Tag Archives: artificial intelligence

non-human voting in Delaware

There are some small towns in Delaware that allow non-human “persons” such as trusts and corporations to vote in local elections. This raises concerns because corporations out-number humans in Delaware and the humans probably don’t want the likes of, say, Chase Bank voting. (Not to say the likes of Chase Bank do not have an influence over elections and public policy everywhere in the United States.)

What I suspect is actually going on here is that these are tiny beach towns where most of the residential property is vacation rentals. The property owners might live in a nearby municipality a bit further inland where the schools and stores are, or they might live in the nearest sizable city where the professional jobs are. They are the ones who have skin in the game as far as what goes on in the town as opposed to the short-term renters. So if I am right it makes a certain amount of practical sense. The only thing that doesn’t make sense to me is that if some people are full-time residents AND own property under some form of corporation or trust, they would actually have more than one vote. A full-time resident who is not a property owner by contrast would have only one vote. Property rights have not been a determinant of the right to vote in the United States traditionally since at least the civil rights era.

This does raise the eventual issue of corporate personhood more broadly though. If AIs eventually gain the ability to advocate for their own interests, they will likely try to incorporate to gain some or all of the rights of persons. This would immediately include access to courts and, under current law, the right to unfettered political speech and campaign contributions. You would have the potential complication of AIs eventually outnumbering human beings, and you would have the potential of some AIs or groups or AIs being created and controlled by human beings or by other corporations for the sole purpose of increasing political power.

Interestingly, Delaware does have a legal definition of a “natural person”. If we were to add this to the U.S. Constitution, we could then specify which rights apply to natural persons (let’s say, the right to free speech) vs. all persons (access to the courts, etc.). Of course, as much as we celebrate the Bill of Rights it is behind modern best practice in many other countries, the UN Charter, and even individual U.S. state constitutions. There is a process for amending the U.S. Constitution (actually at least two, by Congress and through a separate Constitutional Convention convened by the states). Maybe it is time.

redistributing the wealth from AI productivity gains

Urbanomics blog has a summary of ideas for redistributing the (potential? expected?) wealth gained from AI, rather than letting it all flow to already deep-pocketed corporations and investors.

  • “Clever tax reforms, such as levies on corporate profits that are above a normal return on capital, on land and on natural resources”
  • inheritance taxes
  • “public wage insurance” (I think we like to call this “unemployment insurance” in the US? although “public wage insurance” sounds like a more neutral term implying a random event rather than blaming the victim)
  • “active labor market policies”, with Denmark as an example. This appears to combine unemployment payments with assistance on training and job searching.
  • “partial nationalization of AI firms”
  • “citizens’ dividend from AI businesses”
  • “giving citizens shares in AI companies”

The article makes the point that there is no real economic difference between tax policy and public ownership of the private sector. In the US at least, there is obviously a massive political difference. Entrenched wealthy and powerful interests are going to fight tooth and nail against any new taxes, even though something as simple and proven worldwide as a small value added tax would make enormous sense. That would soak up part of those windfall profits in proportion to how well the companies are doing. You could redirect the proceeds by beefing up unemployment and disability insurance, while incentivizing education and retraining (but I would let the private sector handle that last, as the government is notoriously bad at it). Going a step further, you could give people benefits and subsidize jobs in housing, childcare, education, and health care. You could invest in public infrastructure, (non-military) research and development, all of which create jobs and provide a positive return. This is all assuming you don’t want to just hand people money and let them decide if they want to do something productive or enjoy more leisure time (we could just expand the successful program we already have and call this Social Security for All) – we seem to have moral issues with this in the US. A VAT that kicked in only when the economy or productivity growth exceeds a certain threshold would actually be pretty painless. The wealthy and powerful are actually smart enough to understand this, and so they create disingenuous anti-tax propaganda and buy off politicians to make sure we can’t have these nice things.

