Note that CHI still has some technical papers. There are also other specialized adjacent conferences that focus more on technical aspects of HCI, like UIST and IMWUT.

The disciplines that fall under the HCI umbrella has also changed dramatically. In the 1980s, it was cognitive psychologists and computer scientists. Social psychology came in, then design, then ethnography, developing countries, social justice, public policy, and many many more. Having been in the field for almost 30 years, I think that this breadth is part of HCI's strength, though also it's weakness.

Aside: I saw Tom Mitchell (of Machine Learning fame) give a talk about how the field evolved over the past 40 years. What I found fascinating is that, for the most part, there was a single question that drove the field: how to make computers learn from experience (or data)? However, there isn't a single such question for HCI. Again, it's part of its strength and its weakness.

Here's an excerpt of the video where he explains his idea: https://www.youtube.com/watch?v=svl_1upFpQo

My summary: Science is like a hike. You have a general sense of what direction you want to go. You explore things along the way. You might make wrong turns but might also discover something new and interesting. While on your journey, you might spot new mountains or waterfalls or other vistas that look like they might be good to explore.

In contrast, AI is like taking a helicopter to your destination. Yes you got there quickly, but you missed a lot on the way.

Related, Tao later talks about the pipeline. It used to be that seminal proofs were rare, so there was lots of time for the community to review them, process them, share them, and summarize them in textbooks. Nowadays, AI helps a lot with generating more proofs, but not so much with the other parts of the pipeline.

Tao also explains the math pipeline issue in his talk at the International Congress of Mathematicians, also shared on HN last month: https://news.ycombinator.com/item?id=49056620

My overall take: Tao is very thoughtful and insightful about progress in AI and math, and about what the tradeoffs are. We're seeing similar problems in computer science (my field), where conferences are now getting over 10x the number of submitted papers over just a few years ago (not an exaggeration). It's straining the research community in ability to review, understand, and present the papers.

https://www.kompas.id/artikel/when-nobel-winner-in-chemistry... https://www.yahoo.com/news/science/articles/why-nobel-laurea...

Nobel Laureate in Chemistry Omar M. Yaghi joins Tsinghua University full-time https://www.tsinghua.edu.cn/en/info/1244/14984.htm

For example, take a look at Albert Einstein's Google Scholar profile. He's not the top cited physicist. Not even close. It's because other researchers don't explicitly cite his papers. https://scholar.google.com/citations?user=qc6CJjYAAAAJ&hl=en...

Same with Tim Berners-Lee and the World Wide Web. Imagine if his original paper were cited every time someone deployed a web site.

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.

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.

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

We also have many other studies along similar lines, e.g. many triggers for talking about and adopting security are social in nature (https://www.usenix.org/system/files/conference/soups2014/sou...), that security settings that are more social in nature are more likely to be adopted (https://dl.acm.org/doi/10.1145/2675133.2675225), and more.

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.

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.

The original site is down, but jump to November 5, 1999 to see the screenshot. https://web.archive.org/web/20030404093458/http://www.monzy....

https://lichess.org/training/underPromotion

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.