Python has been appreciated for its relentless ascent to distinction over recent years. Supported for applications going from web advancement to scripting and procedure mechanization, Python is rapidly turning into the top decision among engineers for AI, ML, and profound learning ventures. Computer-based intelligence or artificial intelligence has created a universe of chances for application engineers. Computer-based information permits Spotify to prescribe artisans and melodies to clients, or Netflix to comprehend what shows you'll need to see straight away. It is additionally utilized widely by organizations in client assistance to drive self-administration and improve work processes and worker efficiency.
The API provides pre-trained object detection models that have been trained on the COCO dataset. COCO dataset is a set of 90 commonly found objects. See image below of objects that are part of COCO dataset. In this case we care about classes -- persons and soccer ball which are both part of COCO dataset. The API also has a big set of models it supports. See table below for reference. The models have a trade off between speed and accuracy. Since I was interested in real time analysis, I chose SSDLite mobilenet v2. Once we identify the players using the object detection API, to predict which team they are in we can use OpenCV which is powerful library for image processing.
A scientist and a researcher, Brian Roemelle, once said that artificial intelligence is the electricity of the future. And it is difficult to disagree, for AI has a huge impact on many industries right now -- from banking to auto. But have you ever thought how AI works for a sextech? Great changes are happening right now, and although you may not even notice it, your sex experience is getting better. The sex industry is booming -- people accept themselves and their bodies, some of them open out, some start experimenting, and some identify themselves as digisexuals (people whose primary sexual identity comes through the use of technology -- they don't need other people to have sex to).
The first season of Westworld focused on the idea of artificial intelligence and humans co-mingling in one epic theme park. The second season saw those same artificially intelligent beings lead a revolution against their human counterparts. In Westworld's third season, Dolores has escaped from the Delos-owned park she called home -- or perhaps prison is the more correct word choice. She teams up with Aaron Paul's character, who she tasks with taking down a mysterious person who took his future away. Maeve, who also escaped, is given a pretty hefty task of her own: kill Dolores.
Garry Kasparov is perhaps the greatest chess player in history. For almost two decades after becoming world champion in 1985, he dominated the game with a ferocious style of play and an equally ferocious swagger. Outside the chess world, however, Kasparov is best known for losing to a machine. In 1997, at the height of his powers, Kasparov was crushed and cowed by an IBM supercomputer called Deep Blue. The loss sent shock waves across the world, and seemed to herald a new era of machine mastery over man.
Not that long ago, the idea that artificial intelligence (AI) would be generally available -- never mind a vital and daily part of all of our lives -- would have seemed impossible. But here we are, with powerful and useful AI in our homes, cars, pockets and even in our workplace. And interestingly, most of the movie- and science fiction- driven fears of AI (you know, 2001's HAL and the Terminator's Skynet) have faded away, with most of us seeing AI as an extremely useful buddy who helps us get things done, entertain ourselves and answer vital trivia questions. However, there are still some areas where people fear AI. One of the biggest areas of concern with AI is in the workplace.
The past few years have witnessed breakthroughs in reinforcement learning (RL). From the first successful use of RL by a deep learning model for learning a policy from pixel input in 2013 to the OpenAI Dexterity program in 2019, we live in an exciting moment in RL research. Consequently, we need, as RL researchers, to create more and more complex environments and Unity helps us to do that. Unity ML-Agents toolkit is a new plugin based on the game engine Unity that allows us to use the Unity Game Engine as an environment builder to train agents. From playing football, learning to walk, to jump big walls, to train a cute doggy to catch sticks, Unity ML-Agents Toolkit provides a ton of amazing pre-made environment.
Reinforcement learning, which spurs AI to complete goals using rewards or punishments, is a form of training that's led to gains in robotics, speech synthesis, and more. Unfortunately, it's data-intensive, which motivated research teams -- one from Google Brain (one of Google's AI research divisions) and the other from Alphabet's DeepMind -- to prototype more efficient means of executing it. In a pair of preprint papers, the researchers propose Adaptive Behavior Policy Sharing (ABPS), an algorithm that allows the sharing of experience adaptively selected from a pool of AI agents, and a framework -- Universal Value Function Approximators (UVFA) -- that simultaneously learns directed exploration policies with the same AI, with different trade-offs between exploration and exploitation. The teams claim ABPS achieves superior performance in several Atari games, reducing variance on top agents by 25%. As for UVFA, it doubles the performance of base agents in "hard exploration" in many of the same games while maintaining a high score across the remaining games; it's the first algorithm to achieve a high score in Pitfall without human demonstrations or hand-crafted features.
Are superhuman computers set to steal our jobs, take over the world and perhaps even kill off humanity? Such fears are commonplace in contemporary culture. Witness how dystopias about super-powerful rogue forms of artificial intelligence (AI), such as Person of Interest, Black Mirror and Ex Machina, have proliferated in recent years. But while scary science fiction may be entertaining, the reality of AI is rather more prosaic – and positive. For good or ill, AI isn't going to be running the world any time soon.
The old stereotype of video game players as spotty, socially isolated boys in basements is finally disappearing after decades, but the popular image of game developers is enduring. They are imagined to be white and beardy, with glasses and a probable fondness for sci-fi and fantasy, and this is hardly unjustified. Cast an eye over the development floor of pretty much any major game developer in the western world and there's an undeniable homogeneity. The same can be said about video games industry executives. Whether clean-shaven or bearded, besuited or smart-casual, creative or corporate, they are almost universally white and male.