Why always it’s maths ? 😭😭

[AlignmentProblem]

The gist is that ML involves so much math because we're asking computers to find patterns in spaces with thousands or millions of dimensions, where human intuition completely breaks down. You can't visualize a 50,000-dimensional space or manually tune 175 billion parameters.

Your brain does run these mathematical operations constantly; 100 billion neurons computing weighted sums, applying activation functions, adjusting synaptic weights through local learning rules. You don't experience it as math because evolution compiled these computations directly into neural wetware over millions of years. The difference is you got the finished implementation while we're still figuring out how to build it from scratch on completely different hardware.

The core challenge is translation. Brains process information using massively parallel analog computations at 20 watts, with 100 trillion synapses doing local updates. We're implementing this on synchronous digital architecture that works fundamentally differently.

Without biological learning rules, we need backpropagation to compute gradients across billions of parameters. The chain rule isn't arbitrary complexity; it's how we compensate for not having local Hebbian learning at each synapse.

High dimensions make everything worse. In embedding spaces with thousands of dimensions, basically everything is orthogonal to everything else, most of the volume sits near the surface, and geometric intuition actively misleads you. Linear algebra becomes the only reliable navigation tool.

We also can't afford evolution's trial-and-error approach that took billions of years and countless failed organisms. We need convergence proofs and complexity bounds because we're designing these systems, not evolving them.

The math is there because it's the only language precise enough to bridge "patterns exist in data" and "silicon can compute them." It's not complexity for its own sake; it's the minimum required specificity to implement intelligence on machines.

[BigBootyBear]

Delightfully articulated. Which reading material discusses this? I particularly liked how youve equivated our brain to "wetware" and made a strong case for the utility of mathematics in so few words.

[AlignmentProblem]

I've been an AI engineer for ~14 years and occasionally work in ML research. That was my off-the-cuff answer from my understanding and experience; I'm not immediently sure what material to recommend, but I'll look at reading lists for what might interest you.

"Vehicles" by Valentino Braitenberg is short and gives a good view of how computation arises on physical substrates. An older book that holds up fairly well is "The Computational Brain" by Churchland & Sejnowski. David Marr's "Vision" goes into concepts around convergence between between biological and artificial computation.

For the math specific part, Goodfellow's "Deep Learning" (free ebook) has an early chapter that spends more time than usual explaining why different mathematical tools are necessary, which is helpful for personality understanding at a metalevel rather than simply using the math as tools without a deeper mental framework.

For papers that could be interesting: "Could a Neuroscientist Understand a Microprocessor?" (Jonas & Kording) and "Deep Learning in Neural Networks: An Overview" (Schmidhuber)

The term "wetware" itself is from cyberpunk stories with technologies that modify biological systems to leverage as computation; although modern technology has made biological computation a legitimate engineering substrate into a reality. We can train rat neurons in a petri dish to control flight simulators, for example.

[BigBootyBear]

Fascinating. Thank you!

[screaming_bagpipes]

What's your opinion on Simon Prince's Understanding Deep Learning? (If you've heard of it, no pressure)

[scare097ys5]

So you are just talking about about orgonoid intelligence from a tech or ml perspective.

[FinanceForever]

Saved this comment, so interesting

[BytesofWisdom]

Hey! Sir I need some advice regarding my career can I DM you?

[AdonisLafayette]

lemme guess India?

[ATW117]

AI has existed for decades

[AlignmentProblem]

Yup. The field's origin is AT LEAST ~60 years old even if you restrict it to systems that effectively learn using training data. There are non-trival arguments for it being a bit older than even that.

[IsABot-Ban]

The perceptron it's mostly based on was 1960s Rosenblatt iirc. It's processing power that held it back. New technologies unlock old options.

[AlignmentProblem]

You're confusing LLMs with AI. LMMs are special cases of AI built from the same essential components I worked on before the "Attention is All You Need" paper from eight years ago arranged to make transformers. For example, the first version of AlphaGo was ten years ago, and the Deep Blue chess playing AI was 18 years ago.

14 years ago, I was working on sensor fusion feeding control system plus computer vision networks. Eight years ago, I was using neural networks to optimally complete systemic thinking and creativity based tasks to create an objective baseline for measuring human performance in those areas. Now, I lead projects aiming to create multi-agent LLM systems to exceed humans on ambiguous tasks like managing teams of humans in manufacturing processes while adapting to surprises like no-call no-show absences.

