For the last 5 to 10 years, connectivity and mobility have been on top of the agenda for many industries, both are essential enablers for a digital transformation. But while mobility and connectivity continue to be important enablers to expand the internet of things, there is something much bigger at stake that starts to emerge now. As more devices get connected you also start to collect more data and gain more insights and intelligence on how to optimize and automate tasks and processes. So here are a few questions related to the impact these changes could have on our society. How many of the tasks done today by various types of employees can be fully automated?

I often get asked on how to get started with Machine Learning. Most of the time, people have troubles understanding the maths behind all things. And I have to admit, I don't like the maths either. Math is an abstract way of describing things. And I think the way machine learning is described is too abstract to understand it easily. I probably try to describe things with foo code or a bit of JS to explain what I'm talking about.

When Facebook (NASDAQ: FB)suggests you "tag" a friend in a photo, it generally suggests that friend's name. That small interaction provides a glimpse into the world of an emerging and powerful aspect of artificial intelligence (AI) in action -- image recognition. With its treasure trove of words and pictures from 1.79 billion monthly active users,it is using that data, combined with recent advancements in AI, to propel this and other technological advances. Facebook may well have the lead in facial recognition, even extending a step further into the realm of facial verification. It released a research paper in 2014 in which it reported 97.35% accuracy, which approaches human levels of recognition.So accurate, in fact, that Facebook removed the feature for European Union citizens at the EU's request to protect privacy.

AMD has traded at this level on only two other occasions before: Q2-Q3 of 2000 and Q4 2005 - Q1 2007. The rise in market capitalization in both occasions came with the release of a competitive CPU processor/architecture. AMD in 2016/2017 is beginning to look a lot like AMD's most successful periods in 2000 and 2006. I'd like to examine catalysts then and now to gain insight on where AMD may be headed. In June 1999 AMD debuted their K7 Athlon processor.

F-35s, F-22s and other fighter jets will soon use improved artificial intelligence to control nearby drone wingmen that will be able to carry weapons, test enemy air defenses or perform intelligence, surveillance and reconnaissance missions in high risk areas, senior Air Force officials said. "This involves an attempt to have another platform fly alongside a human, perhaps serving as a weapons truck carrying a bunch of missiles," Zacharias said in an interview with Defense Systems. An F-35 computer system, Autonomic Logistics Information System, uses early applications of artificial intelligence that help computers make assessments, go through checklists, organize information and make some decisions by themselves – without needing human intervention. "We are working on making platforms more autonomous with multi-infusion systems and data from across different intel streams," Zacharias explained. ALIS serves as the information infrastructure for the F-35, transmitting aircraft health and maintenance action information to the appropriate users on a globally-distributed network to technicians worldwide, said Lockheed Martin, the contractor that built the system. However, despite the promise of advancing computer technology and increasingly levels of autonomy, Zacharias emphasized that dynamic human cognition is, in many respects, far more capable than computers.

What will be the impact of machine learning on economics? NEW YORK, NY - MAY 05: Susan Athey speaks at TechCrunch Disrupt NY 2014 - Day 1 on May 5, 2014 in New York City. The short answer is that I think it will have an enormous impact; in the early days, as used "off the shelf," but in the longer run econometricians will modify the methods and tailor them so that they meet the needs of social scientists primarily interested in conducting inference about causal effects and estimating the impact of counterfactual policies (that is, things that haven't been tried yet, or what would have happened if a different policy had been used). Examples of questions economists often study are things like the effects of changing prices, or introducing price discrimination, or changing the minimum wage, or evaluating advertising effectiveness. We want to estimate what would happen in the event of a change, or what would have happened if the change hadn't taken place.

Our analysis includes companies applying AI algorithms to verticals like healthcare, security, advertising, and finance as well as those developing general-purpose AI tech. Our analysis includes all equity funding rounds and convertible notes. In addition, auto tech company and unicorn Zoox raised $200M in Series A in Q2'16 and cybersecurity startup StackPath raised a $180M private equity round in Q3'16. Last quarter also saw 4 mega-rounds: $130M Series B round raised by life science startup Zymergen, $120M Series B round raised by computer vision startup SenseTime, $100M Series C round raised by facial recognition startup Face, and a $100M round raised by Israel-based Voyager Labs.

Machine learning has been a constant on tech trend lists for years. This year, it's time to embrace what humans can learn by interacting with machine learning. As Google's head of Machine Intelligence, Blaise Aguera y Arcas, noted in a recent Medium article: "Machine intelligence will expand our understanding of both external reality and our perceptual and cognitive processes." In the spring of 2016, Google's AlphaGo software, fueled by machine learning, beat the world's greatest human Go player, Lee Sedol. The victory was a major milestone for a specific type of AI, called deep neural networks, more closely modeled on the way humans think.

Kristen Stewart is quickly becoming one of my favourite people. She's gone from Twilight to indie darling, picking some amazing films that show us she has way more talent than Bella Swan gave her credit for. Stewart is gearing up to debut her directorial debut, a 17-minute short film called Come Swim, which was inspired by a painting Stewart had previously completed. The film integrates the painting using a technique called "style transfer," which uses convolutional neural networks to have an algorithm change a video in real time. Stewart, along with her film's producer and an engineer, drafted a three-page research paper, called "Bringing Impressionism to Life with Neural Style Transfer in Come Swim."

When Facebook (NASDAQ: FB) suggests you "tag" a friend in a photo, it generally suggests that friend's name. That small interaction provides a glimpse into the world of an emerging and powerful aspect of artificial intelligence (AI) in action -- image recognition. With its treasure trove of words and pictures from 1.79 billion monthly active users, it is using that data, combined with recent advancements in AI, to propel this and other technological advances. Facebook may well have the lead in facial recognition, even extending a step further into the realm of facial verification. It released a research paper in 2014 in which it reported 97.35% accuracy, which approaches human levels of recognition.