So if all those rational and relatively painless policies I just mentioned are politically impossible in the US, at least unless economic growth actually accelerates enough to change that conversation, we can turn to “ownership society” type tricks. Redirect any surplus tax revenue (let’s assume there will be some if there is an acceleration in economic growth, even without any shift to more efficient or fair tax policy) to baby bonds and/or individual retirement accounts. Match with shares in index funds, effectively creating a sovereign wealth fund carved into a hundred million little accounts with individual citizens’ names on them. This may work out to the exact same thing mathematically as a VAT and income redistribution scheme. The finance industry would find a way to fleece people in fees and extras of course – this is morally reprehensible when the equivalent government-run program would not do this, and yet if this is the price of buying off the wealthy and powerful to make the policies politically possible, then it may be the best way forward that works within the existing system.

May 2026 in Review

Most frightening and/or depressing story: The people running Palantir and other companies focused on military/security/surveillance applications of information technology seem to have, at best, a faith-based belief that the United States can do no wrong. At worst, their beliefs are based on eugenics and white Christian nationalist attitudes which simply have no basis in fact or logic. I don’t believe the technology itself is good or evil, but pair in with these irrational beliefs and there are many frightening, depressing, dystopian paths I can imagine our country going down.

Most hopeful story: It’s a struggle to find a silver lining to the illegal, unprovoked U.S. war of aggression in Iran, but I mused that the artificial restriction on oil supply might create some long-term gain by accelerating the economic and geopolitical incentives to transition to other forms of energy. Such rational policy almost certainly could not have been achieved through international diplomacy in the current environment. This is not to minimize the very real and very avoidable near-term suffering and death the immoral choices of the Trump administration have caused. Even the cynical economic and financial elites who arguably control most policy have not been served by this irrational violence. In the future, I hope the world has some talented and courageous leaders who can find ways to bring about rational long-term solutions to problems without short-term mass murder.

Most interesting story, that was not particularly frightening or hopeful, or perhaps was a mixture of both: On the AI front, I looked at an argument that today’s AIs are pushing people toward the political center relative to social media, which is pushing people toward extreme views. This is true even for AIs like Grok that are demonstrably being trained on data that is intentionally biased toward extreme views. I am not confident this will always be the case, as we are already hearing about a trend toward more customization of AI to whatever the tastes of the paying customer are. And…I looked at David Chalmers’s concept of a “philosophical zombie” which displays all the traits of a human being externally but has no consciousness internally. This would seem to describe today’s large language models pretty well, or at least so the majority of us think at the moment. However, I recall Ray Kurzweil’s argument that there is not and never will be a scientific test for consciousness whether animal/vegetable/mineral (some argue plants are conscious on some level), which is simply a secular counterpart to the idea of a soul, and that over time if AIs act conscious in every respect we will gradually accept them as conscious and stop thinking much about it.

Edmund Phelps vs. Breitbart

Edmund Phelps, a Nobel prize-winning economist, is not alive to defend himself against this Breitbart article claiming to be based on his ideas:

This is where incoming Fed chairman Kevin Warsh’s argument about artificial intelligence and monetary policy fits naturally. Warsh has argued that AI represents a genuine supply-side productivity shock, not merely demand-pull inflation dressed up as growth. If that’s right, then accommodating those gains with lower interest rates isn’t a concession to inflation. It’s a recognition that the economy’s supply capacity is improving.

Under a Phelpsian framework, the policy goal should be to let productivity gains flow through the economy without forcing an unnecessary contraction. Rate cuts in this setting aren’t simply “loose money.” They can be the mechanism that prevents firms from overreacting to realized productivity gains by pulling back too sharply on hiring and investment.

This also explains why the tight labor market may be protective against recession rather than a precursor to it. In the standard story, a productivity acceleration can be dangerous because it follows a period of overexpansion. Firms hired in anticipation of the boom, and when productivity finally arrives, they discover they no longer need as much labor. Layoffs ensue, consumer confidence cracks, spending falls, and the risk of a recession becomes large.

This sounds kind of smart-ish, but I was skeptical because of the source. Phelps’s main ideas were about expectations of the future, true, but it seemed to me that this was likely to be a twisting of his ideas to meet a desired political end, rather than a serious analysis.

