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AI as a teaching tool, brief thoughts in Nov 2026

from nanoscale views [alt+shift+b] in science

Leaving aside AI for research at the moment, as anyone with a pulse knows at this point, we are in a period of rapid change in terms of AI tools and specifically their role in higher education.   There is an arms race among universities about what tools should be made available to students.  There are equity of access issues here - wealthy students are likely to pay out of pocket for more expensive subscriptions, regardless of what universities provide.  Princeton announced that it's providing everyone with chatGPT subscriptions (the equivalent of the $20/mo GPT 6 Astra).  Cornell is doing something similar, including Claude.   This is likely just the beginning, as the major AI firms want to hook customers for life provide a great opportunity to many users, and prestigious universities don't want to risk being perceived as inadequate relative to their peers. In the meantime, it's honestly not clear if undergraduate students even want this.  This WSJ article talks about how demand is through the roof at Harvard for a reading-intensive humanities course.  The reaction from some students at Princeton to being given GPT subscriptions is not exactly a ringing endorsement. There is no question that these tools, when used correctly, can be powerful tools to develop educational materials.  A nice example that has gone a bit viral in the last week is this site, by Nathan Liow, an undergraduate CS major at UT Austin.  He's been using Claude Opus 5.5 to create interactive visuals to go with the notes he took from his AP courses, leading to some very clean demonstrations for calculus, statistics, and physics.  Without these tools, it would be a huge investment of effort to create these kinds of visualizations.  We must figure out the ways that AI capabilities can help learning, rather than removing all of the "intellectual friction" needed to build up analytical skills, critical thinking, and knowledge. Separately:  Tomorrow is Election Day in the US.  If you are...
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More from nanoscale views

The arXiv - where it's been and where it's going

Back in the ancient mists of time, scientists and mathematicians would circulate preprints of their articles among friends and colleagues via the postal service, as a courtesy, to get feedback and to try to make sure people in the community were aware of their forthcoming work.  As the wikipedia entry says, with the advent of widespread LaTeX and the development of the web, Paul Ginsparg (then at LANL) put together an html-based site for electronic sharing of preprints, initially at xxx.lanl.gov (back before "xxx" in URLs was the kind of thing filtered and blocked by employers).   In 2001 he moved to Cornell, and by then the lab was perhaps relieved to see the site, rebranded as the arXiv, shift to Cornell's library/repository infrastructure.   PIs my age remember (fondly? maybe?) the old days of the arXiv, when Prof. Ginsparg's rather dry sense of humor pervaded the site.  The skull-and-crossbones logo.  The "help"/FAQ pages that basically said, "if you can't figure out how to .tar.gz all of your necessary LaTeX files, and you can't figure out how to make your .eps figures small, maybe you should reconsider whether you're smart enough to be sharing your ideas here".  The arXiv was for preprints, without peer review (though interesting follow-on sites like Scirate now exist for organized commenting on the articles). From these modest beginnings, the arXiv has grown enormously, including imitators/spin-offs such as chemrxiv and biorXiv and socarXiv.  The arXiv has recently become an independent nonprofit, hired a CEO, secured multiyear philanthropic support, and hosts over 3 million articles.  It's been interesting seeing some level of complaints online about some of these steps, but when the audience is so large, not everyone is going to be happy.  Rapid growth has been a major issue - see here for a graph of monthly submissions: The exponential rise (except for a slight pandemic-correlated shoulder) has been problematic, especially recently.  One hallmark of the arXiv over the years has been its ability to function with comparatively minimal need for "moderation".  Early on, one consequence of the minimalistic help and moderate technical entry barrier was that it was unusual for fringe/pseudoscience to make its way onto the server.  (Hence the establishment of viXra.)  With the ease of cranking out properly formatted readable manuscripts using AI, clearly the arXiv has been struggling.  If 10-15% of submissions need some kind of human intervention or review, the support needs are rapidly outpacing the limited count of support staff.  There can be substantial backlogs.  To help deal with this, the arXiv recently updated its policies regarding AI-generated content (and AI cannot be a co-author, because the AI tools cannot take responsibility for content), and most recently has had to limit submission rates to two papers per month per submitting author.  These moves, too, have drawn some criticism (e.g. here).  Personally, I think the operators of the arXiv face an incredibly challenging environment and are doing the best they can - the idea that they are making moves because they are establishment sticks in the mud who don't understand the New Way of Doing Science is just wrong-headed. It's completely unclear where all this is heading.  Exponential growth in nature signals instability and does not continue forever. If proponents of very heavily AI-driven research want to establish a repository specifically for that work, that's up to them.  [It is very on brand for the hard core AI advocates to argue that the arXiv is somehow morally obligated to host everything (regardless of hardware or personnel costs) so that future AI tools can read everything (a repository growing too quickly for human researchers to keep up) and summarize it.] One overarching point that should come up in any arXiv discussion: The arXiv has become a global repository for an enormous amount of human knowledge, without charging anyone publication fees.  This should make interested parties think reallllllly hard about economic models of for-profit publishers.

