A common artificial intelligence model known as deep convolutional neural networks (DCNNs) does not see objects the way humans do – and that could be dangerous in real-world AI applications. That is the conclusion of Professor James Elder, co-author of a York University study published recently, which finds that AI cannot use something called "configural shape perception", which is standard in human perception for recognising shapes. Published in the Cell Press journal iScience, the paper Deep learning models fail to capture the configural nature of human shape perception is a collaborative study by Elder, who holds the York research chair in human and computer vision and is co-director of York's Centre for AI & Society, co-authored with assistant psychology professor Nicholas Baker at Loyola College in Chicago, a former postdoctoral fellow at York. The study employed novel visual stimuli called "Frankensteins" to explore how the human brain and DCNNs process holistic, configural object properties. "Frankensteins are simply objects that have been taken apart and put back together the wrong way around," says Elder. "As a result, they have all the right local features, but in the wrong places."

A 4-year public / private partnership between UC Berkeley Haas School of Business – Fisher Center for Business Analytics' Program Director, Gauthier Vasseur, and Pyramid Analytics (Pyramid), a pioneering decision intelligence platform provider, celebrated the milestone of teaching more than 2,000 learners in 30 countries. The Fisher Center's Alliance for Inclusive AI (AIAI) program provides data & analytics workshops to improve inclusivity and accessibility to analytics. The Step Into Data program uses a combination of in-person and remote learning to reach people who are under-represented in AI, machine learning, and data analytics. The program will soon expand to the Netherlands, France, Georgia, India, and to new locations across the United States.

HitPaw, a company provides the best digital creation tools, releases the video enhancement software – HitPaw Video Enhancer Mac. Utilizing artificial intelligence, HitPaw Video Enhancer Mac upscales videos automatically for a variety of uses and provides a novel way to lessen noise, pixels, and overexposure brought on by subpar cameras and poor lighting. HitPaw Video Enhancer Mac provides three popular models, including General Denoise Model, Animation Model, and Face Model. HitPaw Video Enhancer will automatically make the identical modifications that professional editors would do. All modifications will also be included right into the rendering pipeline.

Whether we're ready or not, artificial intelligence (AI) already plays a role in many health care settings. However, cautiously developing, deploying, and even defining further AI advancements will determine its impact and efficacy in the years ahead, according to a new University of Western Ontario study. Interdisciplinary researchers from family medicine, computer science, and epidemiology have identified key issues regarding the use of AI tools in primary health care by connecting directly with family physicians, nurses, nurse practitioners and digital health stakeholders. Overwhelmingly, the responses show AI could have a positive impact in clinical practice, but many factors must be considered regarding its implementation. "We are ready for AI, but we must be thoughtful about how and when we use it," said Dan Lizotte, an associate professor in computer science and the Schulich School of Medicine & Dentistry and senior author on the study.

A group of artificial intelligence graduate students from several prestigious international universities concluded their participation in the second edition of the Global AI Summit, which concluded last week in Riyadh. The students also visited Masmak Palace in the center of Riyadh to be briefed on the history of the capital. The students represented six countries, joined by several Saudi scholarship students in the same specialization. Their participation came within the knowledge exchange initiative launched by the Saudi Data and Artificial Intelligence Authority, which hosted 19 male and female students of different nationalities including the US, the UK, India, Jordan, Algeria, South Korea and Nigeria. These students study at international universities and institutes, including the Sorbonne University in Paris, Oxford University, University College London, Durham University, Nottingham University, Sussex University in the UK, the Massachusetts Institute of Technology in the US, and King's College London.

