u/NeighborhoodFatCat

It feels that in a standard curriculum on control engineering/theory, the final topic is inevitably MPC.

Everything else that gradually builds towards MPC feels like “discardable” after you learn about MPC. All that wild eigenvalue analysis and drawing those Nyquist contour? Phew, MPC doesn’t involve with any of that!

Plus, MPC is already in discrete time, so it is implementable without any further weirdness that comes from sampling and discretization of other types of controllers.

I think a parallel analogy is the development of the transformer model in machine learning, which I am more familiar with. Now everything in ML is transformer-based and the next big breakthrough seem to be very far away. Is MPC like the transformer of control theory?

Are there situations where other methods might outperform MPC?

Or does MPC require some unrealistic assumption/requirement?

Is there any other possible algorithm that goes beyond the MPC or is this the final story?

There was a period in the development of machine learning where application seemed to be informed by theory. Some of the best known theories include:

• If you train a model with too much data, then you get overfitting and your test performance will be suffer.
• Big models do not generalize because theoretically you will never have enough data.
• Never train on the test set, because it will result in high-bias.
• Never even look at the test set, because you as a modeller will instantly be biased and use the wrong model.
• Good results can only come from "compatible" models and optimization routines. You can't just throw ADAM onto some brand new model and expect it to work well.
• Optimization provides solid theory for machine learning, so use the optimizer with the best performance guarantee in the optimization literature.
• If you want to have good performance, ensure to use several models instead of one model, because stacked or ensemble models are always superior.

Most of these theories started out as mathematical statements (albeit on some contrived examples that have nothing to do with reality). At some point, these theories became folklores and were widely reproduced in textbooks and taught in classrooms, even making their ways into standard interview questions at data science related companies. Every student had to remember that bias-variance "bull's eye" diagram as if it was relevant in practice.

But then some of these theories started to get overturned. It turns out you can just break a lot of these theoretically-guided practices and still get good results. The ones who pushed these theories (especially the authors of various "statistical ML" textbooks), quietly stopped their postulation and instead hopped on the hype train. This left their students confused because there was never ever any retraction or resolution.

So my question is: are there still any theoretically-guided practice that remain in ML today? For example, do people use an optimizer because it is theoretically the best for the class of problems? Do people use a certain model or components associated with the model because it works well in theory? Or is it now a fully empirical field where practice is guided by whatever seems to work for other people?

It should be clear to everyone that the next great breakthrough idea come from syncretic fusion of multiple disciplines.

The people who seem to be most capable of generating such ideas are the ones who have mastered domain A and have serious interest in domain B.

Yet, very strangely, modern academia do not (or no longer) provide this kind of "lateral mobility" in how it selects and train future researchers, i.e., creating a path for someone to move from A to B.

By moving from A to B, I do not mean someone in A and wants to do B-related research while in A (which is always possible). But someone who moves completely between the fields, departments, research practices, identities.

For example, if a department is hiring for a chemist/physicist, it will very unlikely hire someone from other backgrounds (even though the recent Nobel prize winners in chemistry/physics don't have any relevant background.)

If a department is hiring a graduate student/PhD/Post-Doc, it will very unlikely choose someone from outside of the department. Sometimes a prior degree in the specific area is required in the job posting.

I can easily imagine a world where a physicist proficient in multi-particle physics could be good fit as a sociologist specializing in crowd dynamics. Or a researcher who studied industrial automation could be a good fit for international trades and marketing involving industrial automation.

Yet no such interdisciplinary, transdisciplinary, lateral opportunity seem to exist. The hiring is as myopic as ever. Very little chance is taken on someone who is otherwise demonstrably smart and capable.

I think many students still have the archiac stereotype of a researcher from the 70s/80s, who hopped between many disciplines and made serious contribution in each. These opportunities no longer seem exist, or at least nobody is willing to train such researchers.

What happened?

I find that there are just way too many ideas/concepts in ML. The researcher all portray their idea as the most important one which will revolutionize ML, and this makes it very difficult to zoom in one any single topic. A small list of examples include things like:

• Energy-based models
• Contrastive learning
• Contrastive divergence
• Score matching
• Hopfield networks
• Bayesian learning
• Minimum description length (MDL)
• PAC-Bayes learningg
• Online learning
• Multi-task learning
• Meta-learning
• Transfer learning
• Domain adaptation
• Few-shot learning
• ........

A huge amount of effort could be exerted on any of these topics and while they are all interesting, I am wondering what actually matters in 2026. Like what would you actually devote yourself in learning as opposed just learning whatever researchers out there says is important?

