Is AI truly creative or is it, as Noam Chomsky put it, merely “high-tech plagiarism?” Multiple studies have documented that AI is more creative and more efficient at creative tasks than humans. But in a stunning paper that just appeared, researchers demonstrate that images produced with (simple) convolutional neural nets are 90% predictable by purely analytical methods. So how creative is AI really? Let’s take a look.

If you’ve paid any attention to current AI models like ChatGPT, you know that they... have problems. But they have already acquired emergent abilities, and some researchers think that the current AIs will also learn to use logic and reasoning. In a new paper now, researchers say that this is unlikely. Let’s take a look.

With the rise of AI, we’ve been told that the technology can be used to help software engineers code more efficiently. We have heard plenty of warnings that AI might even take their jobs. But a bunch of studies now show that AI doesn't help software development as much as promised -- at least for now. Let’s take a look.

Researchers at Google have created self-reproducing code from noise. Does this mean that they’ve created artificial life? In this video, I look at what they have done and at what this might mean.

Quantum computing, so the fairy tale goes, is the next big thing in technology. News has popped up time and time again noting major advancements in the field, but the latest statement from company D-Wave had people scratching their heads. Are quantum computers really the next big thing? Who’s at the forefront of the field now? Let’s have a look.

A lot of big banks are banking on quantum computing because they think it’ll give them an edge in trading. Though I have on previous occasions noted my doubt that we’ll see any useful quantum computers within the next ten years, two new papers detailing new methods of scaling quantum computers have shifted my perspective. Let’s have a look.

here’s a lot of talk about artificial intelligence these days, but what I find most interesting about AI no one ever talks about. It’s that we have no idea why they work as well as they do. I find this a very interesting problem because I think if we figure it out it’ll also tell us something about how the human brain works. Let’s have a look.

Elon Musk launched an XPRIZE for Quantum Applications! It’s a 3-year, 5 million dollar global contest that’s looking for ways to use quantum computers to solve real-world challenges that benefit society. Doesn't this basically mean quantum computers don’t currently have any good use cases? And what do you need to do to win? Let’s have a look.

A group of researchers had artificial intelligence play wargames, and that gives us a good idea for how we could all die. They tested five different large language models and asked them to make decisions in war situations. They told the AIs that this is a real-world situation and not a simulation! Nevertheless, for all models they found a risk that a nuclear war would develop without provocation out of an initially neutral situation. Some AIs are more war-loving than others though.

I've been trying to understand the limits to Moore's law and looked into what technologies are under development to push it further.



Transcript, links to references, and discussion on Patreon.

For this video we have looked at what AI applications are currently under development and add some wild speculation about where things will be going in the near future. We want to hear your speculations, too, so let us know in the comments.



Transcript, references, and discussion on Patreon.

Today I want to tell you what is artificial about artificial intelligence. There is, of course, the obvious, which is that the brain is warm, wet, and wiggly, while a computer is not. But more importantly, there are structural differences between human and artificial intelligence, which I will get to in a moment.


Before we can talk about this though, I have to briefly tell you what “artificial intelligence” refers to.

What goes as “artificial intelligence” today are neural networks. A neural network is a computer algorithm that imitates certain functions of the human brain. It contains virtual “neurons” that are arranged in “layers” which are connected with each other. The neurons pass on information and thereby perform calculations, much like neurons in the human brain pass on information and thereby perform calculations.

In the neural net, the neurons are just numbers in the code, typically they have values between 0 and 1. The connections between the neurons also have numbers associated with them, and those are called “weights”. These weights tell you how much the information from one layer matters for the next layer.

The values of the neurons and the weights of the connections are essentially the free parameters of the network. And by training the network you want to find those values of the parameters that minimize a certain function, called the “loss function”.

So it’s really an optimization problem that neural nets solve. In this optimization, the magic of neural nets happens through what is known as backpropagation. This means if the net gives you a result that is not particularly good, you go back and change the weights of the neurons and their connections. This is how the net can “learn” from failure. Again, this plasticity mimics that of the human brain.

For a great introduction to neural nets, I can recommend this 20 minutes video by 3Blue1Brown.

Having said this, here are the key differences between artificial and real intelligence.

1. Form and Function

A neural net is software running on a computer. The “neurons” of an artificial intelligence are not physical. They are encoded in bits and strings on hard disks or silicon chips and their physical structure looks nothing like that of actual neurons. In the human brain, in contrast, form and function go together.

2. Size

The human brain has about 100 billion neurons. Current neural nets typically have a few hundred or so.

3. Connectivity

In a neural net each layer is usually fully connected to the previous and next layer. But the brain doesn’t really have layers. It instead relies on a lot of pre-defined structure. Not all regions of the human brain are equally connected and the regions are specialized for certain purposes.

4. Power Consumption

The human brain is dramatically more energy-efficient than any existing artificial intelligence. The brain uses around 20 Watts, which is comparable to what a standard laptop uses today. But with that power the brain handles a million times more neurons.

5. Architecture

In a neural network, the layers are neatly ordered and are addressed one after the other. The human brain, on the other hand, does a lot of parallel processing and not in any particular order.

6. Activation Potential

In the real brain neurons either fire or don’t. In a neural network the firing is mimicked by continuous values instead, so the artificial neurons can smoothly slide from off to on, which real neurons can’t.

7. Speed

The human brain is much, much slower than any artificially intelligent system. A standard computer performs some 10 billion operations per second. Real neurons, on the other hand, fire at a frequency of at most a thousand times per second.

8. Learning Technique

Neural networks learn by producing output, and if this output is of low performance according to the loss function, then the net responds by changing the weights of the neurons and their connections. No one knows in detail how humans learn, but that’s not how it works.

9. Structure

A neural net starts from scratch every time. The human brain, on the other hand, has a lot of structure already wired into its connectivity, and it draws on models which have proved useful during evolution.

10. Precision

The human brain is much more noisy and less precise than a neural net running on a computer. This means the brain basically cannot run the same learning mechanism as a neural net and it’s probably using an entirely different mechanism.

A consequence of these differences is that artificial intelligence today needs a lot of training with a lot of carefully prepared data, which is very unlike to how human intelligence works. Neural nets do not build models of the world, instead they learn to classify patterns, and this pattern recognition can fail with only small changes. A famous example is that you can add small amounts of noise to an image, so small amounts that your eyes will not see a difference, but an artificially intelligent system might be fooled into thinking a turtle is a rifle.

Neural networks are also presently not good at generalizing what they have learned from one situation to the next, and their success very strongly depends on defining just the correct “loss function”. If you don’t think about that loss function carefully enough, you will end up optimizing something you didn’t want. Like this simulated self-driving car trained to move at constant high speed, which learned to rapidly spin in a circle.

But neural networks excel at some things, such as classifying images or extrapolating data that doesn’t have any well-understood trend. And maybe the point of artificial intelligence is not to make it all that similar to natural intelligence. After all, the most useful machines we have, like cars or planes, are useful exactly because they do not mimic nature. Instead, we may want to build machines specialized in tasks we are not good at.