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Article is very heavily weighted toward neural networks

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Neural networks are a small part of the much larger field of machine learning, yet 80% of the events in the timeline involve advances in neural networks.

Ihearthonduras (talk) 16:42, 9 February 2018 (UTC)[reply]


How to decide what goes in the table?

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How are the recent events added to the page on the same scale as the historical ones? Facebook "detailing" an **internal** tool (FBLearner Flow) is an event now? or Google "detailing their work" on a different internal proprietary product is an event worth marking in the timeline of machine learning how? Why is the release of TensorFlow an event, when Theano isn't, while TensorFlow was more or less a copy of Theano at the time of release? The fact that humans have been outperformed in object recognition is not an event, but the fact that Facebook beat a benchmark on identifying faces is?

Most of the events before 2000 are actually worth mentioning and everyone knows about them. However, what has been added to that table after that period can only be described as a mess produced by companies who think they can write history by literally editing Wikipedia and mark their events important.

Is there any sort of standard to the table? Can I add anything I want as long as I have a news coverage to cite? Or perhaps do I need to be a big multi-national first?

Please sign your posts using "--" followed by four tildes. I have removed TensorFlow and FBLearner Flow per your logic. What else needs to be removed and why? Also, what needs to be added? --Hyperforin (talk) 21:45, 19 November 2016 (UTC)[reply]
"What needs to be removed and why?"
My belief is that Facebook's "Leap in Face Recognition" should be removed. There is no indication why the task of face recognition should be regarded higher than regular object recognition, for instance. I would replace this event with Krizvesky et al[1] whose paper was the first to convince the community about the potential of deep learning. Facebook's achievement was noteworthy but it pales in comparison to the above mentioned paper and the number of citations shows it as well. Further, if Krizvesky is added, there is no need to have both, as object recognition and face recognition are very similar tasks.
Secondly, the "Sibyl" entry is only about Google "detailing" their work on an internal system via a PowerPoint presentation and some news article providing additional vague information about it. Is it necessary to form an argument for why this is not on par with the other entries? There's nothing released--nothing of impact on the machine learning community--it's just a closed-source cluster packaging previous research into a system. All the other entries have had an impact on machine learning research community, while this one is the type of news that had an impact of 60 points on Hacker News https://news.ycombinator.com/item?id=8249752
--109.151.246.114 (talk) 15:07, 20 July 2017 (UTC)[reply]
Agreed. I may remove. Looking back at the last 10 years it's clear what were the big events, and this was not one. WillSmith (talk) 21:05, 12 September 2023 (UTC)[reply]
Krizvesky's Alexnet missing from this list jeopardizes it's credibility. Although they weren't the first to use GPUs to train CNNs, their beating ImageNet challenge by a large margin was a hallmark event that made people focus attention to deep learning - ThisFeelsABitOff (talk) 20:47, 28 October 2021 (UTC)[reply]
I have added Alexnet, and also word2vec. I may add ELMO/Bert, what do you think? WillSmith (talk) 21:04, 12 September 2023 (UTC)[reply]

References

  1. ^ Krizhevsky, Alex, Ilya Sutskever, and Geoffrey E. Hinton. "Imagenet classification with deep convolutional neural networks." Advances in neural information processing systems. 2012.
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