Some Thoughts On Quantum Computing

Some remarks by a speaker at a recent professional event inspired me to rant a bit on the topic of quantum computing. For sure, it's not my area of expertise, but I do have a dilettante interest in quantum physics, plus I took a short course at University of Denver a few months ago that was a kind of "Quantum Computing for Dummies", and it is the area of expertise by the Ph.D. physicist who taught it.

[1] Quantum computers will not replace conventional computers. There is a lot of interest in QCs because it is believed that they can do calculations that are not feasible for today's computers. But those calculations are only possible for problems for which there are known - or believed - to be QC-type algorithms. You will never be running a web browser or a spreadsheet on a QC.

[2] One example is Peter Shor's quantum algorithm for large number factorization, which could be used to break encryption schemes that rely on the difficulty of such factorization. Shor is a theoretical computer scientist who developed Shor's Algorithm while at Bell Labs. This is why governments are interested/concerned about QC. A lot of encrypted secrets have been stolen by hackers (theirs and ours), but are just useless bits until they can be decrypted.

[3] Not all encryption is based on large number factorization. Maybe there are as yet undiscovered QC algorithms for the "trap door functions" that those schemes use instead of large number multiplication and factorization. Maybe not. Until then - if ever - such schemes are described as quantum resistant. Switching to such schemes is probably a good idea for sensitive data, just in case QCs eventually work.

[4] QC may never work. Although scaling up quantum computers is talked about as if it were an engineering issue, the people trying to do it are - in my opinion, and whether they realize it or not - trying to solve what physicists call the measurement problem. What constitutes a "measurement" in a quantum system - an action that causes the wave function describing a superposition of states to collapse or decohere into one state - is unknown. QCs work by causing the superposition created by the quantum algorithm to collapse into a state representing the answer, possibly solving a problem what would take a conventional computer years, or centuries, or ... Engineers working on QCs are trying to prevent a measurement - whatever that is - from occurring and the system decohering until they want it to. Defining what constitutes a quantum measurement is Nobel Prize territory, a problem that reaches into the very definition of reality in the transition from the realm of the very small to the realm we perceive. It is a problem that may never be solved, despite what investors are told.

I hope quantum computers do come to fruition. Not just for the practical reasons of solving some very difficult optimization problems and such, but because of the light it would shed on the measurement problem in physics. But I remain cautiously pessimistic.

Events 2

I read a transcript of a science explainer by Dr. Sabine Hossenfelder about physicist David Deutsch's "Constructor Theory", which I had not heard of before, and how it accounts for time.

It sounds like just the 180º opposite of what I've been talking about: creating a model of physics that seems more like the kind of real-time systems I work on as a basis for reality. The shortest time period (Planck Time?) is the recycle time of a kind of null task, a term right out of Real-Time Operating Systems. That's basically how I think of the world around me - based solely on decades of professional experience - but it seems weird to think of it as a legitimate Theory of Everything.

Down deep, real-time computer systems - with their asynchronous, concurrent, and parallel behavior - are a lot more non-deterministic than people might think. It's one of the reasons that it's hard to debug such systems - a bug might only reveal itself under certain timing or certain order of events. Determinism is a kind of emergent property created by engineers who are hiding the details under the hood from the user - kind of like Newtonian physics layered on top of relativity and quantum mechanics.

Once you become accustomed to architecting, implementing, and debugging such systems, it's easy - it was for me, anyway - to start seeing the entire world through the same lens. Maybe I should not be surprised that there's one candidate for a Theory of Everything that takes this viewpoint.

Large Language Models and the Fermi Paradox

(I originally wrote this as a comment on LinkedIn, then turned the comment into a post on LinkedIn, then into a post for my techie and science fictional friends on Facebook, and finally turned it into a blog article here. I've said all this before, but it bears repeating.)

The destruction of the talent pipeline by the use of AI for work normally done by interns and entry-level employees not only threatens how humans fundamentally learn, but leads to AI "eating its own seed corn". As senior experts leave the work force, there will be no one left to generate the enormous amount - terabytes - of content necessary to train the Large Language Models.

Because human generated content will generally be perceived to be more valuable than machine generated content, humans using AI to generate content will be highly incentivized to not identify AI generated content as such. More and more AI generated content will be swept up along with the gradually diminishing pool of human content to use as training data, in a kind of feedback loop leading to "model collapse", in which the AI produces nonsense.

A former boss of mine, back in my own U.S. national lab days, once wisely remarked that this is why the U.S. Department of Energy maintains national labs: experienced Ph.D. physicists take a really long time to make. And when you need them, you need them right now. Not having them when you need them can result in an existential crisis. So you have to maintain a talent pipeline that keeps churning them out.

It takes generations in human-time to refill the talent pipeline and start making more senior experts, no matter what the domain of expertise. Once we go down this path, there is no quick and easy fix.

The lobe of my brain that goes active at science fiction conventions suggests that this anti-pattern is one possible explanation for the Fermi Paradox.

A Science Fictional Idea: Noise and the Fermi Paradox

Today, Sabine Hossenfelder, "my favorite quantum physicist", had an article about an academic paper in which the author proposed that aliens using really advanced technologies might be able to modulate the quantum properties of photons in order to carry information. (Note: this is not the SFal idea of using entangled particles to communicate faster than light, which - sorry - is not possible; there is no way to modulate the effect to carry information.) Such a modulation scheme could carry a lot more information than our current schemes that modulate properties like amplitude, frequency, phase, etc. A communication beam with quantum modulation would have to be extremely narrowly focused, lest it run into some other matter which would cause the quantum properties to decohere, losing all the information content.

The author wrote a possible approach would be to use frequencies in the infrared, which would require antennas on each end about one hundred kilometers across, which is, actually, not a completely crazy idea. Dr. Hossenfelder also mentioned that because of the decoherence issue, the aliens would be careful NOT to aim the beam near any planet, like, you know, ours. But even if we did receive it, we would have no clue how to demodulate it. I got to thinking about this. (That's why I follow Dr. Hossenfelder, and even support her work on Patreon.)

I recently spent three days at the NIST Time & Frequency Seminar held at the National Institute for Standards and Technology (NIST) Boulder laboratories. A big part of that seminar were demonstrations on how to measure and characterize noise in precision frequency sources. Precision frequency sources like the NIST-F2 cesium fountain clock, shown below, which is the principal frequency reference for the definition of UTC(NIST), the United State's contribution to the international definition of Universal Coordinated Time or UTC. This noise measurement and characterization is not completely removed from measuring noise in communications systems, which, by the way, depend on precision frequency references to work. (A big Thank You to Dr. Jeffrey Sherman, below, for the tour!)

