A short history lesson on AI
The holy grail of AI research is to create Artificial General Intelligence (AGI). AGI is what we will get once we get a machine to operate on the same level of intelligence as a human.
There have been countless theories and approaches over the years trying to achieve this goal, but, so far, none of them have succeeded.
The first wave of theories around the 50s and the 60s, was focused on using logical rules in order to represent knowledge and make machines reason like humans. This was based on the assumption that the property that makes humans special is the ability to reason in a logical manner. For example, humans can easily understand logical statements of the form.
“If all men are mortal and Socrates is a man, then Socrates is a mortal”.
This approach is now termed “Good Old-Fashioned AI“. It was invented by researchers like John McCarthy and Marvin Minsky. While this approach helped birth AI as a scientific field, it, unfortunately, ended up being very limited.
Marvin Minsky
Research then moved on to other approaches like machine learning and neural networks. The 90s saw the rise of what was called “computational intelligence”. This was an approach inspired by biology. Computational intelligence had three main “arms”: neural networks, genetic algorithms (or evolutionary computation) and fuzzy logic.
The idea behind computational intelligence was very attractive. Since intelligence exists in nature, we should be able to replicate intelligence if we mimic natural processes. For example, ants display a kind of intelligence in the way they are organised. By mimicking the way they organise we can create algorithms which display the same kind of intelligence.
A very common analogy was that of the birds’ flight. While feathers is a way for an organism to develop the ability to fly, it is not a necessary prerequisite, as there can be other ways to generate flying organisms or machines. However, by creating machines that imitate birds, we can identify the core features that let us create machines that can fly.
Unfortunately, computational intelligence didn’t manage to give us true general AI. It lacked a proper mathematical theoretical framework which could guide research and provide satisfactory explanations as to how these methods worked.
These days, the latest iteration in the effort to create general AI is deep learning. But, even this might not be enough. While deep learning has been very successful in multiple tasks like computer vision and natural language processing, it has been criticised as simply a very powerful curve fitting machine, but nothing. Judea Pearl claims that it is simply a more powerful version the the same thing that algorithms have been doing for the last 150 years. According to his opinion, the only way to generate real intelligence, is to create an algorithm which can understand causality.
In any case, it looks that the debate is far from over. So, let’s go back to the title of this post. What is consciousness-based AI and what does this have to do with general AI?
Consciousness-based AI
Computational intelligence tried to imitate nature in order to generate AI and it failed. Other approaches have tried to use mathematical wizardry, in order to do the same.
I don’t believe that the approach behind computational intelligence was wrong. I think that you can create real AI through imitation. But you need to focus on the right thing to imitate. It is not nature that displays intelligence. It is consciousness.
So, let me elaborate on that. What do I mean by consciousness? There are many definitions, and the truth is that it is something very difficult to bring into words. But all of us, more or less, understand what it is to have a subjective conscious experience.
What kind of entities unequivocally display intelligent behaviour? What is the common theme amongst all of them? If you think about it for a second, you will soon discover that it is consciousness.
From a microscopic ant, to birds and humans, any kind of creature that is conscious, is also intelligent, at least to a small extent. Are all intelligent organisms conscious? Well, if you believe that a bee hive is an intelligent organism, then the answer would be no. But the components of the bee hive, the bees themselves, are conscious.
So, what does this have to do with artificial intelligence? What if we focused our efforts into replicating the elements of consciousness? Would this lead to the creation of an artificially intelligent machine? I believe it would.
Think in how many ways you can break down consciousness and the substrates that affect it. We can talk about the cognitive layer, the neural layer (brain), the biological layer (gut-brain axis, etc.), and potentially add other layers the deeper we go.
Once you start analysing all these pieces, then the aggregation provides the basis for intelligence.
A new dawn for Consciousness-based AI
Can AI really be conscious? If we could break down all components of consciousness, then I believe we could replicate intelligence. And we could even use tools like Integrated Information Theory in order to create a consciousness equivalent in a machine. Integrated Information Theory gives us the tool to measure the quantity of consciousness in any organism. The theory implies some kind of panpsychism, where, for example, organisms like plants are conscious, but have a lower Phi (the measure of consciousness) than humans or animals.
What if we could measure someone’s Phi and then create an algorithm that possesses an equivalent Phi? While this machine might not be conscious, it would be a consciousness equivalent of someone’s brain. And this artificial construct should be as intelligent as the real person.
But are there any instances of intelligence where there is no consciousness? Our autonomic nervous system, for example, can react to changes in the internal or external environment, but we wouldn’t call it conscious (even though it might be). This is a simple kind of intelligence, and I would argue that higher levels of intelligence, usually imply some kind of conscious experience.
Is this thesis the one to lead us to Artificial General Intelligence?
I think it might well be. That’s why I decided to become part of Within[u], so that we can do the necessary research to find out. I can’t share many details for now, but what I can say is that we are looking for researchers on all kinds of field relating to AI, cognitive science, neuroscience and biology.
If you are curious about this, just drop me a message.