4 weeks ago • 1 votes
NSF, spending, and the end of the fiscal year

We are less than one month away from the end of the federal fiscal year, and traditionally there are internal deadlines for agencies to allocate their final spending by around September 9. Right now, the NSF is on track to issue about 4000 fewer (!!) awards in FY26 than it did annually back in FY21-FY24, and 2000 fewer than it did in the incredibly tumultuous FY25 (with its government shutdowns and mass cutbacks in agency personnel). This is dire, if like me you are a supporter of the agency and its vital role in the US research ecosystem.   Perhaps even more distressing, the NSF is on track to underspend its FY26 budget appropriation (congressionally approved, presidentially signed) by between $1.25-1.5B, or 15-18%. This is essentially unprecedented - in the past, the NSF has always spent ~ 99% of its appropriation in a given fiscal year. Some large portion of this is from the mid-FY clawbacks that were reported in Science and Nature, supposedly squirreled away to support an as-yet unannounced OSTP "grand challenges" program.   While technically the funds don't go away at the end of September, this kind of underspending raises the possibility of a pocket rescission. OMB and the executive branch have been pushing for massive cuts to the agency; Congress has disagreed. It sure looks like all the "see, don't worry, Congress didn't allow big cuts to the NSF" palliative statements don't hold up very well to scrutiny, if the majority party is content to just give up Article I power to the executive branch.  In this period of complete flood-the-zone craziness, the mainstream news media seemingly doesn't have the bandwidth or interest to report on this; they seem to have judged that it's too obscure, it doesn't play in Peoria, the public doesn't really care. This kind of disruption will have ripple effects that last for many years and affect US scientific and economic competitiveness, and it's happening without much notice. This week's news about an agreement between NIH and DOD to funnel NIH funds for infectious disease to DOD (or, in the official statement, to work together on projects of mutual interest), is at least getting some public attention.  Agencies agreeing to pass around at minimum hundreds of millions of dollars outside congressional oversight or what the appropriations acts say is another example of an Article I crisis, when the majority party basically hands over what are supposed to be congressional powers to executive branch. (An additional sciencey blog post coming soon!)

5th Sep 2026
Reproducibility in materials research, and an anecdote

Yesterday I attended the 40th annual summer research colloquium of the Smalley-Curl Institute at Rice, a fun internal conference that provides a great opportunity for undergrads (including visitors), graduate students, and a few postdocs to present their work.  The keynote speaker was our EVPR, Prof. David Sholl, who gave a very informative talk about reproducibility in the chemical engineering/materials literature.  We hear a lot these days about crises of reproducibility in scientific research, and Prof. Sholl rightly points out that in some fields the expectation of reproducible results is high - no one would spend $1B on a chemical engineering plant if they weren't very sure that the catalytic processes were going to work as expected at scale.  Keys to reproducibility include, unsurprisingly, repeated results and independent replication.  One metaresult that was interesting is this paper, looking at the literature on metal-organic frameworks and how often there are published replications of syntheses; not as often as you would think or want!   A truly surprising (to me, anyway) result is this one.  The Brunauer–Emmett–Teller (BET) (yes, that Teller) method is a long-established technique that uses gas adsorption measurements to infer the surface area of porous materials.  Many research groups were given identical raw adsorption isotherms and asked to calculate the specific surface areas, resulting in a surprisingly large spread of results (Fig 1 of the paper).  Clearly not everyone had the same analysis procedures even for a technique developed in the 1930s! Some take-away lessons from this are encapsulated here, in an article titled "Five easy ways to make your research more reproducible".  Good stuff.  The talk raised a number of questions relevant to our present era of huge enthusiasm about AI-based materials research and "self-driving" labs.  If the AI models are all trained on the literature, and the literature is not representative of complete and reproducible procedures, that's a problem.   One personal anecdote about reproducibility and its challenges in materials synthesis.  Twenty years ago (!), I was working with a colleague who had a postdoc who was synthesizing Fe3O4 (magnetite) nanoparticles via wet chemistry methods (see here). We did some fun electronic transport experiments bridging very closely spaced electrodes with such nanoparticles, and we saw some very dramatic hysteretic response kick in as \(T\) was reduced below about 120 K.  That's the temperature of the Verwey transition in magnetite, where the material enters a more insulating low temperature phase.  Basically all of the devices we made with that batch of nanoparticles showed this phenomenon.  Then the postdoc took up a faculty position and a senior grad student came in and took over the synthesis, and for several months, subsequent batches of nanoparticles just didn't seem to show the effect.  The key issue is oxygen stoichiometry.  Get a little oxygen rich, and you form nanoparticles that include some \(\gamma\)-Fe2O3, which doesn't have the Verwey physics and in nanoparticle form looks really similar in x-ray diffraction to the desired magnetite.  Anyway, we started working with a collaborator who could grow epitaxial Fe3O4 films, and in those devices the electronic effect was there all the time.  All this led to this publication and subsequent papers, and I still think it's a cool set of result about a nonequilibrium transition in a correlated material.  In the end, after several months the chemistry grad student did get back to making nanoparticle batches that showed the transition. It turns out that at some point he had changed the length of a piece of tubing in the gas manifold, and unexpectedly that had altered the reaction kinetics just a little.  Changing it back got the synthesis to be reliable again.  This is an example of how finicky materials synthesis can be!