I live in a country plagued by unfinished construction projects. At the beginning of the project, everyone's happy and signing deals and getting media coverage, and then someone gets clever:

"If we stall the project, we can ask for more money. The more desperate they wish to see the project finished, the more money we might be able to get. If the whole project fails, the government might bail us out."

As such, a single road project gets delayed for a decade. Millions of taxpayers are wasted in the process, and even when its finished, it is still shit and a new project is planned.

This is an analogy for virtually everything that humans do.

Instead solving problems, humans create problems within problems to ensure that they have a job that keeps the money flowing. They unnecessarily complicates the whole thing and then milk each of the sub-complications they've created. As a result, the more problems humans "fix" (or rather, pretend to fix), the shittier things gets. And now our society is a cascading layers of doodoo caused by deliberate creation of problems for the sake of milking those problems.

AI is providing us with an honest reflection of the pace for which progress should be made and leave little room for people to "milk" the process. If they tried to milk the process, they'll get destroyed by a competitor who goes straight to the heart of the problem using AI and get eliminated.

That's why software engineering is one of the first field to be radically transformed, not simply because AI is good at coding. Programmers deliberately made the field complicated to gatekeep, AI is the first to call it out. All that "agile development", "scrum", "pair programming" are nothing but corporate BS to make some in the game seem relevant, when the entire field could've done without their existence from the very beginning. Just read this page and see how deep humans have dug their hole: https://en.wikipedia.org/wiki/Agile_software_development This is worse in research by the way. Many disciplines are just solving problems that shouldn't have existed in the first place.

I come from a STEM background (though I no longer practice STEM and purely do international sales). By theory-practice gap, I refer to the divide between the abstract knowledge taught in classrooms and the real-world methods used in active professions.

I find that the theory-practice gap has never been larger between what academia is teaching versus what industry is doing.

Part of the reason is this common and very powerful belief in academia that a robust fundamental will carry a person anywhere. As such, many universities teach the same curriculum that they've taught since 20, 30 or even 50 years ago. Because "the math/physics don't change".

This, however, is something that is not valued by industry based on my observation. First, it is hard to prove you have the fundamentals (what are even the "fundamentals"??). It doesn't visibly show up quite as often in day-to-day work. And it is definitely not something that is 100% for sure going to help you on the job, where you are working with cutting-edge tools or very niche/experimental platforms, softwares, hardwares or simulators.

Another thing is that industry only hires based on the latest hype knowledge/tools/ideas, and don't care about fundamentals or just assume you have it.

Now with AI, I feel that the gap has grown enormously. For example, many industry has virtually abandoned any "classical ML" techniques in favor of LLM and demand years of experience in it, while schools are still busy teaching the classical ML. Everyone predicts that it will grow worse, especially now with AI pushing the frontier of many STEM disciplines, with most of the new knowledge being accumulated in industry behind closed-doors, while academia has not invented a feasible path towards that frontier.

Do you feel that your knowledge accumulated in academia is what is actually being deployed/used out in industry (or a non-academic organization)?

By "over-estimate", I mean that the tools/skill is not hard as it appears and can be picked up on the job, but companies insist on many years of (industry/non-academic/practical) experience or some deep level of expertise.

For example, I find that a lot of companies place a lot of emphasis on simulators. In the robotics industry, it will be things like MuJoCo. They demand years of experience in these simulators.

From my experience, simulators tend to be easy to pick up. I never used MuJoCo personally, but for example if a job demanded some deep expertise in "MATLAB" or "Simulink", most people would just kind of chuckle because that's kind of like freshman year college level difficulty and any hard things involving them you wouldn't do it by yourself anyways.

Another example is database skills. I believe SQL is not something that is really needed to have some serious training in and can be understood/learned in a week and be productive.

Similarly with things like PLC. Most school never teach it because it is assumed you can just spend like a day and understand most of it. Yet companies require years of experience in it.

Similarly with some demand in expert-level programming. In practice you are just reading/debugging thousands of lines of code or hundreds of files of relatively simple code. The code themselves can be understood in seconds/minutes. Most of them involve a function takes in some arguments and produces some outputs - no PhD required here. And anything unfamiliar can be looked up and understood in under ~5 minute.

This said, there are definitely many hard things as well.

So this gets me thinking: what are the hard things and what the easy things that the job requirements or descriptions not tell you?

Out of all the countries I've been to and visited, the Chinese stands out as the most rigid and unfeeling when it comes to following rules, and this manifests itself in millions of different ways in daily life.