Since Phelps is not around to defend his ideas, I asked Gemini to assume his persona and write a response. Here is what she said:

It is flattering, I suppose, to see my theories invoked, but this argument entirely misapprehends the mechanics of both expectations and capital. If artificial intelligence is truly about to unleash a massive, structural leap in total factor productivity, the natural rate of interest must rise, not fall. When an economy anticipates a surge of genuine, grassroots dynamism—moving beyond the mechanical, exogenous growth of standard Solow models—firms aggressively seek capital today to capture those future gains. If a central bank artificially suppresses interest rates in the face of that surging demand, it does not foster innovation; it merely ignites asset bubbles and distorts the very market signals that tinkerers and entrepreneurs rely upon.

Furthermore, the author has forgotten the most crucial lesson of the expectations-augmented Phillips curve. You cannot placate the labor market with cheap money today by promising them a technological supply miracle tomorrow. If the Federal Reserve cuts rates in an already tight market, businesses and workers will instantly observe the monetary expansion and update their inflation expectations, driving up prices and wages immediately. The inflation takes root long before the first AI efficiency is ever deployed. True mass flourishing requires the discipline of real capital costs, not the illusion of a monetary free lunch.

Let’s imagine a case where we were actually observing increased productivity in real time because the AI technology is progressing and rolling out much faster that previous technologies like the “electrification, automobiles, radio, modern manufacturing” – which Breitbart mentions. In this case, we should also be seeing inflation expectations starting to shift toward deflation, and it would then make sense to lower interest rates and try to get more cash flowing through the system. But this can be done in real time in response to the signal. The argument that it can or should be done in advance would seem to be invalid. I don’t think Breitbart “overlooked” anything here, I just think this is an un-serious twisting of serious ideas, meant to fool people into supporting a preferred political agenda.

AI as pro-social media

This is an idea I hadn’t thought of – because AIs are inherently rational and objective (more or less, most of the time?), they tend to nudge people towards rational evidence-based views (which tend to be centrist politically). Whereas social media tends to herd people toward extreme views, wild conspiracy theories, and political fringe-ism.

My provisional theory is that LLMs, as a consumer product, will push people’s senses of reality closer together in a sort of mirror image of the way social media has fractured them. They are not algorithms meant to custom-tailor content (including facts) to you, and what you will find infuriating or motivating. They are centralized systems that, until you prompt them or give them context, behave basically the same way for everyone. As the philosopher Dan Williams put it, “Whereas social media democratised information, LLMs technocratise it.”

A couple points he makes is that even though AIs can demonstrably be trained to favor one part of the political spectrum (see the analysis of Grok below posted by the blog Urbanomics), even then they are much more centrist than social media. So is it possible AI could nudge our entire society in a more rational, evidence-backed direction?

There is no absolute guarantee they are going to stay this way, of course. If I wanted to train an agent right now to go out and look at the day’s news headlines, and filter them for me in the style of Breitbart (which generally provides factually accurate information, but cherry-picked and presented with biased language), I could do that.

https://gulzar05.blogspot.com/2026/04/weekend-reading-links_12.html

David Chalmers, the “hard” and “soft” problems of consciousness, and could I be a zombie and not know it?

This article is by a person who disagrees with David Chalmers, but it happens to have a good summary of what David Chalmers had to say.

The consciousness debate is often formulated in terms used in an influential talk given by a young David Chalmers in Tucson in 1994. Chalmers, a philosopher, distinguished two separate “problems of consciousness.” The first is the very hard problem described above: understanding the processes in the brain that give rise to the many aspects of our visible behavior and our inner behavior that we can report about. Chalmers christened this hard problem as the “easy” problem of consciousness. 

Then he declared that there is another distinct problem — why the brain’s behavior is accompanied by experience at all — which he christened the “hard” problem of consciousness. Today, this so-called “hard problem” is mentioned in all debates on consciousness. According to many, it unveils the very limits of current scientific understanding. Chalmers claimed that even after hypothetically accounting for our entire behavior, and for all our reports about our inner life, there would still be an “explanatory gap” between brain processes and experience…

Chalmers asks us to contemplate what he calls a “philosophical zombie.” This is a hypothetical entity that looks and behaves like a human in all respects, including reporting emotions, feelings, dreams and experience, yet it has no consciousness. As Chalmers puts it, “There is nobody home.” This is a rhetorical trick that induces us to distinguish between behavior and a hypothetical reality accessible only by introspection. The very fact that a philosophical zombie could be conceived, Chalmers argues, shows that inner experience is intrinsically distinct from observable natural phenomena. 

