The Marketing Singularity: How Large Language Models Are Changing Marketing

Yeah, everybody's going to say. All right, should we get started? All right. Well, good morning. My name is Chris.

Today we're gonna be talking about the one thing everyone can't seem to stop talking about, which is a technology service and application called Chat GPT. You have likely heard of this software. It has been literally in the news at every possible turn from graphic design to iPhone moments to how Philadelphia area realtors are using the software. If you're unfamiliar with it, now we'll talk about a bit about it in a second, but just to give you a sense of how panicked and or in awe people are of this thing. One of the challenges that we're going to talk about this today is what how this thing even works.

Because for a lot of people it comes across as magic, and it is most certainly not magic. Nothing about machine learning and artificial intelligence is magic. It is all mathematics. And unfortunately, if you got into, say, marketing or media because you wanted to avoid math, I have bad news for you. This is the interface.

If you're unfamiliar with it, uh ChatGPT, it really is a chatbot interface to what is called a large language model. These are pieces of software that are constructed to be able to do things like generate text, summarize text, rewrite text, pretty much anything with text. So what is a large language model? Large language models are the latest evolution of what's called natural language processing, trying to teach computers how to work with words. They are all based on one fundamental principle from 1957.

You shall know a word by the company it keeps. This is the basis for all natural language processing. Computers can't read. Computers have no comprehensive skill comprehensive skills whatsoever. All they know is mathematics.

And so when we try to teach a computer how to work with language, we're really trying to teach it statistics and probabilities. What are the words around a word, what do they mean? For example, if I say that I'm brewing the tea, what am I talking about? I'm probably talking about a beverage, right? A beverage made from a certain type of plant and the process by which I work with it.

If I'm saying I'm spilling the tea, this is different, right? This is jargon, it means gossip. So it's almost the same set of words, but because of the way we use language, the propensity for language, the meaning changes. So if we understand that, we can understand how large language models work because we can start to understand that words mean different things based on their context, and that is the most important thing to know about large language models. It's all just probability.

Word order matters too. If I say I'm brewing the tea, it's pretty straightforward who the subject is, what the object is and what's happening. If I say the tea I'm brewing, right, this is a different sentence, right? It means functionally about the same thing, but in the English language, this changes what the focus of the sentence is about. It's more about the tea and not me, the person and what it is that I'm doing.

If I say brewing I'm tea the, this doesn't make any lick of sense in English. It's the same words, probabilistically, it's the same words, but they're different because of the order in which they occur. This sentence does make sense in Arabic and Gaelic and Irish, where they have a verb-sub subject-object uh format, but it doesn't make any sense in English. And so part of the challenges of large language models is also to understand what language are you working in and how does that language uh function? So how do these things work?

Well, the short answer is like this. This is probably the least helpful answer, however, because it is just a system architecture diagram. What large language models do, or what companies who make them do, is take an enormous amount of text from the internet. The company, Ulithra AI has scraped about 800 million uh uh megabytes of text from uh clinical research papers, news outlets and things. OpenAIs, Chat GPT has scraped even more.

They have scraped uh several billions uh pounds of text out there, everything that they can get their hands on that they're not going to get sued for immediately. Um, so reviews, uh fan fiction, uh all sorts of crazy stuff online. And all they're trying to do is is gather up all the text possible that they can have access to so that they can start assigning probabilities to it. These are some uh reviews, these are from Amazon and a few other sites, and you can see I'm brewing the tea occurs a fairly frequently number of times, right? We can see that there are it's in this review here, it's in that review here, and as part of this, as part of constructing this model, it's starting, you can also see there are words that go with this phrase, right?

We talk about I'm brewing the tea, you talk about how it tastes, how it smells, certain types of words and phrases, um, styles of tea, herbs. Again, this is all just probability. We're trying to now build associations in words. You do this all the time naturally, right? If you were in a person from England, you would say, you know, God save the, and then up until last year, you would the last word in that phrase would be the queen, right?

Now it's no longer the case. But you have known probabilities based on frequency. If you're an American, you say, I pledge allegiance to the right. It's not the it's not a rutabaga. Probably not.

But that's how probability works. We do this all the time. Where this changes is in what the machines do. So the machines, again, they can't read. So what they do is instead they what they assign values to each of the words that occur in all these texts that they've been scraping, you know, millions and millions and millions of pages.

Here's a very short example. I'm brewing the tea. You assign a numeric value to each of these words. And then when you see them in other cases, then the number is stays the same. This is the sequence changes.

Again, machines can't comprehend. But what they do is they just take these reviews, for example, and break these into all these different numbers, and then we start looking for probabilities. So that when somebody says I'm brewing the, based on all the language that's consumed so far, what is the logical next word in this phrase in the sentence? Well, there's a probably a bunch, right? Based on the data you've scraped together.

And each of these has a probability. Each of these has a probability that is based on context. So part of working with large language models is understanding what probabilities you want to invoke. It's a technique called prompt engineering. When you're working in these systems, let's go ahead and see and just pull one up here.

