These things suck and will literally destroy the world and the human spirit from the inside out no matter who makes them
I think this kind of statement needs to be more elaborate to have proper discussions about it.
LLMs can really be summarized as “squeezing the entire internet into a black box that can be queried at will”. It has many use cases but even more potential for misuse.
All forms of AI (artificial intelligence in the literal sense) as we know it (i.e., not artificial general intelligence or AGI) are just statistical models that do not have the capacity to think, have no ability to reason and cannot critically evaluate or verify a certain piece of information, which can equally come from legitimate source or some random Reddit post (the infamous case of Google AI telling you to put glue on your pizza can be traced back to a Reddit joke post).
These LLM models are built by training on the entire internet’s datasets using a transformer architecture that has very good memory retention, and more recently, with reinforcement learning with human input to reduce their tendency to produce incorrect output (i.e. hallucinations). Even then, these dataset require extensive tweaking and curation and OpenAI famously employ Kenyan workers at less than $2 per hour to perform the tedious work of dataset annotation used for training.
Are they useful if you just need to pull up a piece of information that is not critical in the real world? Yes. Is it useful if you don’t want to do your homework and just let the algorithm solve everything for you? Yes (of course, there is an entire discussion about future engineers/doctors who are “trained” by relying on these AI models and then go on to do real things in the real world without developing the capacity to think/evaluate for themselves). Would you ever trust it if your life depends on it (i.e. building a car, plane or a house, or treating an illness)? Hell no.
A simple test case is to ask yourself if you would ever trust an AI model over a trained physician to treat your illness? A human physician has access to real-world experience that an AI will never have (no matter how much medical literature it can devour on the internet), has the capacity to think and reason and thus the ability to respond to anomalies which have never been seen before.
An AI model needs thousands of images to learn the difference between a cat and a dog, a human child can learn that with just a few examples. Without a huge input dataset (helped annotated by an army of underpaid Kenyan workers), the accuracy is simply crap. The fundamental process of learning is very different between the two, and until we have made advances on AGI (which is as far as you could get from the current iterations of AI), we’ll always have to deal with the potential misuses of AI in our lives.
are just statistical models that do not have the capacity to think, have no ability to reason and cannot critically evaluate or verify a certain piece of information, which can equally come from legitimate source or some random Reddit post
I really hate how techbros have convinced people that it’s something magical. But all they’ve done is convinced themselves and everyone else that every tool is a hammer
Yes, LLMs are stupid and they steal your creative creations. There is some real room for machine learning (something that has been just all combined into “AI” now for some reason), like Nvidia’s DLSS technology for example. Or other fields where the computer has to operate in a closed environment with very strictly defined parameters, like pharmaceutical research. How proteins fold is strictly governed by laws of physics and we can tell the model exactly what those laws are.
But it is funny how all the hundreds of billions $$$ invested into LLMs in the West, along with big government support and all the “smartest minds” working on it, they got beaten by the much smaller and cheaper Chinese competitors, who are ACTUALLY opensourcing their models. US tech morons got owned on their own terms.
“let’s just use autocorrect to create the future this is definitely cool and not regressive and reactionary and a complete recipe for disaster”
It’s technology with many valid use-cases. The misapplication of the technology by capital doesn’t make the tech itself inherently reactionary.
This is a stupid take. I like the autocorrect analogy generally, but this veers into Luddite-ism.
Let me add, the way we’re pushed to use LLMs is pretty dumb and a waste of time and resources, but the technology has pretty fascinating use-cases in material and drug discovery.
I work in software development, and AI can generate instantly some code that would take me an hour to research how to write when I’m using an SDK I’m unfamiliar with, or it can very easily find little mistakes that would take me a long time to figure out. If I have to copy and paste a lot of data and have to do boring repetitive work like create constants from it, it can do all of it for me if I give it an explanation of what I want.
It makes me gain a lot of time, and spare me a lot of mental fatigue so I have more energy to do things that I enjoy after work.
how do you measure performance of an llm? ask it how many 'r’s there are in ‘strawberry’ and how many times you have to say ‘no thats wrong’ until it gets 3
Basically speed and power usage to process a query. Also, there’s been tangible progress in doing reasoning with unsupervised learning seen in DeepSeek R1 and approaches such as neurosymbolics. These types of models can actually explain the steps they take to arrive at the answer, and you can correct them.
I suspect “reasoning” models are just taking advantage of the law of averages. You could get much better results from prior llms if you provided plenty of context in your prompt. In doing so you would constrain the range of possible outputs which helps to reduce “hallucinations”. You could even use llms to produce that context for you. To me it seems like reasoning models are just trained to do that all in one go.
Neurosymbolic models use symbolic logic to do the reasoning on the data that’s parsed and classified using a deep neural network. If you’re interested in how this works in detail, this is a good paper https://arxiv.org/abs/2305.00813
They use synthetic AI generated benchmarks
It’s computer silicon blowing itself basically
I’ve been researching this for uni at you’re not too far off. There’s a bunch of benchmarks out there and LLMs are ran against a set of questions and are given a score based on its response.
The questions can be multiple choice or open ended. If they’re open then it’ll be marked by another LLM.
There’s a couple initiatives to create benchmarks with known answers that are updated frequently, so they don’t need to marked by another LLM, but where the questions aren’t in the testing LLMs training dataset. This is because a lot of advancements in LLMs with these benchmarks is just the creators including the text questions and answers in the training data.
Hey america
Nice AI “superiority”
It would be a shame if someone were to… Challenge it…
