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.

This whole policy is dumber than conventional security theater.

But then again, that's the point of this policy. It has the thinnest veneer of being for a legitimate purpose while hurting those that this administration wants to hurt.

For example, in the Shanghai ranking, 16 of the top 20 universities are in the USA. https://www.shanghairanking.com/rankings/arwu/2024

In US News and World reports of top universities, 14 of the top 20 universities in the world are in the USA. https://www.usnews.com/education/best-global-universities/ra...

In The Times Higher Education ranking, 13 of the top 20 universities in the world are in the USA. https://www.timeshighereducation.com/world-university-rankin...

To be blunt, your assertion has extremely weak evidence. I urge you to look at facts and evidence and not your feels.

> At IBM, where he worked from 1952 to 1993, Garwin was a key contributor or a facilitator on some of the most important products and breakthroughs of his era, including magnetic resonance imaging, touchscreen monitors, laser printers, and the Cooley-Tukey fast Fourier transform algorithm. > > And all that was after he did the thing for which he is most famous. At age 23 and at the behest of Edward Teller, Garwin designed the very first working hydrogen bomb...

It also led to the Tiramisu project, which used people's smartphones to track buses and how crowded those buses were. https://tiramisutransit.com/

The original article talks about several of these, including RISC, out-of-order execution, speculative prefetching, vector processing, GPGPU, and multicore.

It's easy to cherry pick and find things that you might personally disagree with. That's true with any system created by us humans. That doesn't mean that you should burn the whole thing down, which is what this administration is doing.

I feel like I've been making the same post over and over on threads like these. NSF-funded research has led to innovations like the above, as well as multibillion dollar companies like Google, Databricks, Duolingo, and more (and that's just in computer science). NSF-funded research has had an incredible Return on Investment in terms of jobs, economic growth, and national security. It took generations to build the American scientific enterprise, and the system has worked incredibly well as is. It's incredibly short-sighted and a massive self-own to destroy something that has advanced the USA and the world so much.

Also, NSF program officers can have conflicts as well, for example if you are on leave from a university then you can't be heading a review panel that has any grants related to that university.

At my university, we also have to do periodic online training about conflicts of interest, and have to fill out financial forms disclosing whether we have a financial stake in the work (e.g. if we own a startup and are trying to direct research funds to that startup).

Basically, I've always felt that we held ourselves to a higher standard than Congress held itself too (e.g. being on a Congressional oversight committee and owning stock in affected companies, but that's a different rant).

See this blog post by Steve Blank talking about the rise of research universities and why the USA is a science powerhouse. https://steveblank.com/2025/04/15/how-the-u-s-became-a-scien...

And the discussion on HN: https://news.ycombinator.com/item?id=43692360

Also, pragmatically, it's a system that has yielded incredible ROI since WW2, in terms of new industries, new companies, jobs, science, economic gains, productivity gains, and national security. The US university system is the envy of the entire world, and it's being targeted for dismantling by very petty, cruel, and incompetent people.

There are also reports you submit showing your progress and how you spent the money, to check that you are spending it on things you said you would.

This thread (not just the person I'm replying to) demonstrates a lot of misconceptions about why we have research funding, how it works, and what the results have been in practice. Please, everyone, don't rely on stereotypes of how you think research funding works.

https://en.wikipedia.org/wiki/Green_fluorescent_protein https://en.wikipedia.org/wiki/Katalin_Karik%C3%B3

Second, while there are always improvements to be made, the system as is (or was) worked pretty well in practice without knowing what the expected ROI was. The PageRank algorithm which led to Google was funded in part by an NSF grant on Digital Libraries. The ROI on that single invention just from taxes, jobs, and increased productivity likely exceeds NSF's annual budget. DataBricks and Duolingo are also based in part on NSF research.

Yeah, the system is imperfect, as all human-oriented systems are, but for the most part it works pretty well in practice and has been a linchpin in the US economic growth and national security.