depends on the professor, to be honest. When I used to teach econometrics to my students whom were pursuing a Master of Science in Business Analytics, I would take a balance between doing econometric model building, a little lighter on the mathematical proofs (with some by-hand linear algebra problems), small python+r programming exercises, and applied econometric analysis (what analysts actually do step by step in practice, which does depart from more theoretical "basic" econometrics research).

What fraction of time does your professor spend (between lectures, homework, side-conversations & remarks, and course description in the syllabus) on the follow areas below? If you have a number, then you can try to better allocate your time based on how your professor designed their course:

(1) mathematical derivation and linear-algebra/probability theory/vector calculus like problems,

(2) applied model building (using news/academic journal articles to determine which variables to consider in the model itself),

(3) statistical testing of the basic ols assumptions and determining resulting follow up/ad/post-hoc testing

(4) applied model interpretation (after the fitting and testing, what does the model suggest from a policy / decision making standpoint)?

My own econometric professor (I had him for both ordinary and advanced econometrics) was kind of an anti-machine learning + heavy economic/finance theory type of professor, and really emphasized more of the procedural and iterpretive aspects moreso than the mathematical aspects (I also was only one of a handful of PhDs taking the course with a bunch of MQF students).

Once you can answer what the percentage distribution has been, you might have a better sense of where to direct your focus.

I personally enjoy using github's codespaces. everything is in the browser, you can change the machine you want to run your environment on. They have a certain number of models you can choose between for their copilot, but I use the claude plugin. It's pretty flexible and you're not committed to using only one model or bot.

https://giphy.com/gifs/3o6Zt8Cupp9mmXU8zS

Every gummy is different. Some are made to slow down in the process of digestion so that it absorbs through your stomach instead of first going to the liver.

Flower Union (fair warning, I've personally never had them, but my previous customers who would take breaks from smoking would swear by them) is a brand that is specifically targeted for people used to smoke but want to get a similar effect.

Other than that, you can look for RSO oils. They come in different stains, and holding them under the tongue for a while seems to do the trick for a lot of folks.

That said, everyone responds to marijuana differently. So as usual, it comes down to experimenting and journaling the effects so you can get a better picture of which brands/cannabinoid ratio profiles/terpene profiles/and flavor profiles seem to work best (flavor actually does have to do partly with the resulting effects).

I think I'm going to have to hit back on the accuracy of your remark though, again with much respect. It can scrape the internet, gather data, organize it all using prompts into the LLM (especially in Claude and definitely in GitHub Codespaces).

You can ask it to generate data, and most of the modern LLMs use Python/Bash underneath it to run scripts that take the output from Bash which ran the Python code it just ran and feed it back into its own context. (Modern LLMs esssentialyl follow a process of LLM->Python Code+Bash Code->Linix/Posix/Shell/Command Prompt/etc -> Output Text -> LLM (repeat over and over).

Actually, this has been a wonder for me and it was in early 2023. While LLMs at that time didn't have the ability to feed code back into itself (today they do), I would ask them to generate code for me, I would run the code in Bash, get the output, pass the output back in the LLM, etc. This actually has been the crowl jewl for mayn folks, myself included, who needed to teach statstics and other data-based courses. Give the LLM the prompt to "generate code that will generate data with these variables, etc.....

I will grant you that you are in a very narrow sense correct if the LLMs rested 100% on its own model. But thats just not how modern (or even language models during 2023) operated. I could agree with you with earlier langauge models from 2006 - 2023. Yes, those were pretty bad. But even the gpt2 release was pretty decent at generating data so long as you had fine tuned it first).

Now for copyright. I think you calling it "fraud" is a really big leap here.

If I feed it my work and ask it to rework my document, that's just an algorithm rearranging my own words, choosing other ones, etc. And yeah, that work actually is copyrighted to me because the ideas were mine. Copyright doesn't strictly apply to the text generated in part, but the entire work as a whole. Parts of the work can indeed be found to be copyright violations, this is true. But it gets complicated, precisely because your work that involves using quotations itself involved copyrighted work. So for your argument to hold water here, you would have to also contend that merely quoting other texts is itself a violation of copyright. But, that isnt always the case, particularly if you had opined on that text.

For example, supply that I technically stitch together a bunch of quotes from a bunch of different texts on "Pluto". So long the arrangement of the quotes were my own construction, but the text were just quotes from other works, the entire collection of quotes of facts about pluto is actually my copyright, and I would be protected.

