Almost Timely News: MAICON 2023 Takeaways (2023-07-30) :: View in Browser

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25% of this newsletter was generated by me, the human. I was too tired to sit down and write out the opening, so instead I did a voice recording and had Whisper transcribe it, then had Claude clean up the errors in the transcript. It’s 100% my words and my thoughts, but the final output text was manipulated by AI – and that’s important for one of the key points.

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Almost Timely News: MAICON Takeaways (2023-07-30)

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What’s On My Mind: Takeaways from MAICON 2023

Let’s talk about all that happened at MAICON, the Marketing AI Conference, all the takeaways, all the fun stuff that made it a really, really just an incredible conference.

One of the people that was new to me was Professor Ethan Mollick from the Wharton Business School who talked a ton about what’s happening in AI. His talk was fascinating. He was the closing day one closing keynote. Now he was talking about how AI was disrupting everything. And one thing he mentioned was that he didn’t really understand and I think this is a valid statement. He didn’t really understand that the strategy that the big tech companies were taking with regards to how they were rolling out AI models, it doesn’t seem to make any commercial sense. And in a lot of ways, it doesn’t. But then he said something very interesting. He said, it seems that they are all in a race of almost religious zeal to be the first company to create an artificial general super intelligence, or in his words, they want, you know, name the tech company or choice, they want to be the first company to invent God.

This brings to mind the quote from Jurassic Park from Jeff Goldblum’s character- “your scientists were so preoccupied with whether they could that they never stopped to think about whether they should”. I think that falls squarely in that category.

The other thing he was saying was, he views the way that models are being released to the public as very irresponsible, handing out just these incredibly powerful models for free. There doesn’t seem to be a whole lot of strategy to it in terms of like, how is this? How is this useful to people? What are the same one thing about the dangers of it? He especially pointed towards Meta’s LLaMa 2 and said, This seems strange. And I had four thoughts on that topic for reasons why Meta might have done such a thing.

The first reason is releasing a model as open source, it really hobbles regulation. When there are just a few companies, Microsoft, Google, OpenAI, Anthropic, when there’s just a few companies publishing these big models, it’s pretty easy to regulate them. It’s pretty easy to say like, Oh, you know, your AI service should be able to do this and not that. That can be problematic, right? You don’t want too much power, or too much capability in just a few companies because that can of course be regulated, it can be censored even meta giving away their model. Basically just through the ingredients for the any AI any generative AI, that’s a large language model out into the world and said, Here you go. Everyone have fun. And everyone have fun also means that you can’t really regulate that anymore. Because now these pieces of software are running on gaming laptops, you can’t regulate that you can’t control what people are going to do with that. And to some degree, that’s not necessarily a bad thing. Because again, too much power concentrated in too few hands is not really optimal. So that’s the first part.

The second thing that I thought of is Meta’s done this to take a baseball bat to the knees of its big tech competitors, particularly Google, but even OpenAI to some degree. Anyone who has sufficient computing power like a gaming laptop can start building apps building companies building whatever they want, rebuilding these models tuning them. And in doing so it effectively hobbles other tech companies from consolidating their power right so open AI might have the best model for now the GPT four model. But now anyone can with a gaming laptop can run the LLaMa 2 model and not pay OpenAI right so it’s kind of an anti competitive move which I thought was very interesting.

Third, if you think about it, Meta basically gets free tech support, right? If you release a model to the world, thousands of developers are going to beat this thing up and find all the bugs find all the issues find all the the ways that the model can be misused. And let their fellow developers and Meta know Hey, this is how the model is kind of broken. that it gets to see how its models succeed or fail at a massive scale much larger than they could do themselves. And in doing so, find all the vulnerabilities or learn how to make models better without having to pay for it, right? They didn’t have to pay these thousands of developers and people like you and I to test these things out.

And fourth, Meta basically gets free R&D, right? Every developer who takes one of these things and builds a new model emerge from it or a new ecosystem to use the model like cobalt or silly tavern. All those improvements are open source under the same license typically. And so Meta can look around say, Hey, this model, they’re doing this with this model. That’s cool. That’s cool. That’s cool. And now they’ve got the R&D essentially of a much larger company without having to invest a huge number of resources on it because the open source communities is building all these these add ons for them. And so it’s a very clever move to take a very powerful model and throw it out to the world.

Second talk that I have a lot of thoughts about, I saw Chief Decision Scientist at Google, Cassie Kozyrkov, who delivered the day two opening keynote, I got a chance to very, very, very briefly just shake her hand, say hi. I’ve followed her work for years, and she’s absolutely brilliant at what she does.

She said a couple things that stood out to me. Now her talk was mostly about whose jobs AI will take and you know, she’s had the same general line that everyone in the industry has had for years, which is AI takes tasks, not jobs. But she said something very interesting. She said, if you find it’s easier to do a task, then explain that that is a task that is ripe for AI, because the instructions are too complex for you to articulate, but it’s good. It’s should be trivial to for you to make examples that AI can learn from.

She also said AI won’t take entire jobs because humans still need to do the thinking but AI does the doing humans do the thinking AI does the doing. And so this was sort of meant as a way to say, don’t worry, you know, as not going to take your jobs.

That is not my take on it. And the reason that is not my take on it is how much of your job is thinking and how much of your job is doing. I know in my own job. 80% my job is doing right doing the work making the the software go writing the reports talking to the clients, the doing of the work, not the ideation.

