u/NeighborhoodFatCat

Information has never ever been more readily accessible.

If you want a textbook on any topic, a few click and you will find the bible of the field.

If you want someone to teach you something, a few click and there's a video from MIT, Stanford, or Princeton.

This has unfortunately created an information overload situation for people doing multidisciplinary, interdisciplinary research or people who are just intrinsically curious or has "scanner personality" (Jack of all trades, master of none).

The most innovative research ideas combine insights from many fields, and it seems that the trend is towards these innovative or even unusual ideas.

Also, if you are thinking out of switching outside of academia later on, read the job requirements. It's almost like a laundry list of topics, typically outside of your immediate area.

How do you find focus in a time like this?

As someone who is not affiliated with any of the big tech companies, I find it particularly difficult to have the confidence or enthusiasm to approach any ML problem with an attitude that my professors probably had at my stage in life. I'm sure I am not the only one having the following thoughts:

• "My research is currently being done better at companies."
• "ML problem I set out to solve is already solved and in fact turned into products and sold for millions at companies X, Y, Z. There is no need for further research."
• "Industry is not interested in theoretical ideas and there is plenty of evidence for that, starting with their hiring practice."
• "Companies wouldn't have millions of dollars in funding or revenues if their models weren't working."
• "Research is like Darwinian evolution. Evolution aims to produce the fittest model. After decades of evolution, the fittest model is already in industry, why should I explore other evolutionary dead-ends?"
• "There may not be a next big thing after LLM. If there were, it would be simply incorporated as a function or a subroutine that LLM simply calls when needed, and the average person would be none the wiser. My contribution would be invisible."

Seems like research outside of big tech companies is pointless (unless you are a prof who is making big $$ while doing it). Because whatever they are working on might be lightyears ahead of whatever you are doing, but you wouldn't know because their model is simultaneously closed-source and omnipotent.

There are tons of people sharing their resumes on other ML/CS subreddits and occasionally you see that their projects are along the lines of "linear regression for Titanic dataset" or "YOLO for pedestrian detection" and they are wondering out loud why nobody is hiring them. Everyone with more ML experience can see because there is zero need for people with this skillset. But what if my very research also looks the same to people in industry? What if my "deep geometric autoencoding variational neural-former" also looks like some silly Kaggle project because industry can already do that much more efficiently?

How do you silence these thoughts?

I took a course on control many years ago. Although I do not work in control-related fields now, I still use it as a tool to understand real-world systems. After all, all systems are feedback systems and control theory is like the physics of feedback systems. But precisely because of this, I have met with many frustrations with the curriculum and textbooks and other references in this area of study and I have perused material from around the world with little success.

To put it simply, all of these resources put enormous focus on the math, while neglecting the various details on the tech that "surrounds the math". Out in the real-world, when you are implementing a system, or understanding a system or even casually engaging in conversation with someone in engineering, the tech that "surrounds the math" becomes very important whereas the math becomes invisible.

The standard control feedback loop simply consists of a controller ("the ying") and a plant ("the yang"). The set-point is often optional (set to 0). Enormous amount of mathematical analysis can be performed just based on this mental image. In fact, almost all analysis in any standard curriculum in this field can be performed knowing just these two things. You can take multiple graduate-level courses based on this alone and even publish papers of the highest calibre.

Then the frustration comes as you move out of the academy.

As a start, it turns out we also need actuator and sensors. But which ones would be suitable? We are not typically taught. The actual interfacing between the controller (soft/middle/hardware) and the actuator (hardware) can often be tricky. Similarly, the actual interfacing between the plant, sensors and controllers can also be tricky (seldom discussed). For example, textbooks, the controller takes in things like voltage or forces values, but in implementation it takes in 1s and 0s. This exact conversion process is under-discussed.