Here are some little examples:

• Subway hires like 4-5 people to check your unopened bottle of Nongfu water for bomb/flammable liquid when they can clearly see that you are not a terrorist, for example, an elderly grandma with some vegetables, a foreigner wearing a suit and tie clearly on business, or some school kid.
• Employer obsessively check to see your large office supplies such as your work desk or large split AC unit is still there on a fairly regular basis, to prevent you from secretly selling those things off behind their back, when no sane person would do that, e.g., sell their work desk.
• You ask for a single band-aid or a Q-tip at the hospital. The nurse/doctor looks at you completely confused and tell you to buy a box of supplies at the store because giving you a single Q-tip is "We don't do that here".
• Your luggage on Chinese airlines is over the limit by a very small amount, say 1 kg. They will directly ask for hundreds in penalty with zero leeway or tell you to ship the items, whereas other countries will just let it slide or try to help you out.
• A form must be signed with a black pen, but cannot be a black pen with a rollerball tip, which will be rejected. There are various rigid rules for forms signing. Except the forms themselves are poorly made, have typos and also change fairly regularity. Also some of the people working there just completely do not know the rule and tell you to "try things out" and fix if rejected later.
• Two train stations with extremely similar names which are easily confused for foreigners and out-of-towners. The problem is well-known to the station staffs. When you purchase the wrong ticket and go there, instead of modifying the ticket at no additional charge or refunding the ticket, they tell you to buy a brand-new ticket while forfeiting the ticket you've already bought.
• Visiting a minor tourist attraction like some mountain without physical passport, but has photos of passport? No, you can't go in. Only physical ID is allowed. (Wait a minute, why do you need a passport to climb a mountain?)
• If a museum closes at 6:00 and the last entry is at 5:00 and you arrive at 5:01, you aren't getting in. Even thought here is like zero person in the museum.

Many other examples like this.

I find this to be the largest difference between Chinese people and virtually people in any other countries I've visited. There seems to be no genuine attempt at human empathy and following the rule trumps pretty much everything in life, even when the rules are downright absurd and cause problems, they'll just keep enforcing the rules like some rule zombie.

All these time I've been in China, I've never seen a Chinese person proactively loosen up some absurd policy/rule to make your life easier.

What happened to this culture?

One thing that stuck out for me when watching the TCAP series (and other vigilante justice shows on Youtube) is that most of the preds look absolutely like shit, to the point you can create a list of common stereotypes, such as,

• 36 month pregnant dude in athletic or cargo shorts,
• Hobo on a 30-day crack binge with a plastic bag,
• McLovin with a beard and a hoodie, and,
• 46 year old balding uncle with a medical degree from India/Pakistan pretending to be 20

(I'm sure there are many more)

What's the psychology with these preds? Don't they have a shred of common sense that women want someone who's clean and good looking? Do these men live in a vacuum with zero understanding of how attraction works, or never went to college, or never watched a romanic movie?

It's a bit mindblowing to be honest to see a tatted up crackhead looking elderly man or a rotund man who is wearing CPAP and taking insulin shots thinking that they got a chance with some petite blonde (let alone an underaged one).

Help me out with this one.

Was just looking at the list of preprints on Arxiv cs.LG https://arxiv.org/list/cs.LG/recent?skip=0&show=500

Everyday 100 - 400 new machine learning papers gets uploaded on this server.

Looking at this unending list of preprints is as if you stepped into a crowded room, like the stock trading floor on wall st. in the 1980s. Everyone is shouting over each other. Nobody is talking to each other. Everyone's trying to prove something, to someone, to themselves, to build some credentials in the ML/AI space to meet those job requirements, or dying to get their truth out. Every title contains some new terminology invented by the authors that feels not worth the effort in keeping it in your working memory. Burn-out by endless novelty.

Frontier research are now corporate trade secrets that politicians and military are watching closely. Research papers are ir/unreproducible he-said-she-saids. Marketing material are research paper and vice versa. Extremely major breakthroughs are announced via tweets, whereas extremely minor results are unannounced via journals. Everything feels simultaneously mostly true and possibly false (because nobody is seriously checking). Nobody knows what's going on, and people who knows what's going on has a non-disclosure clause in their job contract. Is the theory of generalization that we learned in school true or false? It feels false, why hasn't there been any retractions? Many questions like these.

Is it too late to regain some coherence in this field??

I don't gamble. I've been to the casinos in Macau.

