> I never said it wasn’t statistical at any level so I’m not sure what your point is. You said it was true on the surface, and I'm pointing out that it's true

> I never said it wasn’t statistical at any level so I’m not sure what your point is. You said it was true on the surface, and I'm pointing out that it's true at its core. Your amazement at how well it works is based on our inability as humans to find the same patterns in the data as these models do. Ascribing some higher level sense of intelligence or understanding to these systems because of this party trick is anthropomorphizing what they actually are. We would all be better served by keeping in mind how they work, instead of being surprised when they do output the wrong pattern. > Over time the errors and issues will be smoothed away until it’s still there but it’s not noticeable enough to be a major problem. Why do you think this is guaranteed? We can keep throwing data and compute at these systems, and while we might continue to get better results, the current systems will never intuitively understand that e.g. 9.11 is lower than 9.2[1], unless those specific examples are in their training data. As we reach the limits of the data we can feed them, and we have to generate synthetic data, there's no reason to believe that the current approaches will ever fix these problems at their core. > It being statistical doesn’t make it useless. It not being some metaphysical concept of awareness doesn’t make it useless. I never said it was. I agree that this can be useful, but again, as long as we're aware of their limits. It's good keeping this in mind as we're reaching the peak of inflated expectations of the hype cycle. [1]: https://towardsdatascience.com/9-11-or-9-9-which-one-is-higher-6efbdbd6a025 https://towardsdatascience.com/9-11-or-9-9-which-one-is-high...