This talk explores digital transformation accelerators arising from two shocks – the pandemic and the future of artificial intelligence (AI). Presented by Jim Spohrer, retired IBM Executive, and member of the Board of Directors of the non-profit International Society of Service Innovation Professionals (ISSIP). In 2011 during IBM's Centennial Celebration, Jim Spohrer was recognized as an IBM Innovation Champion for his contributions to service science. Service science is one of the 100 innovations celebrated during IBM's Centennial as an IBM Icon of Progress. Will we get a copy of the deck?
When Peter George saw news of the racially motivated mass-shooting at the Tops supermarket in Buffalo last weekend, he had a thought he's often had after such tragedies. "Could our system have stopped it?" he said. But I think we could democratize security so that someone planning on hurting people can't easily go into an unsuspecting place." George is chief executive of Evolv Technology, an AI-based system meant to flag weapons, "democratizing security" so that weapons can be kept out of public places without elaborate checkpoints. As U.S. gun violence like the kind seen in Buffalo increases -- firearms sales reached record heights in 2020 and 2021 while the Gun Violence Archive reports 198 mass shootings since January -- Evolv has become increasingly popular, used at schools, stadiums, stores and other gathering spots. To its supporters, the system is a more effective and less obtrusive alternative to the age-old metal detector, making events both safer and more pleasant to attend. To its critics, however, Evolv's effectiveness has hardly been proved. And it opens up a Pandora's box of ethical issues in which convenience is paid for with RoboCop surveillance. "The idea of a kinder, gentler metal detector is a nice solution in theory to these terrible shootings," said Jay Stanley, senior policy analyst for the American Civil Liberties Union's project on speech, privacy, and technology. "But do we really want to create more ways for security to invade our privacy?
Currently in second place in her league as the 2021-22 football season draws to a close, she gets her AI assistance from one of the UK's most popular providers - Fantasy Football Fix. Offering both a free and subscription-based premium service, it launched back in 2018, and says it now has 500,000 users.
Predicting the results of matches in sport is a challenging and interesting task. In this paper, we review a selection of studies from 1996 to 2019 that used machine learning for predicting match results in team sport. Considering both invasion sports and striking/fielding sports, we discuss commonly applied machine learning algorithms, as well as common approaches related to data and evaluation. Our study considers accuracies that have been achieved across different sports, and explores whether evidence exists to support the notion that outcomes of some sports may be inherently more difficult to predict. We also uncover common themes of future research directions and propose recommendations for future researchers. Although there remains a lack of benchmark datasets (apart from in soccer), and the differences between sports, datasets and features makes between-study comparisons difficult, as we discuss, it is possible to evaluate accuracy performance in other ways. Artificial Neural Networks were commonly applied in early studies, however, our findings suggest that a range of models should instead be compared. Selecting and engineering an appropriate feature set appears to be more important than having a large number of instances. For feature selection, we see potential for greater inter-disciplinary collaboration between sport performance analysis, a sub-discipline of sport science, and machine learning.
At the heart of backpropagation are operations and functions which can be elegantly represented as a computational graph. Let's see an example: consider the function f z(x y); It's computational graph representation is shown below: A computational graph is essentially a directed graph with functions and operations as nodes. Computing the outputs from the inputs is called the forward pass, and it's customary to show the forward pass above the edges of the graph. In the backward pass, we compute the gradients of the output wrt the inputs and show them below the edges. Here, we start from the end and go to the beginning computing gradients along the way.
Fox News Flash top headlines are here. Check out what's clicking on Foxnews.com. The world's fastest ball sport has been dying a slow death for decades. Now, a group of committed enthusiasts is doing all it can to save jai alai, a game that originated in the Basque region of Spain and France but took root in Miami during the go-go days of the 1970s and '80s. What could be jai alai's curtain call is playing out at Magic City Casino, the last place the game is played as a professional sport.
Amazon Web Services (AWS) and Maple Leaf Sports & Entertainment (MLSE) announced a new deal that will see AWS provide technology services to notable Canadian sports franchises like the Toronto Maple Leafs, Toronto Raptors, Toronto Football Club (FC), and Toronto Argonauts. MLSE said it plans to use AWS' portfolio of cloud capabilities -- including machine learning, advanced analytics, compute, database, and storage services -- to support how its teams play; how players stay healthy; how fans connect with each other and experience games; and how sports franchises operate internally. MLSE added that it aims to offer its teams new AWS-powered insights to further improve the caliber of gameplay and develop new technology for sports fans. Humza Teherany, chief technology and digital officer at MLSE, said the company built its Digital Labs program to create solutions and products that drive the evolution of sports and elevate the fan experience. "We aim to offer new ways for fans to connect digitally with their favorite teams while also seeking to uncover digital sports performance opportunities in collaboration with our front offices. With AWS's advanced machine learning and analytics services, we can use data with our teams to help inform areas such as: team selection, training and strategy to deliver an even higher caliber of competition," Teherany said.
The National Football League is hoping that artificial intelligence can help reduce concussions in athletes. The NFL "Digital Athlete" is an artificial intelligence tool that uses TV images and sensors embedded in helmets, mouth guards and shoulder pads to try to reduce injuries. The tool creates a digital replica of an NFL athlete in a virtual environment. Using machine learning and computer vision technology, the tool then pinpoints impacts and injuries and helps researchers find new ways to improve player safety. "Having the computers understand how many times a player hits his helmet during the course of a game [helps] find ways to reduce the amount of helmet contact,"," Jeff Miller, NFL executive vice president, told New Scientist. Within the environment generated by the tool, an infinite number of game scenarios can be run, "giving the ability to test out new safety equipment, test out rule changes and predict player injury events and recovery trajectories eventually", says Dr Priya Ponnapalli, principal scientist at Amazon Machine Learning Solutions Lab. "What we've shown, I would say pretty definitively, is the relationship between years of play and risk of the disease," said Jesse Mez, Associate Professor of Neurology at Boston University. Mez hopes that the NFL will make more data available for study, "right now no helmet sensor data [is] made available to any investigators at universities.
Outside of football, similar machine-learning applications could analyze workplace behavior to ensure employees are following protocols or working efficiently and safely. It's not hard to envision a scenario in which workers are assigned scores based on how well they adhere to safety practices, which could then be used in employee evaluations and promotions. Some companies, such as Tyson, already use machine vision to study packaging inefficiencies; analyzing the behavior of the humans behind them seems like a logical next step.
AI and machine learning technologies are leading the change in almost every area of life. From business and medicine to entertainment and education, AI disrupts how we use tech. One of the most notable examples of this shift in technology is the multimedia industry, where AI continues to deliver results that were simply unimaginable. One of the newest innovations is that of generating AI voices. Three of the most impactful instances of technological innovation for multimedia in 2021 took place when AI helped clone the voices of cultural icons for social and entertainment initiatives.