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I just finished listening to the book "AI Superpowers: China, Silicon Valley, and the new World Order. It was written by a leading scientist and technologist Kai-Fu Lee, who under the supervision of the Turing Award winner Raj Reddy created one of the first continuous speech recognition systems. He then held executive positions at several corporations including Microsoft and Google. I largely agree with Kai-Fu Lee assessment of China's potential, but it is hard to agree with his assessment of AI. The book was written at the peak of deep learning hype and it completely ignores shortcomings of deep learning such as poor performance on long tail samples, adversarial samples, or samples coming from a different distribution: It is not clear why these important issue is omitted. As we realise now, a "super-human" performance on datasets like Librispeech or Imagenet does show how much progress we have made, but it does not directly translate into viable products. For example, it is not hard to see that current dictation systems are often barely usable and the speech recognition output often requires quite a bit of post-editing.

Given the overly optimistic assessment of deep learning capabilities, it is somewhat unsurprising Kai-Fu Lee suggests that once AI is better than humans we should turn into a society of compassionate caregivers and/or social workers. I agree that a large part of the population could fill these roles, which are important and should be well paid! But I personally dream about a society of technologists, where at least 20-50% of the population are scientists, engineers, and tinkerers who have intellectually demanding (or creative) jobs. Some say it would be impossible, but we do not really know. A few centuries ago, only a small fraction of the population were literate: Now nearly everybody can read and write. Very likely our education system has huge flaws starting from pre-school and ending up at the PhD level, which works as a high-precision but low-recall sieve that selects the most curious, talented, and hardworking mostly from a small pool of privileged people. I speculate we can do much better than this. In all fairness, Kai-Fu Lee does note that AI may take much longer to deploy. However, my impression is that he does not consider this idea in all seriousness. I would reiterate that the discussion about the difficulties of applying existing tech to real world problems is nearly completely missing.

Although it is a subject of hot debates and scientific scrutiny alike, I think the current AI systems are exploiting conditional probabilities rather than doing actual reasoning. Therefore, they perform poorly on long tail and adversarial samples or samples coming from a different distribution. They cannot explain and, most importantly, reconsider their decisions in the presence of extra evidence (like smart and open-minded humans do). Kai-Fu Lee on multiple occasions praises an ability of deep learning systems to capture non-obvious correlations. However, in many cases these correlations are spurious and are present only in the training set.

On the positive side, Kai-Fu Lee seems to care a lot about humans whose jobs are displaced by AI. However, as I mentioned before, he focuses primarily on the apocalyptic scenario where machines are rapidly taking over the jobs. Thus, he casually discusses an automation of a profession as tricky as software engineering, whereas in reality it is difficult to fully replace even truckers (despite more than 30 years of research on autonomous driving). More realistically, we are moving towards a society of computer-augmented humans, where computers perform routine tasks and humans set higher-level goals and control their execution. We have been augmented by (first simple) and now by very sophisticated tools for hundreds of thousands years already, but the augmentation process has accelerated recently. It is, however, still very difficult for computers to consume (on their own) raw and unstructured information and convert it into the format that simple algorithms can handle. For example, a lot of mathematics may be automatable once a proper formalization is done, but formalization seems to be a much more difficult process compared to finding correlations in data.

In conclusion, there are a lot of Kai-Fu Lee statements that are impossible to disagree with. Most importantly, China is rapidly becoming a scientific (and AI) powerhouse. In that, there has been a lot of complacency in the US (and other Western countries) with respect to this change. Not only is there little progress in improving basic school education and increasing the spending on fundamental sciences, but the competitiveness of US companies has been adversely affected by regressive immigration policies (especially during the Trump presidency). True that the West is still leading, but China is catching up quickly. This is especially worrisome given a recent history of bullying neighboring states. The next Sputnik moment is coming and we better be prepared.