Along the commuter train line from our neighborhood to downtown Denver there is an old AT&T Long Lines tower with the giant microwave horn antennas that used be the backbone of the long distance telephone system. This was before fiber optic cables were run along every railroad track - because the railroads owned the right of ways (an effort which gave the telecommunications company SPRINT its name: "Southern Pacific Railroad Internal Networking Telephony").

The Spousal Unit is so very tired of me telling the story - which I do virtually every time we ride the train (sorry) - of the two Bell Labs engineers who were tasked with figuring out and eliminating the source of the noise in the early models of these microwave antennas (which were big enough you could easily stand up inside of them). Alas, they ultimately weren't able to eliminate it: they determined the noise was the Cosmic Background Microwave Radiation that was the result of the Big Bang. They were picking up the noise from the birth of the Universe. As a consolation, however, they did win a Nobel Prize in physics. And invented the entire realm of radio astronomy. (I eventually took a little motorcycle ride and found that Long Lines tower, shown below.)

Noise exists in every communication link, whether it's radio, wire, optical, etc. You can't get rid of it completely. Eventually maybe you give up and just declare it's "cosmic background", or "thermal noise", or "electrical noise from other equipment in the room". But noise in communication systems is no small problem; given enough, it can jam your GPS, your WiFi, your mobile phone, etc., or just make your vintage vinyl albums sound bad.

I very dimly recall a result from Information Theory that says something like: the output of a theoretically optimal data compression algorithm is indistinguishable from noise. That is: there is no statistical test that can tell you whether the data stream you're looking at is just noise, or is optimally compressed data. (That's not quite the same as saying, however, that it is random.)

What if the aliens have a nearly optimal compression algorithm? (A perfectly optimal one is impossible.) Sending data from one star system to another is bound to be really expensive, not to mention take a long time. So they would be highly incentivized to use such an algorithm. What if part of the noise we see and hear and receive every day in our own radio communications systems is really alien data transmissions?

We could be awash in extraterrestrial data communications and not even know it.

Are AI Generated Works Intellectual Property?

The U.S. Patent and Trademark Office (USPTO) has once again stressed that only humans can be listed as inventors on patents. And the U.S. Copyright Office, part of the Library of Congress and typically a small bureaucracy with just a few people, is about to make big news as it evaluates whether AI generated works can be copyrighted.

If the USPTO declines to recognize AI "inventors", and the Library of Congress similarly disallows copyrighting of AI generated material, that's going to really put a crimp in the monetization of AI generated intellectual property, since it cannot be protected.

My current thinking is that right now it's right thing to do.

The current technology of Generative Pre-Trained (GPT) AIs are nothing more than gigantic text or image prediction engines based on huge artificial neural network-based statistical models trained with enormous amounts of human created and curated input - input for which the original authors and artists are not being compensated, despite the fact that their work may have been copyrighted. There's no cognition or creativity involved.

But the counter argument is worth thinking about.

We ourselves are nothing but gigantic text or image prediction engines based on huge natural neural network-based statistical models trained with enormous amounts of human created and curated input - material we have read or examined - for which the original authors and artists are not being compensated, despite the fact that their work may have been copyrighted.

The difference is that when we write or make art, we may be trying use the trained neural network in our brain to create what others have not done before. That's creativity.

Update (2024-02-20)

Another counter argument is that there is creativity and cognition involved in the prompt engineering - the term used for the creation of the prompt, or series of prompts, the human operator gives the AI to produce its output. Perhaps, in this respect, using an AI is no different than using tools like Microsoft Word or Adobe Photoshop for your writing or art.

I'm still leaning towards not providing IP protection for AI generated output. But this is a complicated issue. As the subtitle of my blog reminds you, 90% of this opinion could be crap.

Sources

(Perhaps ironically, this article is based on the no doubt copyrighted work of several others that I would like to cite... if only I could remember them. As I do, I'll add the citations here.) 

Emilia David, "US patent office confirms AI can't hold patents", The Verge, 2024-02-13, https://www.theverge.com/2024/2/13/24072241/ai-patent-us-office-guidance

Cecilia Kang, "The Sleepy Copyright Office in the Middle of a High Stakes Clash over A.I.", The New York Times, 2024-01-25, https://www.nytimes.com/2024/01/25/technology/ai-copyright-office-law.html

Louis Menand, "Is A.I. the Death of I.P.?", The New Yorker, 2024-01-15, https://www.newyorker.com/magazine/2024/01/22/who-owns-this-sentence-a-history-of-copyrights-and-wrongs-david-bellos-alexandre-montagu-book-review

Shira Perlmutter, "Copyright Registration Guidance: Works Containing Material Generated by Artificial Intelligence", U.S. Copyright Office, Federal Register, 2023-03-10, https://copyright.gov/ai/ai_policy_guidance.pdf

Katherine Kelly Vidal, "Inventorship Guidance for AI-assisted Invention", U.S. Patent and Trademark Office,  Federal Register, 2024-02-13, https://public-inspection.federalregister.gov/2024-02623.pdf

Military EMSO Versus Commercial Aircraft

Jeff Wise wrote this interesting article about how commercial aircraft are getting all crossways - figuratively and literally - as nation states and other actors are jamming and spoofing GPS/GNSS and using other ElectroMagnetic Spectrum Operations (the broader term that has replaced Electronic Warfare) generally targeted at military activity. Like a lot of embedded systems, the boxes inside commercial aircraft were never designed with malware and malicious signals in mind.

Jeff Wise, "Air Travel Is Not Ready For Electronic Warfare", New York Magazine, 2024-01-02

I belong to the Association of Old Crows, a professional society for EMSO folks, and I get their Journal of Electromagnetic Dominance. It's mostly about RF stuff far more low level than my area of expertise, being an embedded/real-time/telecom software/firmware guy, so I can't really appreciate most of it. But the volume of ads and articles in the journal makes it obvious this is a highly active area for both defense and offense.

