You Ask, I Answer: 2019 AI Milestones in Marketing?

In today's episode, Annika asks, What is what is, in your opinion, the most important milestone that has been reached in 2019 with AI and digital marketing, and how do you see the future of digital marketing? Uh the future. We're gonna leave the future to its own because these days there's absolutely no credible way to forecast the future more than a very short-term horizon now. A funny example of this. Back in 2016, I think it was, someone prognosticated that uh deep neural networks were so uh primitive and non-capable that would take 30 to 35 years before a neural network learned how to play the game Go.

Of course, 18 months later, Google's Deep Mind beat the world's champion at Go, and that prognostication went right out the window. So we're gonna leave the future out of this for now. In terms of milestones that are happening today and in 2019, uh two big ones recently, in fact, one uh literally just crossed my desk uh last night. One, uh GPT2, which is the general purpose transformer from OpenAI, which is for use for uh natural language processing uh and natural language generation, uh has reached its sort of six-month um midpoint in terms of of the project and how people are using it. And it's been used extensively.

OpenAI has been leaking out, well not leaking, just distributing its language models. And people have done some pretty incredible work with the very small model, which was 124 million uh lines uh documents. Uh they released the medium model in May of 2019 at 355 million documents, and literally just days ago, they released the 774 million uh document. So this one is uh substantially larger, and that means that you get better language generation, you get better um capabilities from the the model, uh more impactful language generation, more natural sounding language, all these things are part and parcel of when a new model is released uh to the world that is pre-trained. So you and I probably don't have the tens of thousands of dollars that would be needed to invest in order to build this model on our own, but we can take an existing pre-trained model and fine-tune it, just like um we probably don't have the money to to build a brand new car from scratch, but we could take an existing car and fine-tune it and customize it for our particular purposes.

Same is true with these models. So that one is a big deal, um, because people have generated some pretty cool text with a 355 million parameter model. The 774 million parameter model is going to be much much bigger, um, and should theoretically allow for much more capable text generation, uh, particularly for longer form text. One of the weaknesses of the earlier models is they could do short form text really well. I mean, really well, but they struggled in an untrained way, uh, with larger uh corpuses of text, writing entire books, for example.

So the 774 million parameter model will give the uh the AI community a chance to work with um this bigger model. The institute has said that there's still one more model left, and that's the 1.5 million parameter model, the the mega model. Um 1.5 million billion, I guess, parameter model, the mega model, uh, and that may or may not be released uh down the road. But I think that's an important milestone because again, these transformer type AI models have really changed how we do natural language generation. 2018 was a big year, 2019 has been an even bigger year.

The second um major milestone was the rule, the release of uh the Melnet uh neural network. This is a leap forward over the uh WaveNet model. So WaveNet was a Google Deep Mind project that they released that allows people to train a model on sound and produce credible uh musical compositions and credible voice synthesis. A lot of the things you've heard about uh AI being able to imitate someone's voice comes out of the Wave Network. WaveNet, though, still sounds a little robotic.

Um you have to do a lot of training with it, and that's because the nature of waveform audio. Well, the folks um who came up with MelNet have changed the game by adding an additional important dimension to audio. So in audio, there's there's sort of what three dimensions, right? There's frequency, there's amplitude, and then there's time. Waveforms are very much about amplitude and time.

Right? How loud is something in you've seen those little squiggly line charts. When you add in frequency, you get what's called a spectrogram. Anyone who's done any audio editing work in Audacity or Adobe Audition or any of these, you know, the major pro audio tools are familiar with spectrograms because you can see the frequencies in music. For example, you can differentiate the sound of a trumpet from the sound of uh cello, even though both are playing at the same time because they occupy different frequencies.

You can differentiate different speakers that way. Ah, my voice is going to be different from um uh a human female's voice in who's a soprano, right? Different frequencies, but spectrograms operate differently than waveforms, which means that it this Melnet project has figured out let's use spectrograms to train AI voices and AI music. And the results so far have been really impressive. Now it's not something that is commercially ready yet, it is still very much a lab project, but the results that they've gotten, uh particularly given how uh how easily accessible major heavy duty uh commercial processing power is points to a very, very bright near term future for projects like this.

For us, you and me as marketers to be able to use them in some fashion. Uh, most likely it will be an AI company that will offer it as a service because there are not a lot of folks who can code their own uh AIs, but uh some very very interesting stuff coming along, very, very interesting stuff that's happening in the future broadly, humans will be the conductors of the orchestra rather than the first violins, right? They'll be the ones who are training the machines and guiding them. Like this is what a proper piano solo sounds like. This is what a proper um audiobook reading sounds like, and the ability for us to do this and be able to really alleviate some of the dull work, is going to be a massive gain in productivity for us because we won't need to have an army of interns to you know crank out mediocre copy and then have to copy edit it.

We'll have machines that can at the very least do the same mediocre work, if not better work, and at a much lower cost, um without making the work mind-numbing for other humans, and be able to scale much larger. So that's sort of where things are right now, where they're going. Although it certainly is beneficial if you can, but you've got to understand what they can do, you've got to understand their limitations, and you've got to be thinking about how do I incorporate this technology into my planning for the rest of 2019, for 20, for 20, for 2021. For the next three years? If your planning does not involve leveraging practical uses of AI, you are at risk.

You are at risk from by being blindsided by a more nimble competitor who can leverage these technologies and uh do things much better, much faster, much cheaper than your company is. So something to think about. As always, please leave your comments below and subscribe to the YouTube channel and to the newsletter. I'll talk to you soon. Take care.

Want help solving your company's data analytics and digital marketing problems? Visit TrustInsights.ai today and let us know how we can help you.