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Hey america
Nice AI “superiority”
It would be a shame if someone were to… Challenge it…
There’s so many Chinese scientists and Chinese institutions on the research papers only a
✨ doofus ✨ 
would think the sanctions would work.
A second Chinese AI has hit the western hype bubble.
I’m slowly just beginning to accept that we are all living in an era of inescapable slop that has the potential to destroy the natural world.
To be super reductionist, one of the key components that is driving people to automate everything away because the name of the game is speed-completing whatever banal tasks/exercises at increasingly quicker rates and increasingly significant costs to our humanity. And if that doesn’t directly parallel with technological progression in the 21st century.
I don’t know how you can look at capitalism which is fundamentally based on infinite growth and just ignore what comes with infinite growth (wouldn’t that be infinite problems?) The “system” argument no longer applies because not all systems are predicated on infinite progress/growth.
This also just sounds like capitalists who lead meaningless lives and have bought up everything at this point, so they’re making their existential crisis everyone else’s problem.
I’ve decided I’m gonna start using copilot for all bullshit work tasks
Use the bullshit machine to answer bullshit questions
You want me to set personal KPIs to align to those of my team and the org? You want me to come up with a list of use cases for generative AI?
ChatGPT time
These things suck and will literally destroy the world and the human spirit from the inside out no matter who makes them
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.
Even LLMs have some decent uses, but you put the finger on what I am feeling, that all of AI and machine learning is being overshadowed by these massive investments into LLMs, just because a few ghouls sniff profit
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
that’s a deeply reactionary take
LLMs are literally reactionary by design but go off
They’re just automation
The fact that there is nuance does not preclude that artifacts can be political, whether intentional or not..
While I don’t know whether this applies to DeepSeek R1, the Internet perpetuates many human biases and machine learning will approximate and pick up on those biases regardless of which country is doing the training. Sure you can try to tell LLMs trained on the Internet not to do that — we’ve at least become better at that than Tay in 2016, but that probably still goes about as well as telling a human not to at best.
I personally don’t buy the argument that you should hate the designer instead of the technology, in the same way we shouldn’t excuse a member of Congress’ actions because of the military-industrial complex, or capitalism, or systemic racism, and so on that ensured they’re in such a position.
I don’t see these tools replacing humans in the decision making process, rather they’re going to be used to automate a lot of tedious work with the human making high level decisions.
There’s value in the tedious decisions though
The tedious decisions are what build confidence and experience
People build confidence doing work in any domain. Working with artificial agents is simply going to build different kinds of skills.
That’s fair, but human oversight doesn’t mean they’ll necessarily catch biases in its output
We already have that problem with humans as well though.
They’re not just automations though.
Industrial automations are purpose-built equipments and softwares designed by experts with very specific boundaries set to ensure that tightly regulated specifications can be met - i.e., if you are designing and building a car, you better make sure that the automation doesn’t do things it’s not supposed to do.
LLMs are general purpose language models that can be called up to spew out anything and without proper reference to their reasoning. You can technically use them to “automate” certain tasks but they are not subjected to the same kind of rules and regulations employed in the industrial setting, where tiny miscalculations can lead to consequences.
This is not to say that they are useless and cannot aid in the work flow, but their real use cases have to be manually curated and extensively tested by experts in the field, with all the caveats of potential hallucinations that can cause severe consequences if not caught in time.
What you’re looking for is AGI, and the current iterations of AI is the furthest you can get from an AGI that can actually reason and think.
That’s not the case with stuff like neurosymbolic models and what DeepSeek R1 is doing. These types of models do actual reasoning and can explain the steps they use to arrive at a solution. If you’re interested, this is a good read on the neurosymbolic approach https://arxiv.org/abs/2305.00813
However, automation doesn’t just apply to stuff like factory work. If you read the articles I linked above, you’ll see that they’re specifically talking about automating aspects of producing media such as visual content.
The “chain of thought” output simply gives you the “progress” and the specific path/approach the model has arrived at a particular answer - which is useful for tweaking and troubleshooting the parameters toward improving the accuracy and reducing hallucinations on a model, but it is not the same reasoning that could be given from a human mind.
The transformer architecture is really just a statistical model built to have very strong memory retention when it comes to making associations (in the case of LLMs, words). It fundamentally cannot think or reason. It takes a specific “statistical” path and arrives at an answer based on the associations it has been trained on, but you cannot make it think and reason the way we do, nor can it evaluate or verify the validity of a piece of information based on cognitive reasoning.
Do you actually understand what symbolic logic is?
