Generative AI: is this something new?

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Published on: October 29, 2022

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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[2022] Status of AI based image generation

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Published on: August 3, 2022

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]

[aeon] Scepticism as a way of life

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Published on: July 31, 2022

The desire for certainty is often foolish and sometimes dangerous. Scepticism undermines it, both in oneself and in others.. An interesting article from aeon.

An interesting passage: “…The sceptical way of life, on Sextus’ presentation, follows a certain rhythm. You feel puzzlement about something. You search for knowledge about it. You arrive at two equally weighty considerations about what is happening. You let go trying to find an answer. And once you recognise that you might not find a solution, it brings some mental tranquility.”

 

More of this here: [The Link]
Also of interest:

Summer season & travel to old places

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Published on: July 13, 2022

Faro, Portugal

Studies on effects of social media [NewYorker]

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Published on: June 13, 2022

There’s a general sense that it’s bad for society—which may be right. But studies offer surprisingly few easy answers. A recent article from New Yorker on a complexities of getting data to show the effects of social media. Also a serves as a good launching pad to dig further into this. Points to quite a few studies and researchers in this field.

 

More of this here: [The Link]

XKCD: assigning numbers

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Published on: April 23, 2022

xkcd link

[Perspective] Is Digital Art Real Art?

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Published on: March 27, 2022

Science of Art

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Published on: March 12, 2022

An interesting article from inference-review.com on the growth of science of art. What can we learn from digitizing, coding and using algorithmic tools on database of items and paintings and other art.

Few tools and methods taken from different fields of science: probabilities, evolutionary principle, genealogy trees, networks and others. Interesting lis t of projects are also mentioned in the article.

“…For now, as always, it is humans who find meanings in the world and science is just a way of testing their truth. All that is required for the use of science, or any other rational method of investigation, is a consensus that those interpretations not be solipsistic and equivocal, but public and falsifiable.” 

“…The prospect of a science of art is, to me, dazzling. When I consider it I feel as Aristotle must have felt when he stood upon an Aegean shore and saw, for the first time, that living things might be the objects of science. A small shift of perspective and virgin vistas appear.” Art as objects of science…

More of this article here: [The Link]

[DOI: 10.37282/991819.22.16]

Meritocracy vs others

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Published on: February 13, 2022

Is success achieved only through hard work? It is not so straight forward. Environment, luck, systemic biases all play a role to some extent. Interesting discussions around meritocratic systems and their moral implications..

 

 

Review of The Dawn of Everything from LARB

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Published on: January 27, 2022

Came across this interesting review of book on human history – The Dawn of Everything, A New History of Humanity by Graeber and Wengrow. Recently had read the books from Y. Harari on this topic, which was refreshing. Looks like this could be an interesting book to check on.

Brief summary: “Nevertheless, The Dawn of Everything is a thoroughly mesmerizing book. Its new story about human history is provocative, if not necessarily comprehensive. The book’s great value is that it provides a much better point of departure for future explorations of what was actually happening in the past. There are almost unlimited possibilities here to build upon, and a much more fruitful critical perspective from which to think about human history.”

 

Also has a passage, which summarize the current thinking of human history, a teleological model. ” … Human societies varied a lot. Now they don’t vary as much, but the technology they employ is wildly more complex. People live longer, but they aren’t necessarily healthier or happier during their long lives. The overall average levels of violence may have decreased (although the massive variability in early human societies suggests that “average levels” is not a particularly useful way to think about violence, or really anything else in the archaeological record), but the violence that does happen is more spectacularly destructive. Most importantly: We can now fail on a global scale, and we seem to be in the process of failing.

 

More of this article from LARB here: [The Link]

 

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