Recent advances in generative AI have sparked renewed interest and expanded possibilities for music generation. However, the performance and versatility of these systems across musical genres are heavily influenced by the availability of training data. We conducted an extensive analysis of over one million hours of audio datasets used in AI music generation research and manually reviewed more than 200 papers from eleven prominent AI and music conferences and organizations (AAAI, ACM, EUSIPCO, EURASIP, ICASSP, ICML, IJCAI, ISMIR, NeurIPS, NIME, SMC) to identify a critical gap in the fair representation and inclusion of the musical genres of the Global South in AI research. Our findings reveal a stark imbalance: approximately 86% of the total dataset hours and over 93% of researchers focus primarily on music from the Global North. However, around 40% of these datasets include some form of non-Western music, genres from the Global South account for only 14.6% of the data. Furthermore, approximately 51% of the papers surveyed concentrate on symbolic music generation, a method that often fails to capture the cultural nuances inherent in music from regions such as South Asia, the Middle East, and Africa. As AI increasingly shapes the creation and dissemination of music, the significant underrepresentation of music genres in datasets and research presents a serious threat to global musical diversity. We also propose some important steps to mitigate these risks and foster a more inclusive future for AI-driven music generation.

But some of them make their mark: testing, hardening, and ultimately reshaping the landscape according to inherent patterns and fluctuations that emerge over time. In the paper "Distributed Learning With Sparsified Gradient Differences," published in a special ML-focused issue of the IEEE Journal of Selected Topics in Signal Processing, Blum and collaborators propose the use of "Gradient Descent method with Sparsification and Error Correction," or GD-SEC, to improve the communications efficiency of machine learning conducted in a "worker-server" wireless architecture. "Various distributed optimization algorithms have been developed to solve this problem," he continues,"and one primary method is to employ classical GD in a worker-server architecture. "Current methods create a situation where each worker has expensive computational cost; GD-SEC is relatively cheap where only one GD step is needed at each round," says Blum. Professor Blum's collaborators on this project include his former student Yicheng Chen '19G '21PhD, now a software engineer with LinkedIn; Martin Takác, an associate professor at the Mohamed bin Zayed University of Artificial Intelligence; and Brian M. Sadler, a Life Fellow of the IEEE, U.S. Army Senior Scientist for Intelligent Systems, and Fellow of the Army Research Laboratory. But some of them make their mark: testing, hardening, and ultimately reshaping the landscape according to inherent patterns and fluctuations that emerge over time. In the paper "Distributed Learning With Sparsified Gradient Differences," published in a special ML-focused issue of the IEEE Journal of Selected Topics in Signal Processing, Blum and collaborators propose the use of "Gradient Descent method with Sparsification and Error Correction," or GD-SEC, to improve the communications efficiency of machine learning conducted in a "worker-server" wireless architecture. "Various distributed optimization algorithms have been developed to solve this problem," he continues,"and one primary method is to employ classical GD in a worker-server architecture.

Petuum's CASL research and engineering team has won this year's OSDI 2021 Best Paper Award. This effort is led by Dr. Aurick Qiao who heads the Composability, Automatic, and Scalable Learning (CASL) research and engineering team at Petuum. Dr. Qiao received the Jay Lepreau Best Paper Award at the 15th USENIX Symposium on Operating Systems Design and Implementation (OSDI) 2021 for the paper he co-authored, Pollux: Co-adaptive Cluster Scheduling for Goodput-Optimized Deep Learning which captures the revolutionary work implemented using one of CASL's key components, AdaptDL. Current live application of Pollux can be implemented via AdaptDL that integrates with PyTorch, Microsoft NNI, and with Ray coming soon. Pollux as implemented by AdaptDL improves scheduling performance in deep learning (DL) clusters by adaptively co-optimizing inter-dependent factors both at the per-job level and at the cluster-wide level.

The United Arab Emirates' Mohamed Bin Zayed University of Artificial Intelligence and Israel's Weizmann Institute of Science have agreed to work together, UAE state news agency WAM said on Sunday. The memorandum of understanding follows the UAE's decision a month ago to normalize relations with Israel. Both countries have said they hope normalized ties will bring economic and technological benefits. The MoU is the first signed between Israeli and UAE higher education bodies, WAM said, intending to "advance the development and use of artificial intelligence as a tool for progress. Spheres of possible collaboration include academic exchanges, conferences, sharing computing resources and the establishment of a joint virtual institute for artificial intelligence, WAM said.

