It's worth pointing out that sometimes, some papers just become part of the general context of things and are no longer explicitly cited. Or people cite textbooks or general survey papers instead.
Speaking as someone who has graduated over a dozen PhD students in computer science...
Yes, it is possible to complete a PhD in 3-4 years, but it's not really good for your career. The bar our department sets for a PhD is that at the end of it, you should be a world expert in your specific topic.
A PhD is more like an apprenticeship, where you develop and refine your skills, your background knowledge in your area of specialization, your ability to write and do presentations, and your taste in research problems. These are all things take a lot of time to mature.
The problem with graduating fast is that (a) you wouldn't be able to do internships, (b) you would severely limit your ability to grow your social network (via workshops, conferences, internships, department service, etc), (c) you would limit your ability to deepen and broaden your portfolio of research, and (d) you limit the time your ideas have to percolate out into the rest of the research community and industry.
While I can't speak directly about your friend's experiences, learning how to do peer review and learning how to write first drafts are really important skills that can indirectly help with coming up and executing on a dissertation idea.
We're working on AI user testing, to make it dramatically faster and cheaper for product managers and dev teams to find major usability issues with web sites. Give us a web site and a task users would do (e.g. "Add a pink shirt to the shopping cart"), and we have some AI users try their best to do the task. The output is a report with a prioritized list of problems identified, plus narrated videos that show each AI user trying the site.
If you want to try it out, we offer some free credits at https://fuguux.com
Any feedback you have would be incredibly helpful! We're considering more kinds of reporting, support for QA testing, better integration with CI/CD, and more.
Note: we don't want to replace real user testing, but rather complement it. With AI user testing, you can get quick feedback on potential usability problems in hours for a fraction of the cost, making it so you can iterate much faster. We advocate doing user tests with real people to understand problems that require domain knowledge or nuance.
Two concepts that help explain the original article are Diffusion of Innovations and Social Proof.
Diffusion of Innovations is a widely cited theory explaining why people do or don't adopt any kind of innovation, from boiling water to eating limes on British ships to installing telephones. The concept of innovators, early adopters, and late adopters comes from this theory. More relevant to this post is that this theory posits five factors contributing to adoption, one of which is Observability: you can easily see other people gaining benefit from an innovation. The more Observable an innovation, the more likely it is to be adopted.
https://en.wikipedia.org/wiki/Diffusion_of_innovations
The other is Social Proof. Seeing what other people are doing, especially those that are similar to you in some way, can help steer your behavior, often in subtle and unconscious ways. There are studies about how simple signs like "people who stayed in this hotel room re-used their towels" or "most of your neighbors are reducing their electricity usage too" can shift people's behaviors, even without people explicitly realizing it.
https://en.wikipedia.org/wiki/Social_proof
My colleagues and I used these concepts in several pieces of research on what we called Social Cybersecurity (joking that the term "Social Security" was already taken). The insight we had was that cybersecurity has very low observability, making it hard for innovations to diffuse through one's social network. That is, I don't know what your cybersecurity practices are, and vice versa, making it hard for best practices to be adopted.
One intervention we did was a large-scale intervention on Facebook to improve observability, showing that simple messages like "108 of your friends use extra security settings" did increase clickthru and adoption rates of those settings.
https://dl.acm.org/doi/10.1145/2660267.2660271
I saw Jim Clark (founder of SGI, Netscape, Healtheon) talk one time about entrepreneurship. He said something that compactly explains a lot of issues humanity faces in general: "One person's inefficiency is someone else's bottom line."
A lot of the things that the original post shares has this characteristic. Sure, things in US healthcare are wildly inefficient, but that's how a lot of these companies make a lot of money. And they will lobby and fight to the death that cash flow.
I've used The Design of Everyday Things in many classes I teach. I would agree that it's not practical, but that's not its goal. Instead, it gives you frameworks for thinking about things as well as vocabulary for talking about those things.
Off the top of my head, some of the key ideas include:
* Affordances, that objects should have (often visual) cues that give hints as to how to use things
* Mental models, that every design has three different models, namely system implementation, design model, and user model, and that the design model and user model should try to match each other
* Gulf of Evaluation (the gap between the current system state and people's understanding of it) and Gulf of Execution (the gap between what people want the system to do and how to use the system to do it)
* Kinds of Errors and how to design to prevent and recover from them, e.g. slips (chose the right action but accidentally did the wrong thing, e.g. fat finger) vs mistakes (chose the wrong action to do)
What's particularly useful about Norman's book is that these key ideas apply for all kinds of user interfaces, from command-line to GUI to voice-only to AR/VR to AI chatbot. I'd encourage you to think about this book in this kind of framing, that it gives you general frameworks for reasoning and talking about UX problems rather than specific practical solutions.
Wanted to share this funny SETI@home prank that Monzy (https://en.wikipedia.org/wiki/Dan_Maynes-Aminzade) did in 1999, where he created a fake VB app that tricked a coworker into believing that his computer successfully found an extraterrestrial signal.
Lichess has a series of puzzles you can try where underpromotion is the theme (which is unfortunately a major giveaway to solving these puzzles, since they otherwise be rather hard to solve)
In May earlier this year, the New York Times had a similar article about AI not replacing radiologists:
https://archive.is/cw1Zt
It has similar insights, and good comments from doctors and from Hinton:
“It can augment, assist and quantify, but I am not in a place where I give up interpretive conclusions to the technology.”
“Five years from now, it will be malpractice not to use A.I.,” he said. “But it will be humans and A.I. working together.”
Dr. Hinton agrees. In retrospect, he believes he spoke too broadly in 2016, he said in an email. He didn’t make clear that he was speaking purely about image analysis, and was wrong on timing but not the direction, he added.
Nobel Laureate in Chemistry Omar M. Yaghi joins Tsinghua University full-time https://www.tsinghua.edu.cn/en/info/1244/14984.htm