AI isn't AI.
Hi everyone. Leo Notenboom
here for askleo.com, where
I try to give you a little bit more
confidence and a lot less fear when it comes to technology.
So there's a lot of discussion lately,
especially in recent weeks, about AI or artificial intelligence.
We've seen a lot of things like ChatGPT
or DALL-E, the image generator that basically take text.
You describe something and then they in turn create something.
They create an image that is what you
said, what you described, or they just write text that is basically
fully fleshed out idea from which you have simply provided a prompt.
There are a couple of things going on here
that I want to be very clear about, that there's a lot of misconception about.
First off, none of this is really AI.
Artificial intelligence is, in a sense, basically things trying to act human.
There are no really hard and fast rules
about what it means to be artificial intelligence.
It turns out to be a very common marketing term.
It turns out to be a term that is a lot easier to conceptualize than the reality.
But the bottom line is that no, none of this is intelligence.
It's all artificial.
But I don't think any of it can be called
intelligence in the truest sense of the word.
So if it's not AI, what is it?
Well, in reality it is something that is very powerful, don't get me wrong.
It's called Machine Learning ML,
and it is something that's been around actually for a long time.
The best way to describe what machine learning is, is to use an example.
Machine learning starts with a human or humans writing software.
Everything that I'm going to talk about is nothing more than humans writing software.
Their software is designed to examine large data sets.
So for our example, I'm going to say that we write some
software that is used to analyze photographs.
We feed that software 5 million
photographs, half of which have dogs and half of which do not.
Along with those photographs,
we include the information that says this picture has a dog, this picture does not.
The software then analyzes all of these pictures and what it looks for is
the characteristics of what it means for a dog to be in a picture.
And it also looks at the characteristics
for what it means for a dog not to be in a picture.
It builds this on this huge data set of 5 million dog pictures.
Now, once it's done, it has collected all of this information
about what it means to be a dog in a picture.
Two interesting things can happen.
One is that this data set can then be used by different software.
Again, software written by humans that can
then use it to say, okay, draw a dog, create from all of this data that you've
collected so far, create a new picture of a dog.
And it does.
Here's an example, this is a picture of a dog that I asked
the AI, and I'm going to use the term improperly because everybody's using it,
but it uses the AI in doll E to create a picture of a dog.
I asked it for a photo realistic dog in the chair, and this is what it gave me.
Whether or not it's photorealistic is up to you, but the bottom line is it does
a pretty good job of putting a dog in a chair.
It's not 100% by any stretch.
You don't see the photos that I've weeded out where the dogs just don't look right.
As you might imagine.
I repeatedly ask for corgis, and the corgis never, ever look quite right.
In fact, some of them look quite frightening.
But the bottom line here is that, yeah, there's stuff that's happening.
It created a picture of a dog.
The other thing that this of course can do is say, okay, great, here's a picture.
Use your data set to tell me if there's a dog in it.
Sounds a lot like the captcha exercise.
And this is one of the reasons that captcha exercises are also someday
going to fall by the wayside, because AI or ML is going to be able
to look at the picture and tell you, yes, there's a traffic light.
There's not a traffic light. Yes, there's a bicycle.
There's not a bicycle.
The bottom line here is that we've written
a third piece of software that can then use this large data set that was created
early on to say, yes, this picture that you just gave me, it has a dog.
It doesn't have a dog.
All that is is software.
In our example so far, three types of software data collection,
image creation, and image analysis that use this large data set based on 5
million pictures that were run through and analyzed.
That's machine learning.
Now, interestingly enough,
and in recent weeks, it's gotten a lot more press, is ChatGPT.
ChatGPT does essentially the same thing, but with text.
Rather than analyzing millions and millions of photographs,
what it's been doing is analyzing and creating a data set of written words,
what it means to write, what it means to say things in the written word.
I don't know what its data set is,
I don't know where all of the analysis comes from, but that's the bottom line.
And the bottom line to that is that there is this data set that GPT is using.
Again, it's just software that was written, that analyzed millions
and perhaps billions of pieces of text to determine what it means to write
something and write something relatively well.
Now, there's software where you can in fact enter a prompt,
and ChatGPT will create for you what it is you've asked for.
For example, I have asked ChatGPT to describe the difference between
artificial intelligence and machine learning.
And it came up with, well, what you see on the screen here.
The interesting thing about this is it's not wrong.
In fact, it's not really even that poorly written.
It's a fairly reasonable summary of the difference between artificial
intelligence and machine learning written by a machine learning engine.
So this part is what kind of starts
to scare me a little bit because you can ask it questions and it starts to come up
with reasonable answers, kind of like what I do for a living.
However, I went ahead and I asked it not that long
ago, what are the steps to download and install Windows Eleven?
It began by telling me Windows Eleven wasn't available yet.
Of course, Windows Eleven has been out for a year.
So this is one of those situations where it's clear that the technology is very
interesting in terms of making things sound like proper English,
but it's not always accurate, it's not always getting the facts right.
I asked it to create a biography of Leo Notenboom and it came up with all
sorts of things that are completely untrue.
Maybe they're true for a different
Leo Note and Boom, but they're not true for this one.
And yet it was written very, very, I'll say confidently now,
with all of this being simply extreme data processing software that is,
processing extreme amounts of data that's been collected from wherever
there's an interesting, I'll call it a risk for years, decades.
One of the mantras about computing is very simple garbage in, garbage out.
In other words, your software,
your program is only as good as the data you feed it.
So if you feed it lots and lots of good dog pictures, great,
you've probably got a good model for what it means to look like a dog.
On the other hand,
if you do a bad job of selecting those pictures, or if occasionally you have
an error and you throw in a picture of cat and you call it a picture of a dog, well,
that's going to poison the well, it's going to skew the data.
It's going to make the machine learning algorithm less accurate at what it does.
What's worse is if now you do this maliciously.
So let's say that in amongst our 2.5 million photographs of dogs,
remember there were another 2.5 million of things that were not dogs.
In amongst those dogs, somebody intentionally throws in,
I'll say half a million pictures of gorillas, but it says that they're dogs.
Now, the machine learning data set is very poisoned.
If you ask it to create you a picture
of a dog, the results could be downright scary.
So what is all this?
What does all this really mean to you and me?
Well, AI which is really machine learning is here.
And given how good it is today,
gorilla, dogs or Windows Eleven availability notwithstanding,
given how good it is today, it's only going to get better.
Whether that's a good thing or a bad thing remains to be seen, because,
like any technology, it can be used for good or for evil.
Will I use it?
I'm going to play with it.
I may have it write a first draft of an Ask Leo! Article,
but it still will take that human touch to basically turn it into something
a little bit more relatable, but also make sure that it's right,
which is something that, as of yet, the AI, the ML isn't really up for yet.
So, as with so many things, it's time to get skeptical,
perhaps even more skeptical, in understanding where some
of the information comes from and how it's being generated.
I hope this was helpful.
I hope that this gives you an idea of what's on the horizon
and exactly what you need to be on the lookout for, basically skepticism.
It's been our friend for years.
We need to basically make it be even more our friend in the coming years.
For comments, for updates,
for links related to this and more, visit Askleo.com/151001.
I'm Leo. Notenboom.
This is Askleo.com. Thanks for watching.