In this episode of In-Ear Insights, the Trust Insights podcast, Katie and Chris discuss their upcoming MAICON 2024 talks on managing the people who manage AI and the importance of open AI models. Discover why open models give you more freedom and control over your AI than closed models. Learn why marketers should understand both the potential benefits and risks of open AI models. Find out how open models can potentially give you a competitive edge in your industry.
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Machine-Generated Transcript
What follows is an AI-generated transcript. The transcript may contain errors and is not a substitute for listening to the episode.
Christopher S. Penn – 00:00
In this week’s In Ear Insights, we are about a month away from MAICON 2024, the Marketing AI Conference, and Katie, both you and I are speaking at this event. We have been perpetual attendees ever since the very first one back in 2019. It is a point of pride that I was the first keynote speaker for this event ever. So, it holds a special place in my heart. This week, I figured we’d talk a little bit about what we’re going to be talking about at the event. Obviously, we want people to attend the event, so we’re not going to give away the entire talk. But your talk is on managing the people who manage the machines, and mine is on open weights models. So, tell me more about managing the people who manage the machines.
Christopher S. Penn – 00:41
And so what’s the angle that you’re going at this year?
Katie Robbert – 00:45
So this year I’m— I did a version of this talk last year, and I really kind of focused on the individual contributor, and I realized that it wasn’t exactly the talk that I wanted to give. So, for the past— well, I guess almost year, I’ve been revamping it, and I’ve really been trying to focus on, at a higher level, the organization.
And so I will bring up one of my favorite slideshows. So, for those who are watching this on YouTube or another channel, this is my buddy, Dexter. Dexter is up for adoption at Baypath Humane Society in Hopkinton, Massachusetts. But I— I put him in the deck because we’ve been doing a lot of top down work with Baypath Humane Society because the question on everybody’s mind is, well, can I use AI to solve this problem?
Katie Robbert – 01:42
Can I use AI for this? And there’s not always an easy answer. So, what I do in this talk is I walk through the 5P Framework. And so for those who don’t know, the 5P Framework is purpose, people, process, platform, and performance. And so going through each of those with a heavy emphasis on people because, sure, you can use AI for a lot of different things, but what about the people who have to actually use it? And that’s where the talk, managing the people who manage the AI, that’s the whole premise. That’s the whole point of the talk is you have technology, but you need people to do the tech. And so my buddy Dexter here is asking, can we use AI for everything? Well, we’re going to find out.
Christopher S. Penn – 02:39
I mean, any deck that involves a lot of puppies is generally going to do well.
Katie Robbert – 02:43
That— and that was sort of what I figured. But there is a reason that he’s included because Bay Path Humane Society is not unique in terms of their needs. They’re a business. At the end of the day, they have people, they have processes, they use a lot of platforms, and they’re trying to figure out, just like the rest of us, how can I be the most efficient? How can I do more with less? Especially since they are a nonprofit, they’re trying very rapidly to figure out we have more, need more— need less resources. How do I do more with less? And that’s true of a lot of publicly owned companies, as well. The resources are dwindling, but they still need to be— do they still need to do more? They need to increase profit margins. They need to increase productivity.
Katie Robbert – 03:40
They need to increase client output. They need to increase revenue. It’s all about more, more. And there’s this misunderstanding that AI can just magically do more. Well, AI can help you do more, but there’s a lot of things that you need to do before you can get to the doing more. And that’s a lot of what I walk through in my deck is, what are the steps that you can take to integrate AI into your organization without it being so disruptive that everybody just walks?
Christopher S. Penn – 04:12
That’s a really good point because one of the things you hear about when people talk about AI, particularly with software development, is sort of the ten X programmer, the ten X developer. The person who can do the work of ten and AI properly used, can be that. Because in most cases, you’re not getting rid of people. In most cases, you are helping your people become double their productivity. Now, that will have an impact in terms of how much you hire. You won’t hire as much. But if you are a resource constrained nonprofit, a two X employee is like a godsend.
Katie Robbert – 04:46
Especially when, in the work that we’ve been doing with them, we’ve been able to help them focus in on the repetitive tasks, which is a lot of what you need to have a good handle on in order for AI to be a useful part of your team, is to really understand where is that repetitive— where are those repetitive things happening? And so for animal shelter, you can imagine there’s a lot of the same questions being asked over and over again. And you can say, oh, everything’s on the website. Well, that might be true, but that doesn’t mean that people have gone to the website. They are on the phone with you now, they want the information now, they don’t want to go to the website.