DUBAI (Reuters) - The United Arab Emirates' Mohamed Bin Zayed University of Artificial Intelligence and Israel's Weizmann Institute of Science have agreed to work together, UAE state news agency WAM said on Sunday. The memorandum of understanding follows the UAE's decision a month ago to normalize relations with Israel. Both countries have said they hope normalised ties will bring economic and technological benefits. The MoU is the first signed between Israeli and UAE higher education bodies, WAM said, intending to "advance the development and use of artificial intelligence as a tool for progress". Spheres of possible collaboration include academic exchanges, conferences, sharing computing resources and the establishment of a joint virtual institute for artificial intelligence, WAM said.

The UAE is rolling out its biggest effort yet to develop a workforce versed in artificial intelligence, as the rapidly-advancing technology transforms economies worldwide. The Mohamed bin Zayed University of Artificial Intelligence (MBZUAI), a new graduate-level AI research institution in Abu Dhabi, is accepting applications for its first masters and PhD programmes this month, with classes scheduled to begin in September 2020. As the first university to have a singular focus on AI, the institution aims to attract students from around the world to advance the technology and propel the UAE's economic diversification efforts. The Mohamed bin Zayed University of Artificial Intelligence is an open invitation from Abu Dhabi to the world to unleash AI's full potential To compete with more than a hundred graduate degree programmes in AI – mainly in North America, China and the UK – MBZUAI is offering full scholarships, monthly stipends, health insurance and accommodation to all students. MBZUAI is named after Sheikh Mohamed bin Zayed, Crown Prince of Abu Dhabi and Deputy Supreme Commander of the UAE Armed Forces, who believes in the transformative power of knowledge and scientific thinking.

In September, Mohamed bin Zayed University for Artificial Intelligence (MBZUAI) will welcome its first batch of local and international students. UAE has been long encouraging the cultivation of future talent through knowledge and science. MBZUAI will set up master of science and PhD programs in core AI areas such as machine learning, computer vision and natural language processing. To compete with top AI programs around the world, especially those from North America, China and the UK, all students enrolled will be provided with full scholarships, salary, medical insurance and accommodation. The UAE has been putting great emphasis on education as one of the nation's key strategies, and well-known international educational programs include cooperation with NYU Abu Dhabi and BFSU Zayed Center for Arabic Language and Islamic Studies.

According to media reports more than 3,200 students have applied for the school in the first week admissions were open. Many of the applicants came from the UAE, Saudi Arabia, Algeria, Egypt, India, and China. In October Abu Dhabi announced the Mohamed bin Zayed University of Artificial Intelligence, which will enable graduate students, businesses, and governments to advance AI. The university is named after the Crown Prince of Abu Dhabi Mohamed bin Zayed Al Nahyan, who is an advocate for developing human capital through science. The school aims to create a new model of academia and research for AI and to "unleash AI's full potential."

An AI university has been established in Abu Dhabi, in what is being billed as a world first for artificial intelligence studies. Earlier this month, it was announced that the Mohamed bin Zayed University of Artificial Intelligence (MBZUAI) has been established in Abu Dhabi, the first graduate level, research-based AI university globally. The AI university will enable graduate students, businesses, and governments to advance artificial intelligence. The university's name comes from His Highness Sheikh Mohamed bin Zayed Al Nahyan, Crown Prince of Abu Dhabi. MBZUAI intends to introduce a new model of academia and research to the field of AI, with a view to provide students and faculty access to some of the world's most advanced AI systems to unleash its potential for economic and societal development.

Five years ago, perhaps not many people would have thought of Abu Dhabi and the UAE as global leaders in artificial intelligence (AI). That changed in 2017 when the UAE became the first country in the world to create a dedicated AI ministry. Today Abu Dhabi is taking another important step at placing AI at the core of our future by announcing the world's first graduate level AI university. AI has emerged into the mainstream and is quickly becoming embedded in our daily lives. Machines that learn can now be found across the globe and are transforming organisations and the way things are done, covering everything from antivirus software to IT infrastructure management.