SAN DIEGO, CA--(Marketwired - Aug 15, 2016) - DB Networks, a leader in database cybersecurity, today announced that that it will be exhibiting at the NSA Information Assurance Symposium (IAS) from Aug. 16-18 in Washington, D.C., in booth number 724; and at the CyberTexas Conference from Aug. 23-24 in San Antonio, Texas, in booth number 110. At these upcoming events, DB Networks will hold booth demonstrations of the DBN-6300, an artificial intelligence (AI)-based database security appliance that non-intrusively discovers databases, immediately alerts when databases are under attack and pinpoints credentials that have been compromised. IT security teams are severely understaffed, and presently there's a shortage of more than 200,000 security professionals in the U.S. In addition, security operation centers (SOCs) are deluged with alerts each day and security personnel are able to respond to only a small fraction of the alerts. AI-based security solutions address these issues by being extremely accurate at identifying actual attacks, thus eliminating false positive alerts, and also by alleviating overworked staff from creating and maintaining white lists/black lists. DB Networks is dedicated to protecting mission critical databases through its patented AI technologies that utilize machine learning and behavioral analysis.

An Elon Musk-backed artificial intelligence research group just got a brand new toy from chip maker Nvidia. Nvidia nvda said on Monday that it had donated one of its new supercomputers to the OpenAI non-profit artificial intelligence research project. OpenAI debuted in December with financial backing from Tesla and SpaceX CEO Musk along with money from other high-profile technology luminaries like LinkedIn lnkd co-founder Reid Hoffman and PayPal pypl co-founder Peter Thiel. OpenAI's goal is partly to create a non-profit outside the corporate sector that could research artificial intelligence technologies without a financial incentive. The concern is that many companies like Google and Facebook that are researching artificial intelligence technologies would horde talent and only work on projects beneficial to their financial interests.

Healthcare is a field that is always at the edge of technology, where there is a push to make strides to help better diagnosis and patient care. There are small changes where new technologies can be adapted relatively cheaply and easily, and larger advances that can take a couple of years to find a foothold in the marketplace. When you think of technology and healthcare, you might think about your general doctor picking up more efficient and hygienic thermometers over the years. From oral thermometers to ear thermometers to the forehead wand, hygiene and ease of use has always been the new factor for each of these tools. But one like it is coming. Since the mid-1980s, robots have been steadily incorporated into surgeries to help add precision, comfort, and alleviate pressure off the surgeon.

We have been tracking Amazon for years, but as a reference point consider that in November 2006 BusinessWeek ran a cover story with the title "Jeff Bezos' Risky Bet" where the concept of cloud computing as a business model disruptor was catapulted into the mainstream. Since then the risky bet has paid off for Amazon and has helped transform the business ecosystem in ways few could have predicted. Amazon considers cloud computing to be the on-demand delivery of IT resources and applications via the Internet with pay-as-you-go pricing. Others may use different definitions but Amazon is the 500lb gorilla so for this post at least we will say we agree! The power of the AWS cloud is now driving continuous advancements in Analytics, Artificial Intelligence and IoT.

Linear Discriminant Analysis (LDA) is most commonly used as dimensionality reduction technique in the pre-processing step for pattern-classification and machine learning applications. The goal is to project a dataset onto a lower-dimensional space with good class-separability in order avoid overfitting ("curse of dimensionality") and also reduce computational costs. Ronald A. Fisher formulated the Linear Discriminant in 1936 (The Use of Multiple Measurements in Taxonomic Problems), and it also has some practical uses as classifier. The original Linear discriminant was described for a 2-class problem, and it was then later generalized as "multi-class Linear Discriminant Analysis" or "Multiple Discriminant Analysis" by C. R. Rao in 1948 (The utilization of multiple measurements in problems of biological classification) The general LDA approach is very similar to a Principal Component Analysis (for more information about the PCA, see the previous article Implementing a Principal Component Analysis (PCA) in Python step by step), but in addition to finding the component axes that maximize the variance of our data (PCA), we are additionally interested in the axes that maximize the separation between multiple classes (LDA). So, in a nutshell, often the goal of an LDA is to project a feature space (a dataset n-dimensional samples) onto a smaller subspace (where) while maintaining the class-discriminatory information.

This article offers a brief glimpse of the history and basic concepts of machine learning. We will take a look at the first algorithmically described neural network and the gradient descent algorithm in context of adaptive linear neurons, which will not only introduce the principles of machine learning but also serve as the basis for modern multilayer neural networks in future articles. Machine learning is one of the hottest and most exciting fields in the modern age of technology. Thanks to machine learning, we enjoy robust email spam filters, convenient text and voice recognition, reliable web search engines, challenging chess players, and, hopefully soon, safe and efficient self-driving cars. Without any doubt, machine learning has become a big and popular field, and sometimes it may be challenging to see the (random) forest for the (decision) trees.

Twenty six hundred years ago, a band of Judahite soldiers kept watch on their kingdom's southern border in the final days before Jerusalem was sacked by Nebuchadnezzar. They left behind numerous inscriptions--and now, a groundbreaking digital analysis has revealed how many writers penned them. The research and innovative technology behind it stand to teach us about the origins of the Bible itself. "It's well understood that the Bible was not composed in real time but was probably written and edited later," Arie Shaus, a mathematician at Tel Aviv University told Gizmodo. "The question is, when exactly?" Shaus is one of several mathematicians and archaeologists trying to broach that question in a radical manner: by using machine learning tools to determine how many people were literate in ancient times.

IP Cores Designed for HSA Historically GPT has developed IP specifically for the China market. The company recently announced a range of new IP licensing offerings along with an enhanced geographical licensing program. With the company-wide adoption of HSA standards, GPT now licenses IP worldwide. All GPT processors include HSA support and the company is now offering world-class HSA-enabled processors to its customers. The HSA enabled IP core which is sampling now in silicon is a first implementation of GPT's 3-in-1 Unity architecture designed for multidimensional signal processing including image and video processing.

Deep Reinforcement Learning has recently gained a lot of traction in the machine learning community due to the significant amount of progress that has been made in the past few years. Traditionally, reinforcement learning algorithms were constrained to tiny, discretized grid worlds, which seriously inhibited them from gaining credibility as being viable machine learning tools. Here's a classic example from Richard Sutton's book, which I will be referencing a lot. After Deep Q-Networks [4] became a hit, people realized that deep learning methods could be used to solve high-dimensional problems. One of the subsequent challenges that the reinforcement learning community faced was figuring out how to deal with continuous action spaces.

Charles Wheelus will present his work in machine learning, complete with real-world case studies from business and academia. This presentation is intended for anyone interested in machine learning; whether you are a novice or an experienced practitioner, there should be something for everybody. Speaker: Charles Wheelus is the Principal Data Scientist at Cequint, in Fort Lauderdale, where he specializes in performance analytics for wireless networks. He is also the inventor of the technology behind Candidate.Guru, one of South Florida's hottest machine learning startups. In addition to his professional duties, Mr. Wheelus is a mentor at FAU's Tech Runway and is a Ph.D. candidate in the Computer Science department at FAU.