But this is just the start of it. Take industrial control as an example, we can now ask many more things such as:

• What hardware is appropriate to implement the controller? (Hardware knowledge)
• How do we come up with the model itself? ("System ID", which tends to be more math without discussing the tech that makes it happen)
• How do we monitor the process from a distance? (Inter-networking, database knowledge, software engineering)
• How do we control the process using a remote? (RF engineering)
• The controller is semi-agnostic to the shape and material of the parts involved in the system. How do shape influence dynamics? (Kinematics, material science)
• How do we optimize all the various parts involved in the process? (Optimization/programming softwares)
• How do we incorporate textual, pictorial, audio or video feedbacks via various tools such as computer vision and language models? (machine learning, LLM, RAG, and tech associated with them)
• How do we get the parts? (Need knowledge about how to source and acquire parts)

In real-world control design, I find the latter set of questions to have an out-sized importance in comparison to the algorithm, which apparently is just 3 numbers associated with the PID gains in 99% of the industrial applications (of course, this is not true for all applications), which apparently can also be picked through trial-and-error according to those hobbyist videos on Youtube.

Finally, one of my relative works in industrial control, and he does not have any engineering or control background. All he understands is one component (a PLC) being hooked up to another component (a SCADA system) being hooked up to another (maybe a pump, or a robot arm) and he can very fluently discuss how these various things are hooked up together and how they can be optimized further without going into any internal low-level details.

I feel like the current control curriculum is denying students to have this type of "global picture" that runs the real-world. Am I justified in my observation? Should there be a revamp in the curriculum that puts more emphasis on the various tech that makes control happen in the real-world?

Saying things like "Anthropic stole copyrighted material" (which is demonstrably true and well covered in maybe thousands of news report) gets you downvotes on Reddit, what gives?

Similarly with other things like:

• Anthropic works closely with department of war.
• Anthropic's model was used in the Iranian strike on a middleschool girl's school.
• Anthropic is backed by AI military drone startup, Whitestork.
• Anthropic is actively working with Palantir on mass surveillance and military projects for the department of war.
• Anthropic CEO actively called for integration of AI with the US military, to promote and defend democracy from "foreign adversaries".
• Anthropic actively works with weapons and firearm financier BlackRock.

All extremely easily verifiable news, even from Anthropic's own website "We work closely with the Department of War", "We have much in common with Department of War than we have differences".

But if you mention any of this you get mass downvoted on Reddit with people saying things like "Well, why not build your own AI company if you don't like Anthropic so much??".

Are we already living under SkyNet?

Was reading some research papers put out by Anthropic (and some other organizations/researchers) and one thing I've noticed is that these research papers consistently all share the same quality:

• Oftentimes over 100 pages of pure words, interspersed with screenshots of very dense/hard to read prompts and replies. Extremely-dry writing style.
• Oftentimes almost zero math or even math symbol to be seen.
• Uses some proprietary model with specific versions.
• Seems like a lot of work to (even want to) try to replicate their experiment.
• Discusses very subjective (and boring, at least to me) matters such as LLM emotions or introspections.

Who are these papers even written for? Certainly nobody is sitting down to read 100+ of subjective interpretations for a model that's barely accessible to the public, right? There are assigned readings for highschool english classes that are shorter than these papers. It seems to be a huge effort now to even check one of these papers for correctness or to formulate some thoughts around the paper. Just very confused at the state of LLM research.

One of my relatives is currently hiring and interviewing candidates. She gotten the job she has now almost 40 years ago. And she is complaining how the current generation and the skillset they have is misaligned with the need of her industry.

This got me thinking: something I have observed (in the older generation) is that the requirements they put up for hiring new applicants far far exceeds whatever skills they brought in/had when they first got the job.

