I'm "almost" a lifelong user of a cochlear implant. I got my first one when I was 9. Before I got it, I was communicating through lip reading and speaking, I never knew sign language. Lip reading I still use relatively often -- when I'm at a crowded restaurant, or at an unbearably noisy party, and there's many interlocutors at the table, I persistently stare at their lips. They take me for a great listener, when in fact, I can't hear shit, and I'm desperately switching back and forth between people's mouths to catch what they're saying. I'm out of shape and this takes so much of my brain power to understand people that I often cannot contribute my thoughts.
Though my cochlear isn't perfect, I would never think of not getting it. In fact, I'd probably be rather angry at my parents for not helping me get one as soon as it was possible. During my childhood and up until late college, I've only ever met one person who was so severely hard of hearing and was about my age, and that was where I have been getting my speech lessons before I got my first cochlear implant.
> They take me for a great listener, when in fact, I can't hear shit, and I'm desperately switching back and forth between people's mouths to catch what they're saying.
FWIW you sound like a good listener. It’s more about understanding than hearing and the dedication to understand what people are saying is the hallmark of a good listener. That said, I’m sure it helps that perception that you have to stare so attentively at whomever is talking.
I really like the book's subchapter on colours, wish it was even more elaborated on. Colours are one of the subtle things I so often find difficult to get right.
As to using matplotlib in published research: when I started out as an undergrad, everybody in the research team used OriginLab for plotting -- my impression of it then was pretty good. At some point, I started using matplotlib + Latex + science plots and it caught on, mostly because there's no need to shift all the data around to a separate programme. Scienceplots package does heavy lifting with fonts and styling for specific journals, so it's just a matter of designing the right plot geometry and information density [1].
In larger companies, and, specifically, bigger projects, systems tend to have multiple ML components, and those are usually a mix of large NN models and more classical (ML) algorithms, so you end up tweaking multiple parts at once.
In my case optimising for such systems is ~90% of the work. For instance, can I make the model lighter or go faster and keep the performance? Or, can I make it go faster? Loss change, pruning, quantisation, dataset optimisation etc. -- most of the time I'm testing out those options & tweaking parameters.
There is of course the deployment part, but this one is usually a quickie if your team has specific processes/pipelines for this. There's a checklist of what you must do while deploying, along with cost targets.
In my case, there are established processes and designated teams for cleaning & collecting data, but you still do a part of it yourself to provide guidelines. So, even though data is always a perpetual problem, I can shed off most of that boring stuff.
Ah, and of course you're not a real engineer if you don't spend at least 1-2% of your time explaining to other people (surprisingly often to a technical staff, but not ML-oriented) why doing X is a really bad idea. Or, just explaining how ML systems work with ill-fitted metaphors.
For a numerical "physicist" (yes, the quotation marks are indispensable), Sympy was somewhat of a godsend to me. Great for prototyping even more advanced models before optimising them later on in C++.
I haven't used Mathematica much, but I have a feeling that it's still more symbolically powerful (or requires less wrangling) than SymPy? I'd appreciate if somebody with more experience in Mathematica could lay it out flat for me if that's the case.
Stephen Wolfram notwithstanding, Mathematica is still far ahead of most alternatives as a CAS. Maple is good on some integrals.
Their downsides are that their languages are not very well suited as general purpose languages, many times the algebraic manipulations you have to perform aren't that complicated and you'd rather work in a "real" language.
Yet another case where python isn't the best in class but still workable and able to benefit from its vast general-purpose ecosystem.
Sagemath is a python library that has more capabilities than Mathematica. If you want to do real symbolic computation in python Sagemath is the only way to do it.
That said sympy is quite a cool little library for learning.
Sagemath isn't a python library, it's a collection of packages (of which sympy is one) under a common interface. It is indeed what you would use if you wanted to do real symbolic computation in python, but it's not at the level of Mathematica or Maple.