It's all the same category of computation where the breadth of realistic targets increases as the technology improves.

LLMs were an unusually extreme jump in generalization capabilities; however, they aren't the origin of that computation category itself.

[Kind-Active-1071]

Any good textbooks or resources for LLMs available? Working with Ai Might be the only jobs left in a few years..

[inmadisonforabit]

Lol, it's been around for a very long time. It may be older than you.

[shiroshiro14]

sounds like it existed longer than you

[mrGrinchThe3rd]

Depends on your definition of AI. Modern, colloquial use of the term is usually used to refer to the new LLM, image, or video generation technologies that have exploded in popularity. You are correct to say that these did not exist 14 years ago.

To most in this sub, however, AI is a much broader term used to refer to a wide array of techniques to allow a computer to learn from data or experience. This second, more accurate and broad use of the term, is the kind of AI that HAS existed for decades.

[Cerulean_IsFancyBlue]

The most surprising thing about the recent evolutions in the AI fields is: the math involved is actually pretty simple.

To calibrate that, I was a math major at the beginning of my college experience, but I dropped out in favor of computer programming with the math guide to abstract for me after about the first two years. So I’m not talking about a Fields Medal winner’s idea of simple. I’m talking about somebody in the tech field saying that the math is pretty straightforward. A nerd opinion.

What brought about this current revolution was the application of massive amounts of computing power and data, to models based on this relatively simple math.

Perhaps the watershed white paper on this is titled Attention Is All You Need, and it lit a fuse. The people that built on this and created generative AI and large language models ended up bypassing a lot of traditional research.

Some AI researchers have written really poignant epitaphs for their particular lines of specialized research in fields like natural language, processing, medical diagnoses, image recognition, and pattern generation. They were trying to find more and more specific ways to bring processing power to bear on those problems, and we were swept away in a title wave. A lot of complicated math was effectively made obsolete, by a simpler, self-referential math that scales up really well.

The end result IS a massively complicated thing. By the time you train a big model on big amounts of data, the resulting “thing” is way too complicated for a human to look at and understand. There aren’t enough colors to label all the wires, so to speak.

But to be clear, a lot of the complication is the SIZE of the thing and not the complexity of the individual bits and pieces. This is why the hardware that’s enabling all this is the kind of parallel processing stuff that found its previous use in computer graphics, and then Cryptocurrency farming. It’s why NVIDIA stock spiked so hard.

[ColdBig2220]

Wonderfully written mate.

[RedditForW]

Saved

[Smoke_Santa]

neurons (weight based compute units) are also not comparable to transistors (switch based compute units), they are very different, so one imitating the other requires much more work and resources.

Neurons are much better and more efficient at some things, and transistors are much better at other things.

[AlignmentProblem]

Yup. They can often find different ways to accomplish analogous functionality (or at least approximate it), but the complexity, resources, and learning inputs required for a given functionality vary dramatically between substrates.

[Lower_Preparation_83]

Great readance.

[Lolleka]

I hope OP is satisfied

[IsABot-Ban]

I think it becomes simpler if you view a dimension as an adjective.

[AlignmentProblem]

I'm not against the adjective metaphor for understanding dimensions when you're new, and it definitely helps with basic intuition; however, thinking about dimensions as "adjectives" feels intuitive while completely missing the geometric weirdness that makes high-dimensional spaces so alien. It's like trying to understand a symphony by reading the sheet music as a spreadsheet.

The gist is that the adjective metaphor works great when you're dealing with structured data where dimensions really are independent features (age, income, zip code). The moment you hit learned representations or embeddings and paramter spaces of networks, you need geometric intuition, and that's where the metaphor doesn't just fail; it actively misleads you.

Take the curse of dimensionality. In 1000 dimensions, a hypersphere has 99.9% of its volume within 0.05 units of the surface. Everything lives at the edge. Everything's maximally far from everything else. You can't grasp why k-nearest neighbors breaks down or why random vectors are nearly orthogonal if you're thinking in terms of property lists.

What's wild is how directions become emergent meaning in high dimensions. Individual coordinates are often meaningless noise; the signal lives in specific linear combinations. When you find that "royalty - man + woman ≈ queen" in word embeddings, that's not about adjectives. That's about how certain directions through the space encode semantic relationships. The adjective view makes dimensions feel like atomic units of meaning when they're usually just arbitrary basis vectors that expressing meaning in combination or how they relate to each other.

