This article defines artificial intelligence, neural networks, machine learning, deep learning, and algorithms.
One definition of artificial intelligence is:
"Machines that can think and perform tasks that normally require human intelligence."
Artificial intelligence is when computers using mathematical models and patterns learned through data analysis can simulate human thinking and behavior. Artificial intelligence mimics one or more aspects of human intelligence. For example, the ability to abstract thinking, analysis, problem-solving, pattern recognition, language mastery and understanding, sensible action, etc. Artificial intelligence cannot understand meaning and context!
You typically talk about two types of artificial intelligence: weak and strong. Weak artificial intelligence can perform specific tasks by mimicking/simulating human actions and cognitive functions. This is the form we see right now at ChatGPT, for example.
Strong AI (also generally referred to as AI) contains a deeper mental layer, such as awareness and intentionality, and can understand and acquire human cognitive characteristics, have emotions, and achieve self-awareness. This kind of artificial intelligence is often seen in science fiction movies (e.g., Star Trek, Wall-E, A Space Odyssey) and doesn't exist (yet). There is much debate as to whether this can be done at all. The concept of singularity is referred to in strong artificial intelligence – that is, computer systems can constantly improve themselves and achieve characteristics far beyond human capabilities.
AI encompasses a wide range of techniques, such as machine learning, including deep learning, which allows computers to learn and improve their performance without being programmed directly.
Machine learning can be defined as "A computer program that uses statistics and algorithms to analyze data (experience), learn from the analyzed data, and use what it has learned to make informed decisions/predictions."
Machine learning is a computer program that analyzes large amounts of data and produces a model based on patterns in that data. The model is then used to say something about the analyzed data.
An algorithm is a specific series of instructions, like a recipe, that can be followed to solve a problem or perform a task. Algorithms are a fundamental part of computer technology and can solve many problems, from sorting large amounts of data to finding the shortest route between two points on a map. An algorithm must be precise and efficient, and designing algorithms that can efficiently solve complex problems is often challenging.
Deep learning is a special machine learning in which the computer program mimics how the human brain works. It is powered by a neural network containing multi-layered neurons that communicate with each other and simulate the human brain's cognitive process – enabling the program to make independent decisions. When a deep learning network is presented with a large amount of data, it can train itself by adjusting the weights that control how the individual neural units interact. In this way, deep learning networks can improve their precision and ability to solve complex tasks over time.
Artificial neural networks can process and classify huge amounts of data and ultimately learn from them. It allows them to refine their systems and thus increase their accuracy.
Systems like self-driving cars and also ChatGPT, and OpenAI are all based on deep learning and neural networks, and that's why they can almost imitate humans – though without being conscious and understanding content.
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