The board will consist of independent advisors with specific skills and contacts in areas of importance for the ongoing development of the mPower suite of mobility services. AI and ML News: Why SMBs Shouldn't Be Afraid of Artificial Intelligence (AI) Charles Martin is considered a leading figure across multiple technology disciplines, with an established track record in machine learning, deep learning, data science, and AI software development, complemented by extensive domain experience in Natural Language Processing (NLP) for Search Relevance (as well as Text Generation and Quantitative Finance). Highly sought after in the software development field, he has personally developed and implemented machine learning (ML) systems at companies including Roche, France Telecom, GoDaddy, Aardvark (Google), eBay, eHow, Walmart, Barclays/BGI, and Blackrock. Much of his recent project work has been done under his advisory firm, Calculation Consulting, which he founded in 2010 to provide data science, machine learning, and deep learning solutions. He has also served as both a consultant and FTE distinguished engineer at GLG, a prestigious international consulting firm, where he developed AI methods for the search and recommendations platform.

Machine learning is a branch of artificial intelligence (AI) focused on building applications that learn from data and improve their accuracy over time without being programmed to do so. In data science, an algorithm is a sequence of statistical processing steps. In machine learning, algorithms are'trained' to find patterns and features in massive amounts of data in order to make decisions and predictions based on new data. The better the algorithm, the more accurate the decisions and predictions will become as it processes more data. Machine learning has led to some amazing results, like being able to analyze medical images and predict diseases on-par with human experts.

The board will consist of independent advisors with specific skills and contacts in areas of importance for the ongoing development of the mPower suite of mobility services. Charles Martin is considered a leading figure across multiple technology disciplines, with an established track record in machine learning, deep learning, data science, and AI software development, complemented by extensive domain experience in Natural Language Processing (NLP) for Search Relevance (as well as Text Generation and Quantitative Finance). Highly sought after in the software development field, he has personally developed and implemented machine learning (ML) systems at companies including Roche, France Telecom, GoDaddy, Aardvark (Google), eBay, eHow, Walmart, Barclays/BGI, and Blackrock. Much of his recent project work has been done under his advisory firm, Calculation Consulting, which he founded in 2010 to provide data science, machine learning, and deep learning solutions. He has also served as both a consultant and FTE distinguished engineer at GLG, a prestigious international consulting firm, where he developed AI methods for the search and recommendations platform.

The educational program focuses on robot safety awareness and closing the skills gap in advanced manufacturing. SICK and Festo Didactic have partnered to create a Safety Awareness Bundle that combines curriculum and hardware to teach students robot safety holistically. The Safety Awareness Bundle's curriculum focuses on robot risk assessments and the implementation of the system approach. Festo Didactic developed the curriculum to help students learn industry best practices. The hardware components consist of a Festo Didactic manufacture production system (MPS), a simulated Cyber-Physical Smart Factory with a six-axis robot, SICK area scanners, safety PLC and safety relay.

Real-time interception of a fast-moving object by a robotic arm in cluttered environments filled with static or dynamic obstacles permits only tens of milliseconds for reaction times, hence quite challenging and arduous for state-of-the-art robotic planning algorithms to perform multiple robotic skills, for instance, catching the dynamic object and avoiding obstacles, in parallel. This paper proposes an unified framework of robotic path planning through embedding the high-dimensional temporal information contained in the event stream to distinguish between safe and colliding trajectories into a low-dimension space manifested with a pre-constructed 2D densely connected graph. We then leverage a fast graph-traversing strategy to generate the motor commands necessary to effectively avoid the approaching obstacles while simultaneously intercepting a fast-moving objects. The most distinctive feature of our methodology is to conduct both object interception and obstacle avoidance within the same algorithm framework based on deep manifold learning. By leveraging a highly efficient diffusion-map based variational autoencoding and Extended Kalman Filter(EKF), we demonstrate the effectiveness of our approach on an autonomous 7-DoF robotic arm using only onboard sensing and computation. Our robotic manipulator was capable of avoiding multiple obstacles of different sizes and shapes while successfully capturing a fast-moving soft ball thrown by hand at normal speed in different angles. Complete video demonstrations of our experiments can be found in https://sites.google.com/view/multirobotskill/home.