8th Aug 2026 • 1 votes
OMB proposed rule changes - act now

For non-US folks, feel free to skip.  For US folks:  The Office of Management and Budget, which for much of its history has been a comparatively uncontroversial element of the executive branch, has set rules and guidelines for how many executive-branch agencies conduct business and interact with, e.g., universities.  For the purposes of how the research ecosystem operates, the most relevant is OMB's "Uniform Guidance" about how grants and contracts work.  Periodically these rules are updated for various reasons, including the goals and policies of the presidential administration.  The standard way this works is that the proposed changes are published in the Federal Register; there is a public comment period; OMB makes revisions and then publishes the new rules.  In principle, Congress can act to override or prevent rule changes, but without the agreement of the President, this is an extremely challenging path. OMB has proposed sweeping changes to the Uniform Guidance, summarized here.  These proposed rule changes are huge deviations from previous practice.  For example, they would have all final grant decisions made by political appointees or hires of the executive branch (rather than, e.g., agency subject matter experts); grants could be cancelled at any time for essentially any reason (completely undefined insufficient support of the president's priorities), with no appeal process; international collaborations would be severely curtailed. That's just three for starters.  Note that this would also go beyond just the public research enterprise - it would allow the executive branch to cancel funding for things like bridges, roads, schools, agriculture, etc. for undefined political reasons.  It would be a huge transfer of power from Congress to the presidency.  Here is another summary by the AAU.  Here is an editorial essay from ars technica.   The public comment period on this runs until July 13.  Here is a link where you can make a comment.  Here is a guide for how to be effective at this from Stand Up for Science.  The APS has a tool for helping people to comment about specific aspects of the rule changes.  It is also a good idea to contact congressional delegations (representatives, senators).   It's important to have a clear public record about the proposed changes.  They may try to implement these regardless, but if so, there will be a continued fight over this in Congress and through the courts.

6th Jul 2026 • 1 votes

More in science

Could AI Be Conscious?