Escape PowerPoint. All these things are just doing is regurgitating based on the amount of information that you have and that and what it was what it had learned about. This is pretty good. This is based on a lot of publicly available information. And the more frequent and common a phrase is used, the easier it is for the machine to put something together.

The more obscure or arcane something is, the harder it is for these machines to do what we want them to do because they don't have nearly as much knowledge to associate with those words. Among other things, I mean we've been feeding Google for since that long, but these other these language models now are starting to consume a lot of that information as well. Okay. So with prompt engineering, again, you know a word by a company it keeps. When you write a prompt, like write a paragraph about B2B marketing strategy, right?

It's going to spit out something fairly generic because I didn't give it much to work with. Remember, it's association. So it comes up with a you know reasonably okay, kind of bland paragraph. If I lengthen that and say, write a paragraph about B2B marketing strategy with an emphasis on email marketing and lead generation, what it comes up with now is more specific because it has more words to work with in the initial prompt and it will narrow those probabilities. Just like if I say I'm brewing the tea, and my previous sentence is about a tea house, right?

It's going to come up with completions that make a lot more sense because it knows what guardrails I want. So when we're writing to talk with these machines, these doing these prompts, we are putting guardrails on, and the guardrails are the words we use in the prompts. If I say, write a paragraph of a B2B marketing strategy with an emphasis on email marketing and lead generation, focus on the reduction of churn and increased audience loyalty, include details about marketing automation and lead scoring, right in a professional tone of voice. Now there's a lot more guardrails. And the more guardrails you put on these prompts, the better the output gets.

Again, making these things work is all about just knowing the company of the words that you want to keep. So let's take a look at a couple of uh use cases, some abridged use cases. This is one I said you will act as a blogger. You have expertise in blogging, content creation, long-form content, content marketing, and content for SEO. Again, guardrails.

I'm trying to tell it what to focus on. You have subject matter expertise in statistics, data science, machine learning, and supervised learning. Your first task is to draft a four-part outline for the following question. How do you build a predictive analytics algorithm? And what it spits out is credible.

So this style of prompt is sort of what we call the role prompt. Like you're gonna be this type of person. And the reason this prompt works, again, those guardrails, the more I say you're gonna be focusing on content marketing, content for SEO, it knows then what words to associate with it. Right? It's if I had said you're going to write a biography or you're gonna write some fan fiction, you're gonna, it's gonna change what words get associated with that and what it spits out.

You're an expert social media manager, you're skilled at crafting social media posts that garner high engagement on services like Twitter, TikTok, Instagram, and LinkedIn in your capacity, so on and so forth. Write a write some uh some uh summaries for this text. So, again, summarization is one of the most powerful things this software can do. If you take uh information that already exists but maybe is not in great format, you can have it summarize and condense it down. So one of my favorite things to do with these tools, I use uh you know voice recorders and voice recording apps, and I will, as I'm driving around, uh just sort of foam at the mouth and and and rant.

And the automated transcripts I get back, you can kind of what you can see here at the bottom of the screen, these are legible, they're understandable, but they're not exactly what I call publication ready. There's a lot of uh and um and you know, and and filler and occasional distraction. But I can take the automated text that these things create and feed it into a system like ChatGPT and say, I want you to revise this, I want you to rewrite this, I want you to make it less, you know, angry man yelling at cloud and more something ready for a publication. And that's what these tools are really good at. It's the the phrase GPT stands for generative pre-trained transformer.

And what that means is that these tools are good at creating, but they're really good at revising. They're really good at rewriting content because in a lot of ways they don't have to work nearly as hard to just rephrase things as they do to come up with something net new. Another example, I told it um, given a piece of text, give me the big five personality scores of the author based on you know several thousand words of text. And so it came up with an explanation of of these psychological scores. If you believe in sort of the big five methodology, this is a handy way to evaluate text and understand the personality of the of the author at the time.

Here's an example. I said you're an expert programmer in the programming language, R, and they gave it this long use case of what I want the software to do. And it writes the code for me, which is an enormous time saver if anyone codes. It will get your code about 90-ish, 95% correct. The last 10% still doesn't work.

Yeah. Does it matter if you say expert or not? It does not matter. Oh, okay. I was just curious because you kept saying you're an expert social media manager, you're an expert something.

Like if you say novice, is it going to give you a crap out there? No. I use that just for my own natural language construction because I want those keywords, you know, tidyverse and R and you know shiny apps and stuff like that. You could actually just list those words out without any proper grammar at all, and it would still function just about the same. It's more for our use as humans to look at the prompt and go, okay, well, this is what you're trying to get the machine to do.

It's all word frequency. So as long as you get the right words. I mean, prompt engineering, if you think about it, it's kind of like SEO in in a lot of ways. It's just prompt optimization. What are the words and phrases that I need to have that are relevant that will put guardrails on the machine's output?

