The implications are staggering.

❤️ Check out Weights & Biases and sign up for a free demo here: https://wandb.com/papers.
📝 The paper “Co-Writing Screenplays and Theatre Scripts with Language Models: An Evaluation by Industry Professionals” is available here:
https://deepmind.github.io/dramatron/details.html.
My latest paper on simulations that look almost like reality is available for free here:
https://rdcu.be/cWPfD
Or this is the orig. Nature Physics link with clickable citations:
https://www.nature.com/articles/s41567-022-01788-5
🙏 We would like to thank our generous Patreon supporters who make Two Minute Papers possible:
Aleksandr Mashrabov, Alex Balfanz, Alex Haro, Andrew Melnychuk, Benji Rabhan, Bryan Learn, B Shang, Christian Ahlin, Edward Unthank, Eric Martel, Geronimo Moralez, Gordon Child, Jace O’Brien, Jack Lukic, John Le, Jonas, Jonathan, Kenneth Davis, Klaus Busse, Kyle Davis, Lorin Atzberger, Lukas Biewald, Matthew Allen Fisher, Matthew Valle, Michael Albrecht, Michael Tedder, Nevin Spoljaric, Nikhil Velpanur, Owen Campbell-Moore, Owen Skarpness, Rajarshi Nigam, Ramsey Elbasheer, Richard Sundvall, Steef, Taras Bobrovytsky, Ted Johnson, Thomas Krcmar, Timothy Sum Hon Mun, Torsten Reil, Tybie Fitzhugh, Ueli Gallizzi.
If you wish to appear here or pick up other perks, click here: https://www.patreon.com/TwoMinutePapers.
Thumbnail background image credit: https://pixabay.com/images/id-1668918/
(Sponsor) Take this survey and you can win new gear and help the Developer community (and mine): https://www.developereconomics.net/?member_id=whatsai&utm_medium=youtube.
References:
►Read the full article: https://www.louisbouchard.ai/musiclm/
►Agostinelli et al., 2023: MusicLM, https://arxiv.org/pdf/2301.11325.pdf.
►Listen to more results: https://google-research.github.io/seanet/musiclm/examples/
►My Newsletter: https://www.louisbouchard.ai/newsletter/
►Support me on Patreon: https://www.patreon.com/whatsai.
►Join Our Discord community, Learn AI Together: https://discord.gg/learnaitogether.
#ai #artificialintelligence #MusicLM
ChatGPT, the artificial intelligence tool that has been used in everything from high school essays to a speech on the floor of Congress, has added another accomplishment to its résumé: passing exams from law and business schools.
The AI tool was presented with several tests from both the University of Minnesota’s law school and the University of Pennsylvania’s Wharton School of Business, passing them all.
That said, the AI didn’t necessarily ace the exams with flying colors. The chatbot answered 95 multiple choice questions and 12 essay prompts across 4 of UM’s law school tests, averaging about a C+ performance overall. The tech did better in Wharton’s business management course exam, scoring between a B to B-.
I used an AI voice changer (AI voice generator) to make money on Fiverr using voiceover services and a Morgan Freeman voice and deepfake.
I’ll be using voice.ai and Adobe AI Audio enhancing software, Adobe Podcast.
This video was generated by using proprietary AI software.
As neural networks become more powerful, algorithms have become capable of turning ordinary text into images, animations and even short videos. These algorithms have generated significant controversy. An AI-generated image recently won first prize in an annual art competition while the Getty Images stock photo library is currently taking legal action against the developers of an AI art algorithm that it believes was unlawfully trained using Getty’s images.
So the music equivalent of these systems shouldn’t come as much surprise. And yet the implications are extraordinary.
A group of researchers at Google have unveiled an AI system capable of turning ordinary text descriptions into rich, varied and relevant music. The company has showcased these capabilities using descriptions of famous artworks to generate music.
There are two aspects to a computer’s power: the number of operations its hardware can execute per second and the efficiency of the algorithms it runs. The hardware speed is limited by the laws of physics. Algorithms—basically sets of instructions —are written by humans and translated into a sequence of operations that computer hardware can execute. Even if a computer’s speed could reach the physical limit, computational hurdles remain due to the limits of algorithms.
These hurdles include problems that are impossible for computers to solve and problems that are theoretically solvable but in practice are beyond the capabilities of even the most powerful versions of today’s computers imaginable. Mathematicians and computer scientists attempt to determine whether a problem is solvable by trying them out on an imaginary machine.
German automaker Mercedes-Benz claims to have achieved Level 3 autonomy — “conditionally automated” vehicles that can monitor their driving environment and make informed decisions on behalf of the driver, but still require humans to occasionally take over — in the United States, an incremental but noteworthy step towards a future void of steering wheels and foot pedals.
“It is a very proud moment for everyone to continue this leadership and celebrate this monumental achievement as the first automotive company to be certified for Level 3 conditionally automated driving in the US market,” said Mercedes-Benz USA CEO Dimitris Psillakis in a statement.