Smart.

Gifted. It is one of the greatest endorsements you can get from a teacher as a parent for the teamwork.

How do you raise someone to be gifted?

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Education was the first topic I addressed in this blog. It will probably be the main fallout of growth from the current wave of artificial intelligence. Artificial intelligence is an inexpensive and quality tool to teach us, challenge us, and spread prosperity.

Try to play this game next time you hold a presentation. Come up with ten questions of a topic and ask the audience to answer the questions individually. Each person should then find a random peer and verify each other’s responses. How many are matching? Probably seven. You may discuss the rest of the differences and convince each other. Now they agree on eight answers. What about the other two? Those two questions are the opportunity for growth based on the peer reviews.

Progress. These two questions are opportunity to do more research. It is the similar math by the way that I described in my articles about the Law Of Energy And Independence. If you see noise, you collect more data. Ten years of work of tech companies gave us dozens of tools to help with this.

Researchers will hop on those two questions, and they will spend years to figure out the details. Graduate professionals will write books, present and organize training, so that everybody answers eight questions correctly. Undergraduate degree holders will use the knowledge to produce, make value, money, and scale out.

Our education system is rooted in the Middle Ages both in the West like the University of Bologna, or the East like the hierarchical education of Confucianism. Hierarchy is set by comparing knowledge. It is proportional to the amount of time invested. Guilds allowed member to be called Master, if they passed the exams and spent some time abroad in training.

I learned this from a PhD while working at Amazon Web Services. You start in the middle of a circle, and you drift to the edge as you learn more. Once you are there you own a share of the arc. You are a PhD. It is lonely. You need to walk back the trail, find professionals to talk to. They implement and review your work. You walk back even more and you find customers. You can make money and build great things.

Hierarchy on the other hand can cause groupthink, inefficiencies, corruption, or even wars. Sacrifice and martyrdom is part of many ethical theories.

Problem. A good education system allows affordable and flexible education.

Solution. Products that use knowledge should be abundant. This means their supply will be elastic, and scalable. Such a property requires more educated professionals than the current demand. Excess demand for education will lower costs, and increase the supply of talent. Books, and related certificates are the best way to provide such educational services.

Some learn faster, some learn slower. My favorite explanation was comparing us to artificial intelligence with equivalent knowledge. It gives the same answers to the same questions. It is Turing's model. Pick the training set that was used to teach the person. Use that to train a machine.

If the training set contains lies, it is inconsistent, training will be slower. If it contains lots of empirical information, training is slower. It may be an overfit. If the training set is concise, training is faster. A similar concept can be applied to people. They study hard. When they are tired, they relax, and clean their mind.

If you just collect the training set, but do not do the actual training, all you have is an index. If you need smart people, give them the freedom to process and analyze what they need.

Why? It is not just about learning accurately. Information requires time to be processed by the brain. You need a good night’s sleep. You also need plenty of news or Lego time to make your knowledge consistent.

Can you measure good learning? Learn the books of Marvin Minsky, especially The Emotion Machine. My model is similar to his concept. We collect accurate information throughout our lives. If we have issues with consistency we wait or collect more information to clarify. Emotions build up, when the accurate information is inconsistent. It is stressful, and the brain waits before learning more.

Whether we need more information depends on emotions. Emotions give the feedback to learn more, think more, or act fast.

Emotions are not random feelings. Emotions are connected to the knowledge and experiences that we collected. All these need processing time. Emotions triggered by brain chemicals are attached to groups of neurons, until they align well with others.

Psychologists explain this as emotions having three roots. These are biology, like using tobacco, sociology, like being bullied, and psychology like organizing your mind.

We spent a lot of time hiking during the Pandemic having health insurance with a high deductible. We had concerns about the virus. A single hospitalization could have cost thousands of dollars. The family improved each other’s immune system by spending time together hiking.

I was able to remember memories from the past during this time. I also resolved them by coming up with answers, how I reacted wrongly, and what to do next time in similar situations. Each resolution gave more power.

A new memory appeared that triggered the emotions that I reacted to. I resolved this one as well. I walked back the emotion domino chain of my memories.

I remembered more and more of my life. I was able to remember even things from my early childhood of year seven. I was able to build a consistent model of life and society, so that I could make safer decisions.

Children are similar. They do not just need knowledge, they need time to test it and make their world view consistent. Constant input distorts the consistent learning process.

