In the passing year many breakthroughts of Artificial Intelligence (AI) occurred. But right now the technology industry reaches a new step of evolution: Using Artificial Intelligence - or better Machine Learning (ML) - to advance and enhance Cyber Security. As Ken Levine - an american (video) game developer - mentioned: a human hacker will one day not be able to defeat an AI, because it won't make any mistakes. So why don't use AI to improve the Cyber Security, which is extremely needed in times of the Internet of Things (IoT) and Financial Technology (FinTech)? Google, as one of the big companies in the race of AI, made an experiment in which two Artificial Intelligences had to communicate in a secure way, while a third one had to decrypt what they encrypt.

Underground mining poses a number of threats including fires, methane outbreaks or seismic tremors and bumps. An automatic system for predicting and alerting against such dangerous events is of utmost importance – and also a great challenge for data scientists and their machine learning models. This was the inspiration for the organizers of AAIA'16 Data Mining Challenge: Predicting Dangerous Seismic Events in Active Coal Mines. Our solutions topped the final leaderboard by taking the first two places. In this post, we present the competition and describe our winning approach.

Businesses are often data-rich but information-poor. Machine learning is changing that. The use of artificial intelligence to let computers learn independently through algorithms without being explicitly programmed can help companies process vast quantities of complex data to improve analytics, predictive accuracy and decision-making. Machine learning is already being used in everything from fraud detection to self-driving cars, and in sectors from marketing to government. "We are currently working on machine learning to pick up early signals of ill health. My current role is to ensure that this is implemented in line with national recording guidance which does not cover machine learning. This is currently in pilot phase in the A&E in Salford."

There have been some alarming reports recently about the possibility of artificial intelligence leaving half of the world potentially unemployed. Recent research shows that within 30 years, robots will be in a position to perform almost all jobs that are held by humans right now. A recent detailed study from the Martin Oxford School has speculated that approximately 47 percent of U.S. jobs are at risk of automation. As there may be some truth in this report, it is not likely that half of the world's jobs will be taken by machines in 30 years. Additionally, some of the jobs facing the risk of automation might not be automated because of technical, societal and economical reasons.

The time of year when crystal balls get a viewing and many pundits put out their annual predictions for the coming year. Rather than thinking up my own, I figured I'd regurgitate what many others are expecting to happen. Chris Preimesberger (@editingwhiz), who does a monthly #eweekchat on twitter, covers many of the worries facing organizations. People focus so much on the'things' themselves rather than the risk of an internet connection. This list discusses how IoT will grow up in 2017, how having a service component will be key, the complete mess of standards and simply, 'just because you can connect something to the Internet doesn't mean that you should.' NW talks about how cyber attacks will get worse due to IoT and gives some ideas on how to protect your data in 2017.

Domain Mondo's weekly review of technology news: Feature • Tech Story of the Year: Tay, Microsoft's Artificial Intelligence (AI) Chatterbot: "As many of you know by now, on Wednesday [March 23, 2016] we launched a chatbot called Tay. We are deeply sorry for the unintended offensive and hurtful tweets from Tay, which do not represent who we are or what we stand for, nor how we designed Tay. Tay is now offline and we'll look to bring Tay back only when we are confident we can better anticipate malicious intent that conflicts with our principles and values ... The logical place for us to engage with a massive group of users was Twitter. Unfortunately, in the first 24 hours of coming online, a coordinated attack by a subset of people exploited a vulnerability in Tay. Although we had prepared for many types of abuses of the system, we had made a critical oversight for this specific attack. We take full responsibility for not seeing this possibility ahead of time. We will take this lesson forward as well as those from our experiences in China, Japan and the U.S. Right now, we are hard at work addressing the specific vulnerability that was exposed by the attack on Tay."--Learning from Tay's introduction blogs.microsoft.com

Machine learning is being applied in recommendation engines, marketing automation, financial fraud detection, language translation, and text-to-speech applications. Apple recently announced that the iPhone 7 would use machine learning in its camera to recognize faces, imagery, and even the lighting in a room, making Apple the latest tech company to give primacy to its use of machine learning. But machine learning is no longer exclusive to digital companies: Businesses in every industry are utilizing this technology to improve processes. The NFL uses machine learning to gather deep insights into player movements, positions, and passes to reorganize play style. In the medical sector, machine learning analyzes patients and predicts the likelihood of their returning. Even hiring and talent management in most companies is now handled by algorithms that dig out desired characteristics and, hopefully, remove biases.

The gold rush for artificial intelligence (AI) is officially in full swing. Big players like Google and Facebook and small teams alike are in an all-out sprint toward the goal of creating the next generation of AI assistants that will fundamentally change how we live and work. I am in awe at the pace of progress, because every week it feels like a new barrier is breached, a tool grows more robust, or a new startup is launched with the ability to transform an industry. However, the most surprising observation continues to be people's underestimation of AI. Specifically how the general population seems so unable, or unwilling, to imagine that a machine could ever match a human's ability in any job -- particularly their own.

Aircraft fuel is without a doubt the biggest cost for any airline and often receives widespread attention, especially when airlines hedge their bets the wrong way. Cathay Pacific reported a HK$4.49 billion fuel-hedging loss in the first half of 2016, which has hurt the airline's profitability. The second biggest expense for an airline is human capital, and researchers from Hong Kong Polytechnic University and University of Nottingham Ningbo China Business School may have found a solution to ease some of Cathays financial woes through an unlikely source – Machine Learning and Data Science. The researchers say that a "poorly designed airline crew schedule can result in unreliable flight schedules, significantly jeopardizing airline operations and profitability if insufficient crew members are available or other glitches occur. For that reason, managing airline crew scheduling and costs are one of the most crucial topics for airlines because it yields enormous economic benefits and ranks as the second highest expenditure after fuel costs."

From car accidents to bridge collapses, 1.25 million road fatalities occur across the world annually. Another 20 to 50 million people suffer road injuries per year, some resulting in lifetime disabilities. Road incidents are also the source of economic damage to the victims, their families and nations themselves as they generate medical treatment expenses and repair costs as well as reduce productivity. There's fear that without noticeable action, roads will become the seventh leading cause of death across the world by 2030. However, there's hope that with the help of emerging technologies such as artificial intelligence and Internet of Things, we can make our roads and bridges much more safer, save hundreds of thousands of lives, and spare billions of dollars in maintenance and repairs costs.