Philosophy Dictionary of ArgumentsHome
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| Neural networks: Neural networks are computational models inspired by the human brain, designed to recognize patterns and solve complex problems. They consist of layers of interconnected nodes (analogous to neurons) that process input data and learn to perform tasks by adjusting the strength of connections based on feedback. Used extensively in machine learning, they enable applications like image recognition, language processing, and predictive analysis. See also Artificial Neural networks, Connectionism, Computer models, Computation, Artificial Intelligence, Machine learning._____________Annotation: The above characterizations of concepts are neither definitions nor exhausting presentations of problems related to them. Instead, they are intended to give a short introduction to the contributions below. – Lexicon of Arguments. | |||
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Chris Anderson on Neural Networks - Dictionary of Arguments
Brockman I 148 Neural networks/Chris Anderson: [the optimization within the network] consists of the following steps: 1. Define a cost function” that determines how well the network solved the problem. 2. Run the network once and see how it did at that cost function. 3. Change the values of the connections and do it again. The difference between those two results is the direction, or “slope,” in which the network moved between the two trials. 4. If the slope is pointed “downhill,” change the connections more in that direction. If it’s “uphill,” change them in the opposite direction. 5. Repeat until there is no improvement in any direction. That means that you’re in a minimum. Problem: that might be only a local minimum. >Local minimum/Anderson, >Universe/Anderson. Brockman I 149 Solution: Random walk. Contrary to humans and most other life-forms, that often stuck in local minima, Brockman I 150 AI systems can consider many moves ahead. AI can find solutions in a few years that 7 million years of evolution never found. Anderson, Chris “Gradient Descent” in: Brockman, John (ed.) 2019. Twenty-Five Ways of Looking at AI. New York: Penguin Press._____________Explanation of symbols: Roman numerals indicate the source, arabic numerals indicate the page number. The corresponding books are indicated on the right hand side. ((s)…): Comment by the sender of the contribution. Translations: Dictionary of Arguments The note [Concept/Author], [Author1]Vs[Author2] or [Author]Vs[term] resp. "problem:"/"solution:", "old:"/"new:" and "thesis:" is an addition from the Dictionary of Arguments. If a German edition is specified, the page numbers refer to this edition. |
Ander I Chris Anderson The Long Tail: Why the Future of Business is Selling Less of More New York 2006 Brockman I John Brockman Possible Minds: Twenty-Five Ways of Looking at AI New York 2019 |
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> Counter arguments against Anderson
> Counter arguments in relation to Neural Networks
Ed. Martin Schulz, access date 2024-04-25