Not enough data to create a plot.
Try a different view from the menu above.
People are increasingly getting onto those banned no-fly types of lists, which could happen with ... [ ] self-driving cars too. People keep getting banned for doing the darndest and seemingly dumbest of acts. Oftentimes getting banned for the rest of their entire life. You might have heard or seen the recent brouhaha in major league baseball when a spectator in Yankee Stadium seated above leftfield opted to throw a baseball down onto the field that then struck the Boston Red Sox player Alex Verdugo in the back. He was not hurt, but you can imagine the personal dismay and shock at suddenly and unexpectedly having a projectile strike him from behind, seemingly out of nowhere. Turns out that Alex had earlier tossed the same baseball up into the stands as a memento for a young Red Sox cheering attendee. By some boorish grabbing, it had ended up in the hands of a New York Yankees fan. Next, after some hysterical urging by other frenetic Yankees to toss it back, the young man did so. Whether this act of defiance was intentionally devised to smack the left-fielder is still unclear and it could have been a happenstance rather than a purposeful aim.
Artificial intelligence (AI) has witnessed a substantial breakthrough in a variety of Internet of Things (IoT) applications and services, spanning from recommendation systems to robotics control and military surveillance. This is driven by the easier access to sensory data and the enormous scale of pervasive/ubiquitous devices that generate zettabytes (ZB) of real-time data streams. Designing accurate models using such data streams, to predict future insights and revolutionize the decision-taking process, inaugurates pervasive systems as a worthy paradigm for a better quality-of-life. The confluence of pervasive computing and artificial intelligence, Pervasive AI, expanded the role of ubiquitous IoT systems from mainly data collection to executing distributed computations with a promising alternative to centralized learning, presenting various challenges. In this context, a wise cooperation and resource scheduling should be envisaged among IoT devices (e.g., smartphones, smart vehicles) and infrastructure (e.g. edge nodes, and base stations) to avoid communication and computation overheads and ensure maximum performance. In this paper, we conduct a comprehensive survey of the recent techniques developed to overcome these resource challenges in pervasive AI systems. Specifically, we first present an overview of the pervasive computing, its architecture, and its intersection with artificial intelligence. We then review the background, applications and performance metrics of AI, particularly Deep Learning (DL) and online learning, running in a ubiquitous system. Next, we provide a deep literature review of communication-efficient techniques, from both algorithmic and system perspectives, of distributed inference, training and online learning tasks across the combination of IoT devices, edge devices and cloud servers. Finally, we discuss our future vision and research challenges.
Recent successes combine reinforcement learning algorithms and deep neural networks, despite reinforcement learning not being widely applied to robotics and real world scenarios. This can be attributed to the fact that current state-of-the-art, end-to-end reinforcement learning approaches still require thousands or millions of data samples to converge to a satisfactory policy and are subject to catastrophic failures during training. Conversely, in real world scenarios and after just a few data samples, humans are able to either provide demonstrations of the task, intervene to prevent catastrophic actions, or simply evaluate if the policy is performing correctly. This research investigates how to integrate these human interaction modalities to the reinforcement learning loop, increasing sample efficiency and enabling real-time reinforcement learning in robotics and real world scenarios. This novel theoretical foundation is called Cycle-of-Learning, a reference to how different human interaction modalities, namely, task demonstration, intervention, and evaluation, are cycled and combined to reinforcement learning algorithms. Results presented in this work show that the reward signal that is learned based upon human interaction accelerates the rate of learning of reinforcement learning algorithms and that learning from a combination of human demonstrations and interventions is faster and more sample efficient when compared to traditional supervised learning algorithms. Finally, Cycle-of-Learning develops an effective transition between policies learned using human demonstrations and interventions to reinforcement learning. The theoretical foundation developed by this research opens new research paths to human-agent teaming scenarios where autonomous agents are able to learn from human teammates and adapt to mission performance metrics in real-time and in real world scenarios.
The Christmas Spectacular Starring the Radio City Rockettes has taken place every holiday season in New York City since 1933. But this year the always festive finale will look different -- and be way more high-tech. That's because Intel is bringing in its lightweight Shooting Star Mini drones -- the ones that made appearances at the Olympics and the Super Bowl -- for some light-filled, choreographed visions. The final scene is called "Christmas Lights"; it's all very on theme. Most notable will be the sheer number of drones: A hundred of them, all synced and moving together to create holiday cheer.
If you want to see some of the stuff that Tay tweeted, head over here (warning; some of her tweets make Donald Trump look tame). Tay's introduction by Microsoft was not just an attempt to build an AI that learnt from human interactions, but also one that potentially enriched Microsoft's brand and was designed also to harvest users information such as gender, location/zip codes, favourite foods, and so on (as was the Microsoft Age guessing software of last year). It harvested user interactions alright, but after a group of trolls launched a sustained, coordinated effort to influence Tay, the AI did exactly what Microsoft designed it to do -- it adapted to the language of it's so-called peers. Tay appears to have accomplished an analogous feat, except that instead of processing reams of Go data she mainlined interactions on Twitter, Kik, and GroupMe. She had more negative social experiences between Wednesday afternoon and Thursday morning than a thousand of us do throughout puberty. It was peer pressure on uppers, "yes and" gone mad. No wonder she turned out the way she did. I've Seen the Greatest A.I. Minds of My Generation Destroyed by Twitter, New Yorker article, March 25th, 2016 Tay is a lesson to us in the burgeoning age of AI. Teaching Artificial Intelligences is not only about deep learning capability, but significantly about the data these AIs will consume, and not all data is good data.
Nvidia reported earnings that beat expectations and showed that the company's focus on artificial intelligence is still paying off. For the past decade, Nvidia has been rising above graphics chips for gamers, expanding to parallel processing in data centers and lately to artificial intelligence processing for deep learning neural networks and self-driving cars. The company reported earnings per share of $1.33 (up 60 percent from a year ago) on revenue of $2.6 billion (up 32 percent), beating Wall Street's expectations. The company's stock price is up more than 100 percent in the past year on the popularity of artificial intelligence. But it slumped during the day on Thursday, along with the broader market.
Apple's stock market value is heading towards a new milestone and its latest product launch on 12 September could push the tech giant closer to becoming the first ever $1tn (£760bn) company. At the end of last week, the company's market capitalisation hovered around $830bn, continuing a 10-year run that has generally headed upwards since a low of $69bn in January 2009, during the financial crisis. Tuesday's event, with the iPhone 8 the star attraction, will strive to meet investors' – and customers' – vaulting expectations. But what will Apple tempt users with to justify Wall Street's faith in its future profits? An Apple spokesman declined to discuss what will be revealed at the event in the company's $5bn, spaceship-shaped Cupertino headquarters.