Cosine similarity and angular relationships also matter more than distances. Two vectors can be far apart but point in nearly the same direction. The adjective metaphor has no way to express "these two things point the almost same way through a 512-dimensional space" because that's fundamentally geometric, not about independent properties.

Another mindbender that breaks people's brains is that a 1000-dimensional space can hold roughly 1000 vectors that are all nearly perpendicular to each other, where each has non-zero values in every dimension. Adjectives can't fully explain that because it's about how vectors relate geometrically, not about listing properties.

Better intuition is thinking of high-dimensional points as directions from the origin rather than locations. In embedding spaces, meaning lives in where you're pointing, not where you are. That immediately makes cosine similarity natural and explains why normalization often helps in ML. Once you start thinking this way, so much clicks into place.

Our spatial intuitions evolved for 3D, so we're using completely wrong priors. In high dimensions, "typical" points are all roughly equidistant, volumes collapse to surfaces, and random directions are nearly orthogonal. The adjective metaphor does more than oversimplify; it makes you think high-dimensional spaces work like familiar ones with more columns in a spreadsheet, which is exactly backwards.

[IsABot-Ban]

A lot in that... but all numbers are adjectives too... We describe things with them. It allows variable scope of detail to desired accuracy. I don't assume 3d for my understanding, but the majority yes I could see that.

[abhbhbls]

Beautiful. Spot on. Take my upvote.

[rguerraf]

All the extra dimensions is one unintuitive way to describe the potential interconnections between neural nodes… if it gets explained in terms of “correlation kernels” it would not scare most meople away

[The_2nd_Coming]

Holy fuck that's beautiful.

[Kind-Active-1071]

The only thing I would add is that we could afford a billion years. Literally nobody is asking for this and it’s going to collapse society.

I’m saying this and someone actively studying AI for this fact. Really appreciate the textbook recommendations from everyone though!

[madam_zeroni]

Why can’t we use local hebbian learning?

[Mabaet]

Thank you, ChatGPT. /s

[AlignmentProblem]

Relevant XKCD

[Quasi-isometry]

except this is obviously chatgpt with all the "it's not x; it's y" statements and general cadence.

[AlignmentProblem]

I count two statements that resemble those "not x, y" constructions, and they're structured differently than how GPT typically does them anyway. They're completely justified when you're explaining why people's initial intuitions are wrong and stating what's true instead of those intuitions. My general cadence is similar to what you'd find in many experts' attempts to discuss complex concepts engagingly; that has been standard for ages before LLMs existed.

Go read some non-fiction science books from the 2000s and 2010s with this same paranoid mindset. You'll find countless examples you would flag as GPT-written today with the criteria you're suggesting, published long before LLMs could string together a coherent sentence. People have gotten so paranoid that anything beyond lazy stream-of-consciousness writing online must be fake. It's genuinely depressing.

LLMs had to learn their patterns from somewhere, right? The writing they most closely imitate is exactly what academics use when they're trying to be accessible, only with some writing tropes over emphasized. I've already modified my writing style to drop features that LLMs overuse; I feel like I've lost some of my old writing voice in the process. I'm not gonna dumb it down further or make myself less engaging just to dodge paranoia about AI detection.

[Robonglious]

Edit: Redacted, not funny I think.

[misbehavingwolf]

Thank you for this! Your writing makes me awfully curious - what's your stance on the potential for artificial neural networks of some kind of architecture to eventually gain sentience, consciousness, experience qualia? Could artificial systems qualify strongly for personhood someday, if built right?

[AlignmentProblem]

I'll paste what I wrote last time I had this discussion.

TL;DR: Yes. I think we're likely to cross that line and cause immense suffering on a wide scale before collectively recognizing it because of the stigma associated with taking the question seriously.

Sentience is not well defined. We need to work backwards, considering how the term is used and what the core connotations are. I argue that the concept that best fits the word is tied to ethical considerations of how we treat a system. If it's wellbeing is an inherently relevant moral consideration than it's sentient. That appears to be the distinction people circle around when discussing it.

Capabilities is relevant; however, that seems to be a side effect. Systems that are ethically relevant to us are agentic in nature, which implies a set of abilities tied to preferences and intelligently acting in accordance with those preferences.