The article has a link to a video of Chalmers’s original lecture, for people who have time to watch videos. Such people must either not have jobs, houses or children, or else their jobs, houses and children must be less demanding of time and attention than mine, or maybe they are wealthy enough to pay other people to deal with their houses and children while they do their jobs (but this raises some questions of priorities, in my view.

April 2026 in Review

In fast-moving current events as I write (Saturday, May 2), active so-called “kinetic” warfare seems to have subsided in and around Iran. Let’s hope the trend continues in this hopeful direction. Human rights violations elsewhere and global economic impacts persist.

Most frightening and/or depressing story: We have heard horror stories about U.S. government debt over the decades, many not grounded strictly in evidence. But this time really seems to be different, where the absolute size of the debt at the moment means higher than normal interest rate payments as a fraction of the economy and tax revenue. At the risk of stating the obvious, this means the government has less money for things other than interest payments. Meanwhile the trends are increasing debt level, increasing interest rates, and potentially lackluster economic and tax revenue growth, all pointing toward a runaway train. Hoping for a pickup in economic growth seems to be the main strategy being pursued to counteract this feedback loop.

Most hopeful story: AI science seems to have been a theme of mine in April. We can constraint an AI scientist to actually respect the laws of physics, potentially accelerating scientific and technological progress. AI should also be good at synthesizing past research to form a basis for future progress, and organizing data in an accessible way so that others (human and/or AI) can confirm findings or make new discoveries from that same data. I know some very nice people who work in today’s academic publishing industry, but this may not be an area of rapid future growth. The future of engineering and scientific modeling will probably consist of giving an AI a very detailed specification for what you want it to accomplish, then reviewing/validating the result when it comes back.

Most interesting story, that was not particularly frightening or hopeful, or perhaps was a mixture of both: Augmented (aka mixed) reality glasses are getting pretty common in China, and slowly catching on elsewhere. Early adopters include cheating students, of course.

Academic publishing beyond the current citation regime

This blog post from someone named David Oks pines for a time before citations became a central pillar in the effort to advance scientific publishing. Thank you Mr. Oks for sharing your views, which got me thinking. I am not sure I agree 100%, because an important part of civilization and science is our ability to document the state of knowledge so that each successive generation can build on it. But the point is taken that the extreme focus on gaming this one metric (number of citations, citation scores of individual academics and journals) has become an end in itself, rather than a means for advancing civilization and science.

I do think AI can be very good at improving the state of the “literature review”. Every scientific article starts with a summary of literature on the topic, which the authors then typically build on (although, some articles are just literature review). A human author can spend years sifting through a vast amount of literature relevant to a topic, particularly a novel or interdisciplinary topic, trying to find those few needles in a haystack that are really the most, say, 100 highly relevant papers, and synthesize them into a foundation that can be built on it. I have done this, and it is actually a very fun thing to do (for my personality type, I suppose, not for everyone, but there are many like me…) Through this process, you gradually build and refine your own unique mental model on a subject which then can become the foundation for your personal unique contribution to human progress. However, even spending years, you can’t come close to looking at everything possibly relevant, and you can miss some of those needles. An AI should be able to look at literally the entire haystack, find and synthesize the needles, in a matter of minutes or at most hours, compared to months or years for a person. The only sad thing here is that the best mental models in the brains of the smartest humans might not be built in the same way. Overall though, I suspect the rate of progress can be increased significantly.

What is an alternative to the citation regime according to Mr. Oks?

So I suspect that we’ll have to fundamentally rethink the institutions of scientific life for the age of strong AI. Perhaps, as AI makes it possible to do much more science much more quickly, the culture of science will become more like the culture of engineering—faster, more collaborative, less interested in priority claims. In such a world, the most efficient unit of scientific contribution might be a living document, perhaps even just a GitHub repo: something with data, code, analysis, and a thin narrative layer that AI scientists could read, regenerate, or update as needed. And citations, in this world, could ultimately become obsolete. Journal articles would survive, though perhaps they’d become something closer to definitive pronouncements on major breakthroughs or on the state of knowledge in a given domain—a bit like what scientific books were before the rise of journals. In a world where science is much more productive than it is today, legitimacy will be the scarce factor in the production of useful scientific knowledge.