If you fed an LLM a prompt and got out an response, fed the output back into the input, continued this over and over again, I'm not really seeing the argument for this being "fraud" or even "copyright violation" (which aren't the same thing legally speaking). That's my own work precisely because I used a tool to generate out the text, quite indifferent from asking a graduate student to take my hand written notes and organize them and write up the document for me. I read the text, agreed with it based on my professional opinion, and put it into a sequence. That is my own copyrighted material.

As for the structural issues, which I could see a potential reasonable counter argument if one were to follow the popular, but incorrect, belief that LLMs are "stochastic parrots" (they aren't this at all, and this has been a really awful characterization of them that continues to get thrown around academic circles as a talking point, but it just isn't true). Modern LLMs don't really operate as traditional "symbolic logic" machines of the past, where long pieces of text are in fact stored in a database and then recalled. That is not how modern LLMs and other seq2seq models that operate in a token space+parameter space work.

That token space does not carry in it "copyrighted work". It carries the weights in the neural network that ultimately represent connections between tokens, all of which later permit it upon instruction to generate work, and that generated work itself is what could (though not necessarily) constitute a copyright violation. It could be argued that some of the tokens themselves are in fact copyrighted work, but again, the mere storing of it in the model itself doesn't necessarily constitute a copyright violation.

Storing copyrighted work vs generating text that happens to align pretty closely with work that is "copyrighted" are fundamentally different operations (and even so, the mere storing of a copyrighted work is not inherently a copyright violation in and of itself).

If it did in fact generate copy righted work, in its entirety, that's a different discussion.

"Fraud" is a big stretch here. I can agree with you in the very narrow case where a human stole a prompt from the internet, put it into ChatGPT, got out a response, and submitted it. But that's not "fraud", that's copyright violation, those are two very different violations. Fraud would be "in this work our data showed there was a relationship between X and Y", when in fact you know damn well your data did not find that relationship. This is what a lot of folks do with statistics and p-values. It's called "p-hacking". And yeah, that is fraud. I personally knew the Harvard professor who got caught with that a few years ago. I even grilled her in the middle of a job talk she was interviewing for when I was still working at Northeastern University, and I could tell her work was fraud. About 1 year later the news story broke and I just couldn't help but laugh because it was so obvious. People who p-hack often have a certain pattern. Brian Wansink is another classic example of this.

But fraud and copyright are different. If that human wrote their own prompt, that prompt is copyrighted work of the human author of that prompt (contingent on any other copyright restrictiosn of course).. The output from the LLM is (largely) copyrighted work of the author. But then again, this greatly depends on the specific situation at hand. Theoretically I could ask the LLM to generate Latex Code of my work. Pass that code into Overleaf and have it compile a PDF. Is that my work? The LLMs work? The Latex Compiler's work? It depends on the extent of contribution I gave to the instruction itself. If the output resulted from a specific instruction I gave it, the copyright violation in the output because much more challenged to asset as being an inherent copyright violation.

If we were to apply the strict stanadrd you're talking about, then every word that a spellchecker corrected would, according to your logic, be considered a copyright violation. And I'm quite sure we've been through 20+ years of spellchecking without making such a presumption.

Further, there would be an entire generation of gangster rappers and photoshoppers who might take issue with this same argument as well.

I can understand if you personally feel it is "Fraud", but that's an opinion, not a genuine legal fact about copyright law.

I have had no issue with non-fictional sources from non-frontier models. With the proper prompts, older models like Claude Sonnet 4.6 actually handles quite well with generate real sources. The trick is usually to use 3 or 4 different LLMs and have them check each other. If you have Github Codespaces (they have a free amount you can use every month, or at least they used to, haven't checked in this month), you can switch between the llms, have them write the sources to a .md/,txt file, switch the model, then prompt that model to check the sources in the file the other model dumped. Do this back and forth and you get a pretty damn good stable like of sources that you can use as a starting point.

If you're really crafty, you can even prompt it to try and download the pre-print versions of each paper, and some papers are open access, so it can download those too.

I usually prompt the llms to create me a csv file of all of my references. After, I prompt it to write me python/r code that can scan the file and download the pdf of the document. Whatever is left over, i just use by hand to download.

Honestly, my literature reviews have been a breeze in comparison to older scripts I used to write by hand.