Black Box AIs in Air Defense Systems

I've said many times - everyone is probably tired of hearing me say it - that I think the use of neural network AI - like used in LLMs/GPTs - in air defense systems for target identification is inevitable. And putting the AI in control of firing to reduce response time will also happen. Accidental shootdowns of commercial aircraft due to human error is common enough that it has its own Wikipedia page, so the AI will actually probably be more accurate than humans. But it's just a matter of time until a commercial aircraft is misidentified by an AI as an enemy target. And when there's the resulting U.S. Congressional investigation about the loss of innocent civilian lives, many are going to be surprised when the defense contractors say that not only does no one know why the aircraft was misidentified, no one can know. That's how these massive neural network algorithms work; they're so far mostly black boxes.

Model Collapse In Air Defense System AIs

We need an enormous volume of high quality content created and curated by human experts to correctly train LLM/GPT-type AIs. Because such data sets are labor intensive, and therefore expensive, to create and to assemble, there will be enormous pressure to train AIs with AI-produced data. This might even happen unknowingly (as has already in fact happened) if the provenance of the original content isn't well documented (or the people building the AI just don't care). (There will be strong incentives not to reveal that content is AI generated, because human-created content will be so much more highly valued.) Training AIs with AI-generated data leads to model collapse, a kind of feedback loop in which errors and hallucinations in the training data are reinforced.

This is likely to occur with the air defense AIs I described above.

And there will be no quick way to fix this. We will likely have eliminated all the career paths of those very same human experts by our use of those same LLMs for their entry level jobs. As the existing cohort of experts retire, die, move into management, or otherwise quit producing content, there will be no one to take their place. See also: "eating your own seed corn".

Time, Gravity, and the God Dial

Disclaimer: my knowledge of physics is at best at a dilettante level, even with more than a year of the topic in college, one elective course of which got me one of the only two B letter grades of both of my degrees. (Statistics similarly defeated me.)

I've read that there is no variable for time in the equations used in quantum physics, no t, because (apparently) time doesn't play a role. That's why quantum effects that are visible at the macroscopic level - even something as simple as stuff that absorbs light to glow in the dark - are random processes time-wise.

Yet time t plays a crucial role at the macroscopic level, in classical, or "Newtonian", mechanics.

Not only that, time is malleable, in the sense that it is affected by velocity and acceleration (special relativity) and gravity (general relativity), effects that are not only measurable, but stuff we depend on every day (like GPS) have to make adjustments for it.

So suppose God has a dial that controls the scale of their point of view, all the way from the smallest sub-atomic scale we know of, the Planck length, to the largest cosmological scale we know of, the observable Universe. At some point as God turns this dial on their heavenly tele/micro/scope, zooming out, out, far out, time goes from not being a factor at all to being an intrinsic factor for whatever they’re looking at.

Does this transition happen all at once? Does it happen gradually - somehow - in some kind of jittery change? What the heck is going on in this transition? What other things similarly change at this transition point? Is this the point at which particle-wave duality breaks down? Where Schrödinger's Cat definitely becomes alive or dead? Where does gravity starts to matter?

Gravity? Yeah, gravity. Because we currently have no theory of quantum gravity. Yet it seems necessary that at the quantum level gravity ought to play a role in a wave/particle. If a particle is in a super-position of states, what does that say about the gravitational attraction associated with the mass of this particle? At what point on the dial does gravity make a difference? There's a Nobel prize for sure for the first person to make significant progress on this question.

This is the kind of thing I think about while eating breakfast.

Model Collapse

A few decades ago I was working at the National Center for Atmospheric Research, a national lab in Boulder Colorado sponsored by the National Science Foundation. Although our missions were completely different, we had a lot in common operationally with the Department of Energy labs, like Los Alamos and Lawrence Livermore, regarding supercomputers and large data storage systems, so we did a lot of collaboration with them.

I had a boss at NCAR that once remarked that the hidden agenda behind the DoE labs was that they were a work program for physicists. Sometimes, often without much warning, you need a bunch of physicists for a Manhattan Project kind of activity. And you can't just turn out experienced Ph.D. physicists at the drop of a hat; it takes years or even decades. So for reasons of national security and defense policy you have to maintain a pipeline of physicist production, and a means to keep them employed and busy so that they can get the experience they need. Then you've got them when you need them.

This always seemed very forward thinking to me. The kind of forward thinking you hope someone in the U.S. Government is doing.

It came to me today that this is the same issue in the screen writers' and actors' strike.

Machine learning (ML) algorithms, of which Large Language Models (LMMs) are just one example, need almost unbelievably large amounts of data to train their huge neural networks. There is a temptation to use the output of ML models to train other ML models because it's relatively cheap and easy to create more input data, where as expensive humans can take a long time to do it. But training an ML model with the output of another ML model leads to an effect called "model collapse".

I mentioned an article on VentureBeat (which cites an academic paper) on this topic in a prior blog article. The VentureBeat article by Carl Franzen provides the following metaphor:

If you trained an ML model to recognize cats, you could feed it billions of "natural" real-life examples of data about blue cats and yellow cats. Then if you asked it questions about cats, you would get answers containing blue cats, and yellow cats, and maybe even occasionally green cats.

But suppose yellow cats were relatively rarely represented in your data, whether they were rare in the real world or not. Mostly then you would get answers about blue cats, almost never yellow cats, and rarely if ever green cats.

Then you started training your new improved ML model on the output of the the prior ML model. The new "synthetic" data set would dilute out all the examples of yellow cats. Eventually you would have model that didn't even recognize yellow cats at all.

This is one example of model collapse: the ML model no longer represents the real-world, and cannot be relied upon to deliver accurate results.

This is what will happen if you eliminate the human elements from your screenwriting or acting (or software development), using AI algorithms to write and to synthesize and portray characters (or write software). If you don't have a full pipeline constantly producing people who have training and experience at writing or acting (or writing software, or whatever it is you need), you no longer have a way to generate the huge human-created and human-curated datasets you need to train your AIs. The models collapse, and eventually the writing or portrayal of characters (or the software) in no way represents the real world.

That valley isn't even uncanny; it's just wrong.

But you can't just gen up more competent, trained, experienced writers or actors (or software developers) on the spur of the moment. It takes years to do that. By the time you realize you're in trouble, it's too late.

This is the precipice some folks want us to move towards today.

Large Machine Learning Models Are Not Intrinsically Ethical - And Neither Are Large Corporations

I think the screen actors and writers concerns about the use of large AI models is legitimate, since the models cannot exist and could not be successful without being trained using a ginormous amount of human-created input, typically without the permission or even knowledge of the original creators.

But that's just the tip of the iceberg, being currently the most visible public example of this concern.

