rasmi

See: https://csvchain.com/#faq

Congratulations on your success! I'm actually familiar with the author's past talks and research, and am not just assuming he's competent because he lists his affiliation with Princeton.

I encourage you to familiarize yourself with the field of socio-technical systems. It is related to (but not the same as) "ML/DL", and it is important to know about if you are doing research in CS. A good place to start is the FAT* conference [0] (which was previously a workshop at NeurIPS).

Regarding manual scoring: The author cites this study [1] and specifically says: "This is a falsifiable claim. Of course, I’m willing to change my mind or add appropriate caveats to the claim if contrary evidence comes to light. But given the evidence so far, this seems the most prudent view." so by all means, do reach out to him with better evidence.

[0]: https://fatconference.org/2019/program.html

[1]: https://arxiv.org/abs/1702.04690

Not to indulge the troll, but Arvind Narayanan is an (associate) professor of CS at Princeton and is one of the foremost researchers in the field on topics of ML/data privacy and ethics [0]. His papers/talks/tweets regularly attract attention on HN [1]. That you're judging the talk based on which conferences the author hasn't published in says more about your ignorance of the STS field than it does about the author's knowledge of the topic. This is top-notch content!

[0] https://scholar.google.com/citations?hl=en&user=0Bi5CMgAAAAJ...

[1] https://hn.algolia.com/?q=random_walker

Code here: https://github.com/google/robotstxt

Here are two more great articles about Transformers:

The Illustrated Transformer (referenced in the parent): http://jalammar.github.io/illustrated-transformer/

The Annotated Transformer: http://nlp.seas.harvard.edu/2018/04/03/attention.html

Source article here: https://www.zdnet.com/article/amazons-cloud-computing-will-s...

For those interested, you can track Beam Python 3 support progress here:

https://issues.apache.org/jira/browse/BEAM-1251

The paper is "ImageNet Classification with Deep Convolutional Neural Networks" by Alex Krizhevsky, Ilya Sutskever, and Geoffrey Hinton, and is available here:

https://papers.nips.cc/paper/4824-imagenet-classification-wi...

The search features discussed in the article are now available through Google Cloud Source Repositories: https://cloud.google.com/source-repositories/docs/searching-...

I link these resources often, but they are often relevant! See "The ML Test Score: A Rubric for ML Production Readiness and Technical Debt Reduction" [1] and the Rules of Machine Learning [2]. Another classic: "Machine Learning: The High Interest Credit Card of Technical Debt" [3], and recently added: Responsible AI Practices [4].

[1] https://ai.google/research/pubs/pub46555

[2] https://developers.google.com/machine-learning/rules-of-ml/

[3] https://ai.google/research/pubs/pub43146

[4] https://ai.google/education/responsible-ai-practices