Here we illustrate the difference between algorithms and conscious beings playing a simple game on the iPhone: If you ONLY get to use autofill, how long can you keep the game going? Pretty long, so long as you make choices. On the other hand, if you just pick choices at random, or let the phone make the choice for you, you immediately devolve into repetitive gibberish.
This illustrates the fundamental difference between humans and algorithms. Humans anticipate the future, where algorithms can only respond to the present based on patterns in the past. The difference is huge. This problem exists no matter how sophisticated the algorithm. This simple game illustrates one of the most critical limitations of AI as it currently exists.
See below for the transcript:
Transcript: Hi, this is Perry Marshall and I want to illustrate a key property of information and language, using Apple’s autofill feature on the iPhone, so I’m going to create a little game here and we’re going to create a text message three different ways.
One way, the first time, we’re going to create a text message where I can only use the autofill and I’m going to pick the best word that I can, and then I’m just going to pick the first choice, and then I’m going to pick random choices. It’s going to make clear a major problem with artificial intelligence, as well as biology. I’m going to get this started just by typing one word, and then after this I’m only allowed to choose from what’s already on the screen.
“Hi there, are you guys coming over today? Or are you going to be home today? I have some stuff to do that I need to get done with the kids, and tomorrow night I want to do…”
At this point I’ve probably run out of good choices that are going to make a sensible sentence, but so far I actually managed to do it. Let’s start over with a different set of rules. The new set of rules is I’m just going to pick the first choice.
You see something very interesting here. “I can do not get to see you tomorrow night and I can do it tomorrow night and I can do it tomorrow night and I can do it…” It gets stuck.
All genetic algorithms, which are evolutionary programs, have this problem. The way that evolutionary algorithms typically get around the problem is that when they get stuck, they’re programmed to do something random, which might get them out of the loop. I want to illustrate that too, so I’m going to start over.
Now instead of picking the first choice, I’m just going to bounce around and pick random choices. What do I get then? “Yes, I can ask them for a dinner. [emoji] Was a good night for you guys to do dinner? [emoji] Is that the one that you sent him to you get a….” As you see, all this gets you is spam.
What this illustrates is that in order to create language, you have to have intentionality or what philosophers call “agency.” I am a human agent. I am self-aware. I know what I’m trying to do, so I can think forward into the future.
The problem with an algorithm is that all an algorithm can do is look at the present, which is whatever you just typed in, and then make a calculation based on statistical probabilities that have happened in the past, so an algorithm is always looking in the rear-view mirror, if you will.
This is a fundamental problem with all computer programs. None of them have any kind of self-awareness. They can only learn from what has already happened. They don’t actually anticipate the future the way humans or even dogs and cats do.
In information theory, this is the dotted line between mathematical analysis, which works at one level, versus creativity, which the mathematical analysis is unable to address. This is the most fundamental problem in biology and evolution because when Barbara McClintock damaged the chromosomes of her corn plants and found that the plant literally rearranged the chromosomes and evolved in real-time, what a lot of people don’t realize is the plant was actually making a choice based on what might work in the future, and it did something that no corn plant had ever done, because it was in a situation that no corn plant had ever been in. What the plant did wasn’t random. It obeyed some kind of linguistic rules, and the plant was anticipating what might work in the future.
This is a property of all biological systems that does not exist in human systems, and this is arguably one of the biggest unanswered scientific questions of all time, and it’s the essential motivation behind the Evolution 2.0 prize.
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