Every textbook on machine learning will tell you: “You need to have a good set of training data”. An access to a large amount of data is necessary for training an AI. In a way, the intelligence of AI is sucked out of a pool of data.
But once you have created such AI, the resulting solutions do not seem to be much alive. Their responses are quite stereotypical. They don’t seem to come up with new ideas. To refresh behavioral pallet of your AI, you need to repeat the training process: collect sufficient amounts of data, and go into training conducting basically intensive number crunching.
That’s not an exciting life for an AI. Wouldn’t it be much better if an AI would change itself on the go? What if it was learning as it was working, including even single events telling it that it should change its behavior?
We have succeeded in creating something like that. After having studied similar abilities of the real brain, we underwent an intensive creative process. We asked the question: What can be done about AI technology?
The results is quite significant.
We basically created a new technology — a new category of machine learning, if you like.
While usually you need a large training set, each time when you learn something new. Here, we learn from Big Data just once. We collect a Big Data representing a certain domain such as for example, character recognition. What we do then is not creating an agent to recognize the characters from that data set — as you would traditionally do in machine learning. We do not develop an expert for those characters that can be found in the big data set.
Instead, we do something else, something much cooler. We create an agent that becomes an expert in learning new characters. That means that we do not care so much how well is our agent classifying characters. What we care about is how quickly it can learn classifying new characters. And more importantly, we do not stop until the agent is really, really good at this learning.
And to qualify as “really, really good”, the agent has to satisfy the following two criteria:
- It must be able to learn new characters that it has never seen before. For example, it should be able to learn Hebrew, Cyrillic or Glagolitic letter as quickly as Latin, even if its learning expertise was trained on Latin letters only.
- It must learn new characters instantaneously, in one shot. A single example should be in most cases enough. (We consider it extremely embarrassing if it needs more than five examples).
Now, take this technology for a creative mental ride and imagine what you could possibly do with it.
You could have an AI that is alive, that changes itself in a natural way as the world changes around it. It never stops learning and hence, it never gets out of sync with the ever-changing surrounding world. Even if the world gets crazy and suddenly, from one morning to the next, an A is no longer A, but is suddenly a B. No problem. Or a government decides to change spelling; which actually they do sometimes? Again, no problem. Or an AI gets a human owner who is quite weird, was educated at some remote island and had a really weird teacher who was as an ayahuasca researcher in the same time, and hence spells things a bit awkwardly. This is not an issue. The AI adjusts to the person; no need for person to adjust to AI.
But this is only a beginning of the ideas for using that fast-learn technology. How about economical, political and legal conditions that change all the time? Stock rise and fall, interests fluctuate, political situations change, nations that are friends become foes, unexpected leaders come to power, companies suddenly crumble, parliaments bring up new laws. How are you going to respond to those, if you did not have had even the time to collect Big Data — and by the time you collect enough data, it is already over, the world has changed once again. So with traditional machine learning you can never properly catch up.
But this is still not all. Think context. Much of what has been said, made, done depends on the context. To understand what a Siri user wanted, depends on what the user was just doing right before that. To train a traditional AI to respond to contexts, you need a data set of all possible contexts. But you don’t have that data set. There is no way you could have had all possible contexts. The combinatorics is way too large. Much like the total number of all possible characters that one can come up with is mind blowing (the actual number is flirting with infinity), so is the number of possible contexts too vast.
Imagine having then an expert on sounds, walking, driving on a road … whatever, that learns every new environment as the situation evolves — i.e., in real time, as the context is being defined. This can be then done for new contexts that haven’t ever occurred before, provided an initial training on a representative subset of known contexts, and provided that you learned how to quickly learn.
Is this theoretically doable at all? Of course, it is. This is exactly how we humans go around our own lives. So, why wouldn’t we give such lives to our AI?
Now we can.
Please explore our platform MENTAL and try out our proof of concept, Mr. Character, who is an expert for learning hand-written decimal digits. Feel free to stress him up and make up your own digits. See how quickly he can learn characters that you invent, on spot.
After having evolved Mr. Character to a mature point, we can now teach him to read a new writing system even if having just one exemplar per letter.
Don’t believe it? So, you’ll be able to try it. You will be able to train Mr. Character online using your own writing system.
Heck, you will be able to make up a completely new alphabet. Mr. Character won’t mind.