I don't know exactly how to put this in words, but some of the issues I see are:

1. Many new technologies were invented during the last couple of decades. They didn't need to know any of these, but now you do.
2. Many jobs require knowing specific things that they very gradually learned on the job for the past decades, and they want you know these things despite not going through their experience.
3. They want you to perform a large variety of roles that they gradually assimilated over the course of their career, right at the offset.
4. They complain about candidates not knowing niche proprietary technologies and bemoan how schools is failing their students, while ignore the fact that they are using niche proprietary technologies that 99.9% of humanity have never even heard of.
5. They ask for quality internship experiences while ignoring the fact that the "internship" (especially paid internship) is a very recently invented concept (similarly with things like "hack-a-ton" or "adjunct faculty") stemming from companies not wishing to pay full-time employees. There weren't that many internships around just a few decades ago! These people never even have an internship to begin with!

I'm sure there is a lot of more fucked-up things to be said. Sometimes I look at the job requirement and think out-loud if this is something humanly possible, i.e., if a human's brain capacity can actually manage all that complexity.

I don't know how society can deal with this gradual credential/requirement creep and total unwillingness for people who have jobs to reflect on their own experience.

In control-related jobs for industry, the most commonly desired experience is PLC (Programmable Logic Controller).

But from my own experience, PLC is something that is virtually never taught (outside of a trade/manufacturing school), most of the stuff is quite niche/proprietary, seems to require little theory and seems to be something that can be learned quickly if someone were to be put to the task.

I think if you asked professors working in academia "how many years of PLC experience do you have", most would say "zero".

So where does PLC sit in terms of control engineering?

It seems to be simultaneously extremely important industry while simultaneously completely disregarded in academia. I don't think PLC is a hot topic in any research conferences. Also, from what I've seen, nobody uses PLC for a hobby project.

So where do you gain the experience? Or do you even need to gain the experience to begin with? Is this an example of a topic that employers don't want spend the time or energy to train employees, while academia don't see as something important to be explicitly taught?

I think the same question can be asked for a lot of these acronymed tech, like SCADA.

Anyone else feels that restaurant is no longer about the food?

Your experience at a restaurant almost solely depends on how much you tip.

You can have amazing food and only to receive a stink-eye, an ugly scowl, a rude remark, get yelled at or even get escorted out of the building, which obviously ruins your overall experience.

What you think you are doing: "I am going out to grab lunch." (WRONG)

What you are actually doing: "I am going out to tip." (Correct)

I think this is the most correct way of thinking about our daily actions in a North American context. The same goes with any other service (sports game, manicure, so and so forth).

The thing you are actually trying to get is secondary to the tip (+ service charge, + large party charge, + employee benefit, + whatever). The actual product is you not getting a bad/rude experience by complete stranger. Kinda like the protection fee for mafia/local gang if you think about it.

For me,

• Certain words have much higher frequency than expected in daily speech, e.g., "ironna". Everyday I can hear people use this word.
• Very common usage of words in Katakana such as マナーモード which I would have never expected. It always take a second to read the Katakana and guess the word in English, some of them are very non-standard from the English perspective.
• Usage of single character kanji that you wouldn't know how to pronounce shows up a lot, every other sentence in an announcement/sign could be a guessing game.
• Pronunciations of 下 literally changes so frequently. One moment you are arriving at 下関 the next you are taking the 下エレベーター, the next you are reading a sign that says 足下気をつけて and now you are walking next to 下水道.
• Pronunciation of numbers is slightly harder than expected due to occasional irregularity, e.g., 四時 is yoji not yonji and 六百 is ro-pyaku rather than ro-hyaku. Hard to react to a sentence and pronounce the number in the correct way.
• Omission or stress of certain sounds in standard textbook examples is difficult to prepare in advance, as well as tense of verbs. お疲れ様でした becomes お疲れ様ですうううう and ありがとうございます becomes あとざいまあああああああああす.
• Polite form of simple words or verbs (e.g., to see, to say) can be difficult to expect and takes a moment to react.
• Can never truly be sure when a verb associated with an action is used in practice as a suru-verb or a ru/u-verb. For example, I've heard both 停止する and 停まる being used and there is very little contextual cue as to why one is used over the other.

I've seen many posts on here on getting advice to get jobs or start careers outside of academia. However, a fairly common pattern I am seeing is that people follow what I will refer to as the academic's mindset, which I believe is something that is holding many people back (myself included).