Um so yeah, you don't need to use the word expert, I just like it because it looks nice. It will write code. It writes about 90-ish percent of the code correctly. This is one of the challenges with the software. If it can it gets it mostly right.

But mostly right does not mean it runs. Particularly computer code, right? It has to be a hundred percent correct. So you still need some subject matter expertise to be able to get it to do what you want a hundred percent of the time. Uh you have to be able to look at the code and go, okay, well, clearly you you know you you missed something here, or this makes no sense.

But conversely, you will also do things in ways that you normally wouldn't. So you will learn from it as as you work with it. How does it compare stack Overflow? So Stack Overflow and GitHub's bots are in the previous version of Chat GPT, uh they were better. In the current version, the GPT 3.5 Turbo, it's better.

It's gotten better, which is alarming. Um part of the reason is because it has uh what it's called reinforcement learning. So every time you create an output and you don't make changes to that output, it essentially interprets it like, okay, I got it right. And so uh over time it I it the model basically is learning along the way and improving very, very rapidly. Chris, yes.

Will this uh retrieve relevant quotes for you or case studies for examples? It will do examples and templates. It will not ret it doesn't retrieve anything. Uh part of the challenge of a large language model is it's not like a search engine where it goes out and finds new stuff, it is constructing stuff from what it knows. So will it be able to create something that is a solid template?

Absolutely. In fact, I've had it uh I think one of my examples in here is I gave it uh an uh a really badly written NDA that somebody sent me, and I said, I'm not signing this because whoever wrote this uh it was an idiot. Um and the guy was like, oh, well, I kind of wrote it from copying and pasting from the internet. I said, I can tell. Uh can I follow up that you said it it will it only writes things based on what it knows?

How often does it update what it knows? Um to me, that would be if it's constantly doing that, it functions somewhat like a search engine. It's somewhat like a search engine. The the core of GPT 3.5 turbo is a knowledge base that ends at the end of 2021. Um there have been seven updates to the model since then, but there's smaller updates based on the feedback people give.

Um it is due for a factual update uh sometime in in Q1 or Q2 of this year when GPT 4 gets released. Um but is it is a slower, it is a fairly slow release cycle. So it's about a year out of date. Um it would be a huge undertaking that makes that absolutely. It's it's I mean you're talking about hundreds and hundreds of millions of pages of scraped text.

Right. So I had it revise this NDA and come up with a better version of it. I said, you know, tell me the things that are missing from this NDA and and and rewrite it. And so we we rewrote it and saved some time. Um writing mission statements, doing search engine optimization keyword lists.

I gave it some background, said give me some suggested keywords. And then you feed these into like your SEO tool of choice and get your keyword scores to uh to make improvements. Uh let's see. Uh oh yeah, writing privacy policies, GDPR compliant policies. Again, you should have some subject matter expertise.

Um the tool gets 90, 95% correct, which again with law is something you probably you know, 95% is probably not good enough. There's always that one thing that that hoses you, but it gets you 95% of the way there. Um this affects any industry that uses language, right? So if you use words, which is pretty much most industries, um, this software will have some impact on what you do. There are risks inherent to this.

Again, this is these models, and this is true across the spectrum, but because it's open AI is not the only company that has one of these. There are many of these. They are trained and they are built on the language that is available publicly. Now, the old joke is think of the average person and realize 50% of the population is dumber than that, right? Which is mathematically true.

OpenAI talks about we found evidence of bias in our models, right? Say, we found evidence that says our models more strongly associate European American names with positive sentiment when compared to African American names and negative stereotypes with regard to black women. That's because of the text that went in. As these models learn, they are picking up our biases. They are picking up our flaws as human beings.

It's one of the reasons why some companies, like your Lothar AI, restrict the text they scrape. Like they say we're not going to go out to the broad internet because the broad internet is basically a sewer. So let's stick with the Library of Congress, perhaps, and things like that. But you have to be aware that these models are not something you just kind of fire and forget. You have to be constantly checking their output to make sure that they're not doing something you would not want them doing.

Now, here's where it gets interesting. Everything that people have done so far has been in this lovely chat interface. And there are a gazillion and a half uh experts who all ran out of money doing crypto, so now they're AI experts talking about chat GPT. And everyone's talking about here's how you use these prompts to shorten your work cycle, make yourself more efficient, etc. This is not the end game.

This is this is the playground for humans to toy with the model. The end game is building software with the underlying model. Right? If you're familiar with the software development lifecycle, you, when you're writing these prompts to f to make the machine do something interesting, you are more or less doing the first three stages of software development. And now this two weeks ago now, OpenAI opened up its API, its application programming interface to ChatGPT to say, okay, now you can write software that will connect to this and let your software do the work.

So for example, this is the this is the sort of the programming interface to test these things. Very similar, you know, you will act as a blogger, there's the the predictive outline, same prompt as earlier, but now I can say I want to run this inside of the R program environment or Python and things like that. Instead of creating one blog post, I can just rerun this software a thousand times or a million times and generate millions and millions of pieces of content. Once you figure out a prompt that works for you, once you figure out a prompt that does something that you think is valuable or important to your job, you can now build software that does it at scale, and that's the value of the software of what's happening. That's the the way you're gonna make your money.