Consistency is important. Very liberal people are proud but tolerant to differences. Ultra liberal people consistently agree on principles that make them work seamlessly with everyone.

Consistency is the key to sharpness. Sharpness leads to happiness signaling there is no need of more processing. Emotions are cleared. It is time to relax.

How do you train artificial intelligence to be smart? The model says just tell them the same information that you tell a child.

Accurate information is essential. Random garbage will not teach anything good.

Consistency requires training time. Eventually it builds a concise model.

Some information may remain inconsistent. These trigger beliefs. Emotions stimulate us to learn more, or depress us to process more. Learning does not just need information, it also needs processing or training time.

How do you measure intelligence? The traditional way is linear intelligence. It is like an IQ test. The way it is built is that they ask people to answer questions. If the number of right answers can map people to a linear hierarchy, they stop. If not, they drop out questions, until they collect a set of questions that can be projected to a linear line.

Linear intelligence is limited as a result. Real knowledge can be more complex. Our knowledge is defined as a graph in GPUs, not as a book with linear chapters one after each other. Doctors may be as smart as an architect answering different questions correctly. Linear intelligence relies on an external tutor. I am not against the Mensa style intelligence definition. It is a model that was used by tutors to motivate their audience by racing to learn more of the same topic.

Learning intelligence is a better approach to define what is smart. If we have emotions like motivation to learn, then we will learn more. We build consistency otherwise. It is never compared to an outsider. It is compared to our own beliefs, whether we need more information or not. We are smart, if our emotions give feedback to stop there.

Learning intelligence is similar to unsupervised learning. Learning intelligence treats the tutor and the student as a whole system. They can be human or machine. Learning intelligence is better, if all the questions are answered and agreed upon in the example at the beginning of this article. The knowledge must be consistent.

The learning intelligence system becomes an unsupervised learning system. Eureka moment is reached, when the system does not require more information to process or learn. There are more examples of such systems. They are typically sciences.

Systems may contain logical and empirical knowledge. Logical knowledge can be derived from the training set. Empirical knowledge must be explicitly described. Empirical knowledge was the result of tribes and languages evolving during the centuries. The reasoning of social behavior varies, it may be related to resources or disease prevention. Still, humankind is social. You can read about this in the book The Social Animal of Elliot Aronson.

This explains that Ivy league edge of certain schools and jobs. As you study more, you follow the path of the previous generations, and you will eventually reach the edge of that knowledge circle, where nobody is better in a topic than you. You are asked as an expert related to a court case for example.

That knowledge secures a good job. Period. Ivy league gives you that discussion that tells you the empirical secret after a late class. Empirical knowledge keeps the group together, like accents.

Problem. The process of consistent rule based learning are the following.

Solution. Curiosity is the first. If there is no curiosity, no more accurate information will be collected. Curiosity requires enough time to build that consistent model and to resolve emotions. Aha! The eureka moments in the mornings after resting give feedback to learn more. The first rule is self motivation or simply motivation. This is Education. This is the church, temple, or skyscraper telling you, that if you follow the knowledge, you can build something similar. This is the concept of Marvin Minsky.

Solution. If the consistent knowledge did not support the learning of others, it would be limited. The system would become a zero or negative sum game limiting the reason for learning. Exceptional learning intelligence respects the learning process of others as a result. The system is going to be incomplete without explaining it all. Learning enables even more learning understanding the system as a whole. Lying limits this motivation. The second rule of respect and no lying is the concept similar to the concept of the Hippocratic Oath by Hippocrates of Ancient Greece. It is the key to smart individual decisions.

Solution. Lastly, knowledge does not destroy information. Accurate information may be useful later to build an even better model. Learning systems keep their past. Think how evolution kept species like bacteria, sharks or rays in habitats even though some orcas, and people are better in many areas if not most of them. Dinosaur genes are kept in birds. New species need to compare to these ancient species as well. It is Evolution by Charles Darwin. Evolution is the key to smart group decisions.

These three rules explain the reason, why we are smarter than other animals. Evolution taught us respecting our community by learning and helping out our sick people. We learned that some berries are helpful in certain quantities, some others are poisonous. We learned that talking and feedback improve learning. We learned that people talking our dialect may have different thinking to learn from. We supported our sick, and they supported us when we were sick. We could tolerate habitats and climate this way that other animals could not.

Why are we smarter than other species? It is probably because we do Medicine.