Almost Timely News, April 23, 2023: The Dawn of Autonomous AI

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Almost Timely News: The Dawn of Autonomous AI (2023-04-23)

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What’s On My Mind: The Dawn of Autonomous AI

This past week was another wild one, this time with the dawn of autonomous AI. Well, that’s not strictly true; we’ve had autonomous AI for a while, but not specifically with large language models and not in ways that are easily accessible. Let’s talk about what this stuff is, what it means, and what you and I should be doing about it.

First, what is autonomous AI? Autonomous AI is AI that does stuff itself. We give it some general directions, and then it goes and does those things. Probably the most well-known type of autonomous AI is the self-driving car. You put in a destination, and the car drives itself, figuring out how to get from where you are to where you want to go. Along the way, it drives, navigates, and communicates how the trip is going, dealing with traffic, pedestrians, etc. We’ve seen plenty of demos about how this sort of technology works, and for the most part, it does work about as well as a human driver – perhaps slightly better. At least the AI driver isn’t staring at its phone while changing lanes at 80 miles an hour on the highway.

We see examples of autonomous AI even within our homes. If you’ve ever gotten one of those smart robot vacuum cleaners, that’s autonomous. Given a programmed schedule and the restrictions you want it to obey (usually programmed by you in an app), it does its thing until either the task is done or it’s devoured an errant power cable on your floor for the third time this week.

Now, in the context of large language models, models like the GPT family from OpenAI, Google PaLM, StabilityAI’s Stable LM, and many others, what does this mean? We’re used to interacting with these models in a singular fashion. Open up an instance of ChatGPT, start typing your prompt, and it does whatever you direct it to do. (assuming it’s in compliance with the terms of service etc.) That’s a single instance of the model within the interface, and for many tasks, that’s good enough.

Suppose you were able to start two instances of ChatGPT. Suppose one instance could hear what the other instance was saying and respond appropriately to it. You’d sign into one instance and tell it to start writing a blog post. You’d sign into the other instance and tell it to correct the blog post for grammatical correctness and factual correctness. Both instances would start almost competing with each other, working with and against each other’s output to create an overall better outcome.

That’s the essence of autonomous AI within the context of large language models. They’re multiple instances of a model working together, sometimes adversarially, sometimes collaboratively, in ways that a single instance of a model can’t do. If you consider a team of content creators within an organization, you might have writers, editors, producers, proofreaders, publishers, etc. Autonomous AI would start up an instance for each of the roles and have them perform their roles. As you would expect in a human organization, some tasks are collaborative and some are adversarial. An editor might review some writing and send the copy back with a bunch of red ink all over the page. A producer might tell the editor they need to change their tone or exclude negative mentions about certain brands or personalities.

So, why would someone want to do this? There are plenty of tasks – complex tasks – that require more than a single prompt or even a series of prompts. They require substantial interaction back and forth to work out key points, to deal with roadblocks, to collaborate and create greater outputs than they could individually. These tasks are the same ones people often work together on to create better outputs than they could individually. I might have an idea I want to write about, but I know that for a significant number of them at work, my ideas get better when I discuss them with Katie or John. Sometimes they behave in a collaborative way, asking “what if” questions and helping me expand on my ideas. Sometimes they behave in an adversarial way, asking “so what” questions and making sure I’ve taken into account multiple points of view and considerations.

That’s what an autonomous AI does. It plays these roles against itself and with itself, working as a team within a computational environment. It’s like an AI office, where the individual office workers are AI instances.

What would this look like as an example? Here’s the setup I devised in AutoGPT, one of the most popular versions of this technology. AutoGPT asks for an overall purpose and five goals to accomplish. Here’s what I told it to do:

  • You are a nonfiction author. You write books about marketing, marketing analytics, marketing attribution, attribution modeling, marketing mix modeling, media mix modeling, media spend, marketing strategy, marketing budgeting. You will write the outline for a book about marketing mix modeling using LASSO regression. You will write in the style and voice of marketing author and expert Christopher S. Penn.
  • The book you will write will be a total of 60,000 words about marketing mix modeling. You will write 20 chapters of 3,000 words per chapter.
  • You will write about why marketing mix modeling is important, what marketing mix modeling is (with examples), and how to implement marketing mix modeling in the R programming language with plenty of examples.
  • You will review your writing to ensure the book is 60,000 words or more, grammatically correct, coherent, and appropriate for business book readers. You will ensure that you have correctly captured the writing style of marketing expert Christopher S. Penn.
  • You will export your work in Markdown format, one Markdown file for each chapter of the book. The book’s author is Christopher Penn. The year of publication is 2023. The publisher is The book is published in the United States of America.