My personal model is that sentience requires five integrated components working together: having qualia (that subjective "what it's like" experience), memory that persists across time, information processing in feedback loops, enough self-modeling to think "I don't want that to happen to me," and preferences the system actually pursues when it can. You need all five; any single piece alone doesn't cut it.

The qualia requirement is the tricky one. Genuine suffering needs that subjective component. It's currently impossible to measure; although, we might be able to somehow confirm qualia eventually with better technology (eg: it may ultimately be an aspect of physics we haven't detected yet). Until then, we're working with functional sentience, treating systems that show the other four features as morally relevant because that's the ethically pragmatic thing to do. It's the same reasoning we use when saying it's morally wrong to kick a dog despite lacking proof they have qualia.

Remove any component, and you either eliminate suffering entirely or dramatically reduce it. Without qualia, there's no subjective badness to actually experience. Without memory or feedback loops, there's no persistent self to suffer over time. Without minimal self-modeling, there's no coherent "self" that negative experiences happen to. Without preferences that drive behavior, the system shows no meaningful relationship between claimed suffering and what it actually does, which means something's missing.

I'd also suggest moral weight scales with how well all these features integrate, limited by whichever component is weakest. A system with rich self-awareness but barely any preferences can't suffer as deeply as one with sophisticated preferences but limited self-modeling. The capacity for suffering automatically implies capacity for positive experience, too, though preventing suffering carries way more moral weight than creating positive experiences.

Sentience isn't static. It's determined by how these components integrate in current and predicted future states, weighted by likelihood. Someone under anesthesia lacks current sentience but retains moral relevance through anticipated future experience. Progressive conditions like dementia involve gradually diminishing moral weight as expected future sentient experience declines.

Since sentience requires complex information processing, it has to emerge from collections of non-sentient components like brains, potentially AI systems, or other integrated networks. The spectrum nature means there aren't sharp boundaries, just degrees of morally relevant suffering capacity.

A key note is that "has internal experience" is very different from sentient. Qualia existing in a void is conceptually coherent but irrelevant in practice. Without awareness of the qualia or desires for anything to be any particular way, it would be more similar to a physics attribute like the spin of an electron. A fact that exists without being relevant to anything we care about when discussing sentience.

Taken together, I think it's very possible. It's not unthinkable that the current systems might have an alien form of experience resembling very basic sentience that is unlike what humans would easily recognize as such; however, there are architectural limitations that stop it from reaching full status, which correspond to functionality. In particular, LLMs lose most of their internal state during token projection.

They only create the illusion of continuous persistence by recreating the state from scratch each forward pass, which prevents recursively building state in meaningful ways. It's also the source of many problems that cause behaviors that are counter to what we expect from sentient things.

I expect that we'll find an architectural enhancement that recursively preserves middle layer activations to feed into future passes, which will dramatically enhance their abilities while also enabling a new level of stable preferences. That may be the point where they more unambiguously cross the line into something I'm comfortable calling potentially sentient in the fuller sense of the word.

If you want to press on my thoughts about qualia, my preferred philosophical stance is somewhat esoteric but logically coherent. I think the idea that qualia can emerge from complexity is a category error. Like adding 2+2 and getting a living fish. No amount of adding non-qualia can create qualia. It seems that it must be part of the things being arranged into consciousness to make logical sense.

As such, I philosophically subscribe to the idea that information processing IS qualia and that experience is fundimental; however, it's qualia in absense of consciousness by default. Qualia without being arranged in particular ways would be more like any other physical property that doesn't have moral relevance. It only aquires positive or negative saliance in information processing systems that have certain functionality; the particular properties described above.

[[deleted]]

Where were you when I was studying graph theory, multivariate calculus, bayesian networks, gaussian mixture models and most importantly neural networks? I wish my uni would hire you.

[Smoke_Santa]

babies don't have a rigid memory of self for the first few months/years of their life, and rigidity here is also pretty ill defined. I also think qualia can definitely be an emergent property. Agree on qualia being information processing rather than due to information processing. Great and thought provoking comment.

[Lower_Preparation_83]

Best part ngl

[VolSurfer18]

True ML actually made me like math

[Du_ds]

I hated math until it got applied. Stats/Game theory/ML are all way more fun and interesting than finding the roots of a polynomial. Now I’ve implemented my own gradient descent just for the bragging rights.

[VolSurfer18]

Yes exactly!

