Quanta Magazine - The Uselessness of Useful Knowledge – Today’s powerful but little-understood artificial intelligence breakthroughs echo past examples of unexpected scientific progress. According to the prominent AI researcher Ali Rahimi and others, today’s fashionable neural networks and deep learning techniques are based on a collection of tricks, topped with a good dash of optimism, rather than systematic analysis. Modern engineers, the thinking goes, assemble their codes with the same wishful thinking and misunderstanding that the ancient alchemists had when mixing their magic potions. It’s true that we have little fundamental understanding of the inner workings of self-learning algorithms, or of the limits of their applications. These new forms of AI are very different from traditional computer codes that can be understood line by line. Instead, they operate within a black box, seemingly unknowable to humans and even to the machines themselves.
This discussion within the AI community has consequences for all the sciences. With deep learning impacting so many branches of current research — from drug discovery to the design of smart materials to the analysis of particle collisions — science itself may be at risk of being swallowed by a conceptual black box. It would be hard to have a computer program teach chemistry or physics classes. By deferring so much to machines, are we discarding the scientific method that has proved so successful, and reverting to the dark practices of alchemy? Not so fast, says Yann LeCun, co-recipient of the 2018 Turing Award for his pioneering work on neural networks. He argues that the current state of AI research is nothing new in the history of science. It is just a necessary adolescent phase that many fields have experienced, characterized by trial and error, confusion, overconfidence and a lack of overall understanding. We have nothing to fear and much to gain from embracing this approach. It’s simply that we’re more familiar with its opposite…”
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- “I did not need Chinese Philosophy to understand analytic philosophy, and vice versa… There are some deep structural differences between their fundamental conceptual frameworks” — an interview with Hiu Chuk Winnie Sung (Nanyang), who has two PhDs (one in Chinese philosophy, one in analytic philosohy)
- “If a lion could speak, we could not understand him.” What about a whale? — researchers aim to use machine learning, language models, and other technology to figure out what whales are saying
- “Though their relationship was not primarily sexual, they were in love in the sense of having a deep desire to know and be known” — Sukaina Hirji (Penn) and Meena Krishnamurthy (Queen’s) on the idea of “romantic friendship” and the example of it between Iris Murdoch and Philippa Foot
- “A combinatorial system is one in which a relatively small number of simple things are combined to form a relatively large number of more complex things… Could morality be such a system?” — yes, say an interdisciplinary team of researchers who explain “moral molecules” and provide a “periodic table of ethics”
- Break each article into segments of up to five words long, then publish each of those segments as a separate file in a publicly accessible index — how technologist Carl Malamud is freeing the world’s paywalled research for data analysis. His index contains material from over 100 million journal articles. Is it legal?
- What’s good and what’s bad about being a child, and why — Anca Gheaus provides a conceptual map to two views about childhood
- “Epistemology is a normative enterprise, ethics is a normative enterprise” — and the two areas should be “consistently informed by an appreciation of each other’s problems,” says Mark Schroeder (USC)
