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A deep learning framework to estimate the pose of robotic arms and predict their movements


As robots are gradually introduced into various real-world environments, developers and roboticists will need to ensure that they can safely operate around humans. In recent years, they have introduced various approaches for estimating the positions and predicting the movements of robots in real-time. Researchers at the Universidade Federal de Pernambuco in Brazil have recently created a new deep learning model to estimate the pose of robotic arms and predict their movements. This model, introduced in a paper pre-published on arXiv, is specifically designed to enhance the safety of robots while they are collaborating or interacting with humans. "Motivated by the need to anticipate accidents during human-robot interaction (HRI), we explore a framework that improves the safety of people working in close proximity to robots," Djamel H. Sadok, one of the researchers who carried out the study, told TechXplore.

Genetic Algorithms + Data Structures = Evolution Programs: Michalewicz, Zbigniew: 9783540606765: Books


Zbigniew Michalewicz is Emeritus Professor of Computer Science at the University of Adelaide in Australia. He completed his Masters degree at Technical University of Warsaw in 1974 and he received Ph.D. degree from the Institute of Computer Science, Polish Academy of Sciences, in 1981. He also holds a Doctor of Science degree in Computer Science from the Polish Academy of Science. Zbigniew Michalewicz also holds Professor positions at the Institute of Computer Science, Polish Academy of Sciences, the Polish-Japanese Institute of Information Technology, and the State Key Laboratory of Software Engineering of Wuhan University, China. He is also associated with the Structural Complexity Laboratory at Seoul National University, South Korea.

Taxonomy Makes Machine-Learning Model Features More Understandable


Researchers focused on the ability for humans to understand how an ML model makes its decisions. MIT researchers are striving to improve the interpretability of features of machine learning models so that decision makers will be more comfortable using the outputs of those models. Drawing on years of field work, they developed a taxonomy to help developers craft features that will be easier for their target audience to understand. "We found that out in the real world, even though we were using state-of-the-art ways of explaining machine-learning models, there is still a lot of confusion stemming from the features, not from the model itself," says Alexandra Zytek, an electrical engineering and computer science Ph.D. student and lead author of a paper introducing the taxonomy. To build the taxonomy, the researchers defined properties that make features interpretable for five types of users.

How the U.S. Can Advance Artificial Intelligence Without Spending a Dime


Federal officials have largely come around to the idea that research funding is crucial for U.S. leadership in artificial intelligence, but there are ways to accelerate innovation besides pouring in more money, according to tech experts. For one, they said, the government could map a long-term strategy for advancing the technology. "The U.S. has been slow in making this a national imperative," Dean Garfield, president and CEO of the Information Technology Industry Council, said Thursday on a panel hosted by Politico. "The signal that comes from the top … is critically important here and has the opportunity to really catalyze that action in a way that wouldn't happen without it." The Office of Science and Technology Policy on Wednesday requested industry input on updating an AI research and development strategy the White House published in 2016.

FIFA will track players' bodies using AI to make offside calls at 2022 World Cup


FIFA, the international governing body of association football,* has announced it will use AI-powered cameras to help referees make offside calls at the 2022 World Cup. The semi-automated system consists of a sensor in the ball that relays its position on the field 500 times a second, and 12 tracking cameras mounted underneath the roof of stadiums, which use machine learning to track 29 points in players' bodies. Software will combine this data to generate automated alerts when players commit offside offenses (that is: when they're nearer to the other team's goal than their second-last opponent and receiving the ball). Alerts will be sent to officials in a nearby control room, who will validate the decision and tell referees on the field what call to make. FIFA claims this process will happen "within a few seconds and means that offside decisions can be made faster and more accurately."



Originally published on Towards AI the World's Leading AI and Technology News and Media Company. If you are building an AI-related product or service, we invite you to consider becoming an AI sponsor. At Towards AI, we help scale AI and technology startups. Let us help you unleash your technology to the masses. Machine Learning Algorithms have historically been used in the Credit and Fraud Space.

IT Threat Detection using Neural Search


If you spend more on coffee than IT security, you will be hacked! Warned U.S. Cybersecurity Czar Richard Clarke, speaking at RSA Conference. This quote would make a great bumper sticker if it weren't for network attacks. According to research by IBM, it takes 280 days to find and contain the average cyberattack, while the average attack costs $3.86 million. But what are network attacks, and how can we leverage a next-gen search tool like Jina to mitigate our exposure to the threat?

Amazon's Echo Dot drops to $20 ahead of Prime Day


Prime Day is still a week away, but Amazon is getting the jump on one of its biggest events of the year by putting a bunch of its own products on sale a little early. One of those is the fourth-gen Echo Dot. The company has slashed the price of the Alexa-powered smart speaker by 60 percent for Prime members. It's down to $20, which is $30 off the regular price. That's the best price we've seen to date.

20 Year Indian Student's Machine Learning Software To Be Sent To Space


A student from India studying in the Nanyang Technological University (NTU) in Singapore, has developed a machine learning software along with his team consisting of 4 other students from the same institution. This software is said to be sent up to the International Space Station (ISS). Archit Gupta's team won a competition recently which was on developing different ways to apply artificial intelligence on space applications. This victory gave his team an opportunity to test their software in the International Space Station (ISS). The software will be installed into a supercomputer which is an artificial intelligence box and after that it will be sent up to the ISS.

Conference on Reinforcement Learning and Decision Making


The 5th Multi-disciplinary Conference on Reinforcement Learning and Decision Making (RLDM) 2022 took place at Brown University from 8-11 June. The programme included invited and contributed talks, workshops, and poster sessions. The goal of RLDM is to provide a platform for communication among all researchers interested in learning and decision making over time to achieve a goal. Over the last few decades, reinforcement learning and decision making have been the focus of an incredible wealth of research spanning a wide variety of fields including psychology, artificial intelligence, machine learning, operations research, control theory, neuroscience, economics and ethology. The interdisciplinary sharing of ideas has been key to many developments in the field, and the meeting is characterized by the multidisciplinarity of the presenters and attendees.