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) …
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.
Describing visual content in a natural-language utterance is an emerging interdisciplinary problem, which lies at the intersection of computer vision (CV) and natural language processing (NLP) ((1)). As a sentence-level short image caption ((2, 3, 4)) has a limited descriptive capacity, (5) introduce a paragraphlevel caption method that aims to generate a detailed and coherent paragraph for describing an image in a finer manner. Recent advances in image paragraph generation focus on building different types of hierarchical recurrent neural network (HRNN), e.g., LSTM ((6)), to generate the visual paragraphs. For HRNN, the high-level RNN recursively produces a sequence of sentence-level topic vectors given the image features as the input, while the low-level RNN is subsequently adopted to decode each topic vector into an output sentence. By modeling each sentence and coupling the sentences into one paragraph, these hierarchical architectures often outperform the flat models ((5)). To improve the performance and generate more diverse paragraphs, advanced methods, extending the HRNN based on generative adversarial network (GAN) ((7)) or variational auto-encoders (VAE) ((8)), are proposed by (9) and (10).
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The rapid progress in artificial intelligence (AI) and machine learning has opened unprecedented analytics possibilities in various team and individual sports, including baseball, basketball, and tennis. More recently, AI techniques have been applied to football, due to a huge increase in data collection by professional teams, increased computational power, and advances in machine learning, with the goal of better addressing new scientific challenges involved in the analysis of both individual players’ and coordinated teams’ behaviors. The research challenges associated with predictive and prescriptive football analytics require new developments and progress at the intersection of statistical learning, game theory, and computer vision. In this paper, we provide an overarching perspective highlighting how the combination of these fields, in particular, forms a unique microcosm for AI research, while offering mutual benefits for professional teams, spectators, and broadcasters in the years to come. We illustrate that this duality makes football analytics a game changer of tremendous value, in terms of not only changing the game of football itself, but also in terms of what this domain can mean for the field of AI. We review the state-of-the-art and exemplify the types of analysis enabled by combining the aforementioned fields, including illustrative examples of counterfactual analysis using predictive models, and the combination of game-theoretic analysis of penalty kicks with statistical learning of player attributes. We conclude by highlighting envisioned downstream impacts, including possibilities for extensions to other sports (real and virtual).
Video game developers have long held a fascination with virtual tennis. All the way back in 1958, a game called Tennis for Two became one of the first documented video game prototypes. Fourteen years later, a similar game called Tennis debuted on the Magnavox Odyssey and made its way to American living rooms. This unlikely relationship between electronic entertainment and digital sportsmanship culminated in the 1975 home version of Pong, one of the first commercially successful home video games. But a new game out on Steam, called qomp, might be the first to consider that playing as the table tennis ball rather than the paddle could make for a novel gameplay experience.
Most of today's chatbots are highly specific in their conversations (according to their domain of usage) and users can't afford to drift away from their expected use. They are not good with retaining context from past conversations, sometimes give meaningless, illogical responses and quite easily give the response, "I don't know". Open-domain chatbots are conversational agents that can chat about anything and have basic knowledge about the real world. In the research paper "Towards a Human-like Open-Domain Chatbot", Google introduced Meena. Meena is claimed to be the smartest chatbot, highly sensible and specific in its responses, unlike other chatbots.
Joy Buolamwini is a researcher at the MIT Media Lab who pioneered research into bias that's built into artificial intelligence and facial recognition. And the way she came to this work is almost a little too on the nose. As a graduate student at MIT, she created a mirror that would project aspirational images onto her face, like a lion or tennis star Serena Williams. But the facial-recognition software she installed wouldn't work on her Black face, until she literally put on a white mask. Buolamwini is featured in a documentary called "Coded Bias," airing tonight on PBS.
Artificial intelligence (AI) can distinguish a dog from a cat, but the billions of calculations needed to do so demand quite a lot of energy. The human brain can do the same thing while using only a small fraction of this energy. Could this phenomenon inspire us to develop more energy-efficient AI systems? Our computational power has risen exponentially, enabling the widespread use of artificial intelligence, a technology that relies on processing huge amounts of data to recognize patterns. When we use the recommendation algorithm of our favorite streaming service, we usually don't realize the gigantic energy consumption behind it.
But these scenarios depend upon an unanswered question: are machines intelligent to begin with? Computers are essentially logic machines that process digital information. But in a recent paper entitled "The Emperor of Strong AI Has No Clothes," physicist Robert K. Logan in Toronto and Adriana Braga in Rio de Janeiro argue that the dream of a super intelligence has limits that its adherents choose to ignore. The things the Singularity will never get right amount to a long list, to quote the two researchers: "… curiosity, imagination, intuition, emotions, passion, desires, pleasure, aesthetics, joy, purpose, objectives, goals, telos, values, morality, experience, wisdom, judgment, and even humor." A clever programmer can figure out how to get a computer to answer human questions like "How is your mother feeling?", "What does chocolate taste like?", and "Don't you just love fresh snow?"
The news recommender systems are marked by a few unique challenges specific to the news domain. These challenges emerge from rapidly evolving readers' interests over dynamically generated news items that continuously change over time. News reading is also driven by a blend of a reader's long-term and short-term interests. In addition, diversity is required in a news recommender system, not only to keep the reader engaged in the reading process but to get them exposed to different views and opinions. In this paper, we propose a deep neural network that jointly learns informative news and readers' interests into a unified framework. We learn the news representation (features) from the headlines, snippets (body) and taxonomy (category, subcategory) of news. We learn a reader's long-term interests from the reader's click history, short-term interests from the recent clicks via LSTMSs and the diversified reader's interests through the attention mechanism. We also apply different levels of attention to our model. We conduct extensive experiments on two news datasets to demonstrate the effectiveness of our approach.