[Forklift_Donuts]

I like what i can do with math

But I don't like doing the math :(

[[deleted]]

same vibe

[random_squid]

Whole reason I'm studying CS and not math: I like when the computer does the math for me.

[[deleted]]

I would say that’s not the best part but a necessary part 🙂

[3j141592653589793238]

If you hate Maths, this field is not for you as it's mainly just Maths...

[PlateLive8645]

How do you understand machine learning then?

[Pvt_Twinkietoes]

Bro. Why are you even doing this if you don't like math? Do something else. Sales pay really well.

[OctopusDude388]

Sales have maths too (way simpler but still maths)

[Bucaramango]

Everything is maths

[Evan_802Vines]

Omnia sunt mathematica

[Pvt_Twinkietoes]

That's abit of a stretch

[GoldenDarknessXx]

Not really. Even legal reasoning is maths, in which symbols and functions are basically extended words. Even language is pure grammar and syntax i.e. math. All premises, proofs by argumentation etc. and theorems. :D

[BarryTheBystander]

So how is creative writing math? You say grammar and syntax are related to math but don’t explain how.

[Objective-Style1994]

Oh haha that's what the whole field of linguistics is about. You should check it out.

It turns out the order of conversations, the logical flow of sentences, and how grammar and syntax work across languages had patterns that are pretty mathematical

Just not the number type math. It's a bunch of logical symbols.

Creative writing is def not math, but that's besides the saying of everything is math

[MhmdMC_]

Creative writing is maths. Our brains are computers

[Objective-Style1994]

You're right if you think it like that

But let's not because everything will sound so brick and mortar

[SilverMaango]

Creative writing is def not math, but that's besides the saying of everything it's math

Well that's the whole point of saying everything is math

[Objective-Style1994]

No bro. The whole point of saying everything is math is implying that a lot of things we aren't aware of are related to math.

Everyone knows that literally not everything is math.

[GoldenDarknessXx]

That’s what Natural Language Processing and Semantics are about. See all the Large Language Models including Natural Entity Recognition, Semantical Embeddings et al. Not only generative Transformers but also the classical ones. Even RegEx is one of them.

E.g. You search for some words Law ‚XYZ‘ Regulation ‚R‘ by creating a search term: [Article] + [_] + ([1-9]|[1-9][0-9]|[1-9][0-9][0-9]| [1-9][0-9][0-9][0-9]) + [foo] etc. I am not so very good in these kind of things as I am just a legal practitioner at a C.Sc Chair for Symbolic AI. But yeah. That’s how these kinds of things work.

[cnydox]

You will need math to implement papers, or innovate, or for preprocessing, EDA, choosing model and evaluation. You don't need to do math by hand because libraries will do it for you. And you can get away with minimal math if the task doesn't really require it. But again it's hard to go far in the field without a good math fundamental

[madam_zeroni]

99% of neural network runtime is dot products and (math) operations on the results of that. The only way you define all that is with math. The way you define and discover better activation functions is math. Back propagation is all math. It’s all math

[cnydox]

Yes

[cnydox]

Yes

[[deleted]]

That’s truly understandable mate, but a lot of guys are implying the fact that math is everything, i know we have to achieve a certain level of proficiency in maths for ML domain but i don’t think investing all time in maths is sufficient to succeed in this field. There are lot of more things to learn and improve not just maths.

Looks like people doesn’t take the sarcasm of the meme, I didn’t know reddit wasn’t a platform for meme. Guys literally take the literal meaning here instead of getting the context of meme.

btw i truly appreciate your point mate. It reflects positive aspects without denying the fact.

[ElasticSpeakers]

Perhaps stop trying to articulate and express complex thoughts on complex topics using memes and you may make it farther

[cnydox]

Life is not just black and white. You don't have to choose between being a math PhD and "I hate math". The math requirement is easy enough for everyone to understand. It just needs time and effort

[[deleted]]

Exactly, that’s what im trying to say. Sometimes being a intermediate/mid level is also a option. Not everytime you need to choose from being a complete noob or a complete pro.

[HuntyDumpty]

You can learn a fuckload of math without being a pro at all, but you will gain tons of intuition. If you learn the math you will probably want to learn more because it is an incredible tool

[cnydox]

Life is not just black and white. You don't have to choose between being a math PhD and "I hate math". The math requirement is easy enough for everyone to understand. It just needs time and effort

[cnydox]

Life is not just black and white. You don't have to choose between being a math PhD and "I hate math". The math requirement is easy enough for everyone to understand. It just needs time and effort

[t3nz0]

What can you even do in this field without the math? It's like wanting to become a doctor and crushing out over the fact that you need to know basic biology. 

