Blog: Machines that Dream: Developing Artificial General Intelligence (AGI) through AI-Kindergarten
Development of artificial general intelligence (AGI) may not be possible exclusively through human-created algorithms. Many aspects of human brain are not understandable to human scientists and engineers. Instead, AGI may require machines to create their own algorithms i.e., machines that learn to learn. It has been proposed that this can be achieved through AI-Kindergarten. In AI-Kindergarten machines are not left alone to figure out on their own the necessary algorithms, but they are heavily guided through human feedback. The feedback comes in a form of everyday interactions but also in a form of scientific knowledge about development of species and individuals. Information obtained from humans is integrated through a computational process that corresponds to the biological function of sleep and dreams. Importantly, AGI created that way has no danger of going rogue. It is completely safe while maximally benefiting humanity.
There is a long-lasting dream in creating artificial general intelligence (AGI). And AGI would be as good as human in most of the cognitive tasks. Today’s artificial intelligence (AI) is not yet there.
The approach of today is to rely on insights of human programmers and engineers in order to create and implement algorithms for AI learning. Hence, much effort is being invested into engineering new learning algorithms and information processing systems. The hope is that a right set of algorithms will be eventually created — making up a machine that will be able to learn on its own so extensively that it becomes one day an AGI.
A possible problem is that this effort based on human-developed algorithms may not be sufficient to bring about AGI. The reason is simple: It is likely that a human engineer cannot understand the complex processes of the brain and mind sufficiently well in order to create computer programs that would result in AI that is generally intelligent.
There is rich evidence suggesting that our capability of understanding the engineering details of the brain is highly limited. For example, for most part a biologist cannot infer what changes on behavior of an animal or physiology will be caused by a change of a nucleotide sequence in DNA. The interactions between genes themselves and between genes and their environment are simply too complex to ever be understood by a human mind with a precision needed to engineer and AGI.
Similar evidence comes from mathematical theory of dynamical systems. From mathematical chaos we know that there are systems consisting of only a few equations that are too complex for a human to understand the behavior of the system. The only way to know how equations will behave is to run them in a computer simulation. Often these incomprehensible mathematical systems consist of only a few equations — a minimum of three for continuous systems, and already a single equation can be chaotic in discrete systems (e.g., a logistic map).
What are then our chances to understand the brain, which probably has the number of interacting equations in the order of thousands if not millions or billions? How can we possibly understand the brain to a sufficient engineering detail? Creation of AGI based on human insights into workings of the brain may not be a very productive strategy simply due to the underlying complexity.
Today’s efforts in developing AI present no significant alternative to human-created learning algorithms. The only known alternative would be to use raw computing power to try out randomly created learning equations, and select them on the basis of a fitness function — much like the natural evolution did. Unfortunately, this approach is computationally unfeasible. Therefore, there seem to be no other option but employ human engineers to think up novel algorithms.
The result of this effort is a large number of solutions, but for very specific problems. New general algorithms, ones that could bring us closer to AGI, do not seem to come out easily from such efforts. Some of the best general algorithms used today (e.g., deep learning) stem largely from 1980’s, making the appearance that not much theoretical development has taken place since.
So, what can we do? Is there any alternative or are we simply stuck with specialized AI?
One possible answer is: Yes, there is an alternative in the form of AI-Kindergarten. AI- Kindergarten is a method for development of AGI that uses a novel approach to the problem (Nikolić 2015a).
First, AI-Kindergarten is not much about developing new algorithms by human engineers. In fact, only a few relatively simple human-created algorithms are needed to operate AI-Kindergarten.
AI-Kindergarten is more about offering the intelligent agents different levels of organization at which they can learn and thus become able to create their own algorithms. The algorithms created by humans operate at much lower levels of organization that in the traditional AI.
These simple algorithms lay behind the ability of the agents to create (or “learn”) more complex learning algorithms that human themselves could not possibly think of. And then these more complex algorithms operate in order to create behavior at human-level intelligence.
For this, a theory of organization of biological systems was needed that was more general than any theory so far such that the theory would be equally applicable to different levels of organization within living systems (cells, organs, organisms) and non- living adaptive systems (AI). This theory is called practopoiesis (Nikolić 2015b) and is fundamentally describing the workings of a hierarchy of cybernetic controllers as it is founded in two fundamental theorems of cybernetics: requisite variety (Ashby 1947) and the good regulator theorem (Conant and Ashby, 1970). But this was not enough. Practopoiesis only provides the basic structure of adaptive systems. It was necessary also to specify how many levels of organization were needed and what the function of each level of organizaiton was. It turned out that, to create AGI, we need more levels of organization than what has been imagined by the current brain theories or AI theories. Namely, adaptive agents that mimic biological intelligence need to operate at three levels of organization (see the tri-traversal theory of the mind in Nikolić 2015b). This implies that for an AGI, it is not sufficient to have an advanced learning algorithm or multiple such algorithms. An AGI needs to rely on a set of algorithms that enable it to ‘learn’ new learning algorithms. And this has to be done on the fly, with the speed of creating novel long-term meories. In effect, this requires conceiving an agent capable of AGI as having one more level of adaptive organization than what we thought so far.
