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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.
Accurate time-series forecasting is vital for numerous areas of application such as transportation, energy, finance, economics, etc. However, while modern techniques are able to explore large sets of temporal data to build forecasting models, they typically neglect valuable information that is often available under the form of unstructured text. Although this data is in a radically different format, it often contains contextual explanations for many of the patterns that are observed in the temporal data. In this paper, we propose two deep learning architectures that leverage word embeddings, convolutional layers and attention mechanisms for combining text information with time-series data. We apply these approaches for the problem of taxi demand forecasting in event areas. Using publicly available taxi data from New York, we empirically show that by fusing these two complementary cross-modal sources of information, the proposed models are able to significantly reduce the error in the forecasts.
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.
"Today is the slowest rate of technological change you will ever experience in your lifetime," wrote Shelly Palmer in his e-book Data-Driven Thinking (Digital Living Press, 2016). As one of the world's premier voices on the accelerating pace of digital technology, he is increasingly preoccupied with helping companies and individuals prepare for the dramatic changes he sees coming, particularly in entertainment and media. Palmer started his career at age 12 as a musician, playing the clarinet, saxophone, and flute in the 1970s in venues around New York. He was also an early experimenter with analog and digital synthesizers. He holds patents for two major interactive television technologies, one of which -- a method for syncing broadcast TV with server-based text, known as enhanced television -- was adopted by Monday Night Football and Who Wants to Be a Millionaire? His background also includes writing the theme music for Spin City and Live with Regis and Kathie Lee, and conducting the London Symphony Orchestra. Currently, he is Fox 5 New York's on-air tech and digital media expert and the proprietor of a popular and prescient email newsletter that covers the impact of technology on media and daily life, with a special focus on smart cars and smart homes. For the past decade, as a venture capitalist and CEO of his own consulting firm and marketing agency, the Palmer Group, Palmer has focused his attention on the evolution of advertising, marketing, and related businesses, along with leading-edge technologies such as smart home systems and data analytics. We recently talked with Palmer in New York. Conscious of the intertwined trajectories of trends in technology and media, we sought to explore how artificial intelligence (AI) and the churn in business models could affect advertising, media, and related fields over the next few years.
NVIDIA's (NASDAQ:NVDA) graphic cards have long been favorites among hardcore gamers, but who would've thought the chipmaker's stock would explode the way it has in recent times? The share price has more than tripled in just the past year, turning NVIDIA into a near eight-bagger in just five years. It's more an artificial intelligence computing company today, having made huge headway in two of the hottest technology fields of our times: AI and self-driving cars. For investors looking to find the "next NVIDIA," the trick is to find a company that is sitting on a big growth opportunity, or is already tapping into a soon-to-heat-up trend, but that is still flying under Wall Street's radar. These are stocks with the potential to soar.