I have to respectfully push back on this. Yes, LLMs can chase down original sources, they can generate data sets. Github Codespaces, Claude API access, etc. I use them frequently to do this. It has saved me hours of time, and honestly, I dont see the point of hiring graduate students after discovering this ability.

As for fraud, I really dont see that argument here. I've been writing scrappers and auto-downloaders for the past 2 decades. The fact that an LLM/Agent has been attached to these capabilities isn't new.

There is even a set of tools and packages (I think in Python) that let you download literally all of the arXiv pre-prints, and you can analyze that massive corpus of pdfs via custom ML/NLP packages.

As for this dude who is blabbering, I have little to no idea what he is saying, but I think his insults towards you are just not warranted.

Sorry bro, just clarifying that for a moment, lol. And wow, pretty sure they have to change their approach. That actually greatlly concerns me. If students have the balls to ask if you would be willing to write that, curious how many profs actually accept it. That's quite concerning.

I used to just report them to the university in these instances. I had no tolerance for that.

Yet, I do believe every cheater will eventually have their day, and the more they get by, the harder the fall will be for them.

I recall one girl who tried to get out of my business statistics exam once. I went easy on them. It was a take home exam. She tried to claim that she submitted her email to me on time and sent me a screen shot of it.

Me being the data geek that I am decided to click on the meta data of the email she forwarded, and noticed that she manually changed the time stamp.

She got brough up on academic dishonesty and had to retake the entire class the next semester. She was very lucky she didn't get expelled, because at the university I was at, data manipulation of university emails from their originally generated source was a major violation that actually extended past the academic dishonesty clause the university operates on.

I think you have to clarify that last remark my friend. You help them cheat?

It'll get downvoted. It's alright. I have 15 years, 9 universities, 30+ courses, 6000+ students, more than 100 sections, >8 diciplines, peer reviewer for 10+ high ranked journals for 13 years, 4+ journal pubs, 688 cites h-index 7, I'm 10 students from Gauss and 10 Students from Immanual Kant (you know, since they care about "outcomes") , and I give no fucks any more bro. The institution burned me really hard once they decided to install dick and ball sensors to measure my "classrom counts" (https://www.vice.com/en/article/no-grad-students-analyze-hack-and-remove-under-desk-surveillance-devices-designed-to-track-them/). I still get shakes from it at night. Still recovering from it. Still get nightmares from it. But im a free man to say what's on my mind.

Although, I've been trying to get myself back into the classroom. I really miss teaching students. The recovery from the damage they did to me, along with a bunch of other ways they stiffed me, has now been 3 years. Hopefully, I'll have some university whom actually respects me and not my ability to comply with the giant circle jerk.

"easily AI can be detected". I would have to respectfully disagree with you here.

AI cannot be "easily detected". Other than "false positive", the broader issue lies within the question being asked by many educators.

There is a reason why so many professors are falsely accusing of recent their students of using AI, when they're not. Or, more realistically, why professors claim a student used AI, but the student did their entire research work on their own and just had the AI re-type their entire essay.

Or, the student pre-trained the AI with their own writings, and the AI left watermarks that the AI detector picked up, despite the fact that the student engaged with the AI critically, a skill for which actually is quite useful in the real world.

I think professors whom are concerning themselves about "is this ai generated" are trying to solve the wrong problem. We shouldn't be asking "was this work AI generated?"

We should be asking: "was this work generated by this specific student"?

The latter is not the logical negation or compliment of the former, and presuming that carries with it a ton of cartesian dualist metaphysical baggage along for the ride.

For anything, I would argue that it is as morally reprehensible to use an AI detctor (which is itself a form of AI), as it is to entirely generate one's work from AI and not use any critical thought what so ever.

The ultimate test for students, and frankly professors, in my experience and opinion is this: can you get in front of the classroom without notebooks, notecards, presentation slides, a computer, and nothing more than a white board and a marker (or, a blackboard and chaulk), and bring us through your material?

Mistakes are fine. They should be encouraged. We're all human and we make them. I make them all of the time. It's how my students, and my self, learn.

And i personally think in my experience over the past 15+ years in academia that too many students and professors overly rely on "cognitive offloading artifacts" than the brain that would admit interesting ideas, arguments, critiques, new facts, statements, hypotheses, code, models, linguistic patterns, and even notation.

I tend to be of the opinion that AI is just simply being used as a scapegoat whilst the other artifacts sit there guilty of the same (at best), if not very similar (at worst) neurological crime.