Katie Robbert – 05:28
They’re talking to you, give the information, or they’ve sent an email, or they’ve reached out on a social media platform. And so there’s all of these different ways that people are asking the same questions over and over again. And so one of the things that the shelter is trying to solve for is how do we serve up this information in a way that is satisfactory to people, that they don’t just feel like we’ve given them the brush off, so that they will come back and take some kind of action, whether it’s fostering, adopting, donating, spreading the word, whatever the call to action is. Because this is the challenge with, especially when you’re in, like, a customer service field, you’re dealing with people directly, and you don’t want to AI them away.
Katie Robbert – 06:16
And what I mean by that is, a lot of people for years have been complaining about— talk about cable companies or banking systems where you can’t get through their chatbot system to talk to a person. And so it becomes very frustrating. And then they, they leave the company. They no longer give them their business. When you have, like, an animal shelter and you want to make sure you keep that connection with people, you need to figure out where AI fits in so that you’re not alienating people, that you still have that human touch, that relationship. So, where does AI fit in a way that helps you become more efficient and lets you build those human relationships?
Christopher S. Penn – 06:56
And I now have the mental sidetrack of an AI chatbot that’s programmed for the dog’s personality. I mean, I’m sorry, I can’t find the answer. Here’s a stick.
Katie Robbert – 07:08
Well, I mean, I’ll bring it. I’ll bring up a different slide. But, like, look at this guy. He’s fantastic.
Christopher S. Penn – 07:17
Exactly.
Katie Robbert – 07:18
Like, if I knew I was talking to this guy, I would interact with it all day.
Christopher S. Penn – 07:24
Exactly. Like, I’m not getting work done. I’m just going to talk to Dexter all day.
Katie Robbert – 07:29
Well, yeah, obviously. But I mean, but I think that there’s definitely ways to think about, who you are as a brand. And so if you had, like, the official AI mascot that sort of interacted as, like, the personality of a Dexter or any of the other adoptable dogs or cats, I mean, that’s sort of a really nice way to bring people in. Like, that’s an engaging thing because people— yeah, who doesn’t want to talk to a dog? Psychopaths probably. But who doesn’t want to, and so, thinking about, like, is that a good use of AI? Well, we know that there’s a lot of repetitive questions. So, could we build a large language model that answers all of these questions, but then also give it sort of the tone and personality of one of our adoptable dogs?
Christopher S. Penn – 08:23
Exactly. Especially since you have a lot of notes about the dog’s personality.
Katie Robbert – 08:28
Right, exactly. And so you can imagine, Bay Path, along with a lot of other companies, like, the data exists. It’s not for a lack of data, it’s for a lack of what do we do with the data? How do we make the data actionable? How do we get it out of the back of the filing cabinets and into the— in front, with people who actually need to use it and make decisions with it. And so that’s— that’s a lot of where you can focus, but in order to get there, you need to go through the five Ps first. And so that’s what I’ll be walking people through at MAICON.
Christopher S. Penn – 09:06
Nice. That sounds awesome. I look forward to seeing it.
Katie Robbert – 09:11
Excellent. Well, don’t heckle me too much.
Christopher S. Penn – 09:14
I don’t heckle a little bit.
Katie Robbert – 09:17
All right, so Chris, you’re talking about your— I don’t know, your talk sounds very technical. So, I can’t even summarize. What the heck is it that you’re talking about? And why is your session a can’t miss?
Christopher S. Penn – 09:33
It’s called The Future of AI is Open, and we’re talking, believe it or not, this will be a very non-technical session. It’s a conceptual session. Oh boy. Yeah. So, there’s two types of models: there’s closed models and open models. Closed models are ones like the ones that power ChatGPT. You use the tool, you enjoy the tool. You have no say or control over the engine, the model itself, and what it does. You have to interact with it with prompting, but you can’t take that engine out, tune it up, do things with it, and then put it back in. It’s locked down. Open models are models like Meta’s Llama, where you download this. Essentially, it’s a big statistical database. That’s what a model really is.
Christopher S. Penn – 10:17
You run it locally on your laptop or on your own servers at your company, and you have total control over it. There is little to no censorship. There is little to no guardrails except for what you provide. And as a result, you can make this thing pretty much do anything you want, which is really important. For example, one of our— this will be in the talk— one of our clients that we’ve done a bunch of talks with now, it does lab products, and they talk about chemicals that are in other contexts, extremely dangerous, hazardous, restricted, and all the closed models say, can’t talk about that. Sorry, that’s dangerous. Like, no, this is a legitimate use case of this company. Needs to help its customers get to this product. And yes, it is a restricted product.