If you're good at prompts and building a good prompt, you are a developer now. If you've got a working prompt, like hey, take this uh YouTube subtitles file and turn it into an article, or summarize this text, or rewrite this for uh for comedy, or you know, take any of the prompts that people have played with, now you turn it into software in production at much, much bigger scale. Which means that a lot of the people things that people have been talking about, like is this going to take jobs and stuff? Yes, it will. Um there's there's really no debate about that.

For those of you who are marketers, what is the impact going to be on marketing? Well, we've already seen uh Microsoft rolled out their integration into Bing with the ChatGPT, and it was a bit of a rocky rollout, but it is it functional in in production. This is changing how people search, because now instead of having to sift through page after page of information, it just summarizes the information for you. Now you can learn more like clicking on any of those those results. Most people don't.

Most people are okay with the answer that they get and they move on with their day. What that means is that for things like organic search, if you do SEO, a big chunk of your unbranded organic search is going to go away. Where can I learn more about B2B marketing? This is from the chat GPT native interface. Here's all the industry publications and blogs.

There's not a single clickable link in here. So if you are relying on people finding your website through search and visiting it, when they're using these native models, you know, it's it very rarely makes any kind of link-based recommendation. Occasionally it will do that if it's about something that's branded. So branded search, even in the chat GPT native model still does have clickable links. So your first task as a marketer, if you're doing anything with search, is to go into Search Console or Bing Webmaster Tools or whatever your engine of choices and look at the percentage of unbranded organic search that comes to your website.

Whatever that percentage is, whether it's 5% or 95%, realize that as search engines start integrating this technology, you're going to lose that traffic. That traffic is just going to go away. Branded search is probably going to be okay, but unbranded search is not so much. And there's three other three things in general that you should be focusing on as marketers if you care about that impacts your business. Number one is your brand.

Your brand and the ability for people to remember who you are and find you by hook or by crook is the most important thing that you can build. We've been saying this for years, and you we've been doing this podcasting and stuff as part of that for 17 years now. But it's a point now where the machines and particularly the large technology companies have decided they would like to send us less traffic and keep more of it for themselves. And the only way around that is to get people to search for us specifically, and that means our brand. Second, is have some kind of publication that you own.

Something that is yours that others can't take away from you. So uh email newsletters is one of my favorite text messaging lists, maybe some other form of publication. If there are companies that do very well with boutique print. But you need to have some kind of method of reaching people that is not intermediated by a machine. Email is one of those last the last things that's available that there's still very little between you and the reader.

Uh compare that to social media, compare that now to search, etc. And the third thing that you need to have is some kind of community. Uh, some kind of community, ideally that it is hybrid, that is in real life, like we're all sitting here today, uh, or also in virtual space. So this is a Slack group that we run, it's called Analytics for Marketers. There are a gazillion and a half different Discord servers that you can join that have these communities.

These communities are important because they're disambiguated, right? There's no machine arbitrating what you see. It's all you just jump into them, and it's kind of like 2007 Twitter. It's just what's happening as it's happening. It's it's a bit of a fire hose in some of these communities, but it's essential.

So finally, who's gonna lose their jobs? Well the Brookings Institute said AI is gonna take away tasks, but not jobs. That's kind of true. They did they said that mainly to avert panic. But the reality is that if you are a marketer who's skilled with AI, uh or any professional who's skilled with AI, you will replace a marketer who is not.

Any professional who is not competent at AI is an endangered species because of the speed at which these serv these these tools are uh proliferating. How is this going to cost jobs? If you think about someone's workday, right? A bunch of tasks that are broken out over time, and you say, well, you know, how much of this can I automate? How much of this can I you know have uh individual tasks turned over to machines?

What's left then is you know the chunks here that were not automated, right? So in a progressive company, a progressive company will say, Well, let's say let's retrain people and fill up those time slots. A regressive company is gonna say, let's add that block of free time and get rid of a seat, right? And so and save some money. So depending on the kind of company that you work for, that will depend on whether AI consumes jobs or not.

But that's kind of where this stuff is going. So that is the end of this time slot. Any questions? So, what is being trained for AI mean? Like, what does that really look like?

In what sense? What's up? I'm gonna be a marketer who's AI enabled. Mm-hmm. So, for a marketer who is AI enabled, it is using learning how to use the various tools like chatGPT or Go Charlie or Jasper, any of these tools that can do content creation.

It is learning how to use generative uh image AI like Dolly 2 or stable diffusion to create imagery. It is learning the landscape of what the tools are that are out there, that that what capabilities they have, things like transcription and translation, and then figuring out how you apply those tools within your work. So, you know, the the obvious things for like text generation is okay, I can write content a lot faster now. I can I can have machines generate first drafts, but I can also have it do summarization and access a lot more information. I can have machines summarize and identify key points, you know, boil down enormous amounts of text into into very, very small chunks that uh I can make use of better.

