I’ve been very fed up with AI for about past 6 months, completely started to boycott it, discuss it with people of all kinds of different views, and I found joy and pride once again in my own work.
But the world issues coming from AI, and even more so from the billionaires and empires behind it seem to further pile up to insane heights. I’ve been trying to learn more and more about it, and after my bachelor and masters I am considering pursuing phd and research surrounding AI, especially from the critical perspective, which seems to be deeply neglected in the research pov.
This is still a few years in the future, and lot could change, but I am curious what do people here think about pursuing such a thing, and if in current academic world it is even something that would be possible doing, given lot of the grants and funding of AI research comes from these companies that just want to gain even more power through it.
As I said, I am already trying to know as much as possible about it, but I would like to look more deeply into its impact on society, impact on students etc.
Do you think this would be a worthwile endevaour? And if not, where do you think I should be heading to make change about this while not completely starving to death?
I think it’s probably a good idea. Are you already pursuing a bachelor and if so in what field?
There are a lot of angles to critique LLMs and generative AI from. Economic, social, environmental, psychological and technical. Do you want to study how investors and managers are duped into investing massive amounts of money into a huge bubble? How society is being ruined by slop? How huge data centers are destroying the environment? How sycophantic chatbots are driving people into delusional beliefs, so-called “AI psychosis”? Or would you prefer to focus on the technological side and find out how LLMs compare to other forms of AI and machine learning and data processing and what innovations could lead to more effecient, more effective and less harmful AI?
You could basically follow two routes, I think. You could major in something like sociology, economics or philosophy and minor/specialize in the impacts of AI or technology in general. Alternatively, you could major in AI or IT and do some kind of minor or specialization in its impacts on the world. Though I suppose it’s possible you may be able to find a course somewhere in the world that tries to teach both.
I am currently studying Cybersecurity as my bachelor, so my background is very technical… I think I would like to go the technical route, but also sprinkle in a bit of sociology, or maybe philosophy or something politics related… Maybe focusing on being sort of bridge in interdisciplinary research with deep knowledge of the technical aspects, while also focusing on more social aspects and collaborating on research with people that have deeper understanding of these issues. There is a master degree in Prague called prg.ai I believe, which kinda tries to teach about AI from many different povs, which could be interesting for me.
To understand things from a technical point of view, you’ll want a solid understanding of Artificial Neural Networks (ANN) and how they are usually trained (back-propagation). These have been around for decades and are also used for classification tasks and things like image processing in your phone or GPU.
To create an LLM, you feed the output of the ANN back into the network, creating a Recurrent Neural Network (RNN) and you tokenize the input.
It also helps to have some general knowledge of machine learning (ML) and understand terms like supervised learning and unsupervised learning.
Most of the ideas behind LLMs have been around for years or decades. The big difference is that nowadays we can throw enough compute at the problem to make them viable.
There are two ways to make ANNs and LLMs more efficient: better algorithms and more specialized hardware. For example, bitnets with ternary connections run much better on conventional CPUs and GPUs. Hardware like Google’s NPUs, OpenAI’s new chip, analog computers or neuromorphic computing could run them more efficiently.