Amazon SageMaker Studio is the first fully integrated development environment (IDE) for machine learning (ML). With a single click, data scientists and developers can quickly spin up Studio notebooks to explore and prepare datasets to build, train, and deploy ML models in a single pane of glass. We're excited to announce a new set of capabilities that enable interactive Spark-based data processing from Studio notebooks. Data scientists and data engineers can now visually browse, discover, and connect to Spark data processing environments running on Amazon EMR, right from your Studio notebooks in a few simple clicks. After you're connected, you can interactively query, explore and visualize data, and run Spark jobs to prepare data using the built-in SparkMagic notebook environments for Python and Scala.

Students learning classical violin usually have to wait until a session with a music teacher to get personalized feedback on their playing. Soon they may have a new tool to use between lessons: an app that can observe them play and guide them toward better posture and form--key elements both for sounding their best and avoiding overuse injuries. Two University of Maryland researchers are drawing on very different academic backgrounds--one in classical violin and music education, the other in robotics and computer science--to develop this virtual "teacher's aide" system powered by artificial intelligence (AI) technology. In addition to expanding the market for violin instruction, it will allow students who may not have access to private lessons to receive feedback on their playing. Associate Professor of Violin in the School of Music Irina Muresanu, who is collaborating with Cornelia Fermüller, associate research scientist in UMD's Institute for Advanced Computer Studies, said the technology will be revolutionary for a field rooted in tradition.

The computer screen shows a height map of the 1716 'Messiah' Stradivari violin, taken from a 3D scan (left); and an X-ray of the ribs of the 1718 'San Lorenzo' Stradivari (right) The following extract is from The Strad's September 2021 issue feature'Violin Making and AI: Intelligent Design'. To read it in full, click here to subscribe and login. The idea that the shape and thickness of a violin's top and back plates can affect its sound is nothing new. Antonio Stradivari was undoubtedly aware of it 300 years ago, and the science behind it was scrutinised and written up at length by Carleen Hutchins in the 1950s. Since then, the phenomenon of violin'modes' and resonances has been investigated by both violin makers and academics; indeed, for many luthiers, one of the first steps in making a new instrument will be to examine its'tap tones', or speed of sound along the plates.

Increased trust, transparency, fairness and auditability of AI technologies continues to be of growing importance to a wide range of stakeholders,

Maria Schrader’s film, starring Dan Stevens as a robot designed to be the perfect man, confirms comedy as the playground of philosophy: nothing is funnier or more stirring than the sight of somebody learning how to be.

People around the globe have suffered the nerve-wracking anxiety of waiting weeks or months to find out if their homes have been damaged by wildfires that scorch with increased intensity. Now, once the smoke has cleared for aerial photography, researchers have found a way to identify building damage within minutes. Through a system they call DamageMap, a team at Stanford University and the California Polytechnic State University (Cal Poly) has brought an artificial intelligence approach to building assessment: Instead of comparing before-and-after photos, they've trained a program using machine learning to rely solely on post-fire images. The findings appear in the International Journal of Disaster Risk Reduction. "We wanted to automate the process and make it much faster for first responders or even for citizens that might want to know what happened to their house after a wildfire," said lead study author Marios Galanis, a graduate student in the Civil and Environmental Engineering Department at Stanford's School of Engineering.

A newly published report on the state of artificial intelligence says the field has reached a turning point where attention must be paid to the everyday applications of AI technology -- and to the ways in which that technology are being abused. The report, titled "Gathering Strength, Gathering Storms," was issued today as part of the One Hundred Year Study on Artificial Intelligence, or AI100, which is envisioned as a century-long effort to track progress in AI and guide its future development . AI100 was initiated by Eric Horvitz, Microsoft's chief scientific officer, and hosted by the Stanford University Institute for Human-Centered Artificial Intelligence. The project is funded by a gift from Horvitz, a Stanford alumnus, and his wife, Mary. The project's first report, published in 2016, downplayed concerns that AI would lead to a Terminator-style rise of the machines and warned that fear and suspicion about AI would impede efforts to ensure the safety and reliability of AI technologies.

Recommender system has been proven to be significantly crucial in many fields and is widely used by various domains. Most of the conventional recommender systems rely on the numeric rating given by a user to reflect his opinion about a consumed item; however, these ratings are not available in many domains. As a result, a new source of information represented by the user-generated reviews is incorporated in the recommendation process to compensate for the lack of these ratings. The reviews contain prosperous and numerous information related to the whole item or a specific feature that can be extracted using the sentiment analysis field. This paper gives a comprehensive overview to help researchers who aim to work with recommender system and sentiment analysis. It includes a background of the recommender system concept, including phases, approaches, and performance metrics used in recommender systems. Then, it discusses the sentiment analysis concept and highlights the main points in the sentiment analysis, including level, approaches, and focuses on aspect-based sentiment analysis.

Generating portrait images by controlling the motions of existing faces is an important task of great consequence to social media industries. For easy use and intuitive control, semantically meaningful and fully disentangled parameters should be used as modifications. However, many existing techniques do not provide such fine-grained controls or use indirect editing methods i.e. mimic motions of other individuals. In this paper, a Portrait Image Neural Renderer (PIRenderer) is proposed to control the face motions with the parameters of three-dimensional morphable face models (3DMMs). The proposed model can generate photo-realistic portrait images with accurate movements according to intuitive modifications. Experiments on both direct and indirect editing tasks demonstrate the superiority of this model. Meanwhile, we further extend this model to tackle the audio-driven facial reenactment task by extracting sequential motions from audio inputs. We show that our model can generate coherent videos with convincing movements from only a single reference image and a driving audio stream. Our source code is available at https://github.com/RenYurui/PIRender.

All human beings--even famous movie directors like Federico Fellini--have a finite lifespan. But can their talent live on (and continue to create) with artificial intelligence? Campari Red Diaries: Fellini ForwardCampari Red Diaries: Fellini Forward, a short film and behind-the-scenes documentary, premieres at the Venice Film Festival on Sept. 7, and will be featured at the New York Film Festival before an on-demand release in select markets. While there are three humans with directorial credits--Zackary Canepari and Drea Cooper for the documentary, and Maximilian Niemann on the short film--the post-human creative force on this movie is AI, masterminded by innovation production studio UNIT9. We spoke with Marc D'Souza, Production Director at UNIT9, to find out more. The question behind Campari Red Diaries: Fellini Forward is whether AI can be trained to not just imitate Fellini's oeuvre, but extend it into new and original work.