Um a portion will be a big portion will be in customer service and and and customer care, where you can have these models now that can have intelligible conversations with people and deploy them so that customers not only get the assistance they're looking for, but also don't feel like they're talking to a moron. You first then you're have you checked out Snipped, the podcast app yet? It's got some AI built into it that will gather things, and then the second question is how has this impacted your own workflow between marketing over coffee and almost timely? I've been subscribing a long time ago. Well, thank you.

Um I have heard of SNPT, uh I've tried out Descript. Descripto is also a fantastic tool that does a lot of stuff with like voice synthesis, where you can have it trim out stop words and filler and stuff and sound more like NPR and less like you're drunk. Um, which is good. And in terms of how I use this, a lot of it is for generation, a lot of it's for but much more summarized. So, like when I write a newsletter, um very often I'm in the car or I'm you know walking the dog or something like that, and I have a bunch of stuff that I want to get out of my head, but it's not coherent.

And so have feeding the first draft, which is you know probably eight or nine pages of rambling and then saying, okay, summarize, find the key points, and then I can go back and refine that and write that. That improves the output. It doesn't necessarily speed it up, but it improves the quality of the output. And again, that's where I think these tools, particularly the text tools, are really powerful, is improving the quality of output. Um, transcripts are a gold mine.

Uh when you think about uh Bill Marriott was famous for this uh years and years ago. Uh he was the CEO of Marriott Corporation. He used to blog uh fairly regularly on the Marriott blog. He's never never set pen to paper. What he did has been doing for over a decade is leaving voicemails uh to his marketing department of you know whatever's on his mind, and then someone ghostwrites that.

Well, now you don't need to have a staff of people to do that. You can have the machines do a lot of that. When you look at some of the more prolific content creators out there, you know, the folks who have several million folks on their YouTube channels and things, a lot of their teamwork is just admin, right? It's just people following somebody around with a camera, editing, making all these these short clips and stuff, and a lot of the the newer content engines now can do that automatically. Where you can say, okay, take this video and now make a square of video format, make a clip of this and so on.

And with the text-based understanding of the models, they can say, Okay, this these are the t five snippets that are the most relevant points. Let's clip those out and turn them into something. I believe the script does allows you to do that now. Yeah, snippet what is an apple from the user's perspective. If you're listening to the podcast, you can have it do three shows.

I think it's three. Um snipped may not itself be free, but the three shows per day asking it to generate, and it will come back with podcast chapters as well as transcript, and you can make clips inside of it based on that transcript and then share them. Yeah, exactly. All right. My question was kind of covered.

I noticed when you talked about having your own properties uh mentioning email emails, you didn't mention a blog or a website. I didn't. And I didn't mention a blog or a website because I'm still figuring out how we can avoid feeding the models uh all of our stuff uh and giving it all of our content away for free that they then use to not send us any traffic because it kind of the a large language model breaks the agreement we have sort of implicitly with a service like Google. We make a ton of free content for Google when Google Exchange sends us new customers. That's no longer true with these language models.

So the question is where do you put your best stuff? Um I would say your best stuff belongs in publications that directly benefit people who've agreed to sign up to hear from you. And then your less good stuff maybe goes on a blog or something like that. And I also wanted to mention that there are now tools that can summarize a YouTube video. Oh, yeah, yeah, Google Charlie is one of my favorites.

You can give it uh like a two hour video and it'll spit out a nice some synopsis. Really handy. Other questions. Uh uh, you first know. So so how are the folks behind chat GPT or these other things actually determining what they want to feed it?

I mean, because it it's only as good as what it's actually being fed. How are they determining that uh, you know, something on say Wikipedia or whatever is actually uh factual versus you know an opinion on something? They're not. And that's why these things are are somewhat risky. That's why there are more curated models that are are you can say like I only want certain types of information going into the corpus.

And even then, I mean, if you think about it, go on to bioarchive. The number of preprints that have been released in the last three years that have since been retracted is higher than you know, well, the the number of papers have been released in the number of years, period. Um, there's been a lot of retraction things, you know, with the pandemic and such. So there is no guarantee of quality with these large language models. That now, for a lot of the uses we've talked about, like summarization, it's probably not going to impact uh your work substantially.

But if you're doing generative stuff, particularly around, say, political topics or um any kind of inflammatory topic, it's a minefield and it's it's not pretty. Yeah. When you were talking about building community, you mentioned Twitter. Um is that going? Ha ha's doctor with a flashlight'll show, yeah.

Right now it's in a wormhole. And uh, yeah, exactly. And I'm I'm like, I sure I'd love to do that. And I and I've been a Twitter head for a long time, but kind of not confident in the platform. I would not be confident at all on the platform, in fact.

