Machine Learning Isn't Magic and LLMs are boring
See the title, end post.
Ok but really
My masters was in machine learning, specifically Time Series Machine Learning. It's a fascinating, and somewhat underexplored branch of time series analysis.
Time Series Machine Learning applies to a vast array of domains, ranging from Climate Modeling, Stock Markets, and my areas of interest, Electroencephalography ^{1} and Electrocardiography ^{2}.
All that is to say, I find machine learning incredibly interesting, and am somewhat of a dab hand with both it, and time series data in general. Because of this background, people are often surprised when they find out that I am not "excited"/"hyped"/"interested" in the current wave of """AI""" tools that are rapidly proliferating.
The goal of this post is to provide a single source I can point to when I end up in this discussion with people, as well as getting a bit of catharsis by screaming all of this text into the void that is the internet.
How Machine Learning Works
Machine Learning is conceptually simple. In semilaymans terms we write code which when presented with some input and a label, it "remembers" both, "attaching" the label to the input. Then, when given an "unlabeled" input, it tries to find the "closest" "remembered" input.
To make this example somewhat more concrete, we'll discuss the "Hello World" of machine learning. Handwritten character recognition.
Given a bunch of photos of hand written numbers [0..9], and labels for these images (Eg, an image, and a bit of data that says "This image contains the number 3") we "train" a model (We make it "remember" these pairs). Now after giving it a few hundred/thousand of these handlabeled handwriting examples, we can present the model with a new, entirley unlabeled photo of a hand written number. The model will then try and find the "closest" image it has in its "memory" and will confidently say "this is this number".
This process we discussed is generally known as "classification". The other form of machine learning, and the one which has the most hype behind it is "regression" or more broadly, "prediction".
Regression Problems
You know how to do regression! It's one of those things you're taught in high school maths, but usually with the term "find the line of best fit"!
One of the things you were also probably asked to do in the same maths lessions you learned how to draw these lines of best fit, was to make predictions based on that same line. This is using a linear regression to make linear predictions.
In essence, regression analysis is the act of using algorithms to find the line of best fit programatically. There's a vast array of methods for finding these lines, and just as many possible types of line (Linear regression, polynomial regression, binomial regression, logistic regression....).
Prediction then, is using the line we get from regression to make guesses as to what the data would be at a given coordinate, just like you did in maths class!
What makes this interesting however, is how we can generalise regression problems into higher dimensions. Our examples above focused on 2d plots, but we can perform regressions on almost any amount of dimensions ^{3}. This is the foundational model upon which almost all of this ""generative ai"" exists.
How GenAI works for dummies
One of the neat things about maths is that it's often possible to reframe one problem as another one. Classic examples of this are the reduction of NPComplete problems ^{4}. Almost everything in machine learning can be reduced into either a classification problem, or a regression problem, and in fact LLMs as a whole are just one incredibly complex regression problem.
An LLM is a statistical model. Given a text, what are the next words that should be produced? Or to phrase it with our newly found knowledge; Given some input, within this multibilliondimension regression problem, what are the next set of values that should appear on this line?
This is what ChatGPT and other LLMs are doing. Your input is the start of a line, and the machine is "guessing" based on it's training data what the appropriate response is.
The Chinese Room and what it means to be Sentient
In the seminal 1980 paper Minds, Brains and Programs the philosopher John Searle put forward a thought experiment. Assume that some AI has been developed which "understands" Mandarin. The machine takes input in Mandarin, and produces an output also in Mandarin. As a result of this rote translation by mechanical means, and an unfathomal amount of computational power it is able to convince some people, that this is a real machine which can read, and write in Mandarin. Truly an amazing piece of technology!
The thought experiemnt then goes on to posit a further idea. If I decided to lock you into a room ^{5}, with nothing but a Mandarin to Mandarin phrasebook, paper, pens, an english version of the "computer program" detailing the steps to "run the program" with said materials, and an unlimited supply of Nanotrasen SpacePaste to fulfil your dietary needs, you would be able to achieve the same result as the machine.
This then raises an incredibly interesting question. Do either of these machines actually understand Mandarin? The machine and yourself are producing equally rote responses to a given input. You may learn eventually that x input always results in y output, but without any frame of reference in a language you already understand it is unlikeley you will ever have any clue what either the input, or output you produce means.
Much like the idea of transforming the travelling salesman into a SAT problem, we can also transform this understanding of the Chinese Room thought experiment into an interesting philosophical razor for the alleged "intelligence" of LLMs.
An LLM has no way of actually understanding what it's input means, nor what it's actually outputting. All it sees are your inputs translated into a set of numbers, and it responds with another set of numbers which in turn correspond to a set of output symbols. Because of their entirley statistical nature, LLMs are incapable of learning anything new without training. LLMs are incapable of producing differing outputs from the same inputs without baking in some randomness to the model, which also means they will never produce the same output!
LLMs are philosophically interesting, but as someone who is keenly interested in the cutting edge of machine learning, seeing an entire field reduced to a more advanced version of Cleverbot is incredibly disappointing.
Lies, More lies and Statistics
Whatever is at the current edge of discovery is always going to have grifters trying to sell you snake oil. Patent medicine was often either useless or downright toxic ^{6}. Cryptocurrency and "blockchain" was usually just a veil over a pump and dump or rugpull scheme.
Right now, there is huge amounts of money and FOMObased advertising which directly targets the management class of companies in order to sell them a product which in almost all cases is only marginally useful at best.
The potential loss of "not being in on the ground floor" for whatever the next revolutionary technology is, is often horrifying for these types of people, and AI grifers are explicitly targeting this by making promises about the technology of LLMs which are just not possible.
One of the most glaring examples of this in recent memory come from a lot of "Thought Leaders" in the area saying that LLMs are "close to AGI" and "require just a bit more training" which is complete, and utter nonsense. By their very nature LLMs are incapable of reasoning, which is the defining feature of an artificial general intelligence.
"AI" is the new buzzword. LLMs are trivially implementable, it's at most 50 lines of python to deploy an OpenAI powered LLM so you can outsource the work of handling support requests to useless chatbots which have never helped anyone.
I predict the current wave of ""AI"" grifters promising LLMs as a "stepping stone to general AI" are not going to be going away any time soon, and will continue to try and sell the cult's koolaid until some incident along the lines of the FTX fallout happens to OpenAI and it's ilk.
Summary
I don't like LLMs. I don't like the fact that "AI" research has been reduced to an overdeveloped version of cleverbot. I don't like the fact that it's big tech trying to create a hypergrowth market that doesn't and cant exist. I don't like the fact that the web is now being polouted by a bunch of LLM generated Slop. I don't know anyone who actually knows about machine learning that is interested in any of this crap.
Outro
 Music: Untrue by Burial
 Coffee: LA SIGUANABA from Dark Arts still. Brewed with a V60, standard papers, 23 on Commandante C40

I totally didn't need to look up the spelling for this. ↩

Or this. ↩

Of course it must be stated that the more dimensions we're working with the slower, and more complex this problem becomes ↩

for example, the travelling salesman problem can be, through a series of reductions through other NPComplete problems eventually turned into a boolean satisfiability problem ↩

Of course this analogy falls flat if you do speak and understand mandarin but in this case replace all instances of the word "you" with "Some random person" ↩

Obviously aside from Lilly The Pink's Medicinal Compoud which was most efficacious in every case. ↩