A second Chinese AI has hit the western hype bubble.

There's so many Chinese scientists and Chinese institutions on the research papers only a ✨ doofus ✨ would think the sanctions would work.

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.

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.

that's a deeply reactionary take

In the meantime, it's making my job a lot more bearable.

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.

it requires fewer tons of CO2 to tell you that 757 * 128 = 3042

They use synthetic AI generated benchmarks

It's computer silicon blowing itself basically