From Yahoo Finance, January 13:
Generative AI, like OpenAI's ChatGPT, could completely revamp how digital content is developed, said Nina Schick, adviser, speaker, and A.I. thought leader told Yahoo Finance Live (video above).
"I think we might reach 90% of online content generated by AI by 2025, so this technology is exponential," she said. "I believe that the majority of digital content is going to start to be produced by AI. You see ChatGPT... but there are a whole plethora of other platforms and applications that are coming up."
The surge of interest in OpenAI's DALL-E and ChatGPT has facilitated a wide-ranging public discussion about AI and its expanding role in our world, particularly generative AI.
"ChatGPT has really captured the public imagination in an extremely compelling way, but I think in a few months' time, ChatGPT is just going to be seen as another tool powered by this new form of AI, known as generative AI," she said.
It's important to understand what exactly generative AI is – and what it isn't.
"What generative AI can do, essentially, is create new things that would have thus far been seen as unique to human intelligence or creativity," she said. "Generative AI can create across all media, so text, video, audio, pictures – every digital medium can be powered by generative AI. So, I think these valuations that you're seeing for OpenAI are actually going to go up and you're going to start to see even more generative AI companies which have universal applications across many industries in 2023."
This is all still really new, as applications for generative AI have "only really [been] coming to the fore in the last 24 to 6 months," added Schick....
....MUCH MORE
No, AI does not create new things. That is a straight-up lie, an attempt to change one's perception of the reality of what is actually going on.
There is a world of difference between what is being touted and what is actually "in the style of" homage. Here's a post from six years ago:
Machine Learning: Google and Style Transfer
A bit of Rembrandt inspired garageiness:
No, it was so I could refer back to them.
And they were pretty.
In a manly sort of way. Very manly.
Moving right along, that is a type of style transfer, something that takes a very good eye for detail and almost virtuoso skill with the camera.
And it's something that computers are learning how to do, sans camera.
From Google's Research blog, Oct. 26, 2016
Supercharging Style Transfer
Pastiche. A French word, it designates a work of art that imitates the style of another one (not to be confused with its more humorous Greek cousin, parody). Although it has been used for a long time in visual art, music and literature, pastiche has been getting mass attention lately with online forums dedicated to images that have been modified to be in the style of famous paintings. Using a technique known as style transfer, these images are generated by phone or web apps that allow a user to render their favorite picture in the style of a well known work of art. Although users have already produced gorgeous pastiches using the current technology, we feel that it could be made even more engaging.
Right now, each painting is its own island, so to speak: the user provides a content image, selects an artistic style and gets a pastiche back. But what if one could combine many different styles, exploring unique mixtures of well known artists to create an entirely unique pastiche? Learning a representation for artistic style In our recent paper titled “A Learned Representation for Artistic Style”, we introduce a simple method to allow a single deep convolutional style transfer network to learn multiple styles at the same time. The network, having learned multiple styles, is able to do style interpolation, where the pastiche varies smoothly from one style to another. Our method enables style interpolation in real-time as well, allowing this to be applied not only to static images, but also videos...
...In the video above, multiple styles are combined in real-time and the resulting style is applied using a single style transfer network. The user is provided with a set of 13 different painting styles and adjusts their relative strengths in the final style via sliders. In this demonstration, the user is an active participant in producing the pastiche. A Quick History of Style Transfer While transferring the style of one image to another has existed for nearly 15 years [1] [2], leveraging neural networks to accomplish it is both very recent and very fascinating. In “A Neural Algorithm of Artistic Style” [3], researchers Gatys, Ecker & Bethge introduced a method that uses deep convolutional neural network (CNN) classifiers. The pastiche image is found via optimization: the algorithm looks for an image which elicits the same kind of activations in the CNN’s lower layers - which capture the overall rough aesthetic of the style input (broad brushstrokes, cubist patterns, etc.) - yet produces activations in the higher layers - which capture the things that make the subject recognizable - that are close to those produced by the content image. From some starting point (e.g. random noise, or the content image itself), the pastiche image is progressively refined until these requirements are met.
This work is considered a breakthrough in the field of deep learning research because it provided the first proof of concept for neural network-based style transfer. Unfortunately this method for stylizing an individual image is computationally demanding. For instance, in the first demos available on the web, one would upload a photo to a server, and then still have plenty of time to go grab a cup of coffee before a result was available. This process was sped up significantly by subsequent research [4, 5] that recognized that this optimization problem may be recast as an image transformation problem, where one wishes to apply a single, fixed painting style to an arbitrary content image (e.g. a photograph). The problem can then be solved by teaching a feed-forward, deep convolutional neural network to alter a corpus of content images to match the style of a painting. The goal of the trained network is two-fold: maintain the content of the original image while matching the visual style of the painting....MUCH MOREI did not recognize painting B, top right, it's a Turner, The Shipwreck of the Minotaur. Here's the Wikipedia image, the sailors and soldiers look totally effed.
Earlier today:
Interview: Manuela Veloso Head of Machine Learning, Carnegie Mellon University
For pastiches I usually use Bohemian Rhapsody as the example:
(not Queen) Bohemian Gravity
Definitely not Queen.
To quote myself, from "Media: William Shatner Covers Queen's 'Bohemian Rhapsody'":
We've commented on the structural genius of Bohemian Rhapsody a couple times, links below the video....
Lyrical Genius:
Previously:Hold all Tickets. We had thought "The Day the Nasdaq Died" the pastiche* of the decade:*..."Bohemian Rhapsody", by Queen is unusual as it is a pastiche in both senses of the word, as there are many distinct styles imitated in the song, all 'hodge-podged' together to create one piece of music....
Humble Pie (sung to the tune of American Pie)...
(Wikipedia)
GOT THAT? WE HAVE A PASTICHE OF A DOUBLE PASTICHE!!!
Genius.
March 2008:
Lyrical Genius:
A few years later Alphaville stepped up:CDO Remake of Bohemian Rhapsody
Is this the real price?
Is this just fantasy?
Financial landslide
No escape from reality
Open your eyes
And look at your buys and see.
I’m now a poor boy
High-yielding casualty...MORE
September 2011
Genius: "Athenian Rhapsody (updated)"
Pastiche time baby!*
From FT Alphaville:
Alan Beattie, FT International Economy Editor and FT Alphaville’s new A&R man, has sent us a rock opera by two parodists who wish to remain anonymous.
We’ve done all we can to confirm one of them isn’t Ben Elton but we can’t give you our full reassurance.
Our sincerest apologies to Mercury, May, Taylor and Deacon....MORE
