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The date when Arthur Samuel wrote the first program is 1952 as wikipedia and many other online websites say, however this article says 1959, which should be corrected.
could someone possibly add some thoughts on how randomness is needed for ml? https://ai.stackexchange.com/questions/15590/is-randomness-necessary-for-ai?newreg=70448b7751cd4731b79234915d4a1248
i wish i could do it, but i lack the expertise or the time to bring this up in Wikipedia style, as it is evident by this very post and the chain of links in it, if you care enough to dig.
- @Cregox: Random forests and conditional random fields are often used in machine learning, for example. Jarble (talk) 16:45, 18 August 2020 (UTC)
both ideas sound interesting, but they both look like optional techniques rather than necessary tools.
in my mind and from my understanding machine learning would never exist without random number generators.
as i also mentioned in my link there, i'll basically just copy and paste it here:
perhaps we're missing words here. randomness is the apparent lack of pattern or predictability in events. the more predictable something is, the dumber it becomes. of course just a bunch of random numbers doesn't make anything intelligent. it's much to the opposite: randomness is an artifact of intelligence. but if while we're reverse engineering intelligence (making ai) we can in practice see it does need rng to exist, then there's evidently something there between randomness and intelligence that's not just artifacts. could we call it chaos? rather continue there: cregox.net/random 11:12, 21 August 2020 (UTC) — Preceding unsigned comment added by Cregox (talk • contribs)
Simple definition of machine learning is inaccurate
There is no reference to the below definition and it is inaccurate as a definition of Machine learning should not include AI. There are many textbook definitions of machine learning which could be used.
The current text without reference: Simple Definition: Machine learning is an application of artificial intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed. Machine learning focuses on the development of computer programs that can access data and use it to learn for themselves.
Definition with reference: Machine learning is the study of computer algorithms that allow computer programs to automatically improve through experience.
Machine Learning, Tom Mitchell, McGraw Hill, 1997. http://www.cs.cmu.edu/afs/cs.cmu.edu/user/mitchell/ftp/mlbook.html — Preceding unsigned comment added by Tolgaors (talk • contribs) 09:55, 29 October 2020 (UTC)
- They are both commonly used terms of the English language with meanings defined by such. Any claim that there is no overlap is an extra-ordinary implausible claim. A discussion within some specialized view within some specialized topic venue is no basis for such a broad claim. North8000 (talk) 13:43, 29 October 2020 (UTC)
- This was recently added. It was unsourced and redundant, so I just removed it. - MrOllie (talk) 13:45, 29 October 2020 (UTC)
- "Machine learning (ML) is the study of computer algorithms that improve automatically through experience." this is a very bad lede. (1) almost not machine learning improves "automatically". Instead, models are trained and deployed manually. (2) "through experience" same. Models are build from some training data, which is not in any meaningful way what is later on "experienced" by the classifier. E.g., a machine learning method to improve picture quality of cell phone cameras is likely trained on artificially corrupted imagery, not by giving it the actual sensor readings and a improved imagery result. I think the old lede was much better! 184.108.40.206 (talk) 15:49, 30 October 2020 (UTC)
Proposed contents removal
The below text is inaccurate and none of the cited references support the statement:
Yet some practitioners, for example, Dr Daniel Hulme, who teaches AI and runs a company operating in the field, argues that machine learning and AI are separate. This quoted reference states that ML is part of AI: 
They are both commonly used terms of the English language with meanings defined by such. Any claim that there is no overlap is an extra-ordinary implausible claim. A discussion within some specialized view within some specialized topic venue is no basis for such a broad claim. Also, the insertion looks like spam to insert the person into the article. North8000 (talk) 13:45, 29 October 2020 (UTC)
I think that a paragraph be added about the issues that can arise from maximizing a mis-aligned objective function. As an example, just take the ethical challenges arising from recommendation algorithms in social media, with negative effects such as creating distrust in traditional information channels, actively spreading misinformation, and creating addiction. --MountBrew (talk) 18:37, 20 November 2020 (UTC)
- Should optimization algorithms (in general) be discussed on this Wikipedia page? If so, this is likely the best place to do so.
- There is currently no mention of fully decentralized machine learning methods, such as CHOCO-SGD. This should definitely be fixed!
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