Eventually, software engineers will wise up and figure out they have the same issue, with companies training AIs using software - including open source software - written by humans, most of whom are no longer, or never were, employees of theirs, without any compensation, consent, or acknowledgement.

Worse, companies will try to get around using expensive, experienced, and ethical developers by training AIs to generate software that will be used in safety critical or weapons systems.

Eventually, companies will save even more money, and avoid any intellectual property issues, by training AIs using software that was itself generated by other AIs, and... it's turtles all the way down. With each iteration, it will be like a game of telephone, the quality of the output getting worse and worse. Except sometimes with ground to air missiles.

In time, there will be corporate executives for some prime defense contractor sitting in front of a Congressional committee, trying to explain why their automated weapons system shot down a commercial airliner because it thought it was a Russian bomber. They will be forced to admit that no one - not their scientists, not their own engineers, not anyone - really understands how the AI in the system came to that decision.

Because that's how complex large neural network machine learning models are. It's not traditional if-then-else logic, a so-called "rule-based" system, like I studied when I was a graduate student in Computer Science. It's an almost incomprehensibly gigantic simulated network of neurons that was configured by an almost unbelievably huge dataset of input. A dataset whose contents no human moderated or approved or even examined. Or, because of its volume, could examine.

I admit this isn't my area of expertise. But I have a couple of degrees in Computer Science from an accredited program at a university. I have worked for many years in large multi-national corporations, part of that time in the defense-industrial complex. So I feel like I have a reasonably informed opinion on both the technical aspects and how large corporations work.

I believe that the application of very large machine learning models to weapons systems is inevitable. If not by the U.S., then by other countries, perhaps including our allies. The results will be unpredictable. And unexplainable.

Postscript

It only just now occurred to me that how large machine learning models work might be a good metaphor for the hive minds of large organizations.

Not really joking.

Postscript 2

My use of "hive minds" above was quite deliberate, BTW, since my train of thought first connected machine learning modes with the emergent behavior of some insect colonies e.g. bees. The individual bee - and the neural network inside its brain - is relatively simple, but the group behavior of a lot of bees is quite complex - and not even remotely understood by any individual bee.

Postscript 3

I couldn't read this paywalled article from Bloomberg [2023-07-16], but the part I could see, just a few minutes ago, coincidentally, was enough.

"Israel Quietly Embeds AI Systems in Deadly Military Operations

Selecting targets for air strikes and executing raids can now be conducted with unprecedented speed, according to army officials.

The Israel Defense Forces have started using artificial intelligence to select targets for air strikes and organize wartime logistics as tensions escalate in the occupied territories and with arch-rival Iran.

Though the military won’t comment on specific operations, officials say that it now uses an AI recommendation system that can crunch huge amounts of data to select targets for air strikes. Ensuing raids can then be rapidly assembled with another artificial intelligence model called Fire Factory, which uses data about military-approved targets to calculate munition loads, prioritize and assign thousands of targets to aircraft and drones, and propose a schedule."

Postscript 4

There's a very recent article on Vox about how the inner workings of large machine learning models are unknowable.

Postscript 5

VentureBeat: The AI feedback loop: Researchers warn of ‘model collapse’ as AI trains on AI-generated content.

Postscript 6

The article from VentureBeat that I cite just above makes an interesting point: the fact that using AI model output as training data for another AI model leads to "model collapse" means that high-quality human-generated or human-curated training data becomes increasingly more rare and more valuable. I predict this will lead to new open source licenses, GNU and otherwise, that restrict data or code use as training data for machine learning models. (And of course, AI developers will routinely violate those open source licenses, just as they are violated now.)

Widows and Orphans and Working from Home

Terrific - and terrifying - article from The Atlantic's "Work In Progress" blog by Dror Poleg, author of the book Rethinking Real Estate: the next crisis will start with empty office buildings.

The commercial real estate market - once so stable it was considered a widows and orphans investment - is changing radically. 25% of commercial real estate in large cities is empty, and that only counts the space whose leases have expired; it doesn't count leased space that isn't occupied, and is unlikely to be occupied again when the lease expires. Many real estate firms are "handing the keys to the bank" by defaulting on their loans.

https://www.theatlantic.com/ideas/archive/2023/06/commercial-real-estate-crisis-empty-offices/674310/

I've been thinking about this ever since the Spousal Unit and I attended the World Science Fiction Convention in Chicago in 2022, post pandemic. It was held downtown at the Hyatt Regency on Wacker Driver right next to the huge Illinois Center complex.

We've attended conventions in this very same venue many times. I was shocked to see how the pandemic and the work at home movement had changed it. Illinois Center is an office building complex that sits atop a vast underground environment linking many such complexes. When we've been there in the past, on working days, this environment was full of retail, food, and service shops, and people bustling through it. This last time, it was almost empty, with a lot of empty storefronts.

Standing in our hotel room and peering at the adjacent office building, on a working day, the I could see the window office space on several floors were empty; I saw one single office worker, looking at what appeared to be large blueprints or schematics.

The commercial real estate market underpins a lot of city tax revenue and investments including pension plans. The clock is ticking: according to Poleg, a third of all office leases expire by 2026.

The Enrollment Cliff

In 2007-2008 the sub-prime mortgage debacle led to the Great Recession, which in turn led to a drop in the birth rate. Fourteen years later, the Denver Colorado public school board is debating whether to close ten schools in the city/county due to low enrollment (and reduced tax income). Jefferson County Colorado (where I live) is having similar discussions. As you might guess, this is controversial with parents.

A week ago I read this in Vox

https://www.vox.com/the-highlight/23428166/college-enrollment-population-education-crash

which I'm now seeing referenced other places: colleges and universities are facing a similar "enrollment cliff", not just due to the birth rate, but from high school students selecting career paths other than college.

Pursuing a career path that does not include a college degree is a decision which I do not disagree - not every career requires a college degree, contrary to popular opinion (mostly from college marketing departments). And some extraordinary individuals can get all the training and knowledge they need through rigorous self-directed learning, even in technology fields like mine. (It's been my privilege to work with a few terrific folks like this.) But they are the exception, not the rule. Not every person has that skill set.

(I am deeply cynical however of major tech giants telling high school students that they do not need a college degree to work in the tech field, merely a certificate from specialized training. This is in not untrue. But they omit the words "perhaps for a while, and maybe at a lower salary". I wonder if the long term effect of this will be to encourage unionization in the tech field. [I don't object to this, either.])

