Advertisers have been targeting their messages to relevant media for as long as advertising has existed. Kids' TV channels are awash with toy commercials, breaks during wedding shows are full of ads for honeymoon destinations and so on. But now, NBCUniversal has adopted a new machine learning tool that makes the placement of ads on TV more exact, relevant and timely. The Context Intelligence Platform analyzes scripts, closed captions and visual descriptions to identify the perfect moment for particular ads to appear. So if you're watching a USA Network show that features a party scene, for example, it won't be a coincidence if the following ad is for an alcoholic drinks brand.

Watch whatever you want, whenever you want. All you have to buy is one little box, and the monthly subscriptions are up to you. After decades of flipping through TV channels, many of us find the promise of internet-based television too miraculous to pass up. That's why millions of Americans prefer streaming services – the leaner, cheaper version of televised entertainment. Much of the streaming decision is dependent on what you want to watch.

AI: It's not as scary as movies would like for us to think. Explore the everyday uses of this extraordinary tech as well as how enterprises can begin to trust the new insights from AI in addition to human-powered decision making.

The Washington Post, Associated Press, and Reuters are just a few of the industry leaders who turned to AI in 2018. Publishers who adopted AI and machine-learning tools have seen results. Last year, Digiday reported The Washington Post's robot reporter published 850 stories in a year. Next year, the global media industry will begin to use these new tools at a faster rate. Here are a few current stories on AI that will have real implications for journalism in 2019.

Tunde Adegbola, born 1 August 1955, also known as T. A. or Uncle T, is a scientist, musician, engineer, linguist and culture activist. He is best known for his work in setting up most of the pioneering private Television and Radio stations in Nigeria. He is the founder of TIWA systems, and the Executive Director of Alt-i (African Languages Technology Initiative). Tunde completed a bachelor's degree in Electrical Engineering at the University of Lagos, and later specialized in broadcast technology. He subsequently obtained a master's degree in Computer Science from the University of Wales (Swansea).

It's Cyber Monday, but many of the deals you're seeing today started on Black Friday. We've been adding and removing items from our categorized lists all weekend, and you should really check them out if you want to see all of the best tech deals today. We'll keep updating them till the bitter end. But below we wanted to highlight a few of the deals that are really impressing us. Whether you're treating yourself, or hunting for gifts, we think these devices will make anyone happy, and they're all on deep discount through the end of the day.

Over the past years, literature has shown that attacks exploiting the microarchitecture of modern processors pose a serious threat to the privacy of mobile phone users. This is because applications leave distinct footprints in the processor, which can be used by malware to infer user activities. In this work, we show that these inference attacks are considerably more practical when combined with advanced AI techniques. In particular, we focus on profiling the activity in the last-level cache (LLC) of ARM processors. We employ a simple Prime+Probe based monitoring technique to obtain cache traces, which we classify with Deep Learning methods including Convolutional Neural Networks. We demonstrate our approach on an off-the-shelf Android phone by launching a successful attack from an unprivileged, zeropermission App in well under a minute. The App thereby detects running applications with an accuracy of 98% and reveals opened websites and streaming videos by monitoring the LLC for at most 6 seconds. This is possible, since Deep Learning compensates measurement disturbances stemming from the inherently noisy LLC monitoring and unfavorable cache characteristics such as random line replacement policies. In summary, our results show that thanks to advanced AI techniques, inference attacks are becoming alarmingly easy to implement and execute in practice. This once more calls for countermeasures that confine microarchitectural leakage and protect mobile phone applications, especially those valuing the privacy of their users.

Humans are the final decision makers in critical tasks that involve ethical and legal concerns, ranging from recidivism prediction, to medical diagnosis, to fighting against fake news. Although machine learning models can sometimes achieve impressive performance in these tasks, these tasks are not amenable to full automation. To realize the potential of machine learning for improving human decisions, it is important to understand how assistance from machine learning models affects human performance and human agency. In this paper, we use deception detection as a testbed and investigate how we can harness explanations and predictions of machine learning models to improve human performance while retaining human agency. We propose a spectrum between full human agency and full automation, and develop varying levels of machine assistance along the spectrum that gradually increase the influence of machine predictions. We find that without showing predicted labels, explanations alone do not statistically significantly improve human performance in the end task. In comparison, human performance is greatly improved by showing predicted labels (>20% relative improvement) and can be further improved by explicitly suggesting strong machine performance. Interestingly, when predicted labels are shown, explanations of machine predictions induce a similar level of accuracy as an explicit statement of strong machine performance. Our results demonstrate a tradeoff between human performance and human agency and show that explanations of machine predictions can moderate this tradeoff.

Buzz60's Tony Spitz has the details. Elon Musk, CEO of Tesla Motors Inc., unveils the companyís newest products, Powerwall and Powerpack in Hawthorne, Calif., Thursday, April 30, 2015. Mars may not be the kind of place you raise your kids, as goes the Elton John song "Rocket Man." The first flight to Mars will likely happen in seven years, Musk said Sunday night on "Axios," the news site's half-hour HBO TV series. Musk, 47, says there's a "70 percent chance" he will take a flight to Mars in his lifetime.

Deep Learning is a Machine Learning method that has taken the world by storm with its capabilities. In this article, we will discuss the meaning of Deep Learning With Python. Also, we will learn why we call it Deep Learning. Moreover, this article will go through Artificial Neural Networks and Deep Neural Networks, along with Deep Learning applications. To define it in one sentence, we would say it is an approach to Machine Learning.