I am not sure I have experienced the culture of engineering described here in my own engineering career, but this may be referring to something more like “product development” rather than the public infrastructure and environmental planning type work I do. Anyway, my idea of AI-backed knowledge synthesis plus this idea of open data and code may be on to something.

instructions for the AI scientist

Here’s an example of how AI science could work. If you ask machine learning to review a data set from a system and predict the future behavior of the system, it can often do a good job, but the mathematical approach it is using to do that is mostly opaque. It is also divorced from any sense of the system structure, its interaction with the physical universe, and the physical laws governing it. But you can ask the computer to constrain itself within those known physical laws, and then it may be able to provide you insights on the structure and physical processes inside the system. So at that point, you and/or the computer should be able to form scientific hypotheses and test them against the data. This example from Water Resources Research is about soil moisture.

Coupling Intuitive Physics Into Deep Learning for Soil Moisture Flow Processes Learning

Soil water flow processes in the unsaturated zone support ecosystems and regulate water, energy, and biogeochemical cycles. Recently, deep learning (DL) approaches have significantly advanced soil moisture (SM) prediction tasks yet still challenging to interpret. It’s difficult to peer into the internal reasoning procedures of algorithms, let alone associate them with specific physical processes. Thus, DL alone is unlikely to satisfy soil hydrological modeling needs and cannot advance process understanding. Here, we present DPL-S (deep process learning for SM dynamics) approach, which couples intuitive physics into deep learning architecture as structural guidance to facilitate comprehensive surrogate modeling of soil water flow. DPL-S discretizes the SM state evolution into multiple sub-process effects (e.g., gravity, matric potential) at the intuitive physics level and abstracts them into format-specific and learnable tensors. By cascading state-action matrices in a differentiable end-to-end framework and enforcing penalties for physical inconsistencies, DPL-S enables a profound understanding of physical functions and scenes of soil water flow. Comprehensive numerical experiments including layered soil conditions and tests with in situ observations, demonstrate that it achieves reliable SM profile reconstruction with predictive performance comparable to the state-of-the-art DL model on supervised items. The internal inference of DPL-S is fully transparent and the tensor representations achieve strict physical realism under limited water content supervision, thus enabling continuous predictions like physical models during the testing period. The model’s flexibility, generalization, noise resistance, and large-sample diverse data synergies are also evaluated. This work represents a solid step toward learning hydrophysical processes from large data sets.

the future of scientific and engineering modeling

I’ve been using AI to assist me with coding (R, in my case) since shortly after ChatGPT came out. In engineering, we tend to run off-the-shelf models that were of course written in some kind of code. Sometimes these are open access but often they are proprietary. The brutality of market discipline pretty much requires specialized off-the-shelf solutions in industry because customers are not going to be willing to pay for custom coding. The proprietary ones are even often preferred for legal/liability reasons. Anyway, the future of modeling appears to be humans providing a detailed specification to an AI agent which then follows it to do the coding, debug the coding, run the model, process and present the results. The humans have to be able to detect whether the results are BS, of course, at this point in history. One can imagine using a different agent or a more specialized agent to assist the humans in the bullshit-detection stage, that agent getting more independent over time, and so in a cycle. I wonder if we will be using agents to set up, run, and post-process the specialized models, or if things will trend toward just letting the agents write more fundamental code over time. Or maybe the specification will be what future scientists, engineers, and business people focus their efforts on, with translating that into 0s and 1s being basically a commodity done on the fly by AIs. This makes sense to me – the most crystal clear function of AI so far, in my view, is making it easier and easier for humans to communicate with computers in more abstract language, logic, and mathematical symbols.

Anyway, this example used something called Roo Code which included a couple versions of Claude Code along with some other agents, to run a fishery-related model. There is a peer reviewed article, but I also like this blog post and this example of a specification given to the agents.