Why do I care? For years I've been on an advisory board for the computer science department at my alma mater, so am aware of the impacts of not just enrollment but - to be honest - fads in the technology fields have on academia. And I'm watching this traveling wave of change move through the elementary and high schools, to the colleges and universities, and finally to employers.

I put my money where my mouth is on both sides of this: some years ago I endowed a modest scholarship at my alma mater in honor of my mentor and thesis advisor, the late great Bob Dixon, and I annually donate to support a lab at my alma mater that provides facilities and tools for students to do their own projects for self-directed learning (basically a makerspace supported by the computer science department).

I've also made it pretty obvious where I stand on continuous self-directed learning, by constantly doing technology projects of my own, having over thirty open-source software repositories on GitHub, and writing about all of it in this blog since 2006.

An old friend, university classmate, former fellow university employee, and one-time flat-mate of mine remarked recently that colleges are part of a free-market and are going to have to learn cost control. I agree. Although I'm concerned as much with the second order effects of this wave on our society as I am with the effects on, for example, my alma mater.

The Time Police Play Hardball

Alain Aspect, John Clauser and Anton Zeilinger won the Nobel Prize for Physics this year for their groundbreaking experiments in the field of quantum physics that verified Bell's Theorem.

One of the more unsettling discoveries in the past half century is that the universe is not locally real. “Real,” meaning that objects have definite properties independent of observation—an apple can be red even when no one is looking; “local” means objects can only be influenced by their surroundings, and that any influence cannot travel faster than light. Investigations at the frontiers of quantum physics have found that these things cannot both be true. Instead, the evidence shows objects are not influenced solely by their surroundings and they may also lack definite properties prior to measurement. As Albert Einstein famously bemoaned to a friend, “Do you really believe the moon is not there when you are not looking at it?”

...

Blame for this achievement has now been laid squarely on the shoulders of three physicists: John Clauser, Alain Aspect and Anton Zeilinger. They equally split the 2022 Nobel Prize in Physics “for experiments with entangled photons, establishing the violation of Bell inequalities and pioneering quantum information science.” (“Bell inequalities” refers to the pioneering work of the Northern Irish physicist John Stewart Bell, who laid the foundations for this year’s Physics Nobel in the early 1960s.) 

[D. Garisto, "The Universe Is Not Locally Real, and the Physics Nobel Prize Winners Proved It", Scientific American, 2022-10-06]

https://www.scientificamerican.com/article/the-universe-is-not-locally-real-and-the-physics-nobel-prize-winners-proved-it

It might occur to you to watch a 'Tube video or two, or read a popsci article on this work (like the one from Scientific American above). I've done this, with a dilettante's interest in the matter. I urge you to be cautious, and show restraint. I'm not entirely joking.

I've been reading and watching material on this very topic for years now. And I find it deeply troubling. As did Albert Einstein and his colleagues Boris Podolsky and Nathan Rosen (who were far far smarter than I've ever been) when they wrote their now-famous paper on the "EPR Paradox". They basically argued "the universe cannot work this way".

But alas, the results of actual experiments performed by these Nobel laureates (and which, remarkably, you can duplicate some of which just using three linear polarizing camera lens filters, which I've done) prove that indeed the universe is not "locally real". As John Stewart Bell (who died before winning a well deserved Nobel himself) showed, quantum physics implies that one of two things must be true: either things can interact in a non-local manner (exchanging information faster than the speed of light), or objects do not have intrinsic physical qualities (even basic stuff, like color or mass) until we measure them.

Regarding non-local behavior: I remind my fellow science fiction fans that non-local behavior does not imply that we can use it for faster than light (FTL) communication. There is no way to modulate the effect to carry data. And you really do not want FTL, since its implications for our perception of reality - regarding our ability to distinguish between cause and effect - are even more dire, an issue which becomes obvious after a little reading about Special Relativity.

Regarding the measurement issue: the idea that "objects do not have intrinsic physical qualities until we measure them" sounds suspiciously like "as an optimization, reality isn't rendered until it comes into the viewpoint of a character in the game".

This is a lot more disturbing than it might first appear. One of the possible explanations (not the only one, but perhaps the simplest one) is "superdeterminism", in which the future is every bit as fixed as the past. I've heard several physicists - both quantum physicists and cosmologists, for different reasons - say "there's no such thing as free will; get over it".

My favorite quantum physicist and science explainer, Dr. Sabine Hossenfelder, brought this topic up in her blog/vlog just recently.

Many headlines promptly claimed that this means spooky action is real. But this is not correct. Rather, the experiments showed that you either have to stick with quantum mechanics and accept spooky action, or you reject spooky action and accept superdeterminism. Which is why I keep saying we need experiments to test superdeterminism.

[S. Hossenfelder, "Science News Oct 12", Backreaction, 2022-10-12]

http://backreaction.blogspot.com/2022/10/science-news-oct-12.html

The phrase "spooky action" here refers to Einstein's description of non-local behavior - specifically the collapse of the wave function upon observation, not quantum entanglement - as "spooky action at a distance", an effect that directly contradicts his own Special Theory of Relativity (which has been experimentally verified countless times, in fact, every time you use a GPS receiver). I look forward to reading about the design of an experiment for superdeterminism.

But such an experiment might incur sanctions from the Time Variance Authority (TVA). Last night Mrs. Overclock and I started watching the MCU series Loki, streaming on Disney+. It's fun, and funny, and of course Tom Hiddleston (as the Norse God of Mischief) and Owen Wilson (as a TVA agent) are terrific. But the best part of it is that it basically posits superdeterminism in the form of a "Sacred Time Line". It features the TVA, a time-police bureaucracy, that insures that everyone conforms to it. TVA operatives can "reset" (their term) the time line back to before it was disrupted, and "prune" (ditto) alternate history branches off of the time line. These actions typically cause time line violators to cease to exist. Or, indeed, to not ever have existed. (Or do they?)

The funny thing about "no free will" is that I'm still happier if I live my life - trying to be a good a person as I can be - as if I had free will. It's a strategy I suggest for everyone.

(Slightly revised on 2022-10-24 for clarity.)

Informed Delivery

 The U.S. Postal Service (USPS) has a feature they offer called Informed Delivery (ID). It's free to residential customers. I signed up for it. Most every day in which mail is delivered - around 7AM local time for me - you receive an email containing black & white scanned images of the paper mail you can expect to receive at the specified street address in the next few days. The USPS has to scan all paper mail. That's the only scalable way to sort and route it. They have to scan it whether I sign up for ID or not. Considering the volume of mail, the variations in address formats, and the support for even hand written envelopes, it's a remarkable technological achievement.

