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.
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!
The paper is "ImageNet Classification with Deep Convolutional Neural Networks" by Alex Krizhevsky, Ilya Sutskever, and Geoffrey Hinton, and is available here:
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].
>It is expected that the Department of Electrical Engineering and Computer Science (EECS), the Computer Science and Artificial Intelligence Laboratory (CSAIL), the Institute for Data, Systems, and Society (IDSS), and the MIT Quest for Intelligence will all become part of the new College; other units may join the College.
An Introduction to Modern Astrophysics by Carroll & Ostlie is the Bible, but it is quite large physically and in scope and better serves as a reference for most people.
For various topics, I would look at:
Introduction to Cosmology by Ryden for cosmology at the undergraduate level.
Cosmology by Weinberg.
The Exoplanet Handbook by Perryman.
An Introduction to Modern Stellar Astrophysics by Carroll & Ostlie.
Particle Astrophysics by Perkins.
Modern Statistical Methods for Astronomy by Feigelson.
Statistics, Data Mining, and Machine Learning in Astronomy by Ivezic.
I can post more in other topics if anyone is interested.
Actually, the Google guides were initially published in February (and publicly available before that from Martin Zinkevich since 2016!). But I agree, it's great to see more resources around these best practices.