Annika asks, “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.”
Two major milestones: GPT-2 755M and MelNet. Watch the video for what in the world these things are and why they matter.
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What follows is an AI-generated transcript. The transcript may contain errors and is not a substitute for watching the video.
In today’s episode Arctic asks, what is what is, in your opinion, the most important milestone that has been reached in 2019? With AI in digital marketing? And how do you see the future of digital marketing? The future we’re going to leave the future but to itself, because these days, there’s absolutely no credible way to forecast the future more than a very short term horizon.
Now, what’s a funny example of this? Back in 2016, I think it was someone prognosticate it that deep neural networks were so primitive and non capable, that would take 30 to 35 years before a neural network, learned how to play the game go.
Course 18 months later, Google’s DeepMind beat the world championship.
And that prognostication went right out the window.
So we’re going to leave the future out of this for now, in terms of milestones that are happening today, and in 2019.
Two big ones recently, in fact, one literally just crossed my desk last night, one GPT to which is the general purpose transformer from open AI, which is for use for natural language processing, and natural language generation has reached its sort of six month midpoint terms of the project and how people are using it, it’s been used extensively open AI has been leaking out, we’re not leaking, just distributing his language models.
And people have done some pretty incredible work with the very small model, which was 124 million lines, documents, they released the media model in May of 2018 355 million documents.
And literally just days ago, they released a 774 million document language model.
So this one is substantially larger.
And that means that you get better language generation, you get better capabilities.
From the model, more impactful language generation, more natural sounding language, all these things are part and parcel of when a new model is released to the world that is pre trained.
So you and I probably don’t have the 10s 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 we probably don’t have the money to build a brand new car from scratch, what 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.
Because people generate is a pretty cool text with a 355 million parameter model, the 774 million parameter model is going to be much much bigger and should theoretically allow for much more capable text generation, particularly for longer form text.
One of the weaknesses of their earlier models is they could do short form text really well, I mean really well.
But they struggled in an untrained way, with larger corpus of text writing entire books, for example.
So the 774 million parameter model will give the the AI community a chance to work with this bigger model.
The Institute has said that there’s still one more model left and that’s the one 1.5 million parameter model, the mega model 1.5 million billion, I guess, around them all of the mega model, and that may or may not be released 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 major milestone was the lease of the mill net neural network.
This is a leap forward over the wave net model.
So wave net was a Google DeepMind projects that they released that allows people to train a model on sound and produce credible musical compositions and credible voice synthesis.
A lot of the things you heard about AI being able to imitate someone’s voice comes out of the wave network.
Wave net, though, still sounds a little robotic, you have to do a lot of training with it.
And that’s because the nature of waveform audio.
Well, the folks who came up with Mel net 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 and you see the little squiggly line charts.
When you add in frequency, you get it what’s called a spectrum Graham, anyone who’s done in the audio editing work in Audacity, or Adobe Audition or any of these, you know, the major Pro Audio tools are familiar with spectra grams, because you can see the frequencies in music for example, you can differentiate the sound of a trumpet from the sound of cello even though both are playing at the same time because they occupied different frequencies.
You can differentiate different speakers that way, my voice is going to be different from a human females voice in who’s a soprano, right? different frequencies.
But spectral grams operate differently than the waveforms, which means that it this Mel net project has figured out let’s use spectra grams to train AI voices, an 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, particularly given how how easily accessible major heavy duty 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.
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 AI is but 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 violence, 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 a proper audio book 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 and productivity for us because we won’t need to have an army of interns to crank up mediocre copy and then have to copy edited, it will have machines that can at the very least do the same mediocre work if not better work.
And at a much lower cost 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.
I would strongly recommend that if you’re a marketer, you be looking at these technologies be reading up on them be watching YouTube videos on them.
You don’t have to necessarily pick up Python or are or whatever encoding these things, although it certainly is beneficial if you can.
But you got to understand what they can do.
You’ve got to understand the limitations.
And you’ve got to be thinking about how do I incorporate this technology into my planning for the rest of 2019 for 2424 2021.
For the next three years, if you’re planning does not involve leveraging practical uses of AI, you are at risk.
You are at risk for by being blindsided by a more nimble competitor who can leverage these technologies and do things much better, much faster, much cheaper than your company is.
So something to think about.
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