Um there are some very good tools. The uh SN Scrape is one as a command line tool to scrape and extract like all of your data out of Twitter so that you can take it with you. Um I would not build uh anything on Twitter unless I had no other choice. Um and I would absolutely feel free to be here's the thing. There's sort of a an implicit, I guess, um type of manners we use on social media, right?

To to not spam and harass people and things like that. Um when Twitter has its little uh change in management back in October, all those rules went out the window, along with the entire trust and safety team. They're like they're completely gone. Almost all the moderation is gone. It's all automated now.

So pretty much you can do whatever you want on Twitter. So I would say if you've got an audience on there, feel free to direct message them all and say, hey, I'm leaving. I'm going over here, follow me over here, things like that. Um use bots. Uh there's no one che there's no one minding the store about uh automated programs anymore.

Um to the point now where there are some very amusing sort of uh political uh games being played on Twitter between various factions and gazillions of bots all like yelling at each other. Um so I would say, yeah, it's that it would not be my first choice of a place. I would build your first choice. Discord. Yep.

Discord. For anybody on for if your audience is under 35, Discord should be the place that you build your community around because A, it's where all the people are, um, and B, because their revenue model has no advertising. Their revenue model is solely on a variety of subscriptions, which means that you know, individual users can upgrade to to nitro, etc., and and get like more emoji in their profiles and um things. And then companies who run servers or entities who run servers can obviously get these things called boosts to you know pay a monthly fee to have better video quality and streaming and stuff. Um it is by far, I think, the the most community-friendly platform, and it's a private space.

Um search engines can't see it, uh, large language models can't index it. Um and so it's it is your space to do with you know uh within the terms of service um as you please. So I would spend my time there. Jan, and then we'll go. Okay, well, it seems to me like they say it won't affect jobs, but if you get good and you learn how to use it, you won't need any editor or any assistant.

So you can do it yourself. Like let's say you have three people working for you. Yes. Or you're sending it out, those three people won't be needed anymore. Right.

So they're they're out of work. Yes. Until they upgrade their skills to be able to work faster with the help of machines. And second, it's very strange to me how people are just talking about this all the time, everywhere. Like I've never seen any product or event that everybody's talking about, they all think they're experts, or they're saying this is good or this is bad, and it's just weird.

It's weird, but it's it's not surprising. It's not surprising because the last three years have really kind of warped our ability to communicate, um, just in general. Um, and being able to c communicate with a machine that can simulate um consciousness. It is not conscious, it is not sentient. We are far from that.

But it can simulate the kinds of conversations you would have with somebody who is more empathetic than perhaps the the the friend group that you have. That's very appealing to a lot of people, right? There's there's there's a lot of value in having someone to be able to talk to. Like if you are a solopreneur, for example, and you're used to working in an office, having um you know something you can just bounce opinions off of in a very non-judgmental way is very popular. Um so there's there's a lot of value to that from a sociological perspective.

It only happened to me once, but they mentioned they mentioned Siries or Alexa on the television. And then my Alexa and my iPhone started arguing with each other. Yeah. That was just very question there. Okay, without trying to drag you into Discord 101, perhaps just a narrative syllabus.

If indeed Ciscord, uh I'm not familiar with Discord, but if it's uh if it's private, not searchable, how does that change the uh the marketing model uh between that and Twitter when you're so used to using you know the common engines of Google and such like that to actually expand upon your Twitter reach. So this is the great challenge now. Um for folks who have Discord communities, they typically have some kind of publicly facing community where they can essentially recruit and bring in new folks. Twitch, for example. Um a lot of very popular Twitch Twitch and YouTube possi uh personalities have Discord communities and they so they use those essentially as feeders to feed into their community.

Yes. Um as a marketer, you have to be thinking a lot about influencer marketing and identifying who else has your community that you don't yet. Um and working with those folks to uh hopefully in a non-competitive way that can bring in um new audiences to you. And a lot of it's word of mouth. You know, if you've got an audience that is not uh you know completely hermits, uh then they they and themselves have friends and stuff, and so you can grow a community that way.

Communities typically have these odd sort of benchmarks uh uh where you you see phases of growth, um, you know, like around 500 users, around a thousand users, 2,000 users, five thousand users, et cetera, where you see these big steps um as you hit sort of critical mass along the way that bring in a lot more folks. So, like it's it's much harder actually to get to your first thousand users than it is to get to your second thousand on Discord because of the power of word of mouth. Um a lot of it is building those relationships with folks and and finding people who are good at influencer marketing to grow a community. Yeah, two comments. Uh one on your comment that it's not surprising, is because we've been living with artificial intelligence from HAL to C3PO and all of these things.

It's been part of our culture, and now it's for real. Yes. And the second is a question of um how do you see mashups like the one between OpenAI and Wolfram Alpha using the Python temp framework Langchain. Uh do you think that's a key part of where we'll be moving so we get both conversation and accuracy? There's a ton of startups that are working on trying to do uh to essentially build fact checking layers into a uh GPT-style model.

