Foundation of the AI, Before You, Go Any Deeper

Foundation of the AI

Nowadays, AI is a buzzword in the technological world. Before we go deep, we must know about the basics of AI because a solid foundation is essential. AI implements the solutions as to how humans/ animals solve problems. Human beings have the power to think and solve problems. Machines can generate evolvable solutions; reducing time and increasing accuracy will be a more improved solution. Also, AI can solve problems using one or more solutions. AI solutions are extensible means solutions are built on existing systems. For example, we can build software solutions using existing basic programs such as Java, MySQL, PHP, etc. Let’s go to some examples of solutions to problems. Think about automobiles in the past. But hybrid and electric vehicles came to the industry based on old cars; then, the past knowledge was not valid anymore. This is the extensible behaviour of problem-solving in AI. Some solutions work but cannot be justified, such as a technician. Some answers can be explained, such as an Engineer. So this is the nature of human and machine intelligence.

Our brain has two parts, left and right, and we should maintain both sides of the brain. In neuroscience, that kind of person is performing well in everything, and the Following example is about the Einstines’ brain.

“In fact, in the case of Albert Einstein, both hemispheres of his brain were equally active. This could be the primary explanation as to why he was so brilliant.”

Here I explain a few famous problems that have been solved using AI. However, this information should search more for your motivation.

DART(Dynamic Analysis and Replanning Tool -1991): used by the U.S. military to optimize and schedule the transportation of supplies or personnel and solve other logistical problems. Thousands of entities were controlled by DART during the war, and everything was controlled using AI applications.

Pathfinder(1997): An American robotic spacecraft landed a base station with a roving probe on Mars in 1997. With the possibility of functioning in an unmanned and unknown environment.

Deep Blue(1996): A chess-playing computer that defeated the master of Chess, Garry Kasparov. It is a historical event, and that is the first time machine has beaten a man.

IBM Watson: Watson is a question-answering computer system capable of answering questions posed in natural language

Alpha Go: AlphaGo is a computer program that plays the board game Go.

Self-Driving Car: In October 2014, Tesla Motors announced its first version of AutoPilot.

Sophia(2016): Sophia is a social humanoid robot developed by Hong Kong-based company Hanson Robotics. In October 2017, Sophia “became” a Saudi Arabian citizen, the first robot to receive citizenship of any country.

Amazon Alexa: Simply Alexa is a virtual assistant technology developed by Amazon.

Google Assistant: Google Assistant is an artificial intelligence-powered virtual assistant developed by Google

Neuralink chip: A microchip that sits in a human skull with wires fanning out into the brain’s cortex

So in like this way, in the future, the difference between man and machine will be reduced. Even now, machines are defeating humans in many ways.

Nature of the Knowledge

Science: Science is a subject that builds and organizes knowledge in testable explanations and predictions about the world.

Mathematics(Algebra & Geometry) — Mathematics includes studying such topics as calculations, measurements, and patterns.

Engineering — Apply Scientific theories. Such as in physics and mathematics. (such as shape, colour, cost, weight, and durability)

Actually, humans are engineered by nature. Those things are inventing and implementing using these knowledge areas.

Technology: know how to do tasks (but cannot explain) — Developers develop programs but do not know how to build and run the program but know how to do it.

• Technology came before science after everything was discovered by science.

Informal Knowledge: This knowledge comes from Commonsense, beliefs, experience, trial, and error. Informal knowledge is the basis of formal knowledge, and informal knowledge is everywhere.

Formal Knowledge: This knowledge consists of theories accepted by everyone and the same for all. It is made on informal knowledge.

E.g., Newton was under the apple tree. He experienced that situation, and he felt that situation. He gained informal knowledge first, and then he suddenly came up with his law of gravity as the formal knowledge.

Where is AI?

AI is a combination of formal and informal knowledge; humans are with both pieces of knowledge, and AI can model both types.

• Researching means building formal knowledge based on informal knowledge.

Is AI a dream or a reality?

For many years this was a question that was hard to answer. But not hard at all. Think about a pulley. It is a machine for multiplying. When one side goes up by one unit, the other side goes down by two units.

So it is an intelligent machine. Therefore AI is not much far as you think. There has no difference between old and new, and AI has been everywhere throughout history. People will invent machines day by day. And AI is not hidden. We can see a lot of applications in AI from our nature and in our minds.

Why is AI different from Other technologies?

Any technology has two main factors. One is power, and the other is control. A lot of examples you can think of it. Let’s get an example. An automobile has the power that came from technology. But it controls by the man. But in AI, the power is from technology, and also control is from technology. The result is unmanned vehicles. The difference is both aspects are generated by technology itself.

AI is multidisciplinary.

Philosophy

Philosophy is a subject that is combined with AI. Here we are going philosophical thinking about technical things. such as natural/ artificial, mind/matter, intelligent machines

Mathematics

Very important to learn before the AI. We are formalizing our symbolic system, the logic for reasoning, and algorithms.

Economics

This is an area where people were getting into trouble in their day-to-day lives. It is better If we can give control of the economy to systems to solve problems with Resources, profit, etc.

Neuroscience

This is a vital area of AI because our brain and the nervous system are compulsory for study.

Psychology

Mental health is the main area of our life, and therefore learning about Anxiety, and depression-like emotional things are important to embed in AI systems. Also, you must have good mental health to learn these things.

Computer Science

AI is mainly influenced by programming knowledge and Helps to develop intelligent machines as software. Computer Science is one of the most influential areas for AI because, from the beginning, AI was with computer science. Typical BSc in AI degree consists of AI, CS, Mathematics, and statistics.

Computer Engineering

Help to build programmable machines. This is the central part of ai, and this is how we convert our knowledge into reality.

Including these subject areas, Control theory and Cybernetics(1984), linguistics (Science of languages) is also more important to study.

Major Areas of AI

All AI technologies can be classified under symbolic AI(rules/theories based/ cognitive systems) and non-symbolic AI areas(training based/ machine learning).

Symbolic AI subjects — Depend on data

• Expert systems
• NLP
• MAS
• Game Playing
• Fuzzy Logic

Non-Symbolic AI subjects — Depend on data

• Artificial neural networks
• genetic Algorithms
• Robotics
• Computer Vision

AI, Machine learning, and Deep learning

Three different things. Machine learning is a part of AI, and ML is based on statistical and mathematical data analysis. Deep learning is a part of ML. Different is it is based on an artificial neural network. Therefore it is for modelling the brain. This model is called an artificial neural network.

Nowadays, some areas can be considered under artificial cognitive systems and machine learning. They can be modelled using symbolic programming and non-symbolic training data.

E.g., NLP, because we can process language by knowing Grammer(symbols) and using the practice(non-symbolic).

In-game playing, the robot can use the training approach for communication and action, while rules can be used for decision-making.

You can learn theories, but you cannot understand them. After you go into the lab, you will learn through experience and understanding. Therefore both can apply together.

E.g., Deep blue is entirely a symbolic system. While ‘alpha go’ is fully used as a non-symbolic(machine learning) system.

For any theory, there is an inspiration coming from nature, and nature is full of undiscovered wonders. Please observe natural intelligence before you learn Artificial intelligence, which is the secret of AI success.

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