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Sunday, March 19, 2017

  Is Artificial Intelligence Already Actual Intelligence? 

  Is Artificial Intelligence Already Actual Intelligence? 
Recently one can read in many papers that robots finally become almost as smart as humans, and the era of humans is beginning to end.
For example, Scott Santens writes in Boston Globe: “Robots will take your job”.
Or, Emmanuel Marot writes: “Robot CEO: Your next boss could run on code” (find it at
Now Paul Allen wants to dissect a brain to make an artificial one.
It is amazing to see how advances in AI make public think that AI is already almost the same as I (Intelligence).
Yes, the progress in AI is huge!
But so far nothing achieved in this field shows actual intelligence, a.k.a. intellect.
An old but appropriate joke comes to mind.
Researches study the development of intellectual abilities in monkeys. They designed a cage with a tree and hanged a banana on a branch to keep the banana high enough. Monkey enters the cage and sees the banana. He tries to jump but the banana is too high. He tries to clime the tree, but it is covered with plastic so the monkey just slides back. Monkey looks around and sees a stick. He takes the stick and hit the banana down. Success! Researchers are preparing the cage for the new run; they place a new banana on the tree. Suddenly a hungry physics graduate gets in the cage. He sees banana. He jumps, but the banana is too high. He tries to clime the tree but slides back. He starts to shake the tree, but the banana holds tightly to the branch. He keeps shaking the tree. Nothing happens. Finally researches tell the guy via an intercom: “Hey, take a second, think it through.” “What to think about”, the guy says, “it is simple - just shake and shake hard”.
This joke helps to make a clear difference between an intelligent action and the one that is not.
Almost all authors talk about self-driving cars, or a robot that can walk and “talk”, or the three famous AIs, Deep Blue, Watson, and AlphaGo because they won in chess, Jeopardy and Go. Keep in mind, that all these AIs have been trained on using a vast set of previously uploaded information. The work they do is not really cognitive, it is routine. Of course, it is a highly performed routine work, but still – routine. It is impressive, but it could never have been done without advances in the amount of trivial operations a computer can make every second. When trying to separate a “routine” work from a “non-routine” work (meaning “intelligent”) people say that a routine work is the one done by body, and a cognitive work is the one which mostly requires our brain – but that is just plain wrong. Any work, or any action in general, requires functioning brain (that is true for all animals, including humans). The difference is what kind of problems does the brain solve when functioning.
There is a lot of routine work a brain does every day, or even every minute. However, nowadays we need to reassess the meaning of the term “routine”; it is not just something repetitive, it is also something that can be done by “routinely” checking a vast amount of data against a set of given criteria. No doubt a computer can do this kind of a routine work much faster than a human, but that does not make it more intelligent than a human – just faster.
Unfortunately, following AI promoters, public also get a deviated impression of what true intelligence is. Many think that memorizing lots of facts and quickly answering any question from an encyclopedia is the sign of intelligence.
The purpose, the missions, the central ability, the most important feature, and the signature sign of an intellect (a.k.a. intelligence) is an ability to solve problems. If we want to broad this definition of an intellect, we should say that the central ability of an intellect is an ability to self-teach, to self-propel individual knowledge generation, usually via generating a solution to a problem which has not been solved before (hence, the solution has not been stored in the memory and could not be found be a simple search, even via a very deep search, and the action solving a problem at hand could not be previously trained). If we have to select a single action which indicates the level of intelligence it will be making a decision. Simple decisions do not require much of thinking. Although today the term “simple” incudes decisions which can be made based on a fast analysis of a large amount of data, as long as the parameters and values are well established and well prepared. Complicated decisions are synonymous to not trivial decisions, which often come spontaneously, as an insight. Spontaneity means that an action has not been trained before and it requires taking a step outside of the previously trained actions, and it has to happen here and now. No robot can do this so far.
Yes, a robot can recognize our face and voice, can get up if pushed down, can perform commands – but so can a monkey, or a dog. Some dogs might show a very smart behavior, but an intelligent dog does not exist.
A brain is just a biological foundation for intelligence, but learning from an intelligent teacher is the source and the roots of intelligence. You can try to train a monkey, but it will not learn how to solve physics problems. On the other hand, you can find a human child razed by monkeys and his perfectly human brain will not be able to show any sign of a high intelligence.
So, how will we know when AI is truly I (Intelligence)?
Another joke (a.k.a. an anecdote!) comes to mind.
A young math teacher enters the teacher lounge and swears: “My students are so stupid. I explained them a theorem, they did not get it. I explained another time. Still did not get. I explained the third time. I GOT IT! They still didn't”
(FYI: who is acting stupid in this joke?).
There is a reason people say “if you really want to understand something, teach it to someone”. Teaching is a sign of a high intelligence. Of course, it needs to be a true teaching, but not just routine training of repetitive actions. Animals also teach their cubs to live, to hunt. Many of the current “teaching” software” do not go farther than an animal-type intelligence; they train a student in a way a trainer trains an animal do tricks, but no more.
Let’s stress one more time: the key ability of an intellect is not an ability to walk, it is not an ability to talk, it is an ability to solve problems. And so far, the most powerful problem-solving instrument invented by the nature is a human brain. So, what every AI expert should do is to watch what problems, in what order and how a human brain learns to solve.
That means a simple thing, AI experts need to map a problem-solving evolution of a growing human, from a baby (problems to solve: reach, touch, recognize, walk, etc.), to … well, Albert Einstein!
At first a robot should learn how to solve any problem from a standard physics textbook; then a robot should be able to tutor a single student how to solve physics problems, and then, when a robot can replace a high school physics teacher, I will agree that finally AI has become I (Intelligence); but so far no company in the world (NO COMPANY IN THE WORLD) works on this (not "sexy", I guess, or too difficult, or both).