But now I wonder: who else can get this ID email for my address? Can law enforcement request it? Does it require a subpoena or a search warrant? Or is it considered public information, like the stuff in your trash bin waiting for pick up at your curb? Do the laws restricting domestic surveillance prevent the CIA or NSA from receiving it? What about the FBI or the DHS? Who else might have access to it? Can it be used to construct a vast network of implied communication, much as intelligence organizations do today with social media accounts?

Maybe this is how conspiracy theories get started.

Human-Machine Teaming and Autonomous Lethal Weapons Systems

I've been doing a lot of reading - and thinking - lately about autonomous lethal weapons systems. I've never helped develop one, but certainly the skills required to do so are in my wheelhouse. I'm philosophically opposed to them; I'm a fan of Isaac Asimov's Three Laws of Robotics. Yet I also believe that autonomous weapons systems are inevitable, and probably necessary. Such systems - e.g. armed autonomous flying drones used in land or sea battles - can go-where and do-what humans cannot. Physics is ruthless.

If our adversaries use them, I don’t see that we will have any choice but to do so as well in order to remain competitive on the battlefield. There’s a strong economic (and possibly even humanitarian, if they can reduce human error and danger to civilian populations) incentive to use autonomous lethal weapons. They will be particularly attractive to smaller first-world states, or any organization exploiting asymmetric warfare. Such systems may be fully autonomous, partially autonomous, or optionally autonomous. Combining a human operator with automation (a term I prefer to "artificial intelligence") is a form of human-machine teaming (HMT).

Lots of people in other domains are thinking about this. In its SAE J3016 standard, the Society of Automotive Engineers defines six levels of driving automation for vehicles, ranging from 0 (fully manual) to 5 (fully autonomous).

(Click on the image to see a larger version.)

It occurs to me that this might be applied to weapons systems as well. Here are some ways J3016 might be adapted to apply to weapons systems.

• 0 - the human operator has full control over the weapon system at all times.
• 1 - automation may assist the human operator with targeting, stabilization, etc.
• 2 - the human operator may relinquish control to the automation but can override its decisions.
• 3 - the automation may take control if it detects the human operator is impaired.
• 4 - the automation operates the weapon but a human operator is still required to approve a kill decision.
• 5 - the automation makes the kill decision without any human guidance or approval.

(H/T to John Stuckey for the inspiration for this.)

Unintended Consequences of the Information Economy IV

Daniel Kim, CTO of Geosite, a company that provides geospatial tools, wrote an eye-opening essay in the national security blog War on the Rocks. In "Startups and the Defense Department's Compliance Labyrinth", he describes what companies have to go through to comply with the enormous, complex, and often redundant, conflicting, and changing requirements to deal with the U.S. federal government, and especially with the its Department of Defense. Total initial cost for Geosite: US$300,000. Compare this with the typical size of contract that start-ups in the U.S. government's Small Business Innovation Research (SBIR) program receive: about US$1,000,000; the cost of compliance could be more than a quarter of the entire budget.

Much of the overhead is in the realm of cybersecurity. No one can fault the DoD for requiring stringent security mechanisms. But it does place contracting with the DoD out of the scope of many small- to medium-sized companies. And even for large companies, it is an incentive for the business to seek revenue elsewhere where it is more easily made in the commercial sector.

Furthermore, the technical work necessary for compliance either takes time away from the core technical team in smaller organizations, or requires hiring (and paying) additional staff with the most hard to come by (and expensive) skill sets. As I am constantly reminded when I chat with a friend of mine who makes her living as a cybersecurity engineer, there is not a lot of overlap between the skill sets of folks that do the kinds of work I do and the folks that do the kinds of work she does. Kim cites a slew of standards, many from the National Institute of Standards and Technology, that document the processes and infrastructure necessary for compliance. Just being familiar with these tomes would be a significant effort.

I've mentioned before that my tiny one-man corporation has done its share of work over the years in the defense domain, but always as a sub-contractor to a far larger organization that provided all the infrastructure and process that was required to comply with the customers' requirements. Kim also mentions the U.S. Air Force's Platform One and its concept of a "Software Factory": a kind of pre-laid infrastructure surround that temporarily assimilates a start-up and provides it with a much simpler set of requirements. (If you have the kind of LinkedIn network that I have, you have already heard a lot about this.)

Alas, without the kind of support provided by these kinds of organizations, the DoD is not able to innovate in the same way, and at the same speed, as the commercial sector. Nor even easily take operational advantage of new and shiny technology that comes out of successful commercial start-ups. Which means, for the most part, it's another windfall for the handful of existing huge defense prime contractors.

Unintended Consequences of the Information Economy III

In "Unintended Consequences of the Information Economy II" (2021), I mentioned an Executive Order issued in May of this year (2021) by the Biden White House: "Executive Order on Improving the Nation's Cybersecurity". This morning over a leisurely Christmas Eve breakfast I read the fifty-four page EO from start to finish. (It's not that onerous; the way it is formatted on the White House web site, with very short lines, it was expeditious to print it two web pages per physical page.)

I'm tempted to say that I've never read an EO before, so I didn't know what to expect. I've read so much stuff over the past decades,  I can't say for certain. (I once found myself having to read U.S Department of State International Traffic in Arms Regulations in regards to a commercial product I was helping develop for the business aviation space.) But for sure, I didn't expect the EO to be as interesting (or as readable) as it was. Here are some of the things I consider highlights:

• There was a a lot of verbiage about the need for federal agencies to share information with one another on malicious cyber campaigns. This may seem obvious, but in many organizations - government and commercial - this might be a hard sell. There is little incentive for organizations to admit they've been hacked, for lots of reasons. So an EO which requires federal agencies to do so is probably a good thing.

• There was also a lot of talk about the need for vendors who sell into the federal government space, especially cloud service providers (CSP), to share information on malicious cyber campaigns. Good idea, especially since they may detect such campaigns in their commercial environments as well. Same disincentives apply as above.

• The EO requires federal agencies using cloud services to adopt Zero Trust Architecture "as practicable". Also a good idea, and (if I am to be honest) something I haven't been that great about in some of my own work. I need to do better, and so does the U.S. Government.

