Friday, December 22, 2017

The Dawn of The New AI Era.

The Dawn of The New AI Era.
 October 7, 1913 changed the automotive industry forever. This is the day when the assembly line for making the Ford Model T car began to operate.
Before that, the industry just did not exist. Automobiles were manufactured like art pieces, by hand; automakers were researchers, and developers, and manufacturers in one. Everyone new how an automobile should operate, what major parts and functions are involved, and everyone could craft those parts in a shop. Ford ended pre-industrial phase and gave the birth to the true industry. He did more than just invented a good car. He invented the industrial manufacturing process for building cars on a large scale. Before Ford, the most important engineers were the ones who have been inventing and advancing cars and the related parts. After Henry Ford, the new profession has been born – production engineers, responsible for designing the whole process of car manufacturing, and for the corresponded devices and procedures.
Another big impact of Ford’s invention was the birth of a vast amount of a new-type of people – drivers, then the development of the set of new regulations, and the formation of another new professional field – driving schools.
A similar transition is being seen in the field of AI.
The news regularly tells us about new achievement in AI technologies. The list is long:
1. beating the World Chess Champion
2. beating the World Go Champion
3. beating the World Jeopardy Champion
4. teaching a car how to ride
5. automatic translation
6. face recognition
7. handwriting recognition
8. learning patterns in human behavior (social media, regular media, sport and other fans)
9. learning patterns in financial transactions
10. winning over professional poker players
11. analyzing heart beating patterns
naturally, there is more.
What we see in that list is the solid proof that today everyone (in the field, of course) knows how AI should operate, what major structural blocks, parts, devices, and functions are involved, and can design a “personal” AI tailored for solving a specific problem. It is inevitable that very soon the focus will gradually shift from technical aspects of manufacturing AI, which will become as “simple” as manufacturing a car, to all aspects of its “post-production”, starting from training AI to do the job it is supposed to do.
Let’s switch for a moment from talking about AI to talking about HI (human intelligence). Albert Einstein was one of the smartest people in the history of the mankind. Obviously, he had one of the most powerful brains in the world. But let’s use our imagination, what would happen if Albert Einstein – an infant, was left in jungles and was raised by monkeys? Would he really become Mowgli, a human among animals, or would he grew up as an animal? All currently known facts tell us that most probably he would become a monkey in a human body. Because humans become humans not as a mere result of birth, by via social interactions. For a large number of years, the core of those interactions is training, learning, schooling, educating. Take training out of the picture, and even the most powerful brain will remain a baby in an adult body. Of course, a person whose brain is very powerful may need less initial training than a regular person, may start learning from books and begin his or her own productive (creative, critical) thinking much sooner than others, but he or she still needs at least some initial training in reading, writing, counting, etc.
The same is true for any I (Intelligence), including AI.
Everyone who dreams about making a true, strong, actual AI, should start from treating it as it was a child, and ask questions like: what happens if AI has bad parents/teachers; what does make a teacher/parent good or bad; how to assess the quality of a teacher/parent (and many more which people have been asking for many years about actual adults raising actual kids).
That is why I expect to see very soon the growing demand for professionals who can train AI in the most effective way (within a specific professional field). To better understand how to train AI, AI designers will inevitably turn to study people who train HI, a.k.a. teachers (including teachers who train teachers). What they will find is that there are not many teachers who are both: good at teaching and good at explaining what makes them to be good at teaching.
My encounters with some of AI experts (e.g. shows that so far they are not aware of the transition their field is entering. The approach is “all we need is data, good data, a lot of data, and we will learn everything what we need from that data”.
This reminds me an old Russian tale called “Porridge from an Ax” (the Russian version is from, the translation is by the web-based Google Translate, with my editing – a lot, by the way; this AI still needs more training).
“The old soldier was on leave. He was walking for the whole day, he got tired, he wants to eat. He reached the village, knocked on the door of the last hut:
- Please, let the traveler rest!
The old woman opened the door.
- Come on in, solder.
- And don’t you have something to eat, mam?
The old woman had plenty of food, but she was very stingy, did not want to feed the soldier, and pretended to be poor.
- Oh, good man, I myself did not eat anything today: nothing.
- Well, not to worry - the soldier says. Then he noticed an ax under the bench.
- If there's nothing to eat, we can cook porridge from an ax.
The hostess clasped her hands.
- How can you make porridge from an ax?
- Here's how, I’ll show you, just give me a pot.
The old woman brought a pot, the soldier washed the ax, put it into the pot, poured water and set it on the fire.
The old woman looks at the soldier and does not take her eyes off.
The soldier took out a spoon, stirred the brew and tried it.
- Well, how is it? - asked the old woman.
- It will be ready soon - the soldier replies, - it's a pity, though, that there is no little salt in it. 
- I have salt, take some.
The soldier took some salt, salted, tried the brew again.
- Good! If only it had a handful of grains in there! 
The old woman began to fuss, ran out, and brought from somewhere a bag of grains.
- Take it, fill it as you need.
He filled a brew with grain. Brewed, cooked, stirred, tried. 
The old woman is staring at the soldier, she cannot move her eyes away from him.
- Mmm, the porridge tastes good! - The soldier licked his lips - if there were a bit of 
butter in it, it would be a real treat.
And butter was there, the old woman quickly found it.
- Now, old woman, just put some bread and get the spoon: we'll eat porridge from 
this ax!
They ate porridge.
- I really didn’t think that you could make such a good porridge from just an ax - the 
old woman says in awe.
Then the old woman asks:
- Solder! When are we going to eat the ax?
- You see, mam, it did not boil enough, still hard - said the soldier - somewhere on 
the road I'll cook it more and eat for my breakfast!
And at once he hid the ax in his knapsack, said good-bye to the lady, and went on 
walking to another village.
So the soldier and the porridge ate and the ax got!”
This tale resembles the situation with data mining (including in AI development; aside any possible moral implications :) ).
Data mining specialists say to us (public, administrators, politicians, financiers): “Give us the access to your data and we will solve all your problems”. But then they say: “You know, if only we had a logistics manager here, to tell us what he thinks; and a psychologist would not hurt, just as a on-a-side consultant, and since we are here, let’s also call on … (fill the blank: and check”.
And then – “See what your data can do for you! (but also, what you can do to your data)”.
This approach also demonstrates a common misconception, that collecting data is the same as doing science.
Yes, science is based on a collection of reliable data, but mining data does not yet mean doing science – it means, though, enacting a scientific practice. To make a transition from a scientific practice to science, data mining needs to be molded using at least one specific model. The most famous example of such transition is astronomy. The growing amount of data on the motion of celestial bodies had led to the formation and the common acceptance of the Ptolemaic system, which then has been eventually replaced by the Copernican system.
The simplest (streamlined) model of the evolution of a scientific field includes three stages/phases:
1. data collection – a.k.a. measuring; which involves establishing measurable parameters, standards (etalons), measuring devices, procedures, protocols.
2. empirical research – searching for correlations.
3. scientific research – a.k.a. research, establishing a paradigm, accepting the set of fundamental models, using models to make successful predictions. The mission of a science (any science) is to make successful predictions; until then the field is a scientific field, but not yet a science (in the true meaning of this word; no predictability = not yet a science).
Although, in order to make all us to feel better about what we do, we could invent a specific language to call everyone who is involved in any scientific practice “a scientist”, and “a researcher”. For example, in accordance with the three stages of the evolution of a scientific field, we could call ourselves:
1. a researcher of the first level (involved in a research activity of the first level); or a scientist of the first level (involved in a scientific practice of the first level).
2. a researcher of the second level (involved in a research activity of the second level); or a scientist of the second level (involved in a scientific practice of the second level).
3. a researcher of the third level (involved in a research activity of the third level); or a scientist of the third level (involved in a scientific practice of the third level).
To make a transition from a scientific practice to science, data mining needs to be molded using at least one specific model. That has to be done by a professional in the field, not by a data mining professional. Only a professional in the field can develop the model to study (the scientific hypothesis is - this model will work, more or less), state the criteria for the model to be acceptable, modify the model based on the preliminary results, etc. A model must include the list of measurable parameters, the description of the possible values for those parameters, etc. and only a professional in the field has the relevant knowledge (that is why the importance of people who can design models will be growing faster than the importance of people who can build AI) .
That finally brings us back to the topic at hands, i.e. the evolution of the AI development.
Data mining professionals are not experts in the field of training. That is why they will need to forge a closer collaboration with such professionals. But that collaboration has to be focused on solving a specific problem – training AI how to train.
Using the language developed in my field – education – I state that the most important goal in the field of AI development is teaching AI how to teach. Naturally, this task cannot be solved using any abstract theories of teaching or learning. The goal is to teach AI how to teach a specific subject, and while doing that to research the process of teaching AI how to teach. Since I am a physicist by trade, and a teacher by birth, I am confident that the next goal in the AI development is developing AI which can teach physics as good as the best physics teacher does (but as the first stage of the project, this AI should be able to win Physics Olympiads).

