To generate classical music for composers, musicians, or even non-specialists without prior knowledge of classical music theories and backgrounds according to their favorite musical eras. Artificial Intelligence could bring music composition to another level with limitless possibilities as an assistant for human musicians or an AI musician itself.

Summit on Music Intelligence was launched at the Central Conservatory of Music in Beijing on Oct 22. A concert was performed by conductor Zhu Man and the symphony orchestra of the university. Beijing Research Institute of Music and Brain was also announced during the opening ceremony. Co-organized by the Central Conservatory of Music and Chinese Association for Artificial Intelligence, the event was held in Beijing on Oct 23 and 24 with forums highlighting the relationship between and development of music and artificial intelligence. Yu Feng, president of the Central Conservatory of Music, said the university launched department of Music AI and Information Technology in 2019, which focuses on the research of technology and music.

OctoML announced a collaboration with Arm to deploy next-generation machine learning (ML) applications and models across its suite of hardware.

Video question answering (VideoQA) is designed to answer a given question based on a relevant video clip. The current available large-scale datasets have made it possible to formulate VideoQA as the joint understanding of visual and language information. However, this training procedure is costly and still less competent with human performance. In this paper, we investigate a transfer learning method by the introduction of domain-agnostic knowledge and domain-specific knowledge. First, we develop a novel transfer learning framework, which finetunes the pre-trained model by applying domain-agnostic knowledge as the medium. Second, we construct a new VideoQA dataset with 21,412 human-generated question-answer samples for comparable transfer of knowledge. Our experiments show that: (i) domain-agnostic knowledge is transferable and (ii) our proposed transfer learning framework can boost VideoQA performance effectively.

Much recent research has uncovered and discussed serious concerns of bias in facial analysis technologies, finding performance disparities between groups of people based on perceived gender, skin type, lighting condition, etc. These audits are immensely important and successful at measuring algorithmic bias but have two major challenges: the audits (1) use facial recognition datasets which lack quality metadata, like LFW and CelebA, and (2) do not compare their observed algorithmic bias to the biases of their human alternatives. In this paper, we release improvements to the LFW and CelebA datasets which will enable future researchers to obtain measurements of algorithmic bias that are not tainted by major flaws in the dataset (e.g. identical images appearing in both the gallery and test set). We also use these new data to develop a series of challenging facial identification and verification questions that we administered to various algorithms and a large, balanced sample of human reviewers. We find that both computer models and human survey participants perform significantly better at the verification task, generally obtain lower accuracy rates on dark-skinned or female subjects for both tasks, and obtain higher accuracy rates when their demographics match that of the question. Computer models are observed to achieve a higher level of accuracy than the survey participants on both tasks and exhibit bias to similar degrees as the human survey participants.

Artificial Intelligence (AI) is helping doctors diagnose rare diseases in babies and Utah doctors are using the tool with the hope of saving more …

If Hogwarts School of Witchcraft and Wizardry were real, it would be teaching'artificial intelligence' and'machine learning' to thwart …

… helping HSI operationalize Machine Learning and Artificial Intelligence in a … AI and machine learning to support critical investigations.

Artificial intelligence is top-of-mind as Governor Baker, President Reif encourage students to “see yourself in STEM.” Ad.

These wide applications have heightened concerns about their potential functioning amongst Machine Learning and Artificial Intelligence researchers.