Andrea Kuszewski is a behavior therapist and consultant, science writer, and robopsychologist at Syntience in San Francisco. She is interested in creativity, intelligence, and learning, in both humans and machines. Find her on Twitter at @AndreaKuszewski.
Before you read this post, please see “I, Robopsychologist, Part 1: Why Robots Need Psychologists.”
A current trend in AI research involves attempts to replicate a human learning system at the neuronal level—beginning with a single functioning synapse, then an entire neuron, the ultimate goal being a complete replication of the human brain. This is basically the traditional reductionist perspective: break the problem down into small pieces and analyze them, and then build a model of the whole as a combination of many small pieces. There are neuroscientists working on these AI problems—replicating and studying one neuron under one condition—and that is useful for some things. But to replicate a single neuron and its function at one snapshot in time is not helping us understand or replicate human learning on a broad scale for use in the natural environment.
We are quite some ways off from reaching the goal of building something structurally similar to the human brain, and even further from having one that actually thinks like one. Which leads me to the obvious question: What’s the purpose of pouring all that effort into replicating a human-like brain in a machine, if it doesn’t ultimately function like a real brain?
If we’re trying to create AI that mimics humans, both in behavior and learning, then we need to consider how humans actually learn—specifically, how they learn best—when teaching them. Therefore, it would make sense that you’d want people on your team who are experts in human behavior and learning. So in this way, the field of psychology is pretty important to the successful development of strong AI, or AGI (artificial general intelligence): intelligence systems that think and act the way humans do. (I will be using the term AI, but I am generally referring to strong AI.)
Basing an AI system on the function of a single neuron is like designing an entire highway system based on the function of a car engine, rather than the behavior of a population of cars and their drivers in the context of a city. Psychologists are experts at the context. They study how the brain works in practice—in multiple environments, over variable conditions, and how it develops and changes over a lifespan.
The brain is actually not like a computer; it doesn’t always follow the rules. Sometimes not following the rules is the best course of action, given a specific context. The brain can act in unpredictable, yet ultimately serendipitous ways. Sometimes the brain develops “mental shortcuts,” or automated patterns of behavior, or makes intuitive leaps of reason. Human brain processes often involve error, which also happens to be a very necessary element of creativity, innovation, and human learning in general. Take away the errors, remove serendipitous learning, discount intuition, and you remove any chance of any true creative cognition. In essence, when it gets too rule-driven and perfect, it ceases to function like a real human brain.
To get a computer that thinks like a person, we have to consider some of the key strengths of human thinking and use psychology to figure out how to foster similar thinking in computers.
Why Is a Human-like Brain So Desirable?
One of the great strengths of the human brain is its impressive efficiency. There are two types of systems for thinking or knowledge representation: implicit and explicit, or sometimes described as “system 1” and “system 2” thinking.
System 1, or the implicit system [PDF] is the automated and unconscious system, based in heuristics, emotion, and intuition. This is system used for generating the mental shortcuts I mentioned earlier. System 2, or the explicit system, is the conscious, logic- and information-based system, and the type of knowledge representation most AI researchers use. These are the step-by-step instructions, the system that stores every possible answer and has it readily available for computation and matching.
There are advantages to both systems, depending on what the task is. When accuracy is paramount, and you need to consciously think your way through a detailed problem, the explicit system is more useful. But sometimes being conscious of every single move and thought in the process of completing a task makes it more inefficient, or even downright impossible.
Consider a simple human action, such as standing up and walking across the room. Pretty effortless, right? Now imagine if you were conscious (explicit system) of every single muscle activation, shift of balance, movement, have to judge/measure distance, determine amount of force, etc. You would be mentally exhausted by the time you crossed half the distance. I’m exhausted just thinking about it. You wouldn’t be doing it very gracefully, either. When actually walking, the brain’s implicit system takes over, and you stand up and walk with barely a thought as to how your body is making that happen on a physiological level.
Now imagine programming AI to stand up and walk across the room. You need to instruct it to do every single motion and action that it takes to complete that task. There is a reason why it is so difficult to get robots to move as humans do: the implicit system is just better at it. The explicit system is a resource hog—especially in tasks that involve replicating actions in machines that are automated in humans.
Now consider the act of thinking, or generating an answer to a problem. And let’s say you had every possible answer given to you, in a list. But let’s say to answer the question, you had to go through all possible answers in that list, no matter how long that list was, and compare it to the question, until you came upon the correct solution. You would probably be quite accurate with your answers using this method, but it would take a very long time. Your brain intuitively knows there’s a better way—sometimes you may just have a hunch that turns out to be correct, or figure it out after trying out only a couple of potential solutions, rather than all of them.
AI systems that use the explicit system of computation get around this time issue by generating faster computations. They think if only they can get the AI to run through all those possible solutions faster, they can replicate the speed of human thought processing, and thus make the machine more human-like. The biggest problem with these systems is the resource issue. Also, that’s just not how humans think, so the application of this type of system is limited.
But what if you could teach AI to operate using the implicit system, based on intuition, rather than having to run through endless computations to come up with a single solution?
AI: Artificial Intuition
To get AI to use intuition-based thinking would truly bring us closer to real human-like machines. In fact, some researchers are working on this technology right now. Monica Anderson, founder, CEO, and lead researcher at Syntience Labs, has been working on an AI learning process called artificial intuition, which aims to teach machines how to think like humans. This system is learning based—no pre-programmed knowledge or rules. It receives novel information, processes it, then takes away the relevant bits, and uses that knowledge to build on the next solution. For example, artificial intuition is currently being used to understand semantics in language. The computer has no dictionary of words to compare the text to, just the text itself—the computer is actually deriving the meaning from the context, understanding the language. By going about things in this fashion, it has the ability to learn, not just make faster computations.
Artificial intuition is very different in theory than most of the other AI research being done. Because it mimics the way humans actually learn, it can be used to develop the types of thinking systems that humans are currently better at, ones that use the implicit system. No one thus far has been able to do this successfully, until now. What did Monica Anderson do differently? She was one of the few forward-thinking researchers that recognized from the very beginning the importance of psychology to developing human-like AI, and has always had psychologists both on her board of advisors and on staff.
An AI with the ability to think intuitively opens the doors for all kinds of new developments in replicating human-like abilities for AI, but the one I’m most excited about is one that has eluded AI researchers for years: creativity. Many have attempted to engineer creative behaviors, such as getting a computer to paint or write music, but no one has figured out how to successfully engineer creative cognition. I think that now we are rapidly approaching this possibility now.
Will we ever have AI that is truly intelligent—learning, thinking, and feeling, just as humans do? Possibly. Sentient, human-like robots and machines that we see in the movies are still a ways off from reality. When that time comes, the field of robopsychology will likely expand and focus more on ethics and morality, as well as learning and emotion. But while we aren’t quite there yet, I do know that the field of robopsychology and AI psychology will play a critical role in a future that includes truly intelligent, human-like machines.
Implicit Learning as an Ability by Kaufman, DeYoung, Gray, Jimenez, Brown, and Mackintosh
The Creativity of Dual Process “System 1” Thinking by Scott Barry Kaufman, Scientific American.com
Reduction Considered Harmful by Monica Anderson, Hplusmagazine.com
The Reticular Activating Hypofrontality (RAH) Model of Acute Exercise by Dietrich and Audiffrin