Anyone who is interested in a collaboration on this project can reach me at
Thank you,
Dr. Valentin Voroshilov
To learn a little bit more about my, please visit:

P.S. since I am a normal person, my views on things evolve over time. This link guides to an older opinion on the intersection between education and innovations:
P.P.S. There is a common misunderstanding of what does AI mean. The literal reading is "Artificial Intelligence". But we need to keep in mind, that currently a true artificial intelligence - as an entity of this world, as something material, presentable, usable - does not exist. What does exist is an artificially created pattern recognition system(s) (a device - in a general meaning of this word), which is (a) trainable, and (b) has elements of self-training. This system has several specific realizations, which differ by (a) the underlying structure; (b) specific area/domain of recognizable patterns. And currently, none of those realizations can operate outside of the domain it was trained to analyze. Which makes those realizations non-intelligent (or animal-level "intelligent"), because the mission and the core ability of an intelligence/intellect is creating solutions to problems which have never been solved before ( (c) Valentin Voroshilov)
BTW: A human brain is a composition of networks of networks of networks with a huge number of active elements (which makes it able to create solutions to problems which have never been solve before); currently manufactured AI is a network with a dismal number of active elements. Like an excavator is better at digging than a man, current AI is better at certain task than a man. But it cannot think and feel and will not learn it any time soon. That is why all publications about emotions, ethics, morality and danger of AI represent a nice scientifically packed version of science fiction (even an exponential rise will take decades to get from thousands of active elements to hundreds of billions).

For the definition of AI at:

To get to know me better, I would recommend to check the following three web-links 
(would not take more than 20 minutes of total time):
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