My realtime thesis based on my tech career

The utility of an education in EECS is still extremely relevant and valuable. I think it still offers the most leverage over knowledge. The individuals with the highest net worth are EECS adjacent. If you are in this track, I urge you not to stop moving forward in this path. My education in EECS has continued to pay dividends over the decades. Neurosurgeons argue that neurochemistry, quantum biology, UPE in the substantia nigra, and photoreceptors like melanopsin (aka biophysics) are extremely relevant areas of research to focus on if we want to make progress on emotionally conscious ML architectures. They also believe memories are stored in the body, and are epigenetic (kind of like how fine tuning works today) and that mitochondrial DNA in the cell is more important for the way our bodies process information (nature’s transformer architecture). Engineering physicists insist that discovering room temperature superconductors and cold fusion reactions are the path to emotionally intelligent AI (due to higher energy densities and higher propagation efficiency, resulting in faster inference). These paths all look good. The truth is that no one really knows who has the answers. As an architect, I have to decide which ideas have merit and which ideas need more testing, based on our best estimate understanding of math and logic to model real world physics. There are definitely more algorithmic breakthroughs in the way of AI becoming better at understanding our world, and there are also knowledge gaps, like what it feels like to fall in love, or fall down and break a leg. There are also power bottlenecks that we are trying to solve with more efficient solid state Gaussian distribution generators, making uncertainty deterministic, lowering energy costs. I don’t think hot deuterium-tritium fusion is going to be the answer (I’d short fusion energy companies doing tokamak, stellerator, etc)🤦🏻‍♂️ The more we try to scale and test out new architectures, the more we learn about what it means to be human, and that is the universal knowledge worth pursuing and investing in. So no, I don’t think we are in a bubble (except for fusion?), because the value of this knowledge is much more than what we currently know. Dogs can hear inaudible frequencies, and know about earthquakes and tsunamis far before humans can detect them. Other animals have superhuman abilities as well, like cats and bats and whales with their sight or hearing. We as AI researchers are now able to tune how good of eyesight and hearing our models have, so they can appear to exhibit superhuman or supernatural performance in some respects. But when can physics and math equations crack consciousness and emotional intelligence? Seems like we may never get there. Or it may just be around the corner. World models? Sutskever, Murati, Karpathy, Carmack, LeCunn have all split off to pursue their own hypotheses. These are the best programmers of our generation and none of them can agree with each other. The truth is no one really knows the answer. And anyone who tells you they know the answer is either lying or misunderstanding the real boundaries of modern science and technology. Yann LeCunn is right in that there is something fundamentally missing. A lot of people don’t understand what he means by that. I think more people disagree with his stance. But that doesn’t mean we should stop searching. We should all do our part to look for the right answers. That’s what being a creator truly is. Creating artificial life is also ethically and morally questionable. But I still think it’s the best use of anyone’s time right now. It’s one of the most interesting problems to solve in our lifetimes in my own opinion. My answer: Linear algebra equations are memory constrained and demand higher HBM3 capacity. Only 3 companies in the world produce these chips, and they are backordered! Samsung, SK Hynix, and Micron. These companies will remain long for the next 3-6 years at least If you would like to understand why linear algebra equations will continue to dominate algorithmic paradigms for a while to come, just ask yourself: how do you represent directed graphs with N nodes and <N\^2 vertices? All biocellular, neurochemical, and quantum mechanical interactions are representable using N dimensional matrices. Matrices interact using matmuls->Matmuls require parallelism->parallelism requires HBM3 chips. These 3 companies own all of the production capacity for years to come. They will set the price and their shares will continue to benefit from the sustained demand as algorithmic complexity inevitably increases.