Don’t know if this is the correct community to ask this question but here goes.
I am a physics/math major and I am fascinated by computers. I want to work on a field that deals with cutting edge computer hardware (sort of like how ssd was in the age of hdd). But most of the research seems to be on stuff that will be used by corporations (not affordable for common people).
Does anyone have any idea what field is closest to what I’m looking for?
most of the research seems to be on stuff that will be used by corporations (not affordable for common people)
To be fair, I think that’s kind of always been the case for cutting-edge computer research. The first computers were the size of a room or a small building, and only universities, governments, and large companies could afford to have one. Over time, hardware becomes smaller, faster, and cheaper, and now most people have a computer in their pocket.
You need to speak with your prof or Academic advisor, because if you want to avoid the corporate world, you will have to go into Academia. Could be anywhere really. Electronics engineering, photonics, quantum computing, materials science, lasers. It’s an enormous field. Find research you love, reach out to them and see if you can join someone’s team.
I’d get into optical computing.
Look up Intel’s research into optical buses, as an example. Or experiments in pure optical inference. I just find this whole field extremely practical in a wide range, from “usefully augmenting silicon PC chips” to specialized accelerators to farther-out computing paradigms.
I’d also look into Josephine Junction computation. If there’s a sudden breakthrough in room temperature superconductivity, that becomes very important, very quickly.
I also find “biological computers” or those emulating biology interesting, but don’t understand enough about them to even begin to assess it.
I’m less enthusiastic about quantum computing. It’s extremely useful in a few niches, but it feels like it’s hit crypto-like levels of hype: a narrowly useful tech that’s being pushed way beyond what it’s good at.
It is related to the other areas I listed, though.
I’d say quantum computing. It’s not as though this will all be happening quickly, if you want to work with high end hardware before it reaches consumers you’d need to find some sort of company like Samsung’s R&D department. But that’ll only be chip manufacturing, memory, stuff like that.
CPU’s in x86 architecture are on their way out, Arm is becoming more popular (SoC) but the other way really is quantum computing. That’s a field they want, something they’ve not yet cracked. Tever transferred some qubits, but it’s just a big boom waiting to happen that will really accelerate AI as well.
Quantum computing sounds great for a physics and math major. There’s certainly still a lot to learn. I think the biggest challenges are still mostly in the physics aspect of quantum computing, but software development is an important part as well.
I actually think the tech is mostly there, all we really need is application. Once there’s a real world need for quantum computers, they will get more developed over time. If there is nothing to be gained by making them quicker, more efficient, et cetera, then it will remain niche.
Which is partly why I mentioned AI. Currently, of course, most of AI applications run on very powerful GPUs with a huge amount of bandwidth. At some point, it will no longer be profitable to increase scale (not to mention the already disastrous environmental impact). I am hoping that quantum can solve both scale up and evolutionary issues that AI is facing.
Mind that I’m not just talking about LLMs, AI is much broader than just coding assistants or silly chatbots. AI has been around long before ChatGPT, it’s just that LLMs have provided a real world use that appeals to a much wider audience (scale up).
When it comes to its evolution, it’s mostly about ‘putting AI in everything’, something that on this platform is a bad thing about which most people here say they don’t want it. I can sort of see why for some aspects, but there’s always been a voice of resistance for disruptive tech. Combining computers with agriculture was long seen as nonsense, until they started seeing how much data they actually could process in a much shorter time. Enabling internet access on a lot of home appliances was seen as daft until home automation became more widespread. Putting excellent camera tech on portable devices seemed a weird combination until social media required you to share photos all the time. It’s unclear which field will benefit most from AI integration at this point, but I’m fairly confident there will be one. Quantum computing can help solve efficiency issues that will give us the opportunity to combine AI with all kinds of applications to see which one might benefit most.
If we stop trying to see the dystopian part of all these developments, I think AI evolution will solve a lot of our issues pertaining to energy usage and transition away from fossil fuels, climate change and the way we live with environmental shifts, language barriers and other sociological challenges that cause friction, space travel and exploration, our understanding of the planet and the universe, education…
But we need to be wary of power struggles, privacy, fascism, time (in the event our ecosystem collapses before AI can help us solve our issues), inequity of access to improvements gained by AI (ie how will the poorest of us benefit. Computers were once only for the elite, now almost everyone has something like a pc, laptop or smartphone).
I’m split 50/50 between optimistic and pessimistic about the future of AI but if we can leverage quantum the way science has promised us quantum may help, that tips the scale to 70/30.
Computer science, computer engineering, electrical engineering?
Most R&D is funded by business, because it’s expensive. Nobody really says “yes, let’s spend millions of dollars to possibly make a retail product with poor margins”.
Being a math and physics major I would go with algorithms. Look at the current limitations of qam, qpsk, and ofdm algorithms and see where a physics or mathematical gains can be introduced to make fibre and radios more efficient.
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Optics and photonics is the future of tech imo.
But most of the research seems to be on stuff that will be used by corporations (not affordable for common people).
If this is your primary concern, you need to be in law or politics rather than science. You can’t afford principles if you’re chasing grant money, and grant money is impossible to come by if you have principles.
Something like ‘Systems Thinking’ with a ‘Dynamic/Cognitive systems’ specialization… ‘Systems’ is imho the ultimate abstraction in a hierarchical universe and goes well with future AGI that can setup and manipulate all the IO, data-flow, sub/super-systems and state details of any system.
Sabotaging AI
Definitely biotechnology. The garage business revolution has been delayed for years (or is limited to unhelpful drugs of abuse).
I’m doing applied math from a contemporary philosophy (and a little physics) perspective. I want to approach it from the concept of the umwelt and link semicircular canals to dimensional perception using transformers [hagfish(1), lampreys(2), zebrafish(3)] then extrapolate to higher dimensions.
If you are interested in hardware, then the key words are computer architecture. There are a lot of great research going on in this domain! I am thinking of RISC-V which is a “new” instruction set (not so new, but it is currently in heavy development). I am thinking of many dedicated components for audio/video processing (FFT, codec, …), or for tensor processing (for neural network). These things are more and more present in all the computers (including smartphone), and there are game-changing. For example all the machine of Apple now embed a TPU (Tensor Process Unit). If you are more interested in the physics behind all this, there are also a lot going on (stacked semiconductors, hybrid computers, …). But this is not my field of expertise.
You really need to ask yourself if you’re okay being a literal evil person