• There is a section on the need for vendors that sell into the federal space to use a secure software development environment. It recommended such actions as administratively separate build environments, and automated tools to demonstrate conformance to secure development processes. There was a lot of text about the need for products to provide a verifiable software bill of materials (SBOM). Keep in mind this was written back in May of this year, months before the Log4J vulnerability came to light. If software products in use today that depend on Log4J had such an SBOM, a lot of IT folks might be having a much more relaxed Christmas holiday.

• Really an eye opener for me: there is a section proposing a cybersecurity labeling standard for consumer internet-connected (i.e. Internet of Things) devices. I very much look forward to seeing what happens with this. As a cybersecurity engineer friend of mine quips: "the 'S' in 'IoT' stands for 'Security'". I confess to still be grappling with security concerns - and the tradeoff between security and real-time behavior - in my own IoT projects.

• Most unexpected: a long section explaining the use and value of system log files. Preaching to the choir, President Biden. It requires federal agencies and their IT service providers to save such logs in a secure manner, and to periodically verify the logs against hashes to insure they haven't been modified. Such logs are crucial for post-hoc analysis of lots of stuff, not just malicious cyber incidents.

• The EO contains the usual caveats and exceptions for the Department of Defense and the Intelligence Community, but at the same time requires cooperation and leadership across the DoD and IC for this effort.

• Several requirements mention the use of automated software tools to audit, verify, and analyze the security of government IT systems. This is going to attract a lot of tool vendor attention. It will be interesting to see how those vendors address this, since the tools themselves - which are likely to be large and complex - will also be recursively subject to these same requirements. (I find myself reminiscing about the Gödel Incompleteness Theorems from graduate school.)

I still think the major impact of this will be more revenue for the small handful of ginormous prime contractors in the defense space. But I'd like to believe it's a step in the right direction.

Unintended Consequences of the Information Economy II

In "Unintended Consequences of the Information Economy" (2014) I cited an article in Foreign Affairs (2014), the journal of the Council on Foreign Relations, by former Deputy Secretary of Defense William Lynn. He talked in part about how companies in the technology sector typically invest far more of their revenue in research and development than do the handful of prime defense contractors in the United States.

What I didn't mention is how we arrived at the situation we currently find ourselves in: with just handful of big prime defense contractors.

In 1993, during the Clinton administration, then-Deputy Secretary of Defense William Perry convened defense industry executives into a meeting that came to be called "The Last Supper". He informed them that due to a huge reduction in the defense budget, there would have to be a consolidation of the defense industry.

John Mintz wrote about this and its consequences in "How a Dinner Led To A Feeding Frenzy" in the Washington Post (1997).

Perry's warnings helped set off one of the fastest transformations of any modern U.S. industry, as about a dozen leading American military contractors folded into only four. And soon it's likely only three will remain, with Lockheed Martin Corp.'s announcement yesterday that it plans to buy Northrop Grumman Corp. for $11.6 billion.

The unintended side-effect of the consolidation of the defense industry into just a handful of prime contractors was that there is now far less competition in the defense sector. If the Pentagon wants to buy a new major weapons system, there may only be a single contractor capable of delivering it.

Since this happened, the U.S. Departments of Defense and Justice and the Federal Trade Commission have tried to reverse this process by opposing further mergers in the defense industry.

John Deutch, also a former Deputy Secretary of Defense as well as Director of Central Intelligence, argued, in "Consolidation of the U.S. Defense Industrial Base", published by the Defense Acquisition University's Acquisition Review Quarterly (2001), that the consolidation also led to far less stability in the companies that did survive this process

In the 1993–1998 period of euphoria, defense companies experienced significant increases in equity prices based on the expectation of revenue growth and margin improvement from cost savings. In 1998, the outlook for the industry began to darken for several reasons. First, DoD reversed the consolidation policy. Second, expected cost savings were not shared with the companies, and hence margins were squeezed, especially from increasing interest payments on debt required to fund acquisitions. Third, defense companies making acquisitions were overly optimistic about the expected growth in top-line revenues from DoD, foreign military sales, and commercial spin-offs of defense technology. The anticipated increase in defense outlays had not materialized.

Finally, some key companies found it difficult to manage their expanded enterprises effectively in all respects and to meet their optimistic financial targets. The capital markets quickly shifted to more glamorous (at that time) dot.com and high-tech stocks not associated with defense.

My tiny one-man corporation has done its share of government contracting over the years, but always as a subcontractor to a far larger organization that had all the infrastructure, people, and processes in place to deal with the federal bureaucracy. The overhead involved is a significant barrier to entry for smaller organizations. And, remarkably, to larger organizations.

In my original article cited above, I related the story of Boston Dynamics, the spin-off of MIT that designs the human- and dog-shaped robots we all watch on YouTube. After the Defense Advanced Research Projects Agency (DARPA) poured a bunch of funding into the company, it was bought by Google in 2013, who basically said “thanks, but no thanks” to any further DoD involvement. Google went on to sell Boston Dynamics to a Japanese company, which in turn sold it to a South Korean company. All that government funding resulted in intellectual property that didn’t even stay in the United States, much less in the DoD. Many large tech firms have big revenue streams that for the most part don’t rely on the U.S. government; there are easier ways to make (lots more) money in the commercial sector.

This leaves much of the technology needs of the U.S. government in the hands of just a few big prime defense contractors.

In a recent edition of his newsletter "The Embedded Muse", embedded software and hardware technology pundit Jack Ganssle mentions that the Biden White House has issued an "Executive Order on Improving the Nation's Cybersecurity". Jack writes:

Uh oh. Do you sell to the US government? Since they buy pretty much everything, pretty much everyone does. A new executive order re security will make our lives much, much harder. Though the details are still being fleshed out, a pretty good overview here will raise your blood pressure. 

Jack references an article by a vendor who is, of course, trying to sell you something, but is none the less a pretty good overview of the EO. From that sales pitch:

This EO directs these agencies to develop new security requirements for software vendors selling into the U.S. government. These requirements will be incorporated into federal contracts for commercial software and hardware with the intent of imposing “more rigorous and predictable mechanisms for ensuring that products function securely, and as intended.”  This is a monumental shift that will have an immediate impact on global software development processes and lifecycles.

In addition to  a host of new information and operational security measures that government agencies need to implement, the new order establishes a robust approach to supply chain security. The new requirements will include security testing throughout the development process as well as a Software Bill of Materials (SBOM) to address security issues in open source components.

I expect this EO to be a huge boon for the few existing big prime defense contractors, while preventing the small-to-medium, and even large, tech companies from participating in providing innovative technology solutions to the federal government.