Christopher S. Penn – 11:14
The government does not smile upon people who have it without permission. But this company is like, well, is the power of AI locked out from us because you have these other big tech companies who decided what is good and what is bad for us instead of letting us say, okay, we have a legitimate use case. Well, if you use an open model, you can alter that engine and say, this is our legitimate use case. And so they can now unlock that power for the core products and services they offer.
Katie Robbert – 11:52
Will you be walking through, I guess, the pros and cons? And by the cons, I mean more of, like, the risk. So, as you’re talking, you’re saying, with this version of the model, you can make it do whatever you want. My brain immediately goes to danger, Will Robinson. Like, you don’t have to give everything away, but will I— so if I show up to your session, which obviously I’m going to, will I walk away with a sense of, I can do this, but here’s my checklist to make sure I’m doing this in a smart, ethical way? You will.
Christopher S. Penn – 12:34
Walk away understanding why open models are important. And yes, there will be some considerations because ultimately open models are very similar to open source software. The Apache web server, for example, the Python programming language. You can do horrendously bad things with these tools. You can run a website that is all, a white supremacist website filled with hate and vitriol, with the Apache web server. It’s free, it allows you to— you can do whatever you want with it. And this is sort of the heart of AI itself. AI is an amplifier. It takes what’s good and makes it better. It takes what’s bad and makes it worse. The challenge with— there’s a bunch of challenges with having, like, a handful of companies overseeing the technology for everyone else.
Christopher S. Penn – 13:24
One of which is, if, for example, if a company like OpenAI runs out of funding, suddenly Chat- GPT is gone. You’re like, well, that was nice, well it lasted. Now what do I do? Well, if you have a Llama model, you run it on your own servers. You’re like, okay, bye, OpenAI. See you in the next life or whatever, but also for one of the important use cases, I think, is that what a typical Silicon Valley exec, typically, like, a white dude, thinks is moral or good or important gets put into their models. Does that apply if you are a Muslim? Does that apply if you are Indonesian? Does that apply? Put in the demographic that is not your Silicon Valley tech exec into this.
Christopher S. Penn – 14:19
Right now, you’re in a situation where most of the state-of-the-art models are— their internal morality is being dictated by a handful of people. Should that be the case? Or should it be the case where a group can say, like, yeah, no, we want Sharia to be the guiding moral principles of our use of AI. We want orthodox Judaism to be the guiding principles. We want Confucianism or whatever. You can’t do that today with the regular model, the closed models, you can do that today with open models.
Katie Robbert – 14:55
I mean, I feel like that brings up a whole other conversation about— I mean, because you’re— and again, this is a very, like, specific example because I asked you the question, but you’re bringing up religion as the guiding principles in the terms of tech businesses. And I can see there’s a lot of conflict of interest there. And so, I’m definitely curious to know— I’ll be very interested to show up to your talk to find out, like— no, I’m genuinely serious, like, where that goes because I think it’s a big question for a lot of people because, if you just sort of— take the example of religion, where in business does religion fit?
Katie Robbert – 15:41
Especially when we’re talking about, companies that are just sort of, like, checking the box on DEI initiatives or saying openly, like, looking at you, Tractor Supply, we are no longer going to support DEI initiatives, but they’re very clearly leading with their version of Christianity, which is— again, I don’t want to get down this role too much because it’s a big deal, but it’s worth acknowledging because so many people can’t separate their beliefs from business.
Christopher S. Penn – 16:22
And who’s to say which beliefs are correct? We’ve been arguing about this as a species for tens of thousands of years now. But one of the things about open models is could there be censorship in AI inherently in the model itself when you train it for the first time? Any model, it’s— not because you’ve trained it on a massive supply of data, good data, bad data, crap. Also, it’s— you name it, it’s in there. And then you have to provide alignment. Well, and we’ve talked about this on the podcast and on the live stream before. A lot of people talk about the ethics of AI. Well, ethics itself is— it’s a discipline. It’s an academic discipline. So, whose ethics should we be using? Because ethics is all about right and wrong, good and bad, harm and help. Who decides that?
Christopher S. Penn – 17:13
And I think that’s one of the things that makes open models so compelling, is that you no longer have to accept whatever Sam Altman or Elon Musk say is acceptable or right because they provide the models. Maybe I don’t agree with them. Maybe I don’t think their view of the world is good. Should I have the right as a user to tune the model to what I think is right and good? That goes back to what you were saying earlier, which is there’s a bunch of people who have a point of view in the world that is objectively harmful to more people than it is good, and they can do bad things with the technology. It’s like fire and electricity and any major transformative force. You can do really cool things, and you can do really bad things.
