(Part 1 can be found here)

Last time, the discussion focused largely on what happens at the circuit level of a computer system, and whether, starting from that, intelligence and consciousness could arise.  For this installment, I wanted to delve a little more into how we define intelligence.  Much of the hype surrounding AI is that we are soon going to see AGI – artificial general intelligence – as well as ASI – artificial super intelligence.  My skepticism remains solid that neither of these milestones will ever be achieved, certainly not with current computing architectures, if ever.

What is AI Doing?

Instead of focusing on the circuit-level, it’s instructive to go a few rungs up the abstraction ladder and discuss what happens when one sends a prompt to an LLM, or large language model (which encompasses the basis most of the well-known AI chatbots today – ChatGPT, Google Gemini, Grok, etc.).  This is a somewhat simplified explanation, but it’s enough to obtain a basic understanding.

When you send a prompt to say, ChatGPT, the words of that prompt are broken down into tokens.  These tokens can be full words, chunks of words (sub-words), or even symbols.  These tokens are then turned into numbers, in a process called embedding.  The numbers are then turned into numerical vectors that can have thousands of dimensions.  The numerical vectors are then fed into a transformer layer, where many, many matrix multiplications are performed.  Since a matrix multiplication comprises many individual (scalar) multiplications, the number of total multiplications becomes astronomical.  In other words, it’s doing a mountain of math under the hood.  

Each step involves multiplying huge grids of numbers together, and every one of those multiplications expands into millions or even billions of tiny arithmetic operations. Processing a single word can require hundreds of billions of multiplications and additions. To put that in perspective, if you sat with a calculator and did one multiplication every second, it would take you thousands of years to do what the model does in a fraction of a second for just one word.

In doing these kajillion multiplications, the AI model is predicting the next word, based on weights applied during said multiplications.  After all these multiplications are done, the resulting numbers are turned back into words for display on your computer screen.

While the operations described above may be algorithmically new, from the perspective of computers, the individual operations – namely, the multiplications – are nothing new at all.  Electronic computers of all kinds have been doing multiplications since they’ve existed.  This isn’t confined to your desktop or the room-sized behemoths of yesteryear, but also includes the pocket calculators that people like myself relied upon during engineering school before things like smartphones were as ubiquitous as they are today.

There are a couple of upshots to the above.  Thie first is, that while an LLM like ChatGPT may appear to understand language, in reality it does no such thing at all.  It just crunches numbers.  And not only that, the computer doesn’t even know it’s crunching numbers – refer back to the first installment – the number crunching is just the causing of basic switching circuits of the computer system to switch between logic 1 and logic 0 – high voltage and low voltage – really, really, really fast.

So if the computer doesn’t understand language, and doesn’t even know it’s crunching numbers to mimic the understanding of language, can it be considered intelligent?  If the answer is no, then how will computers become intelligent by simply making bigger, more computationally intensive models? 

How do you Define Intelligence?

This is a trickier question than it may appear.  We can recognize intelligence to be sure, which is exemplified by the fact that we can ponder and debate what exactly the term means.  But defining it with precision, drawing a hard line between intelligent and not intelligent?  That’s a much more difficult task.

We define humans in general as being intelligent (not to be confused with being wise).  And yet we still have a hard time drawing that line between what is intelligence and what is not, despite most of us being pretty sure that computers running AI haven’t yet reached intelligence.

And that’s the rub.  The people trying to create artificial general intelligence (AGI) – or any intelligence at all in computers/AI, are trying to solve it as an engineering problem.  But engineering problems require well-defined solutions.  If you want to put a satellite into an orbit with a perigee of 150 miles above the Earth’s surface and apogee of 160 miles, the solution is well-defined.  If you want to design an amplifier circuit that can take an input signal with an amplitude of 2 volts and output a corresponding signal having an amplitude of 12 volts, we know how to do that because, again, the solution is well defined.  There may be different ways to get to the same solution, but having a firm definition of the solution provides a framework and a guide for getting there. 

This is true even for some engineering problems that we haven’t solved, like nuclear fusion.  We know what man-made nuclear fusion it will look like should we ever get there in terms of inputs and outputs.  But that illustrates another point: even when we know what the solution looks like, it can be maddeningly difficult to achieve. 

With intelligence, general or otherwise, we can’t even agree on a definition. Not even AI’s biggest proponents can agree on a definition of intelligence, much less what would constitute true AGI.  What they all have in common is that they are trying to find an engineering solution to something that is essentially a philosophical problem.  And because the definition of intelligence is essentially a philosophical, it will continue to defy an engineering solution. 

So far, we’ve spent a lot of time talking about intelligence, the difficulty in defining what intelligence is, and stating why I believe computers running AI workloads are not even remotely intelligent.  What hasn’t been discussed so far will be the topic of the next installment – the rapacious appetite of AI in terms of resources.

Before I go, however, Apple has published a paper about AI entitled “The Illusion of Thinking.”  If you want to dig a little deeper, it can be found here.