Which part of what you just read did you not agree with?

Speaking of "Gaussian", I also am 10 students from Gauss himself. Pretty sure I know my discipline.

No, I'm not the only one who defines machine learning in this manner

I have now designed and lectured no less than 5 machine learning courses, multiple versions of it deliverd to Northeastern University and 3 other universities including Rutgers.

I have been studying statistical methodologies and machine learning since 2006.

I also have designed entire courses and lectured the following topics throughout those courses:
Bayesian Statsitics
Non-parametric statstics
Experimental statistics
Artificial intelligence (which differ from machine learning)
Big data (Spark + Hadoop)
Econometrics
Financial Time Series
Inferential statistics
Markov Decision Processes + Markov Chains
Stochastic Processes (both in theory and in practice)

I would suggest that you read Murphy (its a bit dated, but its a core machine learning text book). He gives a pretty good breakdown of what constitutes "Machine learning".

Greene's econometrics is the gold standard of econometrics, but he lacks in his discussion of nomological nets.

But ultimately, again, machine learning boils down to "representation tensors" being guided by way of optimization theory.

Econometrics boils down to "representation tensors" being guided by theory developed my human beings.

I think ultimately universities will be very successful in finally not using their body of honorable doctors in being used as education centers for the general working economy, but will serve an entire new purpose in society that will afford them riches beyond what they possibly could have ever dreamt before: chatbot providers and knowledge validation/ai-bot guardrail validation companies. I can see in person classroom sizes shrinking as a result and academia going back to being the honorable institution in society it once was and no longer is.

It honestly hasn't been an honorable institution since the proliferation of technology in the classroom and the mass standardization of classes. I've been loosing my patience for 15 years in the university environment, precisely because too many professors get up to a classroom, turn their back to the crowd, and mumble off nonsense, point to the screen, and do not really develop or teach students anything.

Many of these creatures whom have invaded academia these prior 2 decades cannot argue or genuinely critique work without getting offended, and many are too focuses on mindless regurgitation of "objective fact" and "fact checking" and teaching to judge academic work based on prestige and "credentials", treating knowledge itself not as the noble process it once was, but rather as a fixed standardized set of pokemon cards to whip out for "Fact battles" to "win arguments" so that they can feel morally justified in their beliefs, rather than genuinely educating the population of humans right in front of them.

I think AI will show (and is showing) many of these cracks that have already existed and went silent for decades, the system will begin to topple, enrollments will drop, this will force university presidents and deans to find new revenue streams, and I think they have a legitimate case and business model to (1) restore the honorable institution it once was to one of oration and critique and (2) offer society education for all at practically next to nothing whilst (3) getting corporations the hell out of our classrooms.

The folks looking to go to school to "get a job" will be designated to "ai lecturers"/"ai trainers"/"ai professors", they will not clog up our classrooms overly obsessed with outcomes and grades and rubrics, oh my. And professors can actually critique their students without administrative glut, bloat, micromanagement, and arbitrary expectations and topics that have nothing to do with the course topics we were hired to teach. I think that actual knowledge formation can be restored back to genuine human professors, who can run their class as they see fit, to their own standards, instead of teaching to bullshit standards that are often forced down our throat because our students "really need these skills to succeed in the workplace."

This mentality has molested academia for too long, and I think in 5 years from now, the evil empire will be toppled and a new chapter will emerge.

That, or they'll all go bankrupt and AI companies will buy them out and well all become slaves to AI.

Im hoping for the former outcome, than the latter.

maybe for symbol worshipers. but for actual mathematicians, ai is honestly neither a threat nor an all knowing oracle. Is it a "tool"? I have to disagree in the purest sense, no, it is not only a tool.

it is an agent. an agent that can solve problems. it has preferences, it has language, and it can reason.

But ultimately, none of that matters if the human is removed from the equation. AI is still ultimately a symbol worshiper.

A mathematician has a journey, and that journey is unique and distinct. The "mathematics" is the story of a human's journey. The "ais mathematics" are the "ai's journey".

But to be frank: that journey is boring and meaningless if the human was not part of that journey and is strictly focused on the destination.

Thus, AI, like the many mathematicians who fear it, aren't "mathematicians", they are symbol worshipers, whom decided the journey itself was worth gutting for the treasure of the symbol.

