the misuse or malicious use of artificial intelligence technology for things like deep fakes, uh is the most popular cited example, not the one that I think is probably the most prevalent.

I honestly think that bots with minimal natural language processing capabilities are a much bigger problem because they're so much easier to scale. Deep fakes don't scale well, right? Deep fakes require a lot of computational power, and yes, you can rent it for pennies on the dollar from like a Google cloud or something, but to do so then also makes you not anonymous, right? Because once you sign into something like uh one of big tech's clouds, every single thing you do is tracked and can be identified by law enforcement. So, in a lot of cases, if you're doing something malicious, you need to be doing it in in the dark, uh, away from the prying eyes of every major tech company ever.

So, things like you know, mass armies of Twitter bots or Facebook bots and things are a much more practical application and very easy, very cheap, and they have the ability, thanks to the hyperpartisan world that we live in to really manipulate people. And it's not the machines' fault that humans are gullible and that humans like to have confirmation bias out the wazoo, but it's the machines enable hostile actors to do more faster and better. And at least in the context of the United States of America, our defenses uh have been largely dismantled in the last couple of years with uh uh the uh abolition of the cybersecurity uh council and things like that. So we are in a a case where AI can be maliciously used, and that's very concerning to me as a practitioner because again, we want people to trust this technology. If the technology is being used for malicious means, really hard to build trust around it.

So that's sort of the the opposite of an exciting trend. It's the the most worrying trend. But that's what we have to look forward to in 2020 uh for marketing data science for machine learning for artificial intelligence. Um would love your thoughts. Uh, leave them in the comments box below.

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