And they are a greater or lesser skill right now. Uh Microsoft's uh the Bing one actually is pretty decent uh in terms of what it's uh what it because what they've done is they've basically made uh a summarization layer that can take their search results and then essentially feed that through a language model for to create the language, but it's built on the the search engine data that they have. So they've kind of done a very neat twist on it. Uh I think that will probably, for anything where facts actually matter, which unfortunately seems to be less and less these days, um, that is is going to continue to be the probably the gold standard for a while. The large language model itself, because of this in frequency at which it updates, is unlikely to ever going to be a a gold standard of like correct knowledge because it's so prone to hallucination, which is when it's asked a question, there is no logical answer to, so it just imagines something.

Like if you ask it who was president of the United States in 1492, it will not know what to do, so it will just make something up that's factually completely wrong, but plausible. I mean, it sounds like and and that's the danger with these tools is unless you have subject matter expertise, you don't know when it's hallucinating in a subject that you have no expertise in. Like if you were to ask it about the S1 spike protein in SARS COVID, it's probably not going to know the answer about how that impacts, say, microclots in your vasculature. If you know that, you can assess its output and say, okay, like again, 95% correct, but it in as with many things, medicine is one of those areas where 95% correct is not good enough, right? So an anecdote about the accuracy.

Um I tried Chat GPT. I asked it to produce a playlist of songs for a radio show that my friend does. It's all Jersey artists and music. So a two-hour radio show, and it gave me, you know, John Bon Jovi and Bruce Springsteen, predictable stuff, and then it said McJagger and Rolling Stones. Was born in England but spent his childhood in New Jersey.

And it turns out the reason it thought that was because at some point he was arrested for drug possession in West Sussex in England, and Sussex is a county in New Jersey. And it was absolutely confidently wrong that Mick Jagger grew up in Sussex County, New Jersey. Yep. I mean, if you think about it, it's a lot like humans. Um there are a whole bunch of humans who are confidently wrong about a whole bunch of things.

Does that mean we're approaching consciousness now? I I would say it's we're going the opposite direction, humans are getting lost. So at the risk of sounding really negative, I don't mean this. I'm just trying to get my head around this. If we all start to use Chat GPT for our public-facing content, a lot of blog posts about whether it's marketing or whatever, we're all starting instead of one blog post a week or every two weeks, now we're doing two a day, but we can do that now.

And then in 2024, ChatGPT re-serv reskins everything. Are we gonna end up with some kind of singular voice? Or we all write to kind of a bland level, or are we kind of kind of either dumbed down or smart across them together? So that's an interesting question. No, no, no, it's a valid question.

Um the short answer is we already write bland content. I mean if you read like what most marketers write, it's pretty awful. Um and so uh uh yeah, I'm sure the folks in this room that's not the case, but for there's there's an awful lot of marketing out there, which is just not great. Um so it's already learning from that. The amount of text that is marketing text online is actually relatively small.

You know, we we think of ourselves as as you know being prolific content creators, but it we're really not. When you look at how much text flows through, say Reddit on a day-to-day basis, you know, it is a i it dwarfs anything marketing can create. So conversationally, the tools will continue to to evolve to whatever the standard of language is online as a whole. Um there are you know a gazillion and a half new academic papers being published all the time. And those those uh at least for now have have managed to retain the certain level of of linguistic quality.

But there is the possibility that what it spits out, what the tool spits out is average. So, yes, there will be some homogenization, particularly of marketing content, because marketers, you know, are overworked, and we'll just take the shortest possible path to an outcome. But there that again, that's where the things like summarization and rewriting, I think, come in really handy where you can take the content that you've created with your voice, have it rewrite it, but it preserves your ideas and your language. It just you know fixes the the fact that you forget the Oxford comma all the time. Um we were chatting a little bit about you know what happens, and is this the depth of SEO, or is it the improvement because you have these because you can have keywords into GPT and still pulling out those key phrases.

So do you have to is it good to have those skills because you can optimize the prompts better? Or is you know where is that gonna be relevant again because if things are going in and it's not a branded search, you may not get the juice that you used to get out of being authoritative at the website. The big question we don't know is how BARD will function, um, which is Google's entry in the large language model. Um we've seen obviously you know 100 million people sign up for ChatGPT in less than 60 days. There's obviously an appetite for the application and what it can do.

Um, Google's first attempt at using BARD was not particularly successful, like it hallucinated some things that are that were untrue. Um, but they've clearly demonstrated that they have the capabilities, and how that plays out in their version of search, because they still control like 90-ish percent of the search market in most domains. We don't know what form it will take, and we also don't know how well adopted it will be as the primary method of search. Early indications, again, with ChatGPT success uh show that it is a not a small amount of the audience that would prefer to interact with knowledge bases in a more conversational format. Um so depending on on how Google chooses to implement it will dictate how much of an impact it has on SEO, um, and also what OpenAI continues to do to evolve the product to make it you know it's it's now available in Snapchat.

