Amazon's "artificial intelligence" software or should we say smart assistant, Alexa, was the quiet star of CES 2017, appearing in a huge range of products, from fridges to smart lamps, to cars. Alexa has even made its debut in its first smartphone, the Huawei Mate 9 – Alexa, and this could be the start of a new trend in 2017 that sees smart assistants become important flagship differentiators. Of course, Google and Apple have been at this for a long time already, and Google Now is already baked in to every new Android smartphone release these days. So really the age of the smart assistant has been here for a while already. However, Google Now certainly isn't a unique selling point for manufacturers looking to stand out in the competitive high-end market, hence why custom assistants are starting to pop up.

For every up, there is a down. For every wrong, there is a right, and for every good thing, there is a bad to go with it. There are many benefits that autonomous weapons could bring and if you were ever in a position where you needed a weapon you'd be quite grateful to have one by your side. But, what if that weapon suddenly malfunctioned and aimed at you instead? This is the main worry for a lot of people when they think about autonomous weapons, and it seems that the majority of people in 89 different nations agree too.

I have written previously about Robotic Process Automation (RPA) and its potential impact on the financial services industry and more specifically on the finance and risk functions within the institutions. Most financial services firms have started exploring how to benefit from RPA – using software to mimic the actions a human would perform on a PC, then scaling up these actions as needed – and many have already automated a number of the repetitive, rules-based processes that are the typical starting points for RPA. Some firms that have implemented these tools have already seen dramatic reductions in average handling and/or cycle times. Across the industry, companies have been looking for ways to help address high variable costs and stagnant productivity growth against market challenges. The proliferation of new regulations – as well as firms' own initiatives to improve compliance and reduce risk – have driven up the demand for and market cost of finance and risk talent.

Iran introduced blocks on pornography so aggressive that they broke the internet around the world. The country's censorship laws on adult websites are infamously stringent, and require access pornographic websites to be cut off. But last week the state internet provider did so not only for those in Iran but for people across the world – as far away as Russia and Hong Kong. The strange ban was the result of the way that the internet provider cut off access to those sites. It did so using some of the basic mechanisms of the web – not just stopping people in Iran accessing the websites, but changing the directions that power the internet so that nobody could. The giant human-like robot bears a striking resemblance to the military robots starring in the movie'Avatar' and is claimed as a world first by its creators from a South Korean robotic company Waseda University's saxophonist robot WAS-5, developed by professor Atsuo Takanishi and Kaptain Rock playing one string light saber guitar perform jam session A man looks at an exhibit entitled'Mimus' a giant industrial robot which has been reprogrammed to interact with humans during a photocall at the new Design Museum in South Kensington, London Electrification Guru Dr. Wolfgang Ziebart talks about the electric Jaguar I-PACE concept SUV before it was unveiled before the Los Angeles Auto Show in Los Angeles, California, U.S The Jaguar I-PACE Concept car is the start of a new era for Jaguar.

As the Big Data explosion continues at an almost incomprehensible rate, being able to understand and process it becomes even more challenging. With Building Machine Learning Systems with Python, you'll learn everything you need to tackle the modern data deluge – by harnessing the unique capabilities of Python and its extensive range of numerical and scientific libraries, you will be able to create complex algorithms that can'learn' from data, allowing you to uncover patterns, make predictions, and gain a more in-depth understanding of your data. Featuring a wealth of real-world examples, this book provides gives you with an accessible route into Python machine learning. Learn the Iris dataset, find out how to build complex classifiers, and get to grips with clustering through practical examples that deliver complex ideas with clarity. Dig deeper into machine learning, and discover guidance on classification and regression, with practical machine learning projects outlining effective strategies for sentiment analysis and basket analysis.

I was playing around with Algorithmia for a story about their business model back in December, when I got sucked into playing with their DeepFilter service, resulting in a 4-week long distraction which ultimately became what I am calling my algorithmic rotoscope work. After weeks of playing around, I have a good grasp of what it takes to separate videos into individual images, applying the Algorithmia machine learning filters, and reassembling them as videos. I also have several of my own texture filters created now using the AWS AMI and process provided Algorithmia -- you can learn more about algorithmic rotoscope, and details of what I did via the GitHub project updates. The project has been a great distraction from what I should be doing. After the election, I just did not feel like doing my regular writing, scheduling of Tweets, processing of press releases, and the other things I do on a regular basis.

How do we unlock the true value of data? Nowadays, collecting data has never been easier and faster, but understanding its true meaning and value still remains a challenge. IBM estimates 90% of all the data existing in the world today has been generated in the last 24 months. For data-driven organisations such as the NHS, which has disparate trusts throughout Britain generating vast amounts of data, having the ability to harness this information and analyse it correctly to get the right insights has never been more important. In the technology profession, we may have, ironically, promoted Big Data as a panacea without truly and fully understanding the issues it can solve.

To solve real-world problems with AI, a deep learning system would need to be trained on a trillion parameters in 20 minutes. Even Intel is willing to admit that computers are great at crunching numbers, but not so great that they also make good decision-makers. Based on a recent webinar about the hardware advancements that have made better artificial intelligence (AI) possible, and what the future holds, that is about to change, and much faster than many would believe. Pradeep Dubey, the director of the Parallel Computing Lab at Intel, explained the difference between traditional AI systems and newer implementations like deep learning--primarily, it's about who is making the rules. In traditional AI, humans have to create rule-based systems for understanding which data should be processed, and how.

Advancements in machine learning and artificial intelligence (AI) opens new doors for businesses to make data-informed decisions, and if your business does not take advantage of the growing industry of data analytics, your competitors will steamroll ahead. The advancement of machine learning and artificial intelligence is opening new doors for businesses to make more data-driven decisions at higher accuracy rates. Data and analytics are also changing the way that businesses compete. As leading companies take advantage of the power of big data, and laggards stay behind, the disparity between the two groups will continue to widen in the future. Recently, Forbes has predicted that by 2020, we will have 1.7 megabytes of new information created every second for every human everywhere on the planet.

Whenever one separates sociology and technology, one misses the point.