Although not yet released due to copyright issues.
Google researchers say MusicLM is based on a model generating high-fidelity music from text descriptions such as “a calming violin melody backed by a distorted guitar riff”. You can find the details on GitHub.
This article discusses the risks of Generative AI in the Music Industry and puts a spotlight on Google, MusicLM developments and encourages leaders in the music industry to think harder about the future of their industry.
My last article focused on the recent announcement of Google’s MusicLM, although not accessible to the public, due to copyright issues, it does give one new insights that AI is disrupting the value of human talent in the musical field.
Music has been core to humankind for centuries with the first piece of music, a Hurrian Hymn, discovered in the 1950s on a clay tablet inscribed in Cuneiform text. It’s the oldest surviving melody and is over 3,400 years old. Songs are human’s way of communicating stories and encompassing everything we know of as humans.
This article continues to explore the impact of AI on the music industry and looks at some of the pros and the cons, reinforcing the need for increased legal frameworks and copyright protections for musicians.
Last week, I attended a community event, that Collective Forecast organized where Yann LeCun was the guest speaker. The session was attended by leading experts in AI, ML, CEOs, journalists, and company representatives who simply wanted to hear what was on Yann’s mind. The session was jam packed with everyone engaged and cherishing each word.
Yann is is well known for his work on on convolutional neural networks (CNN), and is a founding father of convolutional nets. Yann received the 2018 Turing Award (often referred to as “Nobel Prize of Computing”), together with Yoshua Bengio and Geoffrey Hinton, for their work on deep learning.
This article summarizes a recent discussion with Yann LeCun discussing why ChatGPT3 is not revolutionary and why AI is not as Smart as a Cat.
Can lightning strike twice? That’s apparently the question being raised today with the public introduction of the next social app built by Instagram’s co-founders, Kevin Systrom and Mike Krieger. The duo have launched a new venture to explore social apps, according to a report published in The Verge, which includes the debut product Artifact, a personalized news reader.
The app itself is not yet publicly available but offers a waitlist where interested users can sign up. As described, it sounds like a modern-day twist on Google Reader, a long-ago RSS newsreader app that Google shut down back in 2013. Except in this case, Artifact is described as a newsreader that uses machine learning to personalize the experience for the end user, while also adding social elements that allow users to discuss articles they come across with friends. (To be fair, Google Reader had a similar feature, but the app itself had to be programmed by the user who would add RSS feeds directly.)
Artifact will first present a curated selection of news stories, The Verge’s article notes, but these will become more attuned to the user’s interests over time. Some of the articles will come from big-name publishers, like The New York Times, while others may be from smaller sites. Other key features will include comment controls, separate feeds for articles posted by people you follow alongside their commentary and a direct message inbox for discussing posts more privately.
Will it? Only time will tell.
The popularity of ChatGPT, the online chatbot built by OpenAI, has brought many to question the survival of search engines such as Google. Paul Buchheit, the creator of Gmail, has also dropped his opinion on the matter, and he thinks that Google’s business will last a maximum of two years, he tweeted.
Launched in November last year, ChatGPT has become the favorite destination to ask questions among millions of users. Instead of delivering a response to a search result that runs into tens of pages, ChatGPT answers the questions in a conversational style, making it easier for the user to ask follow-up questions, too.
JHVEPhoto/iStock.
Even if they catch up on AI, they can’t fully deploy it without destroying the most valuable part of their business!
Google is done.
Compare the quality of these responses (ChatGPT) pic.twitter.com/VGO7usvlIB
— josh (@jdjkelly) November 30, 2022
— Paul Buchheit (@paultoo) December 1, 2022.
Yesterday, the Whitehouse announced that the USA and the EU (European Union) signed an administrative agreement to bring AI experts together to advance AI research as prior outlined in the U.S.-EU Trade and Technology Council (TTC) commitment.
This effort will further drive responsible advancements in AI to advance global complex challenges and develop a joint integrated research approach to achieve benefits in key research domains: extreme weather and climate forecasting, emergency response management, health and medicine, electric grid optimization, and agriculture optimization.
This article focuses on the AI leadership with the USA and the European Union in signing a new administrative agreement to do joint AI research in key global challenge areas like: climate change, healthcare, agriculture, etc.
Is this the breakthrough the world has been waiting for from the Search for Extraterrestrial Intelligence Institute?
A scientist, Peter Ma, has applied machine learning and artificial intelligence to data collected by the Search for Extraterrestrial Intelligence (SETI) Institute, a press statement reveals.
Algorithm finds 8 promising signals that could be of alien origin.
Honglouwawa/iStock.
Based on initial results, there is a slight chance the new method may have unearthed non-Earth-based “technosignatures”. That would mean it had achieved SETI’s goal of finding signs of extraterrestrial intelligence.
If we’re worried about plagiarism why not train students to work with AI writing assistants and learn to think through the dialogue? We could teach them to use chatbots to get ideas, to generate alternative approaches to a topic, to research questions, and to edit what they get into a coherent whole.
At the same time, we also have to teach our students to be careful and think critically about engaging with AIs and assessing the credibility of what they say.
By thinking through dialogue we could all rediscover the rich history and potential of this form of engagement.
Scientists at University of California San Diego School of Medicine have developed an artificial intelligence (AI)-based strategy for discovering high-affinity antibody drugs.
In the study, published January 28, 2023 in Nature Communications, researchers used the approach to identify a new antibody that binds a major cancer target 17-fold tighter than an existing antibody drug. The authors say the pipeline could accelerate the discovery of novel drugs against cancer and other diseases such as COVID-19 and rheumatoid arthritis.
In order to be a successful drug, an antibody has to bind tightly to its target. To find such antibodies, researchers typically start with a known antibody amino acid sequence and use bacterial or yeast cells to produce a series of new antibodies with variations of that sequence. These mutants are then evaluated for their ability to bind the target antigen. The subset of antibodies that work best are then subjected to another round of mutations and evaluations, and this cycle repeats until a set of tightly-binding finalists emerges.
Detailed and tailored guide for undergraduate students or anybody want to dig deep into the field of AI with solid foundation. — GitHub