What does that even mean
they “react” to your input and every letter after i guess?? lmao
Hard disk drives are literally revolutionary by design because they spin around. Embrace the fastest spinning and most revolutionary storage media
sorry sweaty, ssds are problematic
Scratch a SSD and a NVMe bleeds.
Sufi whirling is the greatest expression of revolutionary spirit in all of time.
Pushing glasses up nose further than you ever thought imaginable *every token after
hey man come here i have something to show you
It’s a model with heavy cold war liberalism bias (due to information being fed to it), unless you prompt it - you’ll get freedom/markets/entrepreneurs out of it for any problem. As people are treating them as gospel of the impartial observer -
The fate of the world will be ultimately decided on garbage answers spewed out by an LLM trained on Reddit posts. That’s just how the future leaders of the world will base their decisions on.
Future senator getting “show hog” to some question with 0.000001 probability: well, if the god-machine says so
That’s not the technology’s fault though, it’s just that the technology is produced by an imperialist capitalist society that treats cold war propaganda as indisputable fact.
Feed different data to the machine and you will get different results. For example if you just train a model on CIA declassified documents it will be able to answer questions about the real role of the CIA historically. Add a subjective point of view on these events and it can either answer you with right wing bullshit if that’s what you gave it, or a marxist analysis of the CIA as an imperialist weapon that it is.
As with technology in general, it’s effect on society lies with the hands that wield it.
Feed different data to the machine and you will get different results.
These things have already eaten all the data that there is, and I don’t need to tell you that, but that data, as it has been produced almost solely under capitalism, is just crap.
Put it that way, even if one feeds it cia files to the hearts content, the weights of words which are needed to construct sentences is still sitting somewhere there. (also answering about real role of cia implies llm has any idea about reality, it will just bias answer in another direction, just as marxist analysis: it will just reproduce likeliest answer resembling marxist literature you fed to it, not “have analysis”).
Benign application of llm is natural language processing into fixed functions on the back end (e.g. turn off the lights when it start raining or whatever, something which can be disassembled from millions of ways into same set of instructions, here its fuzziness is great)
“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.
LLMs literally cannot do anything else other than reproduce data it has been given. The closer the output is to the input, the better it is. Now if the input is “all the data that capitalism has produced” then the expected output is “an infinite amount of variations on that data”. That’s why it is reactionary.
It’s incredibly power hungry.
The context of the discussion is that it’s already 50x less power hungry than just a little while ago.
For now. We’ve been seeing great strides in reducing that power hunger recently, including by the LLM that’s the subject of this post.
That also doesn’t make it inherently reactionary.
We’ve been seeing great strides in reducing that power hunger recently, including by the LLM that’s the subject of this post.
Due to the market economy in both the United State and China, further development of LLM efficiency is probably the worst thing that could possibly happen. Even if China did not want to subject LLMs to market forces, they are going to need to compete with the US. This is going further accelerate the climate disaster.
Again, an issue with capitalism and not the technology itself.
Kind of wondering why China needs to compete in this realm? Unless their is something from LLM’s that improves the productive forces in a country, I don’t see any other reason.
At least the space race had something to do with a strategic military advantage
Vacuum tubes were too
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.drug discovery
This is mainly hype. The process of creating AI has been useful for drug discovery, LLMs as people practically know them (e.g. ChatGBT) have not other than the same kind of sloppy labor corner cost cutting bullshit.
If you read a lot of the practical applications in the papers it’s mostly publish or perish crap where they’re gushing about how drug trials should be like going to cvs.com where you get a robot and you can ask it to explain something to you and it spits out the same thing reworded 4-5 times.
They’re simply pushing consent protocols onto robots rather than nurses, which TBH should be an ethical violation.
I should have been more precise, but this is all in the context of news about a cutting-edge LLM using a fraction of the cost of ChatGPT, and comments calling it all “reactionary autocorrect” and “literally reactionary by design”. My issue is really with the overuse of the term “AI”, but I didn’t feel like explaining the difference between a GPT and deep kernel learning or graph neural networks, which have been used for drug and material discovery. Peppersky’s comment came off as very anti-intellectual to me, which I hate to see amongst “leftists”.
I should have been more precise, but this is all in the context of news about a cutting-edge LLM using a fraction of the cost of ChatGPT, and comments calling it all “reactionary autocorrect” and “literally reactionary by design”.
I disagree that it’s “reactionary by design”. I agree that it’s usage is 90% reactionary. Many companies are effectively trying to use it in a way that attempts to reinforce their deteriorating status quo. I work in software so I always see people calling this shit a magic wand to problems of the falling rate of profit and the falling rate of production. I’ll give you an extrememly common example that i’ve seen across multiple companies an industries.