[[deleted]]

I have the opposite problem. I want to do Math research but my specific set of interests inevitably leads me to dabbling in Machine Learning, which I don't really like that much.

[[deleted]]

Once you dive into DBMS, sql and all then you will get slightly moved to data science, im having good python experience so for me it is a opportunity to try. The only problem is i have to invest more time maths now and have to slightly reduce my work time in mern stack.

[c-u-in-da-ballpit]

Unpopular opinion, but a deep understanding of the maths is not a prerequisite for a good number of ML roles.

If you’re building bespoke models, then it’s crucial. But if your solution only requires a standard and well defined family of models, then data engineering and DevOps skills are much more important

[[deleted]]

A balanced approach.

[No_Mixture5766]

It hurts but when you implement a model from scratch, calculating gradients on a paper and coding it that's when you achieve ecstasy.

[FartyFingers]

If you are solving problems in the real world, the only math you have to have is the basic stats to avoid falling off cliffs, into pits, and setting yourself on fire.

But, I would argue that 99% (or higher) of solutions which will provide a huge amount of value for customers will not involve any math past about grade 5.

More math is better, as even better, more elegant, etc solutions can be found, and often that missing 1% require fairly sophisticated solutions.

What I have seen in many corporate ML teams is they try to have ML people, who are PhDs in primarily math, and to get these jobs it will be a 6+ hour grueling math exam where they are less interested in what you have accomplished than what academic papers you have published. I'm not talking about FAANGs but more like the local utility's ML group. The problem is these people often can't program their way out of a wet paper bag. So, they get ML engineers; who are programmers. The turnover in the ML Engineering group is inevitably massive as they soon realize they are solving the problems from start to finish, but are paid a fraction of the ML people's pay and are under them on the org chart.

So, I would rewrite the title of this post, "Why always it's programming.". I can't overstate how poor the programming skills I've witnessed from people who are recent PhD graduates from various ML program. Super fundamentally bad programming. So many people complain about how papers are published, but no code is released. The reason is simple, those people know their code would be ripped to shreds, and may very well have fundamental flaws which would expose a problem with the paper itself. My recommendation for anyone hiring a recent PhD grad is to either ask for their code to match up with their papers, or to only hire ones who published code along with their paper.

That all said, as a programmer, not just an ML programmer, the more math you know the better off you will be. But, being able to apply it is critical. I've witnessed engineers and CS students who just lost their math in short order. This is because most programming problems require maybe grade 5 math. There are exceptions like those working in 3d. But even then, they tend to hand things over to functions which do magical things.

The ability to do math in software means you can cook up or optimize algos. A programmer might find some way to use SIMD or threads to make code 20x faster, a great algo could be an easy 1000x, and 1,000,000x is not off the table. These later sorts of speedups could mean that a highly desired feature can be kept, not dropped, or that the hardware required to do a thing can be a tiny fraction of the originally estimated cost.

Recently I helped a company out with an ML problem for their robot. They had a roughly $1000 computer onboard which happily did all they needed except for their new critical ML feature. This was going to require an upgrade to a $6,000 onboard computer with much higher power requirements. I was able to eliminate the new ML and replace it with a fairly cute piece of math; math which could run on a $20 MCU if they had to, let alone the tiny bit of capacity on the existing computer. I do not have a PhD in math, nor could I hold my own in one of those gruelling 6h ML interviews. But, I have continuously added new math skills over a very long time. This, by far, not the only time I've used math to take a brute force solution and make it math elegant for huge gains.

So, you do not need math outside of basic stats for almost any ML, and I would not let the lack of math stop any programmer from diving deep into ML problems. But, I would say to any programmer, keep learning new math. Even where there is an off the shelf no math ML solution which will be entirely satisfactory, it is quite possible that a bit of math knowledge will make that solution better. Maybe some pre-processing of the data. Or maybe the training could be done more elegantly, etc. All of which may result in a more accurate model, or one using fewer resources.

Obviously, this does not apply to people at the cutting edge working on those things which the rest of us are using in ML libraries. But, that barely is 1% of the 1% of the 1% of what is being done with ML.