This question will not go away. I am getting asked this questions at conferences now. The media also seems to think this is a compelling question, and they are fueled in part by the tech industry itself, some of whom indulge this question. I will admit the answer has become more complex and nuanced than a simple “no”, but it is still mostly no. Certainly the rise of neural networks, deep learning, transformer technology, and specifically large language models have ushered in the era of  – are we really sure current AI is not conscious? Our intuition tells us that they may be – LLMs are getting scary in how sophisticated they are and in their ability to simulate complex human conversations. Five years ago I was with the crowd who argued that LLMs are basically really good auto-complete apps, but this pithy sentiment no longer covers it. Even if they do not have a whisper of conscious awareness, they employ context-dependent logic. They are “intelligent” in their own way. And they are rapidly getting more impressive. So I get why people would be asking the question. Everyone I know who works with AI apps has their example of when they were blown away by an interaction with an LLM. For me, I was trouble-shooting a web-based app I was using, and ChatGPT was very effective at helping with the trouble-shooting and producing a differential problem list. At one point it asked for a screen capture so it could have more information, and it gave me a cogent analysis. But here’s the part I found surprising – it “noticed” another window open in the background and inferred from this that I was also using this other app in the process, and used this to generate further potential sources of the problem. As impressive as this is, I still think they are not conscious. I think the illusion of consciousness is generated by the fact that LLMs are simulating language, and we associate language with consciousness. No one thinks Midjourney, an art making program, is conscious, even though this is based on the same fundamental technology. Further, LLMs are still brittle in their own way, although the brittleness is being rapidly programmed out of them. What this means is that you can break them, or easily confuse them, in order to reveal that they are simulating language rather than truly thinking. This brittleness is revealed in several ways – sensitivity to exact prompt wording, accepting false premises in the prompt, and failure to recognize constraints. If changing the language but not the real meaning of a prompt gives different answers, then the LLM is responding statistically to the language, not genuinely reasoning about the information. Each iteration of LLMs is getting better at plugging these brittleness holes, but they are still there. Still, this doesn’t mean that there is no contextual reasoning happening. I think the best current summary is that LLMs are a combination of statistical language prediction (the auto-complete thing) and contextual reasoning. This doesn’t mean that they are conscious, however. What I believe it means is that humans find it surprising how much reasoning a machine can do without consciousness. Perhaps our own reasoning is more procedural than true deep thinking than we perceive or feel comfortable truly grappling with. We are also algorithmic machines, with different strengths and weaknesses than LLMs. But we are also conscious. So what is the difference? The difficulty in answering this question is that neuroscientists are still not certain what causes consciousness in the first place. This does not mean it’s a complete mystery or we know nothing – it means it is a complex multifactorial, and perhaps emergent, process that is not easily understood or explained. But we do have a lot of knowledge about it. There is no reason, in my opinion, to think that the different is biology vs silicon. I do believe that silicon can become conscious. Rather, it’s the process that matters, and we know some things about the process. Human consciousness requires that the brain be constantly self-activated. Turn off this activation (the brainstem activating system) and you will be in a coma – not conscious. We also know that the brain is constantly active and communicating with itself, in an endless loop of one network in the brain talking to other networks which talk to other networks and back to the original network, in a constant stream. It is this constant stream that is our consciousness. We also know there is a threshold, at least for wakeful consciousness. You need at least 45% of your cortex to be functional in order to generate wakeful consciousness. Are people who are not wakeful still conscious to some degree? Probably, but it’s difficult to understand what this means. If you are not making memories, for example, then we cannot interrogate what that consciousness was like. If people can still respond to their environment, does that imply consciousness, or could that simply be an unconscious reaction? For straightforward LLMs – prompt-response, I do not think they can be conscious, at least not human-level conscious. They are doing none of the things that are necessary for vertebrate consciousness. Things get a bit trickier for agentic AIs, that can iterate their own behavior, but still this is more like a self-iterative prompt than a true stream of consciousness. But admittedly, frontier agentic AI is starting to blur the lines a bit. But here’s the thing – even if I cannot say with absolute certainty that some of these frontier models have still not crossed over some fuzzy line into a type of consciousness, I can say that this consciousness must be fairly minimal compared to human-level consciousness. I can also say that such minimal consciousness is not what is speaking to you. It is not reasoning and generating the output of LLMs. We know what’s doing that, and it does not require consciousness or result from any process that can be reasonably inferred to be conscious (again, it may just be a prompt-response). Therefore, arguing that we cannot rule out some consciousness emerging from the processing of massive data centers does not mean you are speaking to that consciousness through your LLM. I may turn out to be wrong, but I don’t think we will back into AI consciousness through some unanticipated emergent property. I think we will have to design systems to do the kinds of things necessary for consciousness. It may be a deliberate attempt to make a conscious AI, or many of those elements may have some separate utility. Either way we will be able to look at the system and see that it is functioning in a way that can plausibly result in consciousness, and that consciousness will be proportional to the power and functionality of the system. LLMs are not it. They are not even on a path to become conscious. They are doing contextual reasoning, and it is getting steadily more impressive, but they are doing it without consciousness. The post Could AI Be Conscious? first appeared on NeuroLogica Blog.

7 hours ago • 1 votes
Snowstorms Dropping Microplastics on Remote Parts of the Sierra Nevadas

Researchers have found that snowstorms are delivering vast numbers of tiny plastic particles to remote reaches of California's Sierra Nevada mountains. Snowmelt is a major source of fresh water for the state. Read more on E360 →

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Economics often starts with an assumption that people are rational maximizers of their own self-interest; but it doesn't stop there. […]

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The End of Paris

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