There’s been a lot of discussion on HN lately about the implications of GPT-3: are we moving toward general AI or is this just a scaled up party trick? I have

There’s been a lot of discussion on HN lately about the implications of GPT-3: are we moving toward general AI or is this just a scaled up party trick? I have no idea whether scaling up transformers another 100x will lead to something resembling real intelligence, but it certainly seems possible. In particular, I find the arguments against this possibility to be fairly silly. These are the three main arguments I have seen for why GPT type models will never approach AGI, and the reasons I don’t think they are valid: 1. GPT-3 requires vast amounts of training data (hundreds of billions of words from the internet), whereas a human can become fluent in natural language after “training on” much less data. It’s not analogous to compare the GPT-3 training corpus to the education that one human receives before becoming fluent in natural language. We benefit from millions of years of evolution across billions of organisms. A massive amount of “training” is incorporated in the brain of an infant. This must be the case because even if you could somehow read all of the text on the internet to your dog, it would not approach intelligence. 2. There was no intellectual breakthrough in the development of GPT-3 just more “brute force” training on more data, therefore it or its successors can’t achieve a breakthrough in intelligence. We must remember that there was no intellectual breakthrough required for the development of human intelligence, it was just more of the same evolution. The core pattern of evolution is extremely simple: take an organism, generate random variants from it, see which ones do the best, and then create new variants from the good ones. This is perhaps the most basic scheme you could think of that might actually work. Evolution has produced amazing results in spite of its simplicity and inefficiency (random variations!) because it generalizes well to many environments and scales extremely well to millions of generations. These are exactly the strengths of gradient descent. In fact, gradient descent follows the same structure as evolution, except that at each iteration we don’t generate random variations, but instead make an educated guess about what a fruitful variation would be based on available gradient information. This improves learning efficiency tremendously; imagine being able to say: “this Neanderthal died because he stepped into a fire, let’s add some fire-avoidance to the next one” instead of waiting for this trait to be generated randomly. Speaking of brute force and amount of training, it would take 355 years to train GPT-3 on a single GPU. This strikes me as quite fast relative to evolutionary time scales. 3. Machines lack capabilities fundamental to the human experience: in particular feeling pleasure, pain, and an internal drive toward a goal. Indeed, if you turn a computer off in the middle of a computation, there is no evidence of suffering. And if the computer successfully writes a blog post of human quality, it feels no joy in the human sense. My claim is that these sensations are not core aspects of intelligence. In fact, pleasure and pain are very primitive developments that even cockroaches can claim. The most impressively human accomplishments (harnessing vast external energy sources, breaking out of bare subsistence, landing on the moon, etc.) were made in spite of the fact that we are messy bags of emotion that unpredictably feel anger, jealousy, despondence or elation. These emotional responses were selected for because they were useful as proximate goalposts orienting us toward reproduction—basically, to overcome forgetfulness in the pursuit of long-term goals. If in the future we can simply direct a computer to write a captivating novel without needing to program in lots of visceral intermediate stimuli to keep it on track, so much the better.