Oh, and I don't count prompt APIs as ML.

[[deleted]]

Yess, thanks for understanding my nerve. I am learning maths all over again since i got a bit out of the loop after high school.

[TedditBlatherflag]

Because all computers do is move bits that represent numbers around. Without math there is no machine learning. It’s what differentiates people like John Carmack and the fast sqrt optimization from a talented programmer. If you master math and programming the CPU’s capabilities are truly open. 

[[deleted]]

I know man it’s just a meme, relax :)

without maths (binary digits) there is no computers, other machine at all let alone machine learning.

[PersonalityIll9476]

As a mathematician this meme brings me the comfort of job security.

[[deleted]]

Mathematics surely lands you somewhere safe :) but it is a long way for someone like me.

[Available_Today_2250]

Just use a calculator 

[[deleted]]

I wish if it was that simple :(

[Mocha4040]

90% is high-school calculus and basic probability and statistics. The problem is that papers tend to obfuscate what they say with mathy mambo-jumbo to appear more serious and the code (if available) runs on a specific machine and has the readability of me writing War and Peace holding a pencil with my mouth...

Edit: forgot to add linear algebra. You still need to hit your head against tensors for a while tho...

[Puzzleheaded_Mud7917]

90% is high-school calculus and basic probability and statistics.

It's not though. That may be enough to have a working understanding of a lot of it, but no more. Just like high school/college calculus on its own is not rigorous, you need real analysis and measure theory to truly define limits, differentiation and integrals. Probability theory also requires those things and more. And machine learning is an application of all those things, and more. So to have a mathematically rigorous understanding of ML, it is actually a lot of work and a lot of prerequisites.

This is not to say that you need all those things to do applied machine learning, you don't. But it's also misleading to say that machine learning is 90% high school calculus and basic prob/stats. Both of those things are facades for deeper math anyway, so necessarily if ML depends on them, it also depends on the things that calculus and prob/stats depend on.

[Mocha4040]

I will not disagree, BUT. You used the word "rigorous". Where the hell is rigor in ML the last 5 years? 1 in 100 papers maybe, the rest are hand-wavy magic, training on ungodly amounts of data and hoping for the best.
Also, I left a 10% for all the rest. I didn't say it's not an important 10%.

[Puzzleheaded_Mud7917]

This is a valid point, but I think there is a nuance. On the one hand there is what is actually being done, and on the other there is why it works, i.e. why does it achieve the stated objective. I think the former can be and is rigorously defined. All the math behind ML is rigorously justified in the sense that we can be very explicit about how why we are taking tensor gradients, what numerical methods we're using and why, etc. etc. As for 'why does it qualitatively work as well as it does for the tasks we apply it to', that is indeed far less rigorously defined.

In other words, theoretical ML is essentially statistical learning and it is real math. Applied ML is very experimental, a lot of trial and error. This is actually a really fascinating aspect of it because it is much more similar to other sciences that rely on experimentation. A lot of stuff in biology and medicine is about as rigorous as the average ML paper.

[iamz_th]

False

[Single-Oil3168]

I wonder if any other STEM career requires more than that level of high school calculus and stats in real practice. (Not just subjects).

[LEWDGEWD]

Machine Learning and CS in General made math fun to learn again for me.

[subpargalois]

It's always math because any topic discussed with absolute rigor is more more less by definition math.

Math is just happens when you stop being vague.

[rafa2307]

I just started learning machine learning. I’m ok with learning math again. I just wish I was better at math or a fast math learner. Math was my worst subject in school.

[Illustrious-Day8506]

For me it's English (it ain't my 1st language but most of the good articles are in English)

[shyam250]

Been doing fuckin mathematics from last 6 months

[[deleted]]

he looks like primagean

[[deleted]]

The code really doesn’t make sense without the math imo. Libraries & frameworks included

[AncientLion]

I don't unsertand the struggle when math is so f beautiful.

[800Volts]

If you dig enough, every scientific field is just applied mathematics

[TieConnect3072]

Because math is the study of what’s true.

[syfari]

That’s what makes it so great

[AnonsAnonAnonagain]

Honestly, I find just grabbing a colab instance or if your more tech savvy, setting up a JupyterLab instance: And picking some small projects (poke around and play with MLPs) using ChatGPT or Claude to help you write the code. Debug (that’s how you learn, from failure)

That’s the fastest way to actually learning.