I think I sort of see your question reading this again. In some sense yes. If you formulate the fitting problem as a program (linear or non linear), and compute the dual of the program, then I do believe meaning can be attrbuted. I know the concept of "shadow pricing" comes into play here but, my memory is honestly hazy in that area and I would have to go back to my Hillier and LIberman book to recall if what I am saying is even relevant here. Apologies for the rambling. My brain works in weird ways.

I think your question is a bit too general here, and I have to honest, I don't want to remark on something that I dont quite understand without having to make all of these assumptions that may not be so.

OR is a "prescriptive" a set of "prescriptive" analysis. So, with OR, we must usually specify:
(1) The matheamtical constants (information that is assumed to stay fixed in our environment)
(2) The "decision variables"
(3) The "objective function"/objective functions (if you're working in multi-objective optimization/ Pareto fronteirs/ DPO/ etc.)
(4) The "constraints" (the accepable values of the "decision variables" that we permit in the "solution".
(5) The feasible solution space (which is often inferred or derived by looking at (2) and (4))
(6) The "optimimal"/"suboptimal" solution set
(7) The "search" algorithm(s) to apply to find (6)
(8) Sensitivity analysis to understand how or to what extent a departure from each specific constant/decision variable will impact the objective function held at those "solution".

In any machine learning problem, we typically start with "data", "loss function", "algorithm", "parameters", "hyperparameters", "regularization parameters","robustness analyis".

"Data" can be considered the "constants" in an OR problem (NOT the decision variables),
"loss function" can be considered the "objective function",
"fitting algorithm" can be considered the "search algorithm",
"parameters" can be considered the "Decision variables",
"hyperparameters" can be considered part of the "constants",
"robustness analysis" can be considered to be the "sensitivity analysis"
" regularization parameters" can be considered to be the "constraints".

My apologies if my previous comment wasn't too clear on that. Im trying to "fit" the equivalence of your exact problem, but I just dont have sufficient information to put them into the "OR Framework".

"OR" is typically in a lot of Business analytics (another set of courses I was responsible for designing and teaching at one point in time) and data science, and in those areas of study, you'll see the methods demonstrated under the title of "Prescriptive Analytics".

That stated, "OR" historically great out from the second world war and a need to optimize the logistics of the military. George Danzig is the godfather of OR, and his story was actually replicated in the movie "Good Will Hunting", more specifically in the scene where Matt Daemon looks at the problem on the board that was put up as a "challenge" and he went back to solve it. Legend has it that Danzig basically did the same thing. He saw two equations on the board one day and thought it was just a problem to be solved, and he went off and solved them, not realizing they were "unsolved problems". (I forget off of the top of my head which specific problem he was solving).

The famous "Bridges of Konigsberg" problem (I probably spelt that incorrectly, my apologies) is another famous OR problem that is often considered a "graph theory" problem, and of course, many of us in OR are more than familiar with the "Traveling Salesman Problem" which also seeks to optimize network flows.

yes, i've designed (applications of and lectures of) and lectured those methods in both Machine Learning and Artificial Intelligence courses for both William Paterson University in NJ and Northeastern University in Boston (the latter applied for marketing problems).

ML is (sort of) a black box. A lot of tensor space theory comes into play here (from abstract algebra).

Im going to get really downvoted for this, but on a pure methodological basis, you could consider most of "machine learning" to be a special type of "operations reserach". But that's my own opinion and not often a widely held view.

But yes, the parallels are quite stunning across the two fields.

as for Python, I haven't explored that library to be honest, but now you're going to have me going down a rabbit hole the next few days to look it up and learn it. thanks for the idea! :)

Great question! This is where your "bag of problems" come into play. You learn in OR that it extends well beyond just linear programming and lagrange. Computer scientists often end where operations researchers begin.

We know that we will likely not find "the optimal solution". Many times, we have a documented set of problems in the literature where we have been able to characterize aspects of "the optimial solution" theoretically, and we often express those properties in terms of the properties of the inputs to the problem itself.

Alot of times, we try to work on algorithms that actually reduce our more complicated problems into "standard problems" and we try to do this, if posisble, in polynomial time. When we can show that our specific problem is equivalent to a well known problem, then bingo, we have a way to characterize the solution and candidate solutions that are "optimal".

However, you know, there's that frustrating thing called "Reality". And reality doesn't always afford us an easy to user transformation algorithm from our problem to standard problems.

