The potential of Humans plus AI

About this episode

We return with another compilation episode, this time looking at the potential of Humans plus AI. You will hear insights in brief excerpts from the current season of the podcast from Jerry Michalski – Episode 2, Toby Walsh – Episode 14, Anne-Laure LeCunff – Episode 5, Jeremiah Owyang – Episode 9, and Dave Snowden – Episode 24. To tap the potential of AI we need to take a deeply human approach. These leading thinkers share how we should be thinking about the relationship between humans and AI and how we can put that into practice.

What you will learn

• Becoming better cyborgs, navigating AI, creativity, collaboration, and ethics
• Navigating the controversy of AI-generated patents and the synergistic relationship between human inventors and AI assistants
• Unleashing AI as a thinking partner for enhanced creative endeavors
  
• Amplifying humanity, navigating the collaboration of humans and AI
  
• Navigating the intersection of human cognition and AI, dangers, opportunities, and abductive reasoning

Full Episodes

Jerry Michalski on ethical cyborgs, amplifying uniqueness, peak knowledge, and fractal conversations (AC Ep2)

Toby Walsh on the differences between human and artificial intelligence, our relationship to machines, amplifying capabilities, and making the right choices (AC Ep14)

Anne-Laure on metacognitive strategies, mind gardening, bi-directional linking, and AI as thinking partner (AC Ep5)

Jeremiah Owyang on amplifying humanity, enterprise excellence, autonomous agents, and AI-business alignment (AC Ep9)

Dave Snowden on abductive reasoning, estuarine mapping, AI and human capability, and weak signal detection (AC Ep24)

Transcript

Jerry Michalski

Ross: How, Jerry, could we become better cyborgs?

Jerry: Part of it is understanding how the tools work and what the limitations are, and not becoming the lawyer who submitted a brief that they fact-checked using the tool that generated the hallucinations and therefore got themselves really embarrassed in public a month or two ago. You don’t want to be that guy. There are a lot of ways to avoid those errors. Understanding how the tools work and what their limitations are, lets you then use them well to generate creative first drafts of things.

One of the enemies of mankind is the blank sheet of paper. So many people are given an assignment, and they’re like sitting down, and it’s just like, No, and you ball up two words, and you throw it in the trash. And here, all of a sudden, you can have six variants of something put in front of you. We need to become better editors of generated texts. Then the other piece of being a better cyborg is not about being a lonely cyborg. But what does it mean to be in a collective of cyborgs? What does it mean to be in a cyborg space? What does it mean to co-inhabit cyborg intelligence with other people and other intelligences that are just going to get faster and better at this thing? I think it’s really urgent that we figure out the collaboration side of this so we don’t think of it only as, Well, they gave everybody a better spreadsheet and now everybody’s making a lot of spreadsheets, this is different; this is different in type.

The third thing I would bring in is the ethics of it, which is boy, it’s easy to misuse these tools in so many ways. Unless we understand A – how they work and what they’re doing, but B – have some better notion ourselves of what is right and what is wrong to do, and some relatively strong idea of what is right and what is wrong to do, then this is going to evolve. There’s one school of thought. Bill Joyce said this years ago: There is no more privacy; forget about it; privacy is overrated. And the other realm is like what the EU is doing right now, with new privacy regulations. They’re really working hard to try to figure out how to protect us from having our data just sucked out of our lives and used by other people to manipulate us in our lives, which is what capitalism wants to do.

It’s not as easy as I’m going to get good at Photoshop, Final Cut, or whatever, and become an ace with some software. I point to those kinds of people as the early cyborgs. I’m like if there’s any piece of software where you no longer think of the commands, maybe you’re a spreadsheet ace and you do these massive, incredible models with pivot tables and who knows what, and the software you’ve internalized so well that it doesn’t even come to consciousness, you’re down this road of cyborgness. But this is more complicated than that because the issues are so important and because we can now collaborate and communicate better all of those issues.

Toby Walsh

Ross: One of the very interesting examples you used was an AI patent generator called DABUS. The person who created it said that it essentially was an AI inventor and tried to patent it in the name of the AI. You pointed out that, in fact, of course, the person invented the system and it was really just an assistant to him. There was a human plus AI endeavor, as opposed to something that you could attribute fully to the AI.

Toby: Indeed, yes. It’s a very interesting example. There was a court case brought in the US and one in Australia where briefly before the initial judgment was overruled, the AI was actually allowed to be named on the patent as the inventor, but that now has been overturned, at least in the US and Australia. Again, we returned to the place where only humans are allowed to be named as inventors. But the system, as he says, is an interesting example of how humans can be helped. These are really powerful tools for helping people do things that we initially thought required quite a bit of intelligence, coming up with, there’s nothing perhaps more endemic of what is something that’s intelligent is to come up with something that’s patentable. There is a certain mark there that it must be novel, and done something truly creative, otherwise, you wouldn’t be allowed the patent.

DABUS helped Steven Tyler, the guy who wrote the program, to come up with a couple of ideas that have patents that have been filed for. A fractal light. The idea is that you turn this light on, and it flashes in a fractal way. Fractal is that it doesn’t have any repetition in it. The frequencies keep on changing. That will attract our attention, obviously, because it is not going to be flashing like a lighthouse, or in any rhythmic way. It’s actually going to be disturbing our mental perception of it. It will actually be quite a good way of attracting people’s attention. Then another example is, interestingly, we have both fractal inventions, a fractal container. The idea is that the surface of this container would have a fractal dimension to it. Again, if you know something about fractals, it means it’s good to have a huge, truly fractal, in fact, infinite surface area. If you want to have something where you can heat up the container very easily, then having a large surface area to the volume will be very useful.

What’s interesting is that these are the only AI programs that are being used by people to help invent stuff. What people do is that they get the program to define what you might call a design space, a set of ideas, and building blocks that you put together. Of course, the great stake of a computer will be very exhaustive and do things in all the possible ways. Maybe our human intuitions will stop us from doing some of the more extreme, unusual ways, putting these things together. But the computer will beautifully peer it as it won’t be inhibited in those ways. It puts all these things together in interesting ways. But the problem is that it is huge, actually infinite design space. You’ve got to tame it in some way. You’ve got to say, what are the interesting ways of putting things together, and then we come to this ill-defined word interesting.

This is where there was a synergy between the human and the AI, which was that it actually outsourced the idea of saying, what’s an interesting promising direction to follow. If I’m trying to build up this i