Tuesday, March 14, 2017

A Song About The Imortance of Education! :)

This song has been written during a snow storm.
When one is being "locked" in a house, mind starts wondering, and here we are!
Keep in mind, I am an amateur song writer, and I learned English using books, TV and radio shows.
I was just having fun, but anyone is welcome to use this text as a draft for an actual professional song!

To deal with my responsibilities
I need to have the right abilities,
I have to learn, and do it fast,
If I don’t want to be the last:

- The last to wear commencement robe,
- The last to find a better job,
- The last promoted at the work,
- The last erasing boss’s smirk.

Education is important,
Education is a must,
Education is a port at
Path to future from the past.

Take your time and be persistent,
Make your teacher sweat and grow,
Education needs commitment
Same like making into PRO.

(regarding the meaning of PRO - see the picture!)

Monday, March 13, 2017

Will the Yidan Prize Affect the Evolution of Education? Too Soon to Say.

Key words (a.k.a. tags): Charles Chen Yidan, Yidan Prize, Yidan Prize Foundation, Priscilla Chan, Mark Zuckerberg, Chan Zuckerberg Initiative, Steve Jobs, Laurene Powell Jobs, XQsuperschool, education, education reform, NSF, National Science Foundation, education funding, education research, teachers, teaching, STEM, STEM graduates, teacher professional development, professional development, MOOC, learning, laws of learning

