Cylance, a cybersecurity startup that leverages artificial intelligence and machine learning to combat online attacks, has raised $120 million in a series E round of funding led by Blackstone Tactical Opportunities, with participation from other unnamed investors. Founded in 2012 by Stuart McClure, an entrepreneur who sold an Internet security firm to McAfee for $86 million in 2004, Cylance is an endpoint protection platform designed to thwart malware, ransomware, and other forms of advanced threats using AI. Its suite of algorithm-based security protocols essentially inspect networks for weaknesses and shuts them down if any are detected. Cylance claims in excess of 4,000 customers, and said that it has revenues of $130 million for the 2018 fiscal year, representing a year-on-year growth of 90 percent. Prior to now, Cylance had raised around $177 million, including a $100 million tranche two years ago, and with another $120 million in the bank it said that it plans to double down on its global expansion efforts, with a particular focus on Europe, the Middle East, and Asia Pacific, and extend its product range.

Marketing automation has driven a lot of hype in the marketing space in recent years, and for good reason. Innovations like Marketo and Eloqua have given companies the ability to scale up their marketing efforts like never before and run multi-channel campaigns that are truly measurable. That said, this is a fast-paced industry – and now, there's a whole new crop of entrants to the martech space that are transforming the way that marketers think about marketing automation. This year, we're seeing a true shift in how marketers think about campaign automation. Instead of designing campaigns aimed to guide prospects through the sales funnel, now companies are looking to design holistic marketing campaigns that aren't just aimed at converting prospects, but also at walking leads through the entire customer lifecycle.

European banks face'bottleneck' to complete EBA stress test New accounting rules and supervisor demands squeeze teams prepping for 2018's exercise It's not going too far to suggest that, in 10 or 15 years' time, the job of a risk manager will be largely that of a machine overseer. Many risk managers may feel this is already the case. Our coverage this week highlights some of the advantages and the dangers of the change. At JP Morgan Asset Management and Citadel, the development of big data technology is producing tempting results – the companies' existing stocks of data on credit history and investment performance can now be exploited as never before. The implications are interesting; if the edge for asset managers of the future comes from sophisticated processing of existing information – what the intelligence community would call open-source intelligence – then the incentives for market abuse through insider trading may be reduced.

Artificial intelligence (AI) is a form of computer science that is built to have machines think and respond like humans, understanding natural language and reasoning with data in a way similar to the human brain. It refers to programming computer technology, known as machine learning, to complement the human mind when making decisions, making make our jobs easier and minimising repetitive tasks. For some businesses, AI still appears to be in its infancy. However, the technology definitely has the potential to transform many organisations as well as the technology industry as a whole. AI has been adopted to automate a number of roles in businesses.

Today at the Computer Vision and Pattern Recognition Conference in Salt Lake City, Utah, NVIDIA is kicking off the conference by demonstrating an early release of Apex, an open-source PyTorch extension that helps users maximize deep learning training performance on NVIDIA Volta GPUs. Inspired by state-of-the-art mixed precision training in translational networks, sentiment analysis, and image classification, NVIDIA PyTorch developers have created tools bringing these methods to all levels of PyTorch users. Mixed precision utilities in Apex are designed to improve training speed while maintaining the accuracy and stability of training in single precision. Specifically, Apex offers automatic execution of operations in either FP16 or FP32, automatic handling of master parameter conversion, and automatic loss scaling, all available with 4 or fewer line changes to the existing code. Installation requires CUDA 9, PyTorch 0.4 or later, and Python 3. The modules and utilities are still under active development and we look forward to your feedback to make these utilities even better.

Well, before answering that, we must take a look at what they are. Chatbots are automated programs that take on mundane and repetitive work such as answering customer queries. Their surge is the result of an ongoing evolution of AI and machine learning (ML) that never seizes to amaze us with new capabilities. Chatbots facilitate online transactions and open up new growth opportunities. They handle interactions with customers and simulate the experience that they have in a retail store.

A recent MIT Technology Review article titled "The Dark Secret at the Heart of AI" warned: "No one really knows how the most advanced algorithms do what they do. That could be a problem." Thanks to this uncertainty and lack of accountability, a report by the AI Now Institute recommended that public agencies responsible for criminal justice, health care, welfare and education shouldn't use such technology. Given these types of concerns, the unseeable space between where data goes in and answers come out is often referred to as a "black box" -- seemingly a reference to the hardy (and in fact orange, not black) data recorders mandated on aircraft and often examined after accidents. In the context of A.I., the term more broadly suggests an image of being in the "dark" about how the technology works: We put in and provide the data and models and architectures, and then computers provide us answers while continuing to learn on their own, in a way that's seemingly impossible -- and certainly too complicated -- for us to understand.

No list of artificial intelligence stocks is quite right without Alphabet (GOOGL, $1,169.44). Its Google division constantly pushes AI to new and exciting places. For example, a recent Bloomberg report has revealed that Google's Medical Brain unit is using AI to train machines to predict when patients will die. The Google tool uses self-learning neural networks to predict key outcomes including readmission and the length of hospital stay. This powerful data analysis can even be used to predict symptoms and disease, apparently with incredible accuracy.

For the moment, Artificial Intelligence is not regulated in a specific way. This problem needs to be resolved quickly. Any information can be easily stolen by hackers. Highly sophisticated models make us vulnerable to many threats. Moreover, many worry about the control around this technology.

Raw video: Cameras mounted inside the car catches the fatal moment. Authorites are investigating the cause of the crash. A police report released Thursday on the deadly self-driving Uber accident in March reportedly revealed that the female backup driver had been watching "The Voice" prior to the crash. The report from police in Tempe, Arizona, indicated that the crash could have been prevented had the driver, Rafaela Vasquez, not been watching the show, The Associated Press reported. Elaine Herzberg, 49, was killed in the March 18 crash - believed to be the first of its kind - after being struck by the autonomous vehicle while walking outside of the crosswalk, authorities said at the time.