Occasional Musings

An interesting article and study about decline in disruptive science in most research fields.

Few excerpts from the article:

“Disruptiveness is not inherently good, and incremental science is not necessarily bad, says Wang.”

“Finding an explanation for the decline won’t be easy, Walsh says. Although the proportion of disruptive research dropped significantly between 1945 and 2010, the number of highly disruptive studies has remained about the same. The rate of decline is also puzzling: CD indices fell steeply from 1945 to 1970, then more gradually from the late 1990s to 2010. “Whatever explanation you have for disruptiveness dropping off, you need to also make sense of it levelling off” in the 2000s, he says.“

More of this here @nature: [The link]

A nice summary of the recent trends in AI, where we get these cool new images or art generated by AI. See more of this in the article from Techcrunch: [The Link]

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TC: There’s a lot of confusion about generative AI, including how new exactly it is, or whether it’s just become the latest buzzword.

JC: I think what happened to the AI world in general is that we had a sense that we could have deterministic AI, which would help us identify the truth of something. For example, is that a broken piece on the manufacturing line? Is that an appropriate meeting to have? It’s where you’re determining something using AI in the same way that a human determines something. That’s largely what AI has been for the last 10 to 15 years.

The other sets of algorithms in AI were more these diffusion algorithms, which were intended to look at huge corpuses of content and then generate something new from them, saying, ‘Here are 10,000 examples. Can we create the 10,001st example that is similar?’

Those were pretty fragile, pretty brittle, up until about a year and a half ago. [Now] the algorithms have gotten better. But more importantly, the corpuses of content we’ve been looking at have gotten bigger because we just have more processing power. So what’s happened is, these algorithms are riding Moore’s law — [with vastly improved] storage, bandwidth, speed of computation — and have suddenly become able to produce something that looks very much like what a human would produce. That means the face value of the text that it will write, and the face value of the drawing it will draw, looks very similar to what a human will do. And that’s all taken place in the last two years. So it’s not a new idea, but it’s newly at that threshold. That’s why everyone looks at this and says, ‘Wow, that’s magic.’

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Interesting article on the status of automated generation of images using AI. Some snippets from the article

“..Lots of labs and companies are working on similar technologies that turn text into imagery. Google has Imagen, OpenAI has DALL-E, and there are a handful of smaller projects like Craiyon. “

“..AI-generated artwork is quietly beginning to reshape culture. Over the last few years, the ability of machine learning systems to generate imagery from text prompts has increased dramatically in quality, accuracy, and expression. Now, these tools are moving out of research labs and into the hands of everyday users, where they’re creating new visual languages of expression and — most likely — new types of trouble.”

More of this here from Verge: [The Link]

Habit & Routine can be very helpful in some aspects but also can be crippling in other aspects. Came across this very interesting & thoughtful article about habits, its history, its relevance in social lifes from past and present, but also some thoughts on role of automation in society.

An interesting passage towards the end for balance & reflection:

“…But even the most ingrained human behaviors are accompanied by sensations that prompt us to pause and recalibrate when something goes wrong—a truth well known to anyone who has caught themselves driving home to a previous residence or gagging on the hemorrhoid cream they’ve mistaken for toothpaste. Ravaisson calls habit the “moving middle term,” a disposition that slides along the continuum between rote mechanism and reflective freedom. Weil, who similarly saw habit as a continuum, believed that we should strive to remain on the reflective side of that spectrum. The Stoics advised nightly meditation, so as to judge the virtue of the actions they’d taken that day, and Charles Sanders Peirce, the father of pragmatism, noted that in cases where habits have begun to work against a person’s interests, “reflection upon the state of the case will overcome these habits, and he ought to allow reflection its full weight.” It is this connection to thought that allows habits to remain fluid and flexible in a way that machines are not. Habits are bound up with the brain’s plasticity, a term James describes as “a structure weak enough to yield to an influence, but strong enough not to yield all at once.” Unlike algorithms, which lock in patterns and remain beyond our understanding, habits allow us to negotiate a livable equilibrium between thought and action, maintaining, as Weil puts it, “a certain balance between the mind and the object to which it is being applied.” .. ”

More about this article here: [The Link]

A pretty interesting documentary from DW on this topic.

 
Also, learned about this intereresting field of Agnotology: A study of culturally induced ignorance or doubt, particularly the publication of inaccurate or misleading scientific data. Wiki Link