What is the academic's mindset? I would characterize it as believing that your future career will necessarily build upon your past experiences and at some point you can look back and see how one thing built upon another like the pyramid of Giza and you would have a coherent story to tell about your life and career (maybe to your grandchildren before they go to sleep).

For example: As a child, I was always interested in abstract mathematics, and I got a degree in mathematics, afterwards I started working as a senior analyst for a data science company, working on homeomorphic topological data analysis, through which I learned many things and became a solid programmer. This experience grew into decades long career and my algorithms are being used in millions of people's pocket today.

This is a pattern of thinking that sees oneself as building a "life thesis", something almost akin to a PhD thesis.

Unfortunately, life doesn't exactly work like that, and creating this "life thesis" might just be impossible because the opportunity just isn't there. That career that builds so neatly on top of your PhD research? Doesn't exist (for most people)!

What I have observed is that the real-world and among my most financially successful peers is that they don't see their life as following a narrative or a particular trajectory. They see opportunities, gold-rushes. Once the gold is depleted, what is there left to rush? So rush before it dries out. That's what I would characterize as a careerist mindset. Yesterday it was machine learning, and a week before was nanotech, and a week before that was Internet of Things and now it's robotics. These people just latch on to keywords, make-up stuff or even lie if they need to, drop out of college, pick up college again, get an online degree for that new thing, pivot, and just kind of roll with the punches.

An academic's training makes them ill-adapted to the uncertainties in the real-world, because they have been following a script, the same script they followed as child. It is as if you are using one particular strategy at a gambling table and is now losing money round after round but still won't quit because it had worked for you for the first decade of your life. Whereas people outside of academia adapted to the changing rules of the game. They lost some, won some, lost again, but experience were gained during this process and are now in position to play entirely different games if they wanted to.

The typical human life is not a thesis. For most people there isn't some grandiose and coherent life-story to tell to their grandchildren. So be creative! Your career might not have much to do with what you have done so far, e.g., something entirely non-technical as a technically trained person. Hope this makes sense.

As an ex-American who now lives in Asia, I sometimes stumble upon other American tourists and occasionally the topics of tipping comes up (especially when they are here for the first time) and I hear things like:

"Oh IT WAS SOOOO HARD to not tip at first."

"Honestly I am still not use to it at this point."

"I left the bill on the table and the waiter chased me outside to hand it back to me!"

"I'm surprised their service is good even without tipping. How'd they do it?"

"Are you sure they don't accept tips?? I swear I saw a tip jar next to the cashier once."

Almost makes me feel like talking to people who just escaped from some labor camp or a cult.

So something that's exceeding strange about China is that it rarely if ever penalize people for bad social conducts such as spitting, tossing garbage in the streets, smoking in prohibited areas (such as hospitals), public defecation, blaring loud noises, etc.

This stands in sharp contrast with nearby places such as Singapore, Japan, Hong Kong, where if you are caught doing any of the above things you could face a heavy fine.

China doesn't lack public enforcement at all. There are like 4-10 people at each entry of the subway station trying to check if you have a bomb (meanwhile you are wearing a "I love BJ" t-shirt). Also it probably has the most security camera equipped with AI on the planet. I remember these cameras can instantly look up your name and plaster it on a huge screen if you were caught jaywalking.

Also the Chinese government is known to be very money-loving. There is service charge and microtransaction built into everything if you want something done by government agencies.

So the government has a lot to gain by fining people. The public sentiment is in favor of cracking down. But it just...doesn't. That's the strange part.

What is your take on why this is the case?

I've heard from citizens here that if they did crack down, they might cause some other social problems because the public defecators and elevator smokers will be depressed and might "go crazy". Does this hold any water or is this BS?

(Note: this was originally posted on Chinalife and was deleted by the mod for "not relevant to life in China".)