Once I got the software installed on my laptop, configured, and ready for use, I started up the engine and put in my goals:


We see above, it’s getting started and working out the structure of what it needs to accomplish. It knows it needs to extract data about what marketing mix modeling is, what my writing style is, and outline the book. About 20 minutes after I issued these commands, it started cranking away:

AutoGPT mid process

These are the first passes through, just getting together the text. It hasn’t started checking over its work to ensure each chapter is the correct length (it’s not), or that it’s coherent and matches my writing style. But you can see just from these examples, from this process, that it’s going to do what I directed it to do in a very efficient way. This is what autonomous AI looks like.

Now, let’s be clear. This isn’t sentience. This isn’t self-awareness. The machine is not alive in any way, shape, or form. It still needed me to declare what it was supposed to be doing. It has no agency of its own without that initial direction, something to kick off the process, so banish any thoughts of Terminators or Skynet. All kinds of folks are talking about this as the start of artificial general intelligence, of truly intelligent artificial life, and it’s not. This is no more alive than a self-driving car. Your cat has more agency than this, more free will. That is not the threat that this technology poses.

What threats does it pose? A few. First, as you can see from the example, this dramatically increases the complexity of tasks that large language models can tackle in a relatively straightforward way. Up until now, large language models struggled to deal with very large forms of text, like novels and books. They don’t generate those well in a singular fashion. This can do so, dealing with far more complex problems and tasks.

Second, this technology exacerbates issues with copyright. At one point, AutoGPT opened up a web browser and started surfing my website to get a sense of my voice and tone. That’s okay – it’s my website, and obviously I give it permission to do so. Suppose I had suggested someone else’s voice instead? That’s problematic, and there’s no ethical checksums, no checks and balances in the technology to say, “hey, maybe don’t do that”. The tool is truly agnostic, truly amoral. It has no concept of right or wrong, which means that any morality needs to come from us.

And that brings us to the third problem. This tool has no morals, good or bad. It only understands the tasks you give it, and it works to achieve those tasks. Morality is in the eye of the beholder. Suppose I wanted the tool to generate some propaganda. Would it do that? Yes, unquestionably. Suppose I wanted the tool to scrape some data from LinkedIn. Would it do that? Yes, yes it would. Suppose I wanted the tool to find a working login to a secured website. Would it do that? Yes, it would. Without going into any details, I asked it to try to break into my personal website, and it went about trying to figure that out. Did it succeed? Not at the time I tried it, which was 5 days ago.

In the last 5 days, the ecosystem around the tool has introduced dozens of plugins that make the tool more capable, like different kinds of web browsing, connections to services and APIs, all sorts of capabilities. It’s a very small stretch of the imagination to envision tasks that autonomous AI could undertake that you might not want it to. People who work in cybersecurity should be very, very concerned and should be watching these kinds of tools like a hawk. They should be red-teaming with these tools today to understand what their capabilities are and are not.

The output right now out of tools like AutoGPT stinks at the moment. It’s coherent but it’s boring, and the process is janky as hell. It’s not ready for prime time…

… just like GPT-2 wasn’t ready for prime time three years ago. And today, GPT-4 and similarly sized models are in production, in the world, and working really, really well at a large number of tasks. Autonomous AI is just getting started, so to dismiss its shoddy output today and assume it will not evolve is just short-sighted.

AutoGPT animation

Whether or not we wanted this technology, it now exists and is available in the world. So what should we do about it?

At a personal or organizational level, we need to be doing rigorous audits of the kinds of work we perform to see what other tasks AI could take on. I’d initially thought that large language models couldn’t easily take on very large content tasks until next year, and here we are. In what ways could you use technology like this for longer-form content like books, keynote addresses, movie scripts, entire publications? Start today doing an audit, then start testing these tools.

If your writing skills are not better than an AI’s writing skills, now is the time to either level up your writing skills or learn how to operate AI software effectively. There isn’t much middle ground on this – either you get better, or you work with the machines that are better. There isn’t a place at the table for mediocre to poorly skilled writers in the very near future.

At a societal level, we need to solve for some very important issues sooner rather than later, things like universal basic income. As I said, the output today is meh at best. It’s not going to stay that way. We’re already seeing some publications announcing more layoffs of writers as generative AI tools are adopted as cost-cutting measures. That’s going to accelerate. Something like universal basic income is essential to keeping the economy operational, because if you reduce the number of employed people by 40-60% – which is very possible as these tools advance – you will need to provide for them in some fashion.

Of all the AI technologies I’ve seen demonstrated in the last year, autonomous AI is the first one that legitimately unsettles me. Watching the tool running on my laptop screen, seeing how it thinks and reasons – it’s unnerving. As its quality improves, as it can tackle more complex tasks and more nuanced tasks, I believe it poses as many dangers as it does benefits, perhaps more. You owe it to yourself to get smart about it and watch it carefully as it evolves to see what the big picture implications are sooner rather than later. I know I am.

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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 bakeoff that Katie and John did this week while I was traveling on generative AI tools, especially in the context of podcasting and video.

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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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