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At some point, because machine learning is picking out algorithms from the training data, models will converge on functionally equivalent algorithms with the brain. At first, it won't look good, because the training data will contain output from the millions of individual brains that produced it. Only the human processes most commonly shared will be learned, and the inference pass does one gigantic single run of a model to produce output. The brain has multiple parallel systems contributing to cognition, with hierarchical structures and cycles. If you could deconstruct transformer models into multiple networks, gradually mapping the architecture to that of a brain, performance should improve. Even if you can't map at high resolution, every stage between monolithic and full cortical hierarchy should improve performance. At some point, such a system could be used in conjunction with Neuralink or direct bci to allow the brain direct access to a model - a true exocortex. With RETRO and lookup interfaces, you could have the whole of Wikipedia available to you at the speed of thought, or offload specific things like calculation and text memory storage. Aside from the ethics involved, the speed of information consumption will have reached parity with the brain's native performance. Through training or natural development, offloading other functionality to the exocortex will improve on speed, with the biological brain learning to trigger exocortex processes. The neocortex would begin to operate more like an extended hippocampus, sitting at the highest level of the cognitive hierarchy. This form of augmentation is explored in the Accelerando novel in depth, including the manipulation and temporary loss of the exocortex. https://en.wikipedia.org/wiki/Accelerando In 20 years, current gigantic language models should be trivially stored, executed, and trained on microsd sized chips. We'll have better bci, better batteries, better architecture than transformers, and hopefully a much better legal system around data privacy and surveillance. If we avoid wireheading and obvious Black Mirror pitfalls, we could dodge the AI great filters that are lying in wait. |
Curious what you see as a possible incentive for this to play out? Personally stopped watching black mirror a few years ago, i dont know how much focusing the public psyche on a fantastical version of the horrors that are playing out helps us imagine or enable a better future. These days it almost feels like a keeper of the gate to STEM. If black mirror freaks you out you probably aren't going to compete in the r&d space.