Making Sense Podcast - Is AI Already Conscious?
Great podcast about whether AI systems are or could become conscious. I found following part on the difference between traditional software and artificial neural networks and the role of learning quite informative:
One thing that I’ve come to realize is that I don’t think a lot of people have sufficiently rich mental model of what these frontier AI systems actually are and what they’re actually doing.
And it might make some sense to just spend a moment reflecting and talking about this.
So, these systems are giant neural networks.
They are digital in exactly the sense that you described.
Their computations individually are significantly less sophisticated than what individual neurons are doing in the human brain.
But it’s really important for people to understand that these systems are not software in the sense that we ordinarily have meant software for any other kind of code programmatic output.
When it comes to, you know, the operating system on your iPad or it comes to, you know, your Microsoft Office Suite.
This is programmed source code, written by developers that compiles on a computer that we can perfectly inspect the internals of, and the person building this system, understands everything about how the inputs, the way that they constructed the system, relate to the kind of thing that you get out at the end.
Artificial neural networks are not like this in many key respects.
What you basically have is a giant, randomly initialized network that does, in its sort of 1st approximation, resemble, in particular, how neocortex is organized.
You have a bunch of general purpose neural units.
They are connected together.
You basically give the system a goal.
This is called an objective function, a loss function, a reward function depending on the specific class of machine learning.
And you basically subject the system to trial and error learning, whereby given certain inputs, it figures out what it wants to do.
It kind of takes a behavioral guess, that guess is reconciled against what the actual objective of what you want the system to do is, that error is propagated through the system, and it’s rinse wash, repeat, until you get systems that behave in accordance with how you wanted those systems to behave.
What this yields is this extremely complex mathematical object, which is this giant neural network.
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So, for example, taking pixels in an image, and your output might be finding a caption that describes what’s going on in that image.
At the beginning of that process, the system was completely randomly initialized, there were no representations that were learned by the system.
And by the end of that process you have a system that has learned a rich representational structure that, to be very clear, is opaque to the people who initialized this process.
This is why some people say that these systems, it’s more apt to say they are grown rather than engineered.
And I think that this is accurate.
This, I think, is deeply similar to the kind of thing that we see in brains.
We do not have a finished neuroscience or anything like it because what’s going on in brains is incredibly complicated in exactly this respect.
We have nonlinear, learned representations that help us as organisms achieve the various goals that we’ve either been evolved to to undertake or learn through experience or culture to move towards.
This is a fundamentally nonlinear input output mapping between the various inputs that the organism gets and the goals of the organism.
We have instantiated these dynamics in the systems that we’re building, and I think that there’s good reason to think that these dynamics are relevant to consciousness in particular.