Intelligence
- Memory – The ability to preserve information (to store, organize, and utilize data relevant to it’s success and survival)
- Self Awareness to recognize and define objects
- Communication like language ability or social and conversational ability.
- Free Will – The ability of the Agent to generate, organize, choose, and obtain new and unique goal states.
- Working with Symbols or the ability to reduce symbols to concrete meaning and to organize these reductions into conceptual frameworks.
- Pattern recognition
Brain project
The brain and spinal cord are made up of many cells, including neurons and glial cells. Neurons are cells that send and receive electro-chemical signals to and from the brain and nervous system. There are about 100 billion neurons with 100 trillion connections—and you have yourself a human brain, capable of much, much more.
Those neurons send signals across tiny spaces called synapses—of which you have 18–32 trillion—at a rate anywhere from 0.1–2 times per second. That means that somewhere between 18 and 640 trillion signals are zipping around your brain every second
Blue Brain project finds how neurons form billions of synaptic connections. Researchers were able to generate statistical instances of the micro-connectome of 10 million neurons, a model spanning five orders of magnitude and containing 88 billion synaptic connections.
The simulation method allowed the scientists to target volumes several orders of magnitude smaller than would be possible with experimental methods. This allows for the simulation of the electrical activity of individual neurons, entire regions or of the entire neocortex.
Machine Life and Artificial Intelligence Benchmark
- Self Maintenance and avoid annihilation and rebuild and repair itself by drawing materials from the environment.
- Adaptivity: capable of adapting it’s behavior, processes, and components – to changes in it’s environment for survival
- Procreation: ability to create separate instances of itself and pass along its memories and successful adaptations and experience.
- Increased Complexity with more components, more complex relationships between these components, and more complex behavior exhibited by those components.
- Environmental Awareness – The Agent has the ability to sense, map, and navigate it’s environment.
- Fight or Flight – The ability of the Agent to detect possible dangers to it’s existence and to determine whether to attempt escape or self-defense.
Artificial intelligence (AI)
What is artificial intelligence? Depending on whom you ask, the term might refer to sci-fi entities, existing cutting-edge technology or even software we use every day.
Productive conversations about AI is hard, when nobody can agree on what the term even means.
Artificial intelligence (AI) is a machine's ability to perform the cognitive functions we associate with human minds, such as perceiving, reasoning, learning, interacting with the environment, problem-solving, and even exercising creativity. AI technology can process large amounts of data in ways, unlike humans. The goal for AI is to be able to do things such as recognize patterns, make decisions, and judge like humans. John McCarthy was an American computer scientist and the term "artificial intelligence" was coined by him.
1) Artificial intelligence (AI) during 1985-95 was used to refer to development of machine learning algorithms, neural networks, and creating machine models that could mimic human intelligence.
2) Data science is about collecting, analysing, and interpreting data to extract valuable insights, make data-driven decisions, and solve complex problems. Pattern Matching or Pattern Recognition refers to techniques used. Objective is to Predict system behavior
3) Computer vision is about AI systems that can interpret and analyse visual information from the world, such as image and video recognition.
4) Natural Language Processing systems that can understand and generate human language.
5) Machine learning robots are changing the way machines interact with their environment, acquiring knowledge and adapting to new situations. The four largest manufacturers of industrial robots are Fanuc and Yaskawa of Japan, KUKA of Germany and ABB of Switzerland.
Global Market for above AI products will be 267 billion USD by 2027. Apart from US, Nordic countries, Germany, France are leading ones. Israel and Japan are leading Asian countries. India and China are catching up.
world GDP was 101.3 trillion USD in 2022. Agriculture contributed 4.27%, industry 27.22% and services contributed about 63.97%. All need advanced systems, though they get less than 1% share in revenue.
Machine learning
Many AI applications are based on an approach called machine learning. The developers of machine learning algorithms don’t start with fixed rules for how those algorithms should behave. Instead, they begin by specifying a goal and a method that an algorithm can use to learn from data. Then they supply the algorithm with a large set of “training” data and let it adjust its internal mechanisms to improve its performance.
Machine learning comes in different varieties. Large language models and other state-of-the-art systems are built around mathematical structures called artificial neural networks, which are loosely inspired by the human brain. But other applications of machine learning use simpler techniques. That’s often because fancier methods don’t improve their performance: Machine learning algorithms are only as good as their training data, and that data might not capture all the aspects of the problem that a system is designed to solve.
The neural network–based systems responsible for many recent breakthroughs also differ from each other in important ways. Hopfield network is a theoretical model demonstrating how an artificial neural network can mimic the way biological brains store and retrieve memories.
Boltzmann machine has introduced the element of randomness, paving the way for modern AI applications such as image generators.
Human and Machine (Automation) Mistakes
Both make mistakes, as preograms are also written by humans. But mistake gets multiplied large number of times.
ex: Books were written by few before press. So there will be few copies with same mistake. After press thousands of copies with mistakes. With computer, millions of copies with same mistakes.
Billion Forwards with added mistake. Original correct copy is out of circulation. Error is Human and machines made by Humans.
Tolerating mistakes by AI
People tolerate these mistakes because they have not understood technology and AI appears to make certain tasks more efficient. Underlying truth is sales people can sell these products and services, with exaggerated claims. Many real scientists/technologists are advocating the use of AI – with limited human supervision – especially in fields where mistakes have high cost, such as health care.
AI at the most can be an assistant to an expert.
Nobody – and nothing, not even AI – is perfect. Perfection is divine or unattainable. Nothing can be declared with 100% certainity.
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