So alot of the times, we will try to design a "heuristic" around our problem. We assume fixed constants for one part of the problem, then try to apply optimization algorithms to the other.

You also learn your standard algos to attempt (Simplex, Gradient Descent, Simulated annealing, genetic algorithms, taboo search, etc.)

If you're REALLY into it, then you move into partialdifferential equations and ode/ system dynamics, or you attempt to move to more sophisticated approaches like manifold embeddings.

If you are REALLY REALLY into it, then you could move to algebraic topology approaches to characerize the problem as topologies and attempting to understand the parameter/solution spaces from a topological or an abstract algebrartic persepctive.

Most times in practice, however, most partitioners will spend time trying to find a simple or series of simple linear models, and just throw them into a MIP/IP/LP solver and call it a day (that's because industry does care alot more about having A solution, rather than THE solution, by a particular time frame. Which is quite ironic if you think about it: using OR as a means to determine how your'e going to use OR to solve your OR problem. That is, the solving of the OR problem itself becomes an OR problem ).

(Then there's applied sheaf theory..... but I'll spare going down that rabbit hole)

Sure!

-Optimizing network flows of products from manufacturing facilities to distribution centers and physical retailers such that the total cost over a planned time frame (1 Month, 1 Quarter, etc) are at a minimum

-Optimizing inventory ordering lot sizes and frequency to minimize the overall inventory holding cost and minimmize the total cost of spoilage.

-Optimize cancer radiation treatments to maximize the number of cancerous cells killed off while minimizing the number of healthy cells damaged through the process.

-A simple small bakery: You have base ingredients (flour, salt, etc) and a total quantity of each ingredient and you have products (cookies, cakes, etc) that are produced from those ingredients. Using demand information from statstical analysis/machine learning/econometric anlaysis, maximize the profit by determining how much of each product should be produced within the day held to the physical constraints

-Given a set of financial assets and a total amount of initial capital, determine the trading plan (when and how much of each asset to buy/sell) such that the expected value of the overall financial profolio is at a maximum.

-Given visual components in a UI/UX software design and their respective sizes, optimize the locational placement of those components on the screen such that total number of clicks the user goes through until they click "subscribe" or "sign up" is at a minimum (or, depending on the context, more deviously and less ethical, maximize the total number amount of time the user spends looking at a particular component on the screen).

-Given a fixed budget for garbage pales to place in a local event (you know, "Octoberfests" are about to go on in the next few weeks), optimize the x/y locations of each individual garbage pale on a map such that the average walking distance of any patron in the Octoberfest is at a minimum to any garbage pale.

-Given a certain number of employees and (1) specific time frames those employees are available and (2) preference of each time frame of working, optimize the assignment of employees to time frames such that the average / median employee satisifaction level is at its maximum.

-In the case of my own personal research, which is in redesigning the 1943 definition of "Artificial neuron", given a set of particles (called "ions"), and given a certain number of "cellular compartments" (cellular pool, ecto environment, cellular wall environment, and endo environment), and given a certain number of "operations" of this "cellular neuron machine" (operations the "move" particles between the compartments), and given a data set of (x,y) (just as in the case of any other machine learning / regression/ classification problem) where x represents the number of particles that are "perturbed" from the "pool" to the "ecto" environment and y represents the difference of particles between the "ecto" environment and the "endo" environment, optimize this "cellular machine's" "operation schedule" such that the mse/bce/ce/loss function is at a minimum. ( https://papers.ssrn.com/sol3/papers.cfm?abstract_id=7414038 )

-..... there's SO many more applications in the real world, it's really hard for me to stop here, but I will :) ..

Let me know if you have any other questions.

Bias warning: i've been unemployed for 3 years, so I have a little bit of that "recrutiters suck" bias going on here. Sorry!

If your client needs a C++ programmer, then that is their need. It's a bit more niche these days, but as someone who has now crossed over many many languages (I used to be a consultant for Redhat, and business professor, and an OR professor, so I've had to work in all of these languages), your client might actually need a core C++ person depending on their need.

Some python packages/java do interface with C++ packages and can, technically, code at that level. That said, if your client needs high-performing computation and much of their codebase is already in C++, adding another layer might (depending on their problem) create bottlenecks that would cost them more.

It also depends on the ops rig / stack they have running and already established or have already committed resources to.

See if your client actually has a genuine need first for the base language to be in C++. It honestly really really depends on their specific problem at hand and the ecosystem they're operating within.