Artificial Intelligence

Artificial Intelligence (AI) is a field of computer science that consists of algorithms, mathematical and statistical models, programming languages, that aims to mimick human intelligence. Further AI is implemented using cognitive analytics and computing, machine learning, machine vision, along with hardware to enable computers, robots and other systems to operate in an autonomous mode. Humans and animals using natural intelligence can independently react to any situation and take necessary action to progress forward to get preferred outcomes.
Human Intelligence (natural intelligence)
A baby after birth initially cries when the baby is hungry, has some discomfort or needs new diapers and so on. Day by day the baby starts to recognize who the parents are, initially by how they are held and carried around - tactile comprehension, later on by vision - facial/pattern recognition. The baby tries to mimic the parents voice by different types of noise and sound - audio/voice comprehension, until it can master to communicate meaningful words/sentenses. Since humans eat and can react to adverse conditions (foul smell), other sensory intelligence is acquired by retaining how things smell. At each level, the newly acquired intelligence gets permanentaly stored in the human brain (human learning). This results in humans taking logical and intelligent actions to one's best ability for day to day activities. There has been a lot of research to understand how the human brain works. There has been analysis of the brain of the well known genius scientist such as Albert Einstein to understand differences in brain of a normal person and a genius.
Human Intelligence
In 1950's, Alan Turing, a British computer scientist wrote about machines (computers) that could think. He is considered as father of Artificial Intelligence. From then on, there has been research to create machines that can think independently by many research organizations such as DARPA and universities around the world. Another famous US computer scientist, considered as founder of AI, John McCarthy named the area of thinking machines as "Artificial intelligence".
During graduate research on robotic dexterous hand several years ago, I encountered this issue when simulating grasping objects such as a ball by the dexterous hand using software. The software simulation of mechanical grasping of was a success. The dexterous hand was taught (in machine learning terms it is similar to supervised learning ) to grab hard balls such as a baseball. The software simulation was mechanically tested using a industrial robotic gripper with three fingers.
The big challenge encountered was when suddenly the object to grasp was switched from baseball to an egg or a ping-pong ball in a demo robot with same robotic gripper with fingers. Without AI (intelligent decision making algorithms) and also due to lack of visual feedback to the robotic gripper, the egg would get squashed. For the dexterous hand or robotic gripper to seamlessly hold objects autonomously without causing damage, requires AI, machine learning algorithms, software and hardware embedded with machine vision.
An animal running in a certain terrain in a forest will react to jump over an obstacle or turn away using its natural intelligence spontaneously. The AI provides the tools and technology for a machine to achieve or mimick such intelligence. Using AI, for a machine to perform such maneuvers operating autonomously, has to
  1. Compute the speed
  2. Constantly make adjustments to speed based on distance
  3. Avoid obstacles using vision (camera, hardware and software)
  4. Constantly adjust mechanical devices that perform functions of legs or motion systems based terrain
  5. Think and make spontaneous decisions using built-in AI
to slow down, or maintain speed, or change path, or stop or jump over an obstacle. At a high level, this lists the complexity involved (intelligence, cognitive analytics) that needs to be incorporated into a robot. The system also has to learn from new situations it encounters on a constant basis.
transistor count
From 1980's to now there has been enormous progress in the computational power due to advancements in memory chip architecture (CPU, VLSI, ULSI, etc.) and technology, which is making AI a reality in the recent years. The table shows how the number of transistors started from 2500 in a chip in the 1970s to 67 billion transistors in a single chip in 2023. The IBM's deep blue supercomputer was able to achieve the level of intelligence to defeat world chess champion Gary Kasparov in a couple of games in a tournament. Autonomous computers and machines continue to evolve with enhancements in AI. As the computing power and other technologies increases, the evolution of AI will be exponential in the future.
transistor count Graph
One of the CEO of a AI research company was expressing how venture capital companies were ignoring funding of AI and ML in 2010 and around. Going back further to 1990s, AI was considered as a fantasy and recruiters would advice prospective clients to remove AI from the resumes, whom they were promoting for job placement in client companies. The above graph can be a good reason that the computing power has made AI and ML a thing of the present day.
The research that came out of AI and robotics in early years, was used in other technologies in stages. Good examples are machine vision (computer vision) in medical diagnosis, optimal path between two points - shortest path algorithms, obstacle avoidance, to name a few.

Some of the top programming languages used in AI are Python, Prolog, Java, C++ and LISP. In 1980's there was research about expert systems, that would one day be able solve most of human challenges. Now we have the ChatGPT and other AI based tools that use NLP and LLM algorithms that synthesize the user input, provide accurate answers, solutions and are well on the way of achieving highest level of Artificial Intelligence.
Weak AI
Implementing of machine intelligence has been there for quite some time. Most automobiles have intelligence such as emergency stopping when a vehicle in front makes a stop or reduces speed, speen adjustment in adaptive cruise control, again monitoring the car in front by use of intelligent sensors, radar, hardware and software. There are the automonous vehicles that drive on their own. Those that operate with some human interaction are typically weak or basic AI. Many IoT devices, traffic signals and many more, are examples of embedded devices with intelligence.
Strong AI
Those machines that operate in autonomous mode - "think on their own and take corrective actions without human intervention" can be considered as strong or advanced AI. Further there are autonomous robots, AGV that operate independently without human interaction, avoid obstacles, plan the best route between a set of points, make independent decisions, essentially use artifical intelligence to the highest level and can be considered as state of the art. There will be a day when an autonomous robot will evaluate battery life and replace battery in a safe mode on its own when the battery life is reaching or below a certain value - say 15%.
What is Not AI
Too many application are loosely tagging automatic emails as "AI-Message" etc. Majority of them are fixed or pre-designated application workflow management or automation. There is no thinking or intelligent assessment aspect. A good example is, if the we search a court document for a specific string such as money laundering, it just highlights the location of the string in the document - a standard word prcessor feature, which has been present in word processors since the earliest versions.
Enabling AI
The above analysis becomes AI enabled, if it can assess what is searched and provide intelligent assessment such as what types of crimes were committed and what it is questionable or can be excluded - simplify a lawyer's job by use of LLM. The speed of processing is enhanced, errors, wrong assessment can avoided or minimized.

AI In Real-World  
Air Traffic control, intelligent traffic management based on weather/traffic conditions
Automotive and Related Applications
Autonomous/driverless vehicles, intelligent traffic and signal management, autonomous highway speed management based on weather/traffic conditions
Finance Applications
Fraud detection, mortgage data analysis, loan processing, stock/trade automation, crypto-currency, fintech and so on.
IT Applications
Software development, system management, data management, autonomous process management, cyber security, network management
Medical Science
Perform surgery in an autonomous manner, with minimum human intervention, basic diagnosis of a person's health and total automated interaction with all health management systems, drug development
Autonomous operations in manufacturing systems, humanoid robots

1. Bostondynamics - Wildcat
2. Machine Learning
3. AI-Avatar
4. Introduction to Artificial Neural Networks (ANN), Slide Show, Mohammed Shbier, 2014.
5. Neural Network

Last Revised on: December 20th, 2022