A very popular recipe in the data science world is IPython, scipy, Jupyter and matplotlib. This is because it is very convenient to collaborate and share examples by publishing a notebook that can be quickly put on the internet, or shared. Lately this recipe has been used to create and publish deep learning examples with Tensorflow. Here are the projects, if you just want the code well here it is. Although this recipe is very popular, little has been done up to this point, to emulate a similar pattern for deeplearning4j, which is the most popular deeplearning platform for Java.

Are you concerned about securing your users and data in cloud based collaboration applications like Office 365? You're not alone. Over 35% of Microsoft Exchange installed base is now on Office 365. Many of these enterprises are actively seeking to extend the same level of security and consistent policies they have in place for existing on-premise and cloud applications, to Office 365. Consider these statistics from IDC: • Over 50% enterprises have users that access their Office 365 applications using unmanaged mobile devices • Over 90% of threats to enterprises emanate from email • 65% of threats go undetected for weeks/months IT administrators lose traditional visibility and control when enterprises move email, content creation, file sharing, and collaboration to the cloud; making it harder to detect inappropriate behavior. This makes it critical for organizations to extend the basic security capabilities of Office 365 and ensure consistency in the level of security across all their cloud services.

The idea of artificial intelligence (AI) -- autonomous computers that can learn independently -- makes some people extremely uneasy, regardless of what the computers in question might be doing. Those individuals probably wouldn't find it reassuring to hear that a group of researchers is deliberately training computers to get better at scaring people witless. The project, appropriately enough, is named "Nightmare Machine." Digital innovators in the U.S. and Australia partnered to create an algorithm that would enable a computer to understand what makes certain images frightening, and then use that data to transform any photo, no matter how harmless-looking, into the stuff of nightmares. Images created by Nightmare Machine are unsettling, to say the least.

A team of Google Brain researchers used neural networks to develop artificial intelligence-generated (AI) encryption of information processed by the networks. In a research paper entitled "Learning to Protect Communications with Adversarial Neural Cryptography," Martín Abadi and David G. Andersen of Google's deep learning project demonstrated that neural networks could develop their own encryption methods without having been "taught" cryptographic algorithms. Two of the neural networks, code-named Alice and Bob, developed an ability to prevent a third network, named Eve, from "eavesdropping" on their communication. The neural networks were able to complete increasingly complex tasks, such as generating realistic images and solving multiagent problems.

Have you seen sci-fi movies like A.I. Artificial Intelligence, a 2001 US science fiction drama directed by Steven Spielberg that portrays a childlike android programmed to love, or Bicentennial Man, which starred the late Robin Williams and was based on a 1976 novel by Isaac Asimov? Have you seen the movie Surrogates which starred Bruce Willis and portrayed a futuristic world where people live within the safety of their homes while their robotic surrogates carry on their daily chores? If yes, you are also likely to believe that machines endowed with artificial intelligence (AI) can emulate, or even surpass, human intelligence. However, nothing can be further from the truth, say researchers. "The frightening, futurist portrayals of artificial intelligence that dominate films and novels, and shape the popular imagination, are fictional… Unlike in the movies, there is no race of superhuman robots on the horizon or probably even possible," insists a Stanford University-hosted report.

At this time, all participants are in a listen-only mode. A brief question-and-answer session will follow the formal presentation. It is now my pleasure to introduce your host Ken Stillwell, CFO and Senior VP of Pegasystems. Before we begin, I'd like to read our Safe Harbor Statement. Certain statements contained in this presentation, including but not limited to, statements related to future earnings, bookings, revenue and mix of license revenue may be construed as forward-looking statements as defined by the Private Securities Litigation Reform Act of 1995. The words expects, anticipates, intends, plans, believes, could, estimates, may, targets, strategies, intends to, projects, forecasts and guidance, and other similar expressions, identify forward-looking statements, which speak only as of the date the statement was made and are based on current expectations and assumptions. Because such statements deal with future events, they are subject to various risks and uncertainties. Actual results for the fiscal year 2016 and beyond could differ materially from the Company's current expectations. Factors that could cause the Company's results to differ materially from those expressed in the forward-looking statements are contained in the Company's press release announcing its Q3 2016 earnings, and in the Company's filings with the Securities and Exchange Commission, including its quarterly report on Form 10-Q for the quarter ended September 30, 2016, its Annual Report on Form 10-K for the year ended December 31, 2015 and other recent filings with the SEC. Although subsequent events may cause the Company's view to change, the company undertakes no obligation to revise or update forward-looking statements, whether as a result of new information, future events or otherwise, since these statements may no longer be accurate or timely. And with that, I'll turn the call over to Alan Trefler, Founder and CEO of Pegasystems. I'm pleased it was a strong Q3, overall. Q3 is generally provide limited visibility given vacations and schedules especially in Europe. And I had spoken about Brexit on the last call and I'm pleased to say that concerns have not materialized with the exception of currency of course. And I'm pleased to see the continued progress we're making towards having less lumpy quarters despite the inherent lumpiness of this business, even in the face of those currency headwinds. Those currency headwinds caught a couple of points off of our results.

WASHINGTON – It's a scary world out there, risk experts agree, but they say Donald Trump and Hillary Clinton often focus on the wrong dangers -- fixing on hazards that are unlikely, or unlikely to cause massive pain. The Associated Press asked 21 risk experts to analyze the presidential campaign and list what they consider the five biggest threats to the world. Climate change topped the list with 17 mentions, often as the top threat. It was followed by use of nuclear weapons, pandemics, cyberattacks and problems with high technology. Neither Trump's signature issues of immigration and terrorism nor Clinton's major concerns, financial insecurity and gun violence, made the list. "I have not heard or read about any significant deliberations of the major risks that face our country today and tomorrow.

When you want a person to do something, you train them. When you want a computer to do something, you program it. However, there are ways to make computers learn, at least in some situations. One technique that makes this possible is the perceptron learning algorithm. A perceptron is a computer simulation of a nerve, and there are various ways to change the perceptron's behavior based on either example data or a method to determine how good (or bad) some outcome is.

From stopping cyberattacks to operating autonomous vehicles to visually searching through a wine database, 140 startups using AI as a core part of their products raised $958M in funding in Q3'16, making it the second-highest quarter for funding after Q2'16. Since 2012, deals and dollars to AI startups have been on a steady rise, and this year is extending that trend. Our AI category includes companies applying AI solutions to verticals like healthcare, security, advertising, and finance as well as those developing general-purpose AI tech. Our list excludes robotics (hardware-focused) and AR/VR startups, which we've analyzed separately here and here. Our analysis includes all equity funding rounds and convertible notes.