If you are looking for an answer to the question What is Artificial Intelligence? and you only have a minute, then here's the definition the Association for the Advancement of Artificial Intelligence offers on its home page: "the scientific understanding of the mechanisms underlying thought and intelligent behavior and their embodiment in machines."
However, if you are fortunate enough to have more than a minute, then please get ready to embark upon an exciting journey exploring AI (but beware, it could last a lifetime) …
Opsmatix, an innovative provider of AI-powered omnichannel operations automation solutions, announces a significant new hire to lead their platform development. Sateesh Pinnamaneni has joined the firm as Head of Engineering and will be working closely with Mark Barton, Opsmatix's Chief Technology Officer. Sateesh has over 20 years of experience in IT, having worked at some of the world's leading financial institutions, including; Nomura, Goldman Sachs and Credit Suisse. He is a self-proclaimed technology enthusiast and has joined Opsmatix to accelerate the platform development programme. Sateesh is also a keen tennis player and, in his spare time, coaches children.
Following its 2020 hiatus due to the Covid-19 pandemic, Wimbledon finally returns today, much to the delight of tennis fans around the world. If you're planning to tune into this year's tournament you're in for a treat, as a new study has revealed that tennis is the most euphoric sport to watch. The study, by Freeview, using artificial intelligence to decipher the expressions of participants while they watched various sports on their home devices. The findings suggest that tennis sparks the strongest euphoric response, closely followed by gymnastics, football and cricket. If you're planning to tune into this year's Wimbledon tournament you're in for a treat, as a new study has revealed that tennis is the most euphoric sport to watch In the study, Freeview used RealEyes' Emotion AI technology on 150 participants while they watched clips of 10 different sports at home.
For me, Wimbledon is one of the sporting highlights of the year. In previous Championships, I was lucky enough to be invited by IBM to see first-hand how the world's oldest tennis tournament was using cutting-edge technology to create amazing spectator experiences. Today, Wimbledon has become a technology-driven media operation, consistently refining its ability to keep fans engaged with the game in increasingly immersive and personalized ways. The AI of IBM Watson has been at the heart of this digital transformation. Much of it began with the introduction of "SlamTracker."
Artificial intelligence, is the magic technology stimulating intelligent behavior in machines. The core concept of artificial intelligence is to train machines to mimic human activities in performing routine and labor-intensive tasks. Moving out of the confined box, today, artificial intelligence is also being trained to carry out intellectual works like difficult calculations, decision-making, coming up with solutions, etc. The combination of science and engineer, which emerged as artificial intelligence technology, has revolutionized the business industry as well. In the digital world, artificial intelligence companies are providing innovative solutions to almost all sectors.
Detecting what emotions are expressed in text is a well-studied problem in natural language processing. However, research on finer grained emotion analysis such as what causes an emotion is still in its infancy. We present solutions that tackle both emotion recognition and emotion cause detection in a joint fashion. Considering that common-sense knowledge plays an important role in understanding implicitly expressed emotions and the reasons for those emotions, we propose novel methods that combine common-sense knowledge via adapted knowledge models with multi-task learning to perform joint emotion classification and emotion cause tagging. We show performance improvement on both tasks when including common-sense reasoning and a multitask framework. We provide a thorough analysis to gain insights into model performance.
Reinforcement learning (RL) is empirically successful in complex nonlinear Markov decision processes (MDPs) with continuous state spaces. By contrast, the majority of theoretical RL literature requires the MDP to satisfy some form of linear structure, in order to guarantee sample efficient RL. Such efforts typically assume the transition dynamics or value function of the MDP are described by linear functions of the state features. To resolve this discrepancy between theory and practice, we introduce the Effective Planning Window (EPW) condition, a structural condition on MDPs that makes no linearity assumptions. We demonstrate that the EPW condition permits sample efficient RL, by providing an algorithm which provably solves MDPs satisfying this condition. Our algorithm requires minimal assumptions on the policy class, which can include multi-layer neural networks with nonlinear activation functions. Notably, the EPW condition is directly motivated by popular gaming benchmarks, and we show that many classic Atari games satisfy this condition. We additionally show the necessity of conditions like EPW, by demonstrating that simple MDPs with slight nonlinearities cannot be solved sample efficiently.
Neural models have shown impressive performance gains in answering queries from natural language text. However, existing works are unable to support database queries, such as "List/Count all female athletes who were born in 20th century", which require reasoning over sets of relevant facts with operations such as join, filtering and aggregation. We show that while state-of-the-art transformer models perform very well for small databases, they exhibit limitations in processing noisy data, numerical operations, and queries that aggregate facts. We propose a modular architecture to answer these database-style queries over multiple spans from text and aggregating these at scale. We evaluate the architecture using WikiNLDB, a novel dataset for exploring such queries. Our architecture scales to databases containing thousands of facts whereas contemporary models are limited by how many facts can be encoded. In direct comparison on small databases, our approach increases overall answer accuracy from 85% to 90%. On larger databases, our approach retains its accuracy whereas transformer baselines could not encode the context.
A team of researchers at Stanford University has created an artificial intelligence-based player called the Vid2Player that is capable of generating startlingly realistic tennis matches--featuring real professional players. They have written a paper describing their work and have uploaded it to the arXiv preprint server. They have also uploaded a YouTube video demonstrating their player. Video game companies have put a lot of time and effort into making their games look realistic, but thus far, have found it tough going when depicting human beings. In this new effort, the researchers have taken a different approach to the task--instead of trying to create human-looking characters from scratch, they use sprites, which are characters based on video of real people.
During the British summer, conversations about sport become almost ubiquitous. This year, however, one participant in those conversations was very different: IBM Watson, IBM's cognitive intelligence. The All England Lawn Tennis Club knew that 2016 would feature unusually fierce competition for attention, with the Tour de France and Euro 2016 taking place alongside Wimbledon. More than ever before, social media was going to be a vital tool in directing that conversation, and directing attention to SW19. Wimbledon's "Cognitive Command Centre" – powered by Watson's intelligence running on a hybrid, IBM-managed cloud - scanned social media for emerging news and trends.