Many people have this doubt, what's the difference between statistics and machine learning? Is there something like machine learning vs. statistics? From a traditional data analytics standpoint, the answer to the above question is simple. Machine learning is all about predictions, supervised learning, unsupervised learning, etc. Statistics is about sample, population, hypothesis, etc. Well, let's see if they are actually that different!

We have experimented with CaffeOnSpark on a 5 node MapR 5.1 cluster running Spark 1.5.2 and will share our experience, difficulties, and solutions on this blog post. Deep learning is getting a lot of attention recently, with AlphaGo beating a top world player at a game that was thought so complicated as to be out of reach of computers just five years ago. Deep learning is not just beating humans at Go, but also at pretty much every Atari computer game. But the fact is, deep learning is also useful for tasks with clear enterprise applications in the fields of image classification and speech recognition, AI chat bots and machine translation, just to name a few. Caffe is a C /CUDA deep learning framework originally developed by the Berkeley Vision and Learning Center (BVLC).

IBM plans to launch a cloud-based version of Watson's cognitive computing technology, designed solely to zero in on cybersecurity language, as a part of a year-long research project, the company announced Tuesday. The Watson for Cyber Security platform is touted as the first technology to offer cognition of security data. Watson will pull the majority of its cognitive data from the X-Force research library: a threat intelligence platform with 20 years of security research, details on 8 million spam and phishing attacks and more than 100,000 documented vulnerabilities. "Even if the industry was able to fill the estimated 1.5 million open cybersecurity jobs by 2020, we'd still have a skills crisis in security," Marc van Zadelhoff, general manager of IBM Security said in a statement. "The volume and velocity of data in security is one of our greatest challenges in dealing with cybercrime."

A Law Firm Just Hired Its First Artificial Intelligence Attorney . While only a portion of the typical attorney's work involves legal research and creation of memoranda of law and tables of authorities, AI Ross can apparently understand natural language queries, perform legal research, and spit out memoranda of law and answers without human intervention. A human lawyer can then evaluate the response, and advise clients accordingly. Will AI lawyers take over the practice, or will they just enhance the ability of the lawyers still working? What if clients decide that the quaility of the work product and the answers of the AI computer give them the option to cut out the middleman?

Artificial neural networks (ANNs) are a type of information processing system based on mimicking the principles of biological brains, and have been broadly applied in application domains such as pattern recognition, automatic control, signal processing, decision support systems and artificial intelligence. Spiking neural networks (SNNs) are a type of biologically inspired ANN that perform information processing based on discrete time spikes. They are more biologically realistic than classic ANNs, and can potentially achieve a much better performance-power ratio. Recently, researchers from Zhejiang University and Hangzhou Dianzi University in Hangzhou, China successfully developed the Darwin Neural Processing Unit (NPU), a neuromorphic hardware co-processor based on spiking neural networks, fabricated by standard CMOS technology. With the rapid development of the "Internet of Things" and intelligent hardware systems, intelligent devices are pervasive in today's society, providing many services and conveniences to people's lives. But they also raise challenges of running complex intelligent algorithms on small devices.

Leading industry experts are launching a Cognitive Computing Consortium to focus on furthering innovation in cognitive computing. The consortium is an interactive forum for researchers, developers, and practitioners of cognitive computing and its allied technologies. The consortium was co-founded by Sue Feldman, CEO, Synthexis; and Hadley Reynolds, principal analyst at NextEra Research, to fill a gap in the industry. Its mission is to enable professionals to exchange ideas and insights to conduct research and to educate buyers, users and the public on cognitive computing technologies, their uses, and potential impacts. The group was inspired to form after vendors told various experts that they needed an unbiased source to which they can refer potential clients for validation, advice, and background information.

Google is about to set its developer agenda for the next year. So, what kind of news should you expect from Google I/O 2016? Google, as it normally does, has organized I/O around three distinct categories: development, monetization and the future. The conference will have 190 sessions for developers to learn how to make fast and efficient Web apps, optimize Android development and learn about the tools and features that will progressively make the Internet a more intelligent place. If you've never experienced a Google I/O before, the sessions can be very technical.

As you read this essay, you understand each word based on your understanding of previous words. You don't throw everything away and start thinking from scratch again. Traditional neural networks can't do this, and it seems like a major shortcoming. For example, imagine you want to classify what kind of event is happening at every point in a movie. It's unclear how a traditional neural network could use its reasoning about previous events in the film to inform later ones. Recurrent neural networks address this issue.

Periscope may soon be able to identify what's happening in live broadcasts with the help of Twitter's Cortex. Cortex describes itself as "a team of engineers, data scientists, and machine learning researchers dedicated to building a unifying representation of all of the users and content on Twitter, to help build a product in which people can easily find new experiences to share and participate in." Our biggest ever edition of TNW Conference is fast approaching! The team first showed its livestream scanning system off to MIT Technology Review, where it scanned and categorized two dozen streams at once. To achieve this, Twitter has built a proprietary computer made entirely of GPUs, which then feeds its findings to a deep learning algorithm.

Google today is open-sourcing SyntaxNet, a piece of natural-language understanding (NLU) software that you can use to automatically parse sentences, as part of its TensorFlow open source machine learning library. The release includes code for training new models, as well as a pre-trained model for parsing English-language text. The parser, which goes by the name Parsey McParseface and can automatically figure out whether a word is a noun or a verb or an adjective just like your third-grade English teacher, is the most accurate one in the world, Google says, beating out its own technology. So this is a big deal in the world of natural-language research. "The way we evaluate technologies internally is actually pretty different. We care much less about benchmarks and much more about how they impact performance of downstream systems. Our goal is to improve user experiences," Google Research product manager Dave Orr told VentureBeat in an interview at Google headquarters in Mountain View, California, earlier this week.