I've been to multiple art museums in Japan and 80% of them have been disappointment for the following non-exhaustive list of reasons:

1. Often only a few pieces are on display while the cost of entrance can often be exorbitant.
2. Contains multiple exhibits with separate charges for each.
3. Very often no picture allowed except for maybe one artwork that's sort of abstract and nonspecific and you wouldn't want to take a picture anyways.
4. Very commercial-oriented. Kind of feels like they want you to see the art first and then buy the art (and vaguely related ones) at the souvenir store. Even if the art is just some stone or sticks that the artist has deemed to be significant.
5. An army of staffs just standing around doing nothing or very very menial and frankly puzzling work like arranging people so they line up 2 person side-by-side instead one after the other.

Don't believe me just read the reviews of, for example, the Yayoi Kusama museum or the Nakanoshima Museum of Art in Osaka.

All of this is fairly ridiculous when other nearby Asian countries all have completely free (or very low) entrance fees. For example the main art museum in Seoul is 200 yen entrance fee and is consistently amazing.

This is really not to diss Japanese art in anyways. Everybody knows Japan has some of the best art and art scenes in the entire world. I'm more concerned with how these museums operate after repeated disappointments.

Everybody knows that academia has an unspoken stigma against those who teach.

There is an underlying subtext that if you are mostly teaching, then:

1. you are not good enough or unable to do research.

2. you are unserious about academia.

3. you are old and on your way out.

4. you are too young and don't have enough experience yet.

5. you are a weirdo for even wanting to interact with students.

6. you are a weirdo for not chasing the more "exciting" stuff.

7. you are just lazy.

I've heard all of the above and much worse.

I had these professors who could publish 20+ papers a year and couldn't teach out of a paper bag (but knew how to game the system so they had good evals, i.e., giving students high grades). Meanwhile, great teaching professors I've had were treated as "loonie bins", had MUCH lower salaries, shorter contracts, some are fired at will. Many of them don't even have a formal webpage by the school. I've heard some teaching profs even had to teach 4-6 courses per semester (including summer).

Are those who mainly teach tired of being treated as second-class citizen yet? Wonder how all this is going to play out as now tons of research becomes trivialized due to AI.

Has it ever occurred to you that academia and universities in general just refuse to manage any crisis and instead pump out PR that pretend everything's operating as usual?

• For example, why are universities pretending that the grades they give is legit despite decades worth of evidence of severe grade/GPA inflation across the world?
• Why are universities pretending that student evaluation/feedback are worth something given that the feedbacks are highly correlated with their expected grades? And even using it in promotion?
• Why are universities pretending that the research they pump out is still meaningful given that there is overwhelming evidence that nobody is reading anything, nobody is cross-checking each other, and now A.I. can easily fabricate research.
• Why are universities pretending that their researchers are "cutting-edge" when most of the cutting-edge stuff has been moved to industry for most applied/STEM departments decades ago?
• Why are universities pretending that their graduates will have fulfilling academic careers?

It just seems to me at every turn, instead of offering concrete solution to address problems, academia just turns a blind-eye, pretend everything is fine and just keeps on operating. Really feels like the wheels have fallen off of the wagon these days.

Anyone else surprised at the enormous amount of backlash against Arxiv's proposed 1 year ban for authors and coauthors publishing papers with hallucinated reference and other obvious LLM/Gen AI artifacts?
https://x.com/tdietterich/status/2055000956144935055
https://xcancel.com/tdietterich/status/2055000956144935055

Some of the responses:

1. "This is the age of AI, Arxiv should be part of the movement instead of holding onto the old ways"

2. "The P.I. is a macro-manager, not a micro-manager, can't be expected to read every reference that his/her student puts in."

3. "I publish 20+ papers a year with my students, how do you expect me to read everything?"

4. "What about teams with 100s of people? How can you expect the authors to check references?"

5. "Who reads references in depth anyways!?"

These responses are very revealing how academia works. Apparently people have just been slapping names on research papers they've never even read or fact-checked themselves. Very obscene!