Look, we all love open source, but we aren't doing anybody any favors by pretending the open source alternative is better when it isn't. I would encourage anyone whose needs are satisfied by sympy/sagemath to opt for the open alternative, but the question was whether or not Mathematica as of now, early 2024, is better. The unfortunate reality is that it is.
Sagemath is a Python library (and a rather large one at that). It's vastly superior to Mathematica and Maple at some things (e.g., number theory and algebraic combinatorics) and inferior at other things (e.g., symbolic integration).
Your definition of "better" may be wildly different than many. Mathematica is much better in many cases... especially tasks where Mathematica has a built-in function call for something that would be an absolute pain in Python, but worse with respect to price and licensing. I use Python a lot more than Mathematica, but sometimes Mathematica is the best solution.
Your windows/Linux analogy is also not very relevant here. Both are popular in different areas.
I think the windows linux analogy is pretty apt. especially around the win95/98 days.
Getting some random laptop and figuring out what kernel mods to enable and hope that the specific chipset revision was supported, or maybe a patch available that might work was, in fact, a lot of bullshit to put up with to get, say, sound.
sympy will do a lot. but you're probably going to have to reach for a big book of integrals, or find a friendly mathematician to identify the equation and possible approaches. Mathematica as a paid product has a lot of time and effort spent avoiding resorting to asking for help. Much much more built in.
As an undergrad or a hobbyist you probably want to stay "lower" and slog through the calculations when you're stuck. This is part of the process of understanding. But as a professional, or a more advanced user, screwing around for a week looking for a solution is a waste of time and expertise. Spring the cash, and move forward immediately.
I'm not trying to put words in your mouth, but I think there is some nuance that this maybe helps people understand your point.
"Better" really really depends on where you are and what you're trying to do.
I don't think many question that open source won't eventually be equivalent or better than Mathematica for computational work. It just isn't for a lot of things in 2024. It might be fully reversed in another 5 years. Agreed everyone's view of "best" is different as I said above.
I will say a really nice thing about Mathematica is consistency.
I wonder if this is in response to Sci-Hub/Arxiv proliferation or what else.
Anyhow, I find it beyond rich -- the authors usually do all the typesetting, spell/grammar checks, formatting etc. themselves and now they get to pay for that (or their respective funding institutions). I presume that is what would land under a "processing fee". Beyond atrocious -- some journals have their custom LaTex templates only to discard formatting they put in them at the pre-publish stage.
Also, reviewers are probably still not getting paid.
I'm not sure if that's an accurate sentiment, but I feel like there's an academic research winter with grants and funding are being slowly squeezed out of academia and moved to external consortia with loose academic affiliations (at least this is what I _think_ I see happening in Europe). If that's true, this trend of moving the paywall from readership to authorship is just sad.
As an engineer my concern about "google killers" is that I can't see an easy way to scale and control/optimize them in business settings. Apart from factual misstatements happening in the ChatGPT, what about source attribution? How is the relevance of a source determined? How is the flow of information through the network preserved (sourceA => sourceB => sourceC)? With Google we also don't know exactly but I can image some version of PageRank as tuneable. Finally, how to add new pages to index and measure potential "forgetting" that could happen?
Unless somebody could clarify those for me, this is what currently petrifies me -- some uncontrolled black box presenting its clandestine view of the web with no way to follow the breadcrumbs.
I'm a certified member of the Peripatetic school [1]. The ancients knew it and I know it -- I walk therefore I am. It's by the walking alone I set my mind to motion.
Needless to say that my downstair neighbours hate me for it. I guess philosophers were always ostracised ;)
I actually tested a bunch of those on GH Copilot -- sure enough, it does autocomplete certain prompts. No wonder, some are probably known quotes. Anyway, when I'm feeling down in my VSCode environment, I enter some random prompts in search of a pun. Most of the time it's just random rambling but hey:
# (prompt: my dog ...) seems to be interested in the plot.
The README.md contains a screenshot from local testing that's got more results included: https://github.com/zilliztech/VectorDBBench?tab=readme-ov-fi...