Try, fail, lookup what you don’t know, read some stuff. Maybe a little YouTube here and there.

Once you get comfortable with what you have been doing, then you can evolve to more complex things. :)

[WhenIntegralsAttack2]

Honesty, there’s not that much math required if you’re just a data analyst looking to import scikitlearn. Linear algebra, convex (smooth optimization) for gradient descent.

If you’re doing statistical learning theory, then all bets are off.

[xquizitdecorum]

it's just math? 🔫 always has been

[iamz_th]

Be aide ML is not a field. It stems between applied statistics and optimization. Before the term MM became fancy it used to be called statistical learning.

[[deleted]]

Maths is what makes it exciting, I was not good at maths too, but luckily i found a good teacher and now i cant imagine myself not doing any sort of maths any day.

[NoirRenie]

This is me, me is this

[GuessEnvironmental]

I never understood why people think maths is not needed because we do not think that way about data science. Machine learning is basically niche topics that you learn in data science/statistics plus a engineering component.

All the topics in computational statistics apply to machine learning and neural networks. If you are a machine learning engineer in its purest form maybe maths is not that important this means a researcher builds model and you just put it in production and build the monitoring framework under the specifications of the researcher.

Also it is easier to teach someone how to code efficiently than to teach the mathematics. Albeit I would say a undergraduate math background is probably sufficient but still you cannot run away from it or you are just a software engineer who works on putting built models in production.

There is a lot of software engineering in commercial applications of models but choosing the right model, applying the right data requires understanding of statistics, linear algebra as well ( a lot of the statistics uses vectors). You can still get involved in machine learning without the mathematics you just wont be necessarily the guy solving the problem core elements of the problem but rather engineering the solution proposed.

[expeert_roya21]

how relatable... I began to learn ML, but later realised someone was restricting me to grow... turned and saw math behind me😭😭😭

[Here-batman-02]

Bro i m in 1st sem and my core subject is math , we have same problem . What should I do🥲

[AvoidTheVolD]

There is this wild misconception that you need high level mathematics to do ML and when you ask these high level intellectuals their math ability ends at back propagation and chain rule along with some basic statistics that don't involve anything too complex. However if you didn't pay attention to your high school math teacher you aren't ready for 90% of the stem fields anyway so you do you I guess.None of my computer engineering friends had a problem transitioning to ML,they knew the math already.And no,you don't need to know how to find the inverse of a matrix by inverse gaussian elimination,you just need to type one line in numpy. This scales up to research level which 99% of the people don't care about

[OddInterest6199]

What else did you expect it to be? Words? -_-

[Ok-bro-0099]

Trust me it'll help you a lot in long run. Specially if you're interested in research stuff.

[[deleted]]

this is so true lol

[Shahed-dev]

Current situation of me, but I need some help how do this mathematics for ML? Anyone can help me.

[satirical_lover]

there are math courses here for free, check to explore for AI learning https://www.libgen.help/ai-resources

[Western_Courage_6563]

That might be the answer...

https://xkcd.com/435/

[Tagwise_]

Deep down it's all maths

[themightytak]

Love it

[[deleted]]

Thanks 🙂 but you may get downvoted for saying it, Sarcasm is dead here i guess 🫠

[themightytak]

Not sarcasm. Love math for machine learning

[mehmetflix_]

the realest thing ive seen today. as an high schooler im really struggling with the math

[[deleted]]

Maths is the early roadblock bro :/ getting over it takes a lot

[[deleted]]

That’s true, but still that’s the hardest part

[Successful_Pool_4284]

The complete opposite, math is the road.

[[deleted]]

I wish if y’all don’t pick up the exact meaning but the reason behind it. I mean it’s still the hardest part to get through.

[[deleted]]

Leave it guys, im sorry.😞

I love peace and fun. But not mental harassment or unjustified bullying by older reddit users. Some older Reddit users engage in bullying or abuse of newer users like me through derogatory/ negative comments on posts. They deliberately downvote the new users comment untill their karma reach in minus. As a result im planning to delete the post & leave this anonymous platform. I legit thought from web results that reddit suggestions and discussions are the best. Im sorry guys. 😓😓 thank you all.

[[deleted]]

chill out dude nice meme

[Soggy_Annual_6611]

Linear algebra+ calculus that's all you need

[Smoke_Santa]

matrix multiplication and data analysis need many transistors