Something that has been on my mind is that a lot of gamblers seem to be old-timers who seems to know the game inside and out.

How? Where do they get started?

I've look around and found exactly zero people teaching others on how to use the machines or the rules at the table.

The games have very mysterious elements, with various slight gestures from the attendant seemingly signalling some interesting stages of the game. And often times the entire table would be speaking Cantonese and the attendant would be shouting something in Cantonese if someone does something outside of the rules of the game, yet there's like one guy from Europe or Russia or a couple who speaks standard Mandarin who's also playing.

I honestly don't get how anyone can figure out how any of this works.

Not a mathematician and has never done work even remotely close to mathematics. A bit more of a social scientist. But mathematics has been on my feed for a while now, especially recently all over Twitter with the resolution of the Jacobi conjecture and now the Fields medal.

I looked up the Wikipedia page of Field medalists and I quickly spotted a trend of the recipients:

• Both parents are working in the sciences, and often mathematics, physics or computer science, and sometimes at the university level.
• Often grew up in some very politically stable country with little civil or international strife and a lot of free time to do math, play intellectually simulating games like Chess or Go.
• Was aware of major mathematical competitions in their country at young age and participated in them, usually did exceptionally well.
• Often extraordinarily gifted and quickly moved through the entire K12 process.
• Often quickly paired with another famous mathematician either as mentor or supervisor.
• Often immediately got hired at universities and became professors.

To put it more succinctly, these people:

• Were unnaturally gifted in terms of their inherent neuro-biological make-up,
• Had extraordinary upbringing from supportive parents and social environment,
• Had access to information that most of their peers would not have gotten, e.g., to join certain competitions or work on certain problems.

In this sense, it seems a lot of the prizes are just rewarding things that originated in a mathematician's childhood or even at the moment of birth. And while many other accomplished happened, they were all contingent on these "precocious" or "pre-school" factors.

If they didn't have those favorable childhood conditions, it seems to be doubtful that later accomplishments could have been made.

So what do you think? Are prizes in mathematics in general are just rewarding favorable childhood conditions of extraordinarily biologically gifted individuals?

Sorry if this sounds dismissive or jealous. Like I said, I have no skin in this game (not in math at all). I just want to understand how people in math feels about these type of things. If there is any critique, it is towards the reward structure rather than the mathematicians themselves.

Can we talk about how bad review formats are for hotels (and pretty much just anything)?

• A guest goes to a hotel
• A guest leaves a review
• The hotel owner gives a reply to the review

Done, that's it, no more conversations. Can replace hotel with any other establishment.

Just had a situation where the picture and the actual building and room were like totally different. Long story short the hotel and the room was like the hellishly bad (dragging heavy luggages 7 floors up the stairs bad).

After leaving the review, the hotel owner replied with "Oh, the guest tried to request for a refund and it didn't get approved so he left a bad review, we are genuinely sad about this situation".

When the hell did I ever request a refund? The reply is just completely made up. I informed the booking platform regarding this situation and they are like "yeah we reprimanded the owner." But the fake reply is still up. Reality is diverging.

Published July 2026, https://www.whitehouse.gov/wp-content/uploads/2026/07/Science-A-New-Golden-Age.pdf

This document titled "Science a Golden Age" by the White House Tech director and presented to Donald Trump calls for "de-prioritization and re-engineering of legacy institutions" in favor of individual researchers and the creation of an organization that supercedes academia entirely.

• The U.S. research system should prioritize the individual scientist over legacy institutions...Too much of our research enterprise has come to serve itself rather than the scientists within it. Federal funding agencies should support a broader range of performers...rather than the academic silos of the last century.
• We must prepare our research enterprise for the AI revolution...even the most capable AI models will be slowed down in the bottleneck of institutions and systems built for the last century.
• Just as scientific inquiry demands that we revise our theories when evidence contradicts them, evidence of scientific slowdown...should spur us to experiment...new ways of funding, conducting, and translating research. Vannevar Bush’s pioneering spirit calls us to do what he would surely do today: reimagine the entire enterprise for our time

The report goes on detailing problems widely recognized within academia, with sections titled:

• The Incumbency Tax
• Weakened Meritocracy
• Misaligned Incentives
• The Reproducibility Crisis
• A Lack of Accountability

While I agree that the problems mentioned are significant enough to warrant a completely revamping of academia, the report is also quite elitist at points and it is hard to see how disadvantaged population could rise to the top under this system. In either case, if you are in academia or planning to continue to work within academia in the US, be prepared to swim against some major tides soon (if you aren't already).