Blockchain and AI, particularly machine learning, are two quite recent revolutionary technologies that are being adopted by Governments and Businesses in all sorts of ways. In a series of articles (in 4 parts) I reflect in what ways these technologies have the potential to impact the lives of ordinary citizens. Businesses such as the ORS Group are providing innovative tools that will incorporate lots of small businesses into the blockchain, and providing them with AI-based tools that will enable them massively scale up their operations. According to the ORS President Fabio Zoffi, ORS Group has created a new concept known as Hypersmart Contracts, which will serve as the backbone to connect small businesses into global players using he combination of the AI and the blockchain. HyperSmart Contracts are smart daemons with an associated Ethereum account which run second layer, AI algorithms off-chain, to find solutions for complex optimization problems.

In this paper, we propose and develop the novel idea of treating musical sheets as literary documents in the traditional text analytics parlance, to fully benefit from the vast amount of research already existing in statistical text mining and topic modelling. We specifically introduce the idea of representing any given piece of music as a collection of "musical words" that we codenamed "muselets", which are essentially musical words of various lengths. Given the novelty and therefore the extremely difficulty of properly forming a complete version of a dictionary of muselets, the present paper focuses on a simpler albeit naive version of the ultimate dictionary, which we refer to as a Naive Dictionary because of the fact that all the words are of the same length. We specifically herein construct a naive dictionary featuring a corpus made up of African American, Chinese, Japanese and Arabic music, on which we perform both topic modelling and pattern recognition. Although some of the results based on the Naive Dictionary are reasonably good, we anticipate phenomenal predictive performances once we get around to actually building a full scale complete version of our intended dictionary of muselets.

Digital camera pipelines employ color constancy methods to estimate an unknown scene illuminant, enabling the generation of canonical images under an achromatic light source. By taking advantage of large amounts of labelled images, learning-based color constancy methods provide state-of-the-art estimation accuracy. However, for a new sensor, data collection is typically arduous, as it requires both imaging physical calibration objects across different settings (such as indoor and outdoor scenes), as well as manual image annotation to produce ground truth labels. In this work, we address sensor generalisation by framing color constancy as a meta-learning problem. Using an unsupervised strategy driven by color temperature grouping, we define many related, yet distinct, illuminant estimation tasks, aggregating data from four public datasets with different camera sensors and diverse scene content. Experimental results demonstrate it is possible to produce a few-shot color constancy method competitive with the fully-supervised, camera-specific state-of-the-art.

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.