There are I think, McKinsey or somebody said there are 130 million knowledge workers in the US. And their estimates that AI will impact in some way dramatically 100 million of them. Right? If we if we go by what Cassie said that AI is not going to touch the thinking is only going to replace the doing that still 80% of the workforce, or at least 80% of the tasks for that workforce that they won’t be doing anymore in some capacity. Now, they might be supervising the AI, they might be pushing the go button to make it go. But they’re not necessarily going to be doing the doing part. And that is a problem. Right? That is a problem. Because when you have machines that are doing most of the doing work, people aren’t doing that work. And that that to me can be I think very challenging.

The third talk, Jim Sterne gave a great talk on prompt generation 101. The one analogy I liked in there is there are these technical parameters that you can set when you’re using language models like temperature, top P, top K, etc. And I liked his analogy, he said, the temperature setting, which is how creative a prompt will be really should just be called the number of drinks setting, right? And you scale from zero to a lot. And the more the more drinks you give it, the more creative it gets. I thought that was a really clever way of explaining that. So I will probably be using that analogy in the future.

And then the fourth person that I talked to and listened to was Olivia Gambelin, who is an AI ethicist. She had one quote that was hilariously funny, which was compliance – when you’re compliant with regulation basically means you just one step above illegal, right? This is the very bare minimum you can do.

We had a very good discussion about how fairness in AI is challenging because there are two fundamental types of fairness, right? There’s equality of opportunity, which means that everybody starts the race at the same starting line. And then you succeed in the race on your merits on how fast you can run. There’s also equality of outcome where it doesn’t matter where you start the race or how soon or how late you start, everyone should arrive at the finish line at the same time.

And so there are cases where equality of opportunity is important, right? You want people to succeed on their own merits in in a business. There are cases where you want equality of outcome where you want to, to ensure that everyone gets the same treatment, right? customer service, you call into a customer service line, and you should get the same treatment as the next customer where you should not be how good a customer you are should be no everybody gets the same treatment.

But we had an interesting twist in this conversation about how different cultures might implement these. There is the idea of of individuality, individualistic societies, the United States of America, for example, is a super hyper individualistic society. far anomaly. And then there are collectivist society societies where people put the good of the whole above the good of the individual, you see this very prominently in Far East Asian societies like Japan and Korea and China.

And we were wondering, and this is something that’s sort of an open question is, do collectivist societies focus on equality of outcome more than equality of opportunity? And I think the answer there to some degree is yes. When you look at even Scandinavian countries, the countries that have much higher taxes, but provide much more social goods, again, equality of opportunity, having fewer people sleeping, you know, homeless in the streets is a higher priority than equality of opportunity, right? The equality of outcome is no more homeless in the streets. The equality of opportunity would mean Yeah, we’re we’re okay with homelessness, because we want everyone to succeed on their merits.

These questions are important because they govern how we use artificial intelligence, how we deploy it, how we manage it, how we govern it, how we control it, to the point where we want to ensure that artificial intelligence is working for us and not the other way around. And questions about fairness and equality are not a single one size fits all answer, right? It is. It’s case by case, and companies, people have to decide how they want fairness implemented in whatever situation you might be in.

So lots and lots of really cool takeaways from the event I gave my talk, which you can watch the top of the newsletter, I’ll put a link in this section as well. I would encourage you to enjoy the talk. It’s fresh. It’s not out of date yet, as far as I know. And, and see what you think of the takeaways and maybe if they might stir some thoughts of your own.

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ICYMI: In Case You Missed it

Besides the newly-refreshed Google Analytics 4 course I’m relentlessly promoting (sorry not sorry), I recommend the piece on why you get generic outputs from your AI prompts.

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Get Back to Work

Folks who post jobs in the free Analytics for Marketers Slack community may have those jobs shared here, too. If you’re looking for work, check out these recent open positions, and check out the Slack group for the comprehensive list.

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Events I’ll Be At

Here’s where I’m speaking and attending. Say hi if you’re at an event also:

  • ISBM, Chicago, September 2023
  • Content Marketing World, DC, September 2023
  • Marketing Analytics and Data Science, DC, September 2023
  • Content Jam, Chicago, October 2023
  • MarketingProfs B2B Forum, Boston, October 2023
  • Social Media Marketing World, San Diego, February 2024

Events marked with a physical location may become virtual if conditions and safety warrant it.

If you’re an event organizer, let me help your event shine. Visit my speaking page for more details.

Can’t be at an event? Stop by my private Slack group instead, Analytics for Marketers.

Required Disclosures

Events with links have purchased sponsorships in this newsletter and as a result, I receive direct financial compensation for promoting them.

Advertisements in this newsletter have paid to be promoted, and as a result, I receive direct financial compensation for promoting them.

My company, Trust Insights, maintains business partnerships with companies including, but not limited to, IBM, Cisco Systems, Amazon, Talkwalker, MarketingProfs, MarketMuse, Agorapulse, Hubspot, Informa, Demandbase, The Marketing AI Institute, and others. While links shared from partners are not explicit endorsements, nor do they directly financially benefit Trust Insights, a commercial relationship exists for which Trust Insights may receive indirect financial benefit, and thus I may receive indirect financial benefit from them as well.

Thank You

Thanks for subscribing and reading this far. I appreciate it. As always, thank you for your support, your attention, and your kindness.

See you next week,

Christopher S. Penn


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