Christopher S. Penn – 18:05
And the question is, how do we manage this yet another new thing? Again, like you always say, new technology does not solve old problems. In fact, it makes old problems worse.
Katie Robbert – 18:18
It sure does. It makes it— It sounds like it amplifies the problems, which is— I can see that as a double-edged sword because if it’s easy to hide the problem until you introduce new technology, if you amplify the problem and it’s in your face, then maybe now you have a shot of dealing with it, whereas with older technology, perhaps it was more convenient or easier to mask the problem or to be unaware that there was a problem at all. But now, no, new technology doesn’t solve old problems. New technology amplifies old problems, giving you an opportunity to solve said problems.
Christopher S. Penn – 19:10
Exactly. So that’s— it’s a very— the topic sounds like, okay, open models versus closed models, it sounds a little bit wonkish, but it really is about how much freedom and choice should people have with AI? Knowing that a certain percentage of people are going to use it for very bad things, knowing that a certain percentage of people are going to use it for very good things, knowing that we as a species are very diverse in our viewpoints, should we have an environment of diversity that by its very nature has to include the bad stuff or what we consider to be the bad stuff?
Katie Robbert – 19:52
It’s such a big topic. Well, I guess my question to you as a speaker is, how deep do you feel you’ll be able to get in a 50-minute session?
Christopher S. Penn – 20:05
The idea and the ideal of this talk is to introduce the concept, to make people aware that they do have a choice, to explain five or six major big reasons, like, hey, OpenAI might run out of money, you need a backup plan, or you probably don’t want a very specific viewpoint, or the ability for a government to censor AI. You probably don’t want those things. So, we’re going to cover the majority concepts and then show a couple of examples of how you would get started if you wanted to do this. But not a technical session. If people want to do a technical session, I’m happy to do that. But I will tell you upfront, implementing an open model is much more work than opening a browser and typing with chatgpt.com.
Christopher S. Penn – 20:52
It’s a lot more work than that, but it is under your control, which means that for things like sensitive data, PII, protected health information, it’s the best option for dealing with that kind of data.
Katie Robbert – 21:05
It’s interesting because I feel like, so, technology in general, like, let’s say you buy a CRM system, there is some customization available, but for the most part, systems like that, you sort of— you plug them in and you go. And so you don’t feel like you have a lot of control over what the system does. The system is pretty much set, but you buy the system because it meets, like, the majority of your requirements, and then the rest of it you have to figure out. It’s interesting with generative AI because you can set it and forget, you can, like, plug it in, just use it as is or now the difference between this tech and older tech is that you do have so much freedom of choice over how it works.
Katie Robbert – 21:51
And I think that’s something that’s really important that a lot of people might not be aware of. They may not realize how much say they have over what the tech does versus a marketing automation system that literally does one thing, and it either does it or it doesn’t.
Christopher S. Penn – 22:10
Exactly. Just the other week, we’re doing some work with our ideal customer profile and scoring our content, and part of that was a model evaluation. I ran a test set of data through five different models, and you, as our Subject Matter Expert, were the one to score the model outputs and say, this is the one that did the best job. That’s part of open weight stuff is you can say, okay, I’m going to run this task through a bunch of different models, knowing they all perform differently, knowing they all have different strengths and weaknesses, which is the best suited for this particular task.
Christopher S. Penn – 22:46
And so I think that goes with that of, as you start, if you choose to adopt this technology, this branch of generative AI, you have to be willing to do some of the work to say, yeah, this model is good at this and bad at that. We kind of assume, ChatGPT is good at everything. Like, no, it’s not. And there are differences even in the closed models. You will see that in their writing styles, for example. But when it comes to a batch task, like, use our ICP and, score this or do that, the model makes a difference.
Katie Robbert – 23:26
And I think that— I know you don’t get into the different models, like, into the deep dive, but I think that’s something important that people should be aware of, is that not all models act the same way. I mean, very early on, I remember in the infancy of generative AI being in the market, we did a bake off just to see the bias between the models. And the exercise that we did was, we used gender as the differentiator between the two models. And what we found very quickly was that the models were very much biased more positively towards what it perceived as a male gender versus what it perceived as a female gender.