Will the Yidan Prize Affect the Evolution of Education? Too Soon to Say.
When Priscilla Chan and Mark Zuckerberg Initiative announced their goal “to eradicate all diseases” (https://chanzuckerberg.com/) I only hoped it would go better than the project of changing education in Newark (New Jersey). It did not go well: https://www.amazon.com/dp/B00AXS6BIE/ref=rdr_kindle_ext_tmb.
The same approach must be used to eradicate all “the ignorance” in the world by reforming the way education currently is being reformed.
This task however is even more difficult than “eradicating all diseases” (http://www.teachology.xyz/30uS.html). Like in medical and biological research, research in education is being conducted by many independent groups, with a very low level of sharing data – mainly, because there is no comparable data (http://www.teachology.xyz/FW.htm). Many of the activities are not even a research, but an attempt to advance some elements of social reality in the field of education.
When the widow of late Steve Jobs, Ms. Laurene Powell Jobs announced her XQsuperschool initiative, I wrote her a letter, warning that there is a mismatch between the goal (reshaping ALL high schools in America) and the actions (reshaping 5 high school): http://www.teachology.xyz/xq.htm. There are 10 XQsuperschools now, but my premises in the letter still stand.
I got a hope again when Mr. Charles Chen Yidan announced the establishment of the Yidan Prize Foundation (http://www.yidanprize.org/en/). This is the first philanthropist who seems understands the difference between a social project and a scientific research. The distinction is very important for advancing education (http://www.teachology.xyz/wwNSF.html), and I applaud Mr. Charles Chen Yidan.
If I had a chance, I would tell Mr. Charles Chen Yidan the following.
Part I: Initiating a discussion.
Dear Mr. Charles Chen Yidan,
I sincerely admire your intention to support education. I have been in education for almost twenty years, and it pains me to see who slowly it changes to the better.
However, I need to inform you that most probably you will be spending your money with achieving much less than you would expect, at least at first.
I believe that reading the following letter could help you to solidify your views on the functioning of the Foundation.
But first, I want to inform you that very often my views on education – its state, the way to improve the whole system – are “perpendicular” to the mainstream views.
To describe the current state of affairs in education we can use one word – chaos.
There is no science of education, it is in a rudimentary state, similar to alchemy before chemistry:
Currently, educational publications rarely lead to more than a simple statement “more exercises => better student outcomes”.
R&D projects are focused on local goals incoherent with each other.
There are three large fields within education which need a serious reformation:
* Teacher professional development
* Big Data in education
* Detailed study of the time scale of all elementary learning actions and teaching acts
More on this at:
Of course, teachers and schools keep doing the best they can to give students the best education they can. They would appreciate any additional funds which would let them teach better. But simply giving extra money would not lead to a development of a science of education, would not advance a progress in new teaching technologies.
The latest reports show that U.S. system does not help many students to be ready for getting college education, especially in science and engineering.
“The number of U.S. citizens and permanent residents earning graduate degrees in science and engineering fell 5 percent in 2014 from its peak in 2008. At the same time, the number of students on temporary visas earning the same degrees soared by 35 percent”.
“Nearly half of PhD aerospace engineers, over 65% of PhD computer scientists, and nearly 80% of PhD industrial and manufacturing engineers were born abroad”
At this stage, any “innovations” at a college level are more like a game. The focus must be at the advancing pre-college education on a broad scale. However, at a K1 – K12 levels all “innovations” fall into two categories: (a) give teachers more workshops; (b) give students more toys (like tablets, Lego robots, etc.) – they do not represent a scientific research.
Politicians, unions, professionals are stuck debating what is better “charter schools” or “regular school”.
This debate is irrelevant to the real needs of educational reform.
More on this at:
If you really want to make a difference, you need to go beyond orthodox views on what education is, and how science of education should be developed.
For example, you could start from creating a completely new type of a school (a.k.a. a new type of a research facility in education).
Sincerely yours,
Dr. Valentin Voroshilov

Part II: Widening the discussion.
Dear Mr. Charles Chen Yidan,
I have spent some time to study the materials related to the Yidan Prize.
I truly admire the mission of the Foundation, which is to create a better world through education.
I have watched the videos, I read all the information about the Yidan Prize.
The video and the Forecast point at several important problems the world is facing right now, for example how many children are not having any formal education, or that education does not guarantee a job, or on youth unemployment, and STEM graduates.
The Forecast shows the tendency of the future.
But education also has a long history.
We can imagine a long line which represents the trajectory of the evolution of world education. The Forecast indicates how this line will continue in the future.
I assume, that when the Yidan Prize was established, the goal was to alternate the current trajectory, to “bend the line”, so to speak. The actual trajectory of the evolution of the world education should become different from the projected trajectory (without the establishing of the Yidan Prize) due to the fact of the influence of the Yidan Prize.
But the Yidan Prize Foundation is not the only organization with a similar mission.
For example, the U.S. Department of Education appropriates about 69 billion dollars per a year. About 500 million dollars from the budget are spent to support innovations in education.
In addition to it, the National Science Foundation spends about 61 million dollars on research in the field of education.
Largest US foundations and corporations put together about 500 million dollars to advance education (https://www2.ed.gov/news/pressreleases/2010/04/04292010a.html).
Your foundation has an extraordinary team of experts. Those people have been helping to advance education for decades.
I also have been in this business for about 20 years.
I have seen innovations come and go without leaving a mark.
I am pretty sure that when creating the Yidan Prize you also have asked yourself the following two questions:
1. How would you make a difference; how would your actions influence the evolution of the world education; due to what mechanism the Foundation would change education?
2. How would you know it; how would you assess the effects of your actions; how would you measure the impact of the Foundation?
My personal answer would be to concentrate on the projects in three areas:
1. Teacher professional development
2. Big Data in Education
3. The study of the processes of individual learning.
The first area does not involve a scientific research, it is mostly based on the projects of a social type (http://www.teachology.xyz/wwNSF.html).
Those projects usually fall into one of the two categories:
1) “We want our students to do better. For that we plan on trying - this.” – this project mostly involves faculty or teachers who directly teach students.
2) “We want our school teachers to teach better. For that we plan on trying - this.” – this project mostly involves faculty from a university or a school of education helping teachers to teach better (usually via workshops, or other forms of communication).
The second and the third areas represent the areas of a scientific research.
For the second area, the main idea is that data must be collected from a vast number of sources (at least hundreds) – only then it will become the Big Data.
For the third area, I would use an analogy.
Among many new things America brought to the world is potato. There are more than 4000 types of potato. For each type, we know exactly how to grow it: what type of soil is good, when to plant, how often to water, what microelements to add, when the first leaf should start growing, what signs of a good or a bad growing process, etc.
But when we teach, we only know in general how people learn. But we have no idea about specific stages needed to learn a specific skill of a specific subject depending on the economical, racial, geographical, background of a student, his or her age, gender. And so far, no one does this type of a research.
When my students tell me that they want to make a difference in the world, I tell them:
“You want to make a difference? Be different!”
But it is simple - to paint your hair in pink. The true difference comes from thinking and acting differently, and from finding people who think and act differently and supporting those people.
What I see is that the Yidan Prize is expected to be different from others by making a clear distinction between scientific projects in education (Education Research), and social projects in education (Education Development).
I only want to warn you that sometimes it is not easy to recognize the type of a project based only on its textual description.
I wish you good luck!
Dr. Valentin Voroshilov