As both a software engineer and a taxpayer with many decades of experience doing both, I have very mixed feelings about this. I can appreciate the need to make sure that our tax dollars aren’t wasted, that expenditures are all accounted for, and that the products our government purchases are reliable and secure. But I feel pretty confident in predicting that it will mostly mean that giants like Raytheon and Lockheed-Martin will do well.

Small Talk

Despite the fact that I rate off the scale on introversion on every personality test I've ever taken, I am skeptical about the the Work From Home and Remote Learning trends. As necessary as they are during these Plague Times, I don't think we have any idea of the long term effects on our corporate culture, our productivity, our innovation, or on our and our children's emotional maturity and communications skills. We are a species that evolved to live and work in tribes.

This morning I read two articles that I liked on this topic.

In "What a Year of WFH Has Done to Our Relationships at Work" (Baym et al., HBR, 2021-03-22), three Microsoft researchers analyze ginormous datasets to determine how communication between team members have changed. Hint: they've become much more siloed.

In "I've lost my conversational mojo - can I relearn the art of small talk?" (Samadder, The Guardian, 2021-04-17), actor and columnist Rhik Samadder talks - socially distantly - to experts in the art of small talk, including to the neuroscientist for which the "Dunbar Number" is named. Upshot: the experts think we'll adjust back to being social again more readily than we might think.

I am concerned about limiting our interpersonal communications to those channels moderated by technology. It's not just about the loss of body language and other social cues. Or the lack of the productive backchannel that is the spontaneous hallway conversation. Or the magic that happens when you get a few smart people in the same room in front of a whiteboard.

It's also about the way in which I turned from being a socially awkward introvert to being - I am told, anyway - skilled at collaborating, teaching, public speaking, and giving executive briefings (while still being a little socially awkward). Being a strong introvert - some might even say a nerd - those skills didn't come naturally to me; they were learned through years of hard-won, and sometimes even painful, experience. Experience that we are eliminating in the WFH and RL world.

I hear a lot of folks talk about how much they prefer WFH and RL. And a lot of those preferences are totally legitimate. But for some of those folks, the strong introverts, I fear that they prefer it because it gives them an excuse not to grow and learn and exercise important social skills.

I also fear this is just another facet of late-stage capitalism, in the form of another cost - the cost of providing adequate infrastructure - that corporations can push off onto their employees, while the resulting hidden expense to the organization remains largely unaccounted for.

I don't think we appreciate what we're giving up. And we have little understanding for the possible long term consequences.

Update (2021-04-22)

Laurie Kenley, a cloud security wrangler, made these insightful remarks in another forum, and kindly gave me permission to reproduce them here verbatim.

I am off the scales in extroversion and I have been working from home for 2 1/2 years. I had to re-create my life around the fact that I would not be getting social interaction in my job well before the pandemic. I had it all figured out. And when the plague hit, all of that collapsed. It took a lot of figuring out how to be OK. Not great but OK.

One thing I will say that has been “better“ during the pandemic as a remote worker, is that everyone is remote now and it’s a level playing field. I have been able to be more effective and more plugged in to my coworkers than ever before in this remote job. To use a Hamilton reference, "the room where it happens" is now a virtual room. It has democratized decision making in a way I don't necessarily expect to last in the coming months.

Catus Amat Arca Archa

As is well known, cats are the natural enemies of vampires. This has been clearly established by overwhelming empirical evidence. This is because cats recognize that vampires can take the form of bats, which are just a kind of flying mouse (German: "fledermaus" or "flitter mouse"). Also, cats are naturally aggressive towards any creature that tries to unseat them as the apex predator. Finally, cats must challenge anything that threatens the cushy situation cats have created for themselves with their human domestic servants, whom vampires consider to be merely livestock. [Ref: J. A. Lindqvist, Let the Right One In ("Låt den rätte komma in"), St. Martin's Griffin, 2004]

Vampires are repelled by the Christian cross, not due to its religious symbolism, but because vampires have a cognitive bias against right angles. Again, there is a wealth of research about this, the most widely accepted hypothesis being that vampires predate the evolution of humans by hundreds of thousands of years, having evolved long long before our distant ancestors introduced artifacts built with right angles, angles which typically do not appear in nature. Vampire brains never evolved the ability to deal with right angles, which may also explain why humans prefer Euclidian architecture, as a form of defense. [Ref: P. Watts, Blindsight, Tor, 2006]

This is why cats are attracted to boxes. Or even squares drawn on the ground. They feel safe from vampires, their natural enemies, when inside boxes because they know that the vampires will be repelled by the right angles found in boxes. The fact that cats began cohabiting with humans when we began building structures incorporating right angles cannot be a coincidence.

This startling and enlightening revelation came to me this morning during an argument with one of my Beloved Feline Overlords while I was trying to break down some old cardboard boxes for recycling - an argument my BFO, of course, won.

I refer to this hypothesis as catus amat arca archa.

I await the accolades that are sure to follow this major insight.

Acknowledgements

The author would like to acknowledge his BFO and lab assistant Sophia for her significant contributions to this research effort.

As I was walking down the street one day

I think about time a lot. I have a cesium atomic clock "ticking" away in my living room. I've stood alongside one of the official cesium frequency references, and also the experimental ytterbium optical atomic clock, at the NIST laboratories in Boulder Colorado.

For a long time, I thought there wasn't really any such thing as time per se, only events connected by causality. After all, all of our means of measuring time have to do with counting events that occur in some naturally occurring resonator: pendulum, quartz crystal, cesium atom, etc. This is a point of view held by at least some research physicists, so it's not a completely crazy idea.

But in one of Dr. Sabine Hossenfelder's blog posts, a commenter mentioned the effects of special and general relativity on our measurement of time, in which acceleration and gravity wells, respectively, slow time down, according to those very same resonators. That comment completely changed my world view. So now I think time is a real thing, not just a measurement artifact.

Next thing to think about: why does time play a role in the large (classical physics, cosmology, etc.) but not in the very small (where it doesn't appear as a factor in the equations used by quantum physicists)?

Separately: what sometimes keeps me awake at night (really) is that there is much evidence that at both the quantum (Bell's Theorem) and cosmological ("block time" a.k.a. "block universe") level, reality appears to be super deterministic: both the past and the future are fixed, and we are just passive observers playing our roles as we move along the timeline. We can't predict the future; but we can't change it either.

Does anybody really know what time it is?