Problem: Modern companies do not want to be responsible for the development and education of their employees. They do not want to pay for the development of well functioning specialized tools for the problems their company faces. They see it as a money and time sink. This often presents itself as:
- missing, incomplete, incorrect documentation
- horrible time wasting meeting practices
I’ve seen the following be pitched as AI Bandaids:
Proposal: push all your documentation into a RAG LLM so that users simply ask the robot and get what they want
Reality: The robot hallucinates things that aren’t there in technical processes. Attempts to get the robot to correct this involves the robot sticking to marketing style vagaries that aren’t even grounded in the reality of how the company actually works (things as simple as the robot assuming how a process/team/division is organized rather than the reality). Attempts to simply use it as a semantic search index end up linking to the real documentation which is garbage to begin with and doesn’t actually solve anyone’s real problems.
Proposal: We have too many meetings and spend ~4 hours on zoom. Nobody remembers what happens in the meetings, nobody takes notes, it’s almost like we didn’t have them at all. We are simply not good at working meetings and it’s just chat sessions where the topic is the project. We should use AI features to do AI summaries of our meetings.
Reality: The AI summaries cannot capture action items correctly if at all. The AI summaries are vague and mainly result in metadata rather than notes of important decisions and plans. We are still in meetings for 4 hours a day, but now we just copypasta useless AI summaries all over the place.
Don’t even get me started on CoPilot and code generation garbage. Or making “developers productive”. It all boils down to a million monkey problem.
These are very common scenarios that I’ve seen that ground the use of this technology in inherently reactionary patterns of social reproduction. By the way I do think DeepSeek and Duobao are an extremely important and necessary step because it destroys the status quo of Western AI development. AI in the West is made to be inefficient on purpose because it limits competition. The fact that you cannot run models locally due to their incredible size and compute demand is a vendor lock-in feature that ensures monetization channels for Western companies. The PayGo model bootstraps itself.
I think we agree that LLMs like ChatGPT and CoPilot largely will be (and are being) used to discipline labor and that is reactionary. But this feels more like a list of gripes with LLMs and not actually responding to my comment. DKL, GNNs and other machine learning architectures ARE being used in drug and material discovery research, I just didn’t feel like explaining the difference between that and the popular conception of “AI” to peppersky, given how flippant and troll-y their comments were. We should push back against anti-intellectualism in our spaces, and that’s all I was trying to do.
Just like every technological advancement. The problem isn’t the technology but how capitalism puts it to use
Luddites were actually cool and right. They didn’t organize and destroy looms because they just loved the more tedious work of non-powered looms, they destroyed them because they were the beginning of industrial capitalism and wage labor.
🙄
In the meantime, it’s making my job a lot more bearable.
How?
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.
It’s really useful to use a library / language you’re not very familiar with. I’ve used it recently to learn how to use minizinc, a constraint problem modeling language. There’s not a lot data of it on the Internet, and for that reason, sometimes the generated code won’t even be sintatically correct, but even then it was extremely useful to learn the language
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
I appreciate the link but I stand by my point. As far as I’m aware, “reasoning” models like R1 and O3 are not architecturally very different from Deepseek v3 or GPT4, which have already integrated some of the features mentioned in that paper.
Also as an aside, I really despise how compsci researchers and the tech sector borrow language from neuroscience. They take concepts they don’t fully understand and then use them in obscenely reductive ways. It ends up heavily obscuring how LLMs function and what their limitations are. They of course can’t speak plainly about these things otherwise the financial house of cards built up around LLMs would collapse. As such, I guess we’re just condemned to live in the fever dreams of tech entrepreneurs who are at their core are used car salesmen with god complexes.
Don’t get me wrong, LLMs and other kinds of deep generative models are useful in some contexts. It’s just their utility is not at all commensurate with the absurd amount of resources expended to create them.
The way to look at models like R1 is as layers on top of the LLM architecture. We’ve basically hit a limit of what generative models can do on their own, and now research is branching out in new directions to supplement what the GPT architecture is good at doing.
The potential here is that these kinds of systems will be able to do tasks that fundamentally could not be automated previously. Given that, I think it’s odd to say that the utility is not commensurate with the effort being invested into pursuing this goal. Making this work would effectively be a new industrial revolution. The reality is that we don’t actually know what’s possible, but the rate of progress so far has been absolutely stunning.
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.
it requires fewer tons of CO2 to tell you that 757 * 128 = 3042
