Sign up for dot.LA's daily newsletter for the latest news on Southern California's tech, startup and venture capital scene. Computers can now write poems, paint portraits and produce music better than many humans. The case will now head to federal court as the AI program's owner, Stephen Thaler, plans to file an appeal, according to Ryan Abbott, a Los Angeles-based attorney representing Thaler. The case arrives as artists are increasingly using AI to help generate artwork, including works produced by autonomous machines. Abbott, a partner at L.A.-based law firm Brown, Neri, Smith & Khan, noted that AI-produced artwork is creating significant commercial value, such as an AI-authored painting that sold for $432,000 at auction in 2018.

Artificial Intelligence served as an instrument for musicologists and classical music experts to complete Beethoven's X Symphony. A known fact is that Beethoven left his tenth symphony unfinished, for some experts what would have been his masterpiece. But on October 9, 2021, the Beethoven Orchestra Bonn performed the complete work under the baton of its conductor Dirk Kaftan. Ever since Ludwig van Beethoven died in 1827, the notation of his 10th Symphony, which was only written in handwritten sketches in the year of his death, has been nicknamed "the Unfinished". One year after the 250th anniversary of Beethoven's birth, an attempt has been made with the help of artificial intelligence (AI) to transfer the composition fragments into a fully formulated work.

Teresa Carey: This is Scientific American's 60-Second Science. Every morning at five o'clock, composer Walter Werzowa would sit down at his computer to anticipate a particular daily e-mail. It came from six time zones away, where a team had been working all night (or day, rather) to draft Beethoven's unfinished 10th Symphony--almost two centuries after his death. The e-mail contained hundreds of variations, and Werzowa listened to them all. Carey: Werzowa was listening for the perfect tune--a sound that was unmistakably Beethoven.

Beethoven's previously unfinished Tenth Symphony has been completed by artificial intelligence technology. The work will have its world premiere in Germany next month, 194 years after the composer's death. In 1824 Beethoven premiered his final orchestral work, Symphony No. 9 in D minor. However, before his death three years later in 1827, he had begun work on a tenth symphony. All that remains of Beethoven's Tenth Symphony is fragmentary sketches of the first movement which he started before his death in 1827 (read more about the curse of the ninth symphony here).

Artificial intelligence technology has completed Beethoven's previously incomplete Tenth Symphony. Next month, 194 years after the composer's death, the work will be performed for the first time in Germany. Ludwig van Beethoven's final orchestral composition, Symphony No. 9 in D minor, was debuted in 1824. In the latter years of his life, Beethoven began work on what would have been his tenth symphony. However, due to ill health, he was only able to complete a few musical sketches before dying in 1827 at the age of 56.

Playform AI launched the limited-time exhibition this week with work from four artists, who include established names in the fine art word, new media specialists and a pseudonymous Instagram creator. The pieces range in price from about $60 to $1,500 and include wall prints, jigsaw puzzles, framed video screens and masks. The shop also features an augmented reality tool that allows prospective buyers to project artworks into their home before purchase. The startup is one of a handful of companies to venture into the AI art exhibition space as new advances in machine learning research have spawned a small but growing community of creatives, artists and technologists attempting to harness the power of AI in art. The key piece of a technology involved is a form of neural network called a generative adversarial network (GAN), which brands have already experimented with using for everything from deepfaked commercials to product design.

In 1950, Alan Turing developed the Turing Test as a test of a machine's ability to display human-like intelligent behavior. "Are there imaginable digital computers which would do well in the imitation game?" In most applications of AI, a model is created to imitate the judgment of humans and implement it at scale, be it autonomous vehicles, text summarization, image recognition, or product recommendation. By the nature of imitation, a computer is only able to replicate what humans have done, based on previous data. This doesn't leave room for genuine creativity, which relies on innovation, not imitation.

In 2018, a neural network bested the human in a reading comprehension test. The machine was able to answer over 100,000 questions from the Stanford Question Answering Dataset. It read over 500 Wikipedia articles and beat a human by 0.136 points. They are obviously better and quicker at analysing massive arrays of data, and they are keen on spotting the subtle differences and details. Our brain is still credited with the largest number of neurons, far exceeding any AI.

AI has touched just about every industry and discipline known to mankind, but how about the arts? Enter Artrendex and its ArtPI, a new interface or API driven by artificial intelligence that's poised to transform the way art gets discovered, displayed, and sold. It promises to transform art discovery the way Shazam transformed music discovery. ArtPI is the first public API designed and optimized for art. It uses AI (artificial intelligence) and deep learning models trained over 1 million artworks.

Generative Adversarial Networks use a pair of machine-learning models to create things that seem very realistic: one of the models, the "generator," uses its training data to make new things; and the other, the "discerner," checks the generator's output to see if it conforms to the model. Rutgers comp sci prof Ahmed Elgammal runs an Art and AI Lab where they use "Creative Adversarial Networks" to produce new artworks: CANs use a "discerner" that seeks out "novelty," not fidelity to the statistical predictions of the model. The underlying theory is that art evolves "through small alterations to a known style that produce a new one," which, as Ian Bogost (previously) points out, is "a convenient take, given that any machine-learning technique has to base its work on a specific training set." Elgammal recent exhibited a show called Faceless Portraits Transcending Time at Chelsea's HG Contemporary gallery; and his choice of portraiture as a means of showcasing the capabilities of CANs has proven to be controversial: as art historian John Sharp says, "You can't really pick a form of painting that's more charged with cultural meaning than portraiture." Portraits use extensive, coded symbology to say something about their subjects, and CANs do not, by themselves, understand or correctly use these symbols in the works they create.