You can have a conversation you know with uh a machine within Snapchat when you're bored of talking to your your human friends. So he was saying because you're talking about how AI affects the people in this group in the market. I work in education specifically at high school, they're out of their minds with this thing because they're worried about students using it for papers, banned the any kind of uh websites on their Chromebooks, but that doesn't do anything to do at home. So, overall, you know, what are your thoughts on you know how this affects that generation about because most of the teacher are like you know, they're lazy already. What's gonna be is this going to create well, so the getting standards for this?

This raises a very interesting question. What is the value of regurgitating information into an essay? Right? Because the machine can do that. The machine can do that really well.

So, what is the value of that that particular work product? It's like what's the value of a blog post? If it's a mediocre blog post that you've put up about something, does that add any value to the company? Maybe. Does it add any value to the people who are consuming it?

Again, maybe. Education has had a problem for the last 30 years. And education's major problem is that it has failed to recognize it is no longer the gatekeeper of information, but is now the this arbiter of quality, right? So getting a degree does not mean you know anything. I apologize, is anyone else from FNM in here?

But it it certifies that you are at least minimally competent at getting through something in life that takes a fair bit of effort and money to set you apart from the rest of the crowd. So if you're in education and you are trying, you know, you you've got someone who is fluent in using machines, that is the skill today that people need to have. Not being able to write an essay about Columbus's, you know, uh genocidal tendencies. I mean, any you know any machine can do that. Um it is can you fact check it?

Can you can you prove it's true? Can you advance and add knowledge where knowledge did not exist prior? And that's where the machines won't help, right? The machines are incapable of doing that because they can only uh uh work with what they've been given. One of the most interesting things that I think in like clinical papers when you read them, but particularly NIH's papers, is there's a whole little box that says, you know, what is already known and what value is added by this new research.

And in all these papers, they talk about you know, here's what we've learned from our new research. Education's role is to help people become contributors, right? To help people become uh advance the their fields. And so if education is just teaching and regurgitation, then it's doing students a disservice because they're not creating anything net new. Um can a machine do that to some degree, but it still can't synthesize new things.

And the language model is gonna take a while to get there. Um I think we'll probably get artificial general intelligence before then. Uh but is there a good answer for educators? No. I mean, the the existing models of education are so badly broken that a student can use chat GPT for.

I mean, I I helped a student with their their 16th century art paper the other day with a friend of mine in Discord. Um they're like, I need to write this paper. I said, here, let's you know, let's do the first draft with chat GPT, you know, five minutes later they were done. Um and and they got uh a 96 on the paper. Um because the teacher was unable to tell the difference between what a machine had written and what they had written.

Um what was the actual skill there? Was it the knowledge about 16th century art? No. Was it the ability to use AI intelligently? Yes.

Right. So uh you and then you. I I'm curious about Chat GPT is not hard. So the idea that you would have so you made the point around um biased language. Yes.

So people are gonna create their own types of AI products like this that are going to build that initially. Uh how do we get language that's less biased but not biased, or addresses this like indigenous language and and culture and whatnot, right? So I'm curious as a as a product, is this something that you see becoming a competitive space where you're gonna have to do that? Yes. Absolutely.

There are entire groups of people who strongly object to some of the way chat GPT behaves. There's this one conservative group that says, you know, we believe that racist content should be permitted. Um, that you know, freedom of freedom of speech should be absolute, and you should be able to say what machine should be able to say whatever it wants to say without restriction. And that is a group that will, you know, will have some technical skills somewhere in it, and they will craft their own version of it. And I think there's there is space for those systems.

What is missing from the AI field as a whole is there is no really standard of ethics yet, right? There's the right now it's still very much the wild west. Um and how that plays out, we don't know. The one area where I think we have a substantial very high risk is in autonomous um machines, which are not large language models. Um but the technology exists right now for machines to autonomously make kill decisions.

Um this is generally a bad idea. Um at the end of the day, a human being should still decide whether or not uh you know to take another human being's life. Uh I think leaving that up to machines probably is not a great idea, but that is the direction that a lot of militaries are going. Um, and I think that's probably very problematic. So, yeah, go ahead.

So you said that the um bias influences sentiment. Yes, um so if I'm a marketer and I'm looking at um something going back on like focus or something like that, I can see all of that. But at the same time, I'm wondering when I'm writing, does it pick up my voice? Does it understand enough about who I am through the words to sound like me? Or does it come through in this relatively vanilla format?

Um the the out-of-the-box model is still mostly generic, right? Um the previous versions of this model could be done what's called fine-tuning, where you would give it a lot of your content and then it it would essentially uh create its own biases to say I want to use words and phrases and patterns that you use more. Fine-tuning is not available for this model yet, probably will be relatively soon. Um and that at that point you can train it and then essentially build your own model. Um through my lexicon.

Through your lexicon, the way you speak the language you use. That said, the more detailed your prompts are, the more likely it's gonna sound closer to you. And if you are having it rewrite where you're providing the raw text that is your voice, it will preserve much more of your voice that way. Yep. All right.

Well, thank you everyone, and uh have a good rest of the day.