Philosophy Dictionary of ArgumentsHome
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| Machine learning: Machine learning is a type of artificial intelligence (AI) that allows software applications to become more accurate in predicting outcomes without being explicitly programmed to do so. Machine learning algorithms use historical data as input to predict new output values. See also Artificial Intelligence, Deep learning, Strong Artificial Intelligence, 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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Judea Pearl on Machine Learning - Dictionary of Arguments
Brockman I 15 Machine learning/Pearl: Once you unleash it on large data, deep learning has its own dynamics, it does its own repair and its own optimization, and it gives you the right results most of the time. But when it doesn’t, you don’t have a clue about what went wrong and what should be fixed. In particular, you do not know if the fault is in the program, in the method, or because things have changed in the environment. We should be aiming at a different kind of transparency. VsPearl: Some argue that transparency is not really needed. We don’t understand the neural architecture of the human brain, yet it runs well, so we forgive our meager understanding and use human helpers to great advantage. PearlVsVs: I know that nontransparent systems can do marvelous jobs, and our brain is proof of that marvel. But this argument has its limitations. The reason we can forgive our meager understanding of how human brains work is because our brains work the same way, and that enables us to communicate with other humans, learn from them, instruct them, and motivate them in our own native language. Problem: If our robots will all be as opaque as AlphaGo, we won’t be able to hold a meaningful conversation with them, and that would be unfortunate. We will need to retrain them whenever we make a slight change in the task or in the operating environment. Current machine-learning systems operate almost exclusively in a statistical, or model-blind, mode, which is analogous in many ways to fitting a function to a cloud of data points. Such systems cannot reason about “What if?” questions and, therefore, cannot serve as the basis for Strong AI—that is, artificial intelligence that emulates human-level reasoning and competence. >Strong Artificial Intelligence. Brockman I 16 (…) current learning machines improve their performance by optimizing parameters for a stream of sensory inputs received from the environment. It is a slow process, analogous to the natural-selection process that drives Darwinian evolution. It explains how species like eagles and snakes have developed superb vision systems over millions of years. It cannot explain, however, the super-evolutionary process that enabled humans to build eyeglasses and telescopes over barely a thousand years. Brockman I 17 First level: statistical reasoning, which can tell you only how seeing one event would change your belief about another. Second level: deals with actions. (…) [it] requires information about interventions that is not available in the first [level]. This information can be encoded in a graphical model, which merely tells us which variable responds to another. Third level: (…) the counterfactual. This is the language used by scientists. “What if the object were twice as heavy?” “What if I were to do things differently?” Counterfactuals/Pearl: they cannot be derived even if we could predict the effects of all actions. They need an extra ingredient, in the form of equations, to tell us how variables respond to changes in other variables. >Models/Pearl. Pearl, Judea.”The Limitations of Opaque Learning Machines.” 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. |
Pearl, Judea Brockman I John Brockman Possible Minds: Twenty-Five Ways of Looking at AI New York 2019 |
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> Counter arguments against Pearl
> Counter arguments in relation to Machine Learning
Ed. Martin Schulz, access date 2024-04-26