Part III: Topics for further discussion
Education is the most important human practice. If I had to think about how to change education as a whole practice, at first I would ask myself, what is the missions of education in general? Then I would apply this view to the actual practice of education and compared.
The Yidan Prize is “to embrace outstanding achievements in education research and development”; but those achievements might belong to different social scales – individual, institutional, regional. It is advisable to keep in mind that in social practices (like education) an outstanding achievement on an individual level might have no effect on other levels. 
The systemic approach to funding education should include this question: “How to manage funds more efficiently”. The society does not really want to know how students get good education. The society just wants to have students with good education. That is why in principle, it does not matter where and how students have been taught. But we really have to establish a uniform procedure for assessing the quality of education. That will mean that we will be able separate the process of learning from the process of assessing the results of learning. The quality control should be decoupled from a teaching process. This approach will eventually lead to more effective distribution of funds in education.
Every large research university has a long line of students who want to get education in those universities. That is why any internal research in such a university does not really make a broad impact, even if the university has structures which create many teaching tools. But an external outreach to schools, school district might make a big difference.
Why do people select a massive open online course? Because they do not have another option (due to financial, time, geographical restrictions). Currently, there is no MOOC which would be as good as a good regular face-to-face course. Creating such a MOOC would be a true breakthrough (but even bigger achievement would be creating a system of MOOCs: http://www.teachology.xyz/chs.htm).
The challenges education faces today have been facing education for decades. Education has “survived” many waves of innovations, so to speak. Big corporations and small startups develop a vast amount of various teaching tools. Teachers are flooded by innovative tools. It is like you buy a car, but instead of a car you get a kit, a collection of parts, and you need to assemble it, like a chair from IKEA.
Creativity, communicative skills are important. But if people cannot read or count, creativity will be useless. The current discussion is framed as “creativity versus basic skills”. Instead we need to be able to teach basic skills and develop creativity. Teaching creativity is not about what to teach, but how: it is not about the content, but about the process.
Good teaching leads to good results. Period. This statement is a law (http://www.teachology.xyz/6LT.html). If there are no good results, the teaching was not good. Simple. The quality of teaching is based on the quality of teacher professional development; the low quality of teaching is the direct result of the low quality of teacher professional development.  Teacher professional development often goes top-down, which is one of the least effective ways. Essentially, the quality of teacher preparation should be defined by teachers. (http://www.teachology.xyz/np.htm).
Large scale changes require systemic approach. I would recommend to establish “Yidan Institute for Advancing the World Education”. This Institute would become a coordinating force for some of the teams nominated for the Prize, not received it, however, expressing a certain potential (according to the criteria). The Institute would provide some financial, logistical, organizational support (a.k.a. incubator). Even though all the teams would work in different countries, via the Institution they would develop, use, and when necessary modify a common protocol for observations in education, collecting data, sharing the data, analyzing the data.

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