Katie Robbert – 24:13
And I think that today, I mean, I can’t say for certain, but I think a lot of people are choosing models based on ease of use versus what the underlying code and, knowledge base is. And so that’s something that we should probably revisit at some point now that the models have matured quite a bit. Ease of use is a huge thing. If the interface isn’t easy for people to pick up right away, they’re probably going to walk away from it. But the easiest to use model doesn’t necessarily mean it’s the best use model. And I think that it’s interesting that you’re sort of going through the risks, and I think that’s such an important conversation. So, hopefully, your session is jam-packed because it’s so important.
Christopher S. Penn – 25:03
It is. We will not show this in the session because this will just make people leave immediately. But this is an example. This is a piece of software called Cobalt. This is essentially a web interface on a local model. In this case, I’m running Mistral Nemo model. Look at all the buttons that you can choose from now. You would need to know what they all do. But this level of granularity allows you to fine-tune just how the model performs, how it returns answers, how big the answer returns, how much it’s allowed to repeat itself. And there you go again. If people are interested, we’re happy to talk about this stuff in depth, but it’s not the most exciting thing. There’s very little.
Christopher S. Penn – 25:48
So, what here, in terms of your average non-technical user, the technical user would use this, especially in, like, a company. If you were going to deploy this inside a company, you would have a technically— just say, here’s the settings that best fit for our company. You get none of this with a closed model. So, which means that you can’t get results and performance that’s tuned to how you specifically want to use it. If you tune a model— well, if you have all the right settings and all the knobs and dials and stuff all set up properly, you can be more concise in your prompts. You can have less frustration that the model does or does not do something the way you want it to.
Christopher S. Penn – 26:31
So, there’s a lot of technical stuff, and maybe sometime we’ll do a highly advanced class or something, but certainly not for this session.
Katie Robbert – 26:43
Well, what strikes me is, unsurprisingly, how complimentary our sessions are. And so my session is getting you to the point of deciding, open or closed models, what do I need to do? And then your session is, okay, so now you need to figure out what to do. And so our sessions together sort of make that full, complete picture of, all right, I want to integrate AI, how do I get started? What do I need to do? Who are the people? Especially, you’re talking about, Chris, you need to have a technical Subject Matter Expert. Not everybody has that. So, I think it’s worth acknowledging. Like, if you want to do this version, these are the types of resources you need.
Katie Robbert – 27:31
And in my session, I’m saying, first you need to figure out what is the problem you’re trying to solve so that you can even figure out, can you do this version? And so I think it’s a nice— I don’t— unfortunately, I think your session is first and my session comes later, but if they were reversed back to back, they would very nicely dovetail into each other.
Christopher S. Penn – 27:53
We’ll tell people when they use the MAICON recordings, they should watch them in reverse order.
Katie Robbert – 27:59
That’s right.
Christopher S. Penn – 28:02
All right. So, MAICON is going to be September 10 through 12th, I think it is. I don’t remember what it is, but it’s MarketingAIinstitute.com or MAICON AI. If you want to attend, I believe you can use either of our last names, penn100 or robert100 for $100 off the cost if people want to attend. And if you’re going to be there, look for Katie, not Katie. Katie’s easier to spot. She’s much taller. I need to be, like, that guy with a little balloon tied so you can see me in the store aisles.
Katie Robbert – 28:35
Unsurprisingly, because I’m so tall. I have a lot of shorter friends, and they’ve all sent me that video of the short friend being— but anyway, we will put in the show notes the discount codes. The MAICON is September 10 through the 12th in Cleveland, Ohio. Chris and I will be there. You can’t miss us. Chris will have a swarm of people around him with their heads looking like they’ve been imploded, and I’ll just be off to the side, just doing what I do, just walking around and generally being invisible, which is fantastic. But yeah, September 10 through 12th, come see both of us, and we will put our discount codes in the show notes.
Christopher S. Penn – 29:20
All right. And if you’re going to be at MAICON, or maybe if you’re not, but you have some perspectives on managing the people who manage the machines or open models, pop on by our free Slack. Go to Trust Insights AI, analytics for marketers, where you and over 3,500 other marketers are asking and answering each of those questions every single day. And wherever it is you watch or listen to the show, if there’s a channel you’d rather have it on instead, go to TrustInsights.ai/tipodcast, and you can find us on most major channels. Thanks for tuning in. We’ll talk to you next time.
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Trust Insights (trustinsights.ai) is one of the world's leading management consulting firms in artificial intelligence/AI, especially in the use of generative AI and AI in marketing. Trust Insights provides custom AI consultation, training, education, implementation, and deployment of classical regression AI, classification AI, and generative AI, especially large language models such as ChatGPT's GPT-4-omni, Google Gemini, and Anthropic Claude. Trust Insights provides analytics consulting, data science consulting, and AI consulting.
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