Microsoft this week released an updated version of its Microsoft Cognitive Toolkit as an open source Beta. The deep learning system is used to speed advances in areas such as speech and image recognition and search relevance on CPUs and Nvidia GPUs. It also works with Microsoft's Azure GPU offering. The Microsoft computer scientists who developed the toolkit initially were looking for a tool to speed up and improve their own research. Initially called "Microsoft/CNTK," it morphed into an offering that Microsoft customers and flagship product groups depend on for a wide variety of deep learning tasks, the company said.

Predictive analytics / machine learning / artificial intelligence is a hot topic – what's it about? Using algorithms to help make better decisions has been the "next big thing in analytics" for over 25 years. It has been used in key areas such as fraud the entire time. But it's now become a full-throated mainstream business meme that features in every enterprise software keynote -- although the industry is battling with what to call it. It appears that terms like data mining, predictive analytics, and advanced analytics are considered too geeky or old for industry marketers and headline writers.

This year, the Association for Computing Machinery (ACM) celebrates 50 years of the ACM Turing Award, the most prestigious technical award in the computing industry. The Turing Award, generally regarded as the'Nobel Prize of computing', is an annual prize awarded to "an individual selected for contributions of a technical nature made to the computing community". In celebration of the 50 year milestone, renowned computer scientist Melanie Mitchell spoke to CBR's Ellie Burns about artificial intelligence (AI) – the biggest breakthroughs, hurdles and myths surrounding the technology. MM: There are many important examples of AI in the mainstream; some very visible, others blended in so well with other methods that the AI part is nearly invisible. Web search is an "invisible" example that has had perhaps the broadest impact.

Rai said that his AI system shows that the candidate in each election who had leading engagement data ended up winning the election. "If Trump loses, it will defy the data trend for the first time in the last 12 years since Internet engagement began in full earnest," Rai wrote in a report sent to CNBC. Currently most national polls put Clinton and the Democrats ahead by a strong margin. Rai said his data shows that Clinton should not get complacent. But the entrepreneur admitted that there were limitations to the data in that sentiment around social media posts is difficult for the system to analyze.

A team at Google has built a system to show that artificial intelligence can build its own form of encryption. While not very complex currently, this research could set the table for encryption that gets stronger as hackers attempt to crack it. To see if the artificial intelligence could learn to encrypt on its own, the AI researchers at Google Brain, a unit of the search company focused on deep learning, built a game with three entities powered by deep neural networks: Alice, Bob, and Eve. Alice was designed to send an encrypted message of 16 zeroes and ones to Bob, which was designed to decrypt the message. The two bots started with a shared key, a foundation for the message's encryption.

A recent Compliance Week story on how artificial intelligence could revolutionize compliance depicted how technology firms "are offering software platforms that promise to automate otherwise routine tasks and improve upon fraud detection audits, anti-money laundering protocols, and know-your-customer screening." With the advent of cyber-security attacks, developers of advanced artificial intelligence security monitoring solutions have also emerged. However, understanding when and how often monitoring solutions should be executed presents trade-offs to be considered. Legacy approaches to risk monitoring look for recognized threats by known signatures and pre-built event detection logic. Often these standby methods rest on technology confines and as a result are not aligned to business risk.

We've paid a lot of attention lately to Convolutional Neural Nets (CNNs) as the cornerstone of 2nd gen NNs and spent some time on Spiking Neural Nets (SNNs) as the most likely path forward to 3rd gen, but we'd really be remiss if we didn't stop to recognize Recurrent Neural Nets (RNNs). Because RNNs are solid performers in the 2nd gen NN world and perform many tasks much better than CNNs. These include speech-to-text, language translation, and even automated captioning for images. By count, there are probably more applications for RNNs than for CNNs. On one scale RNNs have much more in common with the larger family of NNs than do CNNs which have very unique architecture. RNNs allow inputs of strings of data to be assessed together and those strings can be of widely varying lengths.

While the number of headlines about machine learning might lead one to think that we just discovered something profoundly new, the reality is that the technology is nearly as old as computing. It's no coincidence that Alan Turing, one of the most influential computer scientists of all time, started his 1950 treatise on computing with the question "Can machines think?" From our science fiction to our research labs, we have long questioned whether the creation of artificial versions of ourselves will somehow help us uncover the origin of our own consciousness, and more broadly, our role on earth. Unfortunately, the learning curve on AI is really damn steep. By tracing a bit of history, we should hopefully be able to get to the bottom of wtf machine learning really is.

It's easy to shrug off trends as temporary ways of approaching business. The best approach is to stick to what you know works, right? Wrong, if you ask Reggie Bradford, SVP of product development at Oracle. Bradford spoke Monday at the multinational tech corporation's OpenWorld conference in San Francisco about what he called "megatrends." Megatrends, per Bradford's PowerPoint, "are global, sustained, and macroeconomic forces of development that impact business, economy, society, culture, and personal lives, thereby defining our world and its increasing pace of change."

Want to watch this again later? Need to report the video? This feature is not available right now. Keynote - Bring ML to every corner of your Organization, Danny Lange, Head of Machine Learning, Uber at Applied AI Conference 2016 by BootstrapLabs Danny Lange recaps the history of Artificial Intelligence and Machine Learning, as well as explains the roles it currently plays in companies everywhere. Dr. Lange demonstrates the application of Machine Learning models in Uber and how it affects service.