The real implementation problem arises from the fact that these algorithms on how to learn-to-learn are also incomprehensible to human engineers or scientists. These algorithms correspond to the plethora of plasticity mechanisms that are encoded in our genes and are driving the development of our brains and all our instincts. It is practically impossible to even enumerate those rules, not to mention understanding of the principles of their functioning. To solve that incomprehensibility problem, AI- Kindergarten was invented (Nikolić 2015a) as a method understandable to a human mind and capable of providing the most fundamental learning-to-learn algorithms for AGI.
Second, AI-Kindergarten is not about autonomously self-developing AI. A popular science-fiction meme is that it is sufficient to give a smart AI an access to the Internet. Then the AI can download all the necessary information on its own and develop autonomously. One just needs to wait until the machine spits out a super-smart agent.
To the contrary, in AI-Kindergarten much of human input and supervision is needed and this is required at every stage of the process of developing AGI.
Importantly, this input is not in a form of direct engineering. It is a different type of input. It requires showing our intuition and demonstrating our own skills of dealing with world, and also requires using our scientific knowledge of biology and psychology.
AI-Kindergarten takes advantage of the fact that biological evolution has already performed numerous experiments until it came up with rules for building our own brains and controlling our behavior. AI-Kindergarten is about extracting this existing knowledge from biological systems and implementing it into machines.
To do that AI-Kindergarten uses inputs from human trainers. If human engineering fails to specify the learning rules for the machine, human intuition can specify for the machine what kind of behavior the machine should produce, and then the machine is left to find out on its own the proper rules on learning-how-to-learn. In AI-Kindergarten we tell machines which behavior is in which situations desirable. This information is provided during interactions with AI in a way similar to that in a real kindergarten — where teachers interact with our own biological children.
But AI-Kindergarten requires something else in addition. While our kids learn only at the level of developing their brains (the level of long-term memory), AGI needs to learn at one organizational level lower, at the level of “machine genes”. To achieve that, AI- Kindergarten must combine ontogeny with phylogeny (i.e., development of an individual with development of the species). For that, data from biology and psychology are needed to structure the stages of AI development. That way, existing scientific knowledge on brain and behavior plays a much more important role in AI-Kindergarten than in classical AI in which the engineers are supposed to assimilate scientific knowledge into the algorithms that they invent — and which turns out to be very difficult.
It would be incorrect to think that AI-Kindergarten does not require a high intensity of computation. On the contrary, we cannot run away from intensive computations to develop AGI. In AI-Kindergarten heavy computations are primarily needed for integrating knowledge acquired from humans. The process of integrating knowledge within AI-Kindergarten corresponds to what biology has already invented when endowing us with the capability to sleep and dream. Much like our dreams are needed to internally integrate knowledge that we have acquired throughout the day, the AI developed in AI-Kindergarten needs to integrate knowledge acquired through interaction with humans. Without intensive “machine dreaming”, AGI cannot be developed.
Finally, there is a concern that AI could surprise us with some unintended type of behavior becoming rouge or rebellious (Bostrom 2014). Due to the continuous feedback from humans, which occurs along all stages of AI development, the resulting AI remains safe. It develops exactly the type of behavior and instincts that its creators require. The motives, instincts and interests of the resulting AI are carefully crafted and shaped through this process such that they match the needs of human users. Much like selective breading of dogs makes them reliably human-friendly and non-wild, a super-intelligence produced in AI-Kindergarten has in its “machine genes” even more thoroughly imprinted basic instincts of not harming humans. AI-Kindergarten, by its very nature, produces safe AI.
Ashby, W. R. (1947) Principles of the self-organizing dynamic system. Journal of General
Psychology 37: 125–128.
Bostrom, N. (2014). Superintelligence: Paths, dangers, strategies. Oxford University Press.
Conant, R. C. & W. Ashby, R. (1970) Every good regulator of a system must be a model of that system. International Journal of Systems Science 1(2):89–97.
Nikolić, D. (2015a) AI-Kindergarten: A method for developing biological-like artificial intelligence. (patent pending)
Nikolić, D. (2015b) Practopoiesis: Or how life fosters a mind. Journal of Theoretical Biology 373: 40–61.