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workingatbeingbetter319 karma

About 90% of my job involves quickly and accurately understanding papers in ML/AI from a wide variety of fields (I actually had to revisit two of John Langford's papers from the early 2000s on cross platform remote secure backup systems and his CAPTCHA paper with Luis von Ahn and others a few months ago) so I will give you some of my thoughts.

First, LEARN TO READ SLOWLY AND PRECISELY AND WITH A PURPOSE. These are not novels. You are almost always reading these papers with the intent to learn something. Figure out what that is ahead of time and seek it out in the paper. You do not have to read these papers serially from start to finish. Look for what you need and go from there. If you're very unfamiliar with the subject, read the title and then the abstract first. If you're pretty familiar with the subject, maybe you can jump to the conclusion first and then to the end of the discussion section. The more papers you read, the more you will understand this common structure and the more you'll be able to quickly find what you are looking for.

For example, say I have a paper on a particular way of using confidence labels in ML and I'm trying to figure out if I can use this paper to write better code for an autonomous vehicle system. I will first read the title and then the abstract and I will look up any words I don't absolutely understand. For example, are the terms "class label" or "confidence label" terms of art that have very specific definitions? I didn't know, so I googled "'class label' machine learning" and clicked the first link on stack overflow. Continue doing this until you understand what is happening. If you are even the slightest bit unsure as to what something means, look it up. Use wikipedia and reddit as resources -- they are generally excellent. But don't just try to read this paper like a novel or anything like that. These are primarily utilities for most people. That said, there are a select few who read these specifically to critique them and find holes in the science. That generally requires subject matter expertise and a close reading from start to finish, but I won't go further into that since most people don't do that.

Second, have a decent mathematical foundation, particularly in linear algebra, statistics, and calculus. You don't need to remember every proof or algorithm or even be able to do all of the math yourself, but you need to be able to read an equation and understand how the variables relate to one another in the context of the function. Also, learn about Markov chains, Bayesian statistics and probability, and decision trees. You can get deep into the weeds here with graph theory, topology, PDEs, and even game theory mathematics, but for a foundation you really just need the top few things I mentioned here.

Third, develop an understanding of computers, both programmatically and from the hardware side. There is a lot here, but it helps to understand how memory is allocated to various programs, how parallel programming concepts work, and how various algorithms and/or data structures can optimize certain use cases. Understanding these aspects will help you get a deeper understanding of why certain aspects are being proposed over others. From the hardware side, it's helpful to understand the physical limits of different sensors/approaches in computer vision for example and how those limits can interact with other aspects of your code or be overcome through the use of additional hardware implementations (e.g., adding a magnetometer to a drone that is programmed for autonomous flight so as to account for location relative to the Earth's magnetic field).

Fourth, familiarize yourself with AI/ML concepts. The youtube channel 3Blue1Brown has an excellent video series on Neural Networks and ML/AI to start you off. Once you understand the general ideas and how they operate, slowly read through the wikipedia entry on Machine learning. In particular, look at the "Approaches" section, and feel free to follow linked articles from there. If you want to get into deeper issues at the most advanced levels, go to university websites and look for faculty in CS/ML/AI and then look at their research interests. Or, if you want to bridge the gap between wikipedia and those faculty research papers, read relevant chunks of the "Deep Learning" book written by Ian Goodfellow (the genius son of a bitch who invented generative adversarial networks -- or GANs -- when he was like 28).

If you do even most of those above, you will have an EXCELLENT foundation for reading these papers. After that, it's just practice to get faster. I've been doing it for years and I probably read 20-30 papers in this field on an average day. Sometimes it takes me a couple of minutes to find what I need and sometimes I will try to read the paper for days or weeks on end (if it's the rare time when I need to understand absolutely everything about the paper).

Anyway, I hope that helps.

workingatbeingbetter153 karma

As a follow-up to that, Netflix has a notorious perform-or-perish culture. I'm curious what Marc thinks of the various human consequences associate with this culture (e.g., increased stress levels, substance abuse, early career burnout, etc.) and whether the company is working to combat some of these deleterious effects.

workingatbeingbetter54 karma

For all that is holy, this. VBA is an atrocious language and since I only ever use it for a few Microsoft products, I have to relearn the nuances of VBA every time. It would be amazing to have better integration with Python or even C++ because I could finally redo some of the spreadsheets people at my office have used forever and I wouldn't have to explain to them the various things they might need to do to run a program outside of excel.

workingatbeingbetter31 karma

Hi Lawrence and Andrew! Thanks for doing this.

Here's my question (with more context below):

Why don't you guys (and legal reporters in general) discuss the legal context of cases and rulings in greater detail? And are you doing anything to do better on this aspect?

I'm a lawyer and moderator at /r/Ask_Lawyers, and as such I spend a lot of my time explaining and discussing various legal rulings -- particularly when it comes to U.S. Constitutional Law issues. For example, here is my analysis of SCOTUS's rejection of the challenge to PA's Shutdown Order earlier this month. As you can tell, my analysis provides quite a bit of legal context that wasn't provided in the linked article. This is my one major problem with most reporting in this area. Most articles fail to provide a good explanation of the proper legal context of a given ruling.

For example, your coverage of the South Bay case -- the case involving California's pandemic restrictions on religious institutions -- failed to discuss the legal context for the decision. The article did not mention any of the historical case law, such as Smith, Lukumi, etc.. The article not only failed to mention why Roberts chose to apply rational basis rather than strict scrutiny (like Kavanaugh suggested), but the article failed to even mention levels of scrutiny to begin with. Also, and probably my biggest pet peeve as a news consumer, the article failed to link to the Court document (which was available here). As a reader and someone who participates in this space quite actively, these missing pieces lead to reader confusion and most likely reader polarization, since the absence of a greater context and explanation thereof makes it appear as though the authors or the justices are merely shooting opinions from the hip.

I want to be clear, you are not the worst offenders on this by far. Most other media sources are generally more guilty on these matters. But this brings me back to my question: Why is the greater legal context omitted so often in articles in this field? Is it deadlines? Editor decisions? Word limits?

As an avid reader of legal articles -- particularly regarding SCOTUS -- I am genuinely curious about your response on this matter.

Thanks again for your time!

workingatbeingbetter7 karma

Opponents of student loan cancellation say that Congress never intended to grant the president unlimited, unchecked authority to cancel everyone’s student loan debt — and if Congress intended this, Congress would have written it explicitly in the text of legislation, which Congress didn’t.

I somewhat disagree with the bolded claim above. It's definitely reasonable to interpret the statement that "the Secretary may . . . waive, or release any right, title, claim, lien, or demand, however acquired, including any equity or any right of redemption" as a relatively unlimited and unchecked authority given to the Secretary by Congress to cancel everyone's student loan debt. Assuming the Secretary is not in violation of the APA or other provisions of this section -- and nothing I've seen in this section explicitly restricts the Secretary from performing the above other than the preamble to this section -- then I don't see a good legal argument that (a)(6) is restricted in a manner that would prohibit such cancellation. That is, unless quite a few of the conservative justices on SCOTUS change their legal philosophy starkly compared to their individual histories. In any case, it would require adopting a jurisprudence that gives substantial weight to Congressional intent over the text itself.