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Can Machine Learning Turn Big Data into No Big Deal?

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With technology moving so fast, new ways to automate, and connected machines, how can managers and engineers simplify the complexity of that ecosystem? Is machine learning (ML) or artificial intelligence (AI) the key? This article will define some buzzwords, what they mean, and if they might help simplify these complex technologies so that you can move back into production. New technologies, such as Big Data and the Industrial Internet of Things, are gaining more traction. While security is a concern, some companies push ahead because the benefits are too great.


How AI can help you stay ahead of cybersecurity threats

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Since the 2013 Target breach, it's been clear that companies need to respond better to security alerts even as volumes have gone up. With this year's fast-spreading ransomware attacks and ever-tightening compliance requirements, response must be much faster. Adding staff is tough with the cybersecurity hiring crunch, so companies are turning to machine learning and artificial intelligence (AI) to automate tasks and better detect bad behavior. In a cybersecurity context, AI is software that perceives its environment well enough to identify events and take action against a predefined purpose. AI is particularly good at recognizing patterns and anomalies within them, which makes it an excellent tool to detect threats.


Dell TechnologiesVoice: Machine Learning's Role In Big Data

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Since it launched in 2009, the Kepler Space Telescope has done a good job of collecting data -- too good for human analysis alone. The telescope has produced 14 billion data points about 200,000 stars. It has also amassed 35,000 signals indicating possible planets. People alone would not have been able to keep up. Since it launched in 2009, the Kepler Space Telescope has done a good job of collecting data -- too good for human analysis alone.


How To Become a Neural Networks Master in 3 Simple Steps

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Artificial Intelligence, Machine Learning and Deep Learning are all the rage in the press these days, and if you want to be a good Data Scientist you're going to need more than just a passing understanding of what they are and what you can do with them. There are loads of different methodologies, but for me I would always suggest Artificial Neural Networks as the first AI to learn - but then I've always had a soft spot for ANNs since I did my PhD on them. They've been around since the 1970s, and until recently have only really been used as research tools in medicine and engineering. Google, Facebook and a few others, though, have realised that there are commercial uses for ANNs, and so everyone is interested in them again. When it comes to algorithms used in AI, Machine Learning and Deep Learning, there are 3 types of learning process (aka'training').


A Beginner's Guide to AI/ML – Machine Learning for Humans – Medium

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This guide is intended to be accessible to anyone. Basic concepts in probability, statistics, programming, linear algebra, and calculus will be discussed, but it isn't necessary to have prior knowledge of them to gain value from this series. Artificial intelligence will shape our future more powerfully than any other innovation this century. Anyone who does not understand it will soon find themselves feeling left behind, waking up in a world full of technology that feels more and more like magic. The rate of acceleration is already astounding.


Machine learning capabilities aid healthcare cybersecurity

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As the new year draws near, healthcare organizations are thinking about where to focus their resources. Matt Mellen, security architect and healthcare solution lead at Palo Alto Networks, predicts that, in 2018, machine learning capabilities will not only enhance a healthcare organization's cybersecurity program, but improve patient outcomes as well. Healthcare IoT has the potential to greatly improve patient care – but it's not without its challenges. Download this essential guide in one PDF and learn how to overcome the obstacles: security, data overload, regulations, and more. You forgot to provide an Email Address.


Report on the 1984 Distributed

AI Magazine

The fifth Distributed Artificial Intclligencc Workshop tias held at the Schlumberger-Doll Research Laboratory from October 14 to 17, 1984 It was attended by 20 participants from academic and industrial institutions. As in the past,' this workshop was designed as an informal meeting It included brief research rcport,s from individual groups along with genera1 discussion of questions of common interest. Distributed artificial intelligence (DAI) is concerned with cooperative solution of problems by a decentralized and loosely coupled collection of knowledge sources (KSs), each embodied in a distinct processor node. The KSs cooperate in the sense that no one of them has sufficient information to solve the entire problem; mutual sharing of information is necessary to allow the group as a whole to product an answer. By decentralized we mean that both control and data are logically and often geographically distributed; there is neither global control nor global data storage.


RESEARCH IN PROGRESS

AI Magazine

The Computing Research Laboratory (CRL) at New Mexico State University is a center for research in artificial intelligence and cognitive science. Specific areas of research include the human-computer interface, natural language understanding, connectionism, knowledge representation and reasoning, computer vision, robotics, and graph theory. This article describes the ongoing projects at CRL. Complex Data-Structure Manipulation in Connectionist and Neural Nethe Computing Research Laboratory (CRL) was founded in July 1983 as an autonomous unit in the College of Arts and Sciences at New Mexico State University (NMSU). The laboratory began as a part of the Rio Grande Corridor, a program funded by the state legislature, which links government laboratories, universities, and public-private research facilities across the state with the aim of fostering high technology development. The laboratory currently employs a full-time director; 14 faculty members with joint appointments in the departments of computer science, electrical engineering, mathematics, and psychology; eight full-time researchers; four technicians; and over 30 research assistants.


Networks and Learning

AI Magazine

On 15-16 November 1989, I attended the Massachusetts Institute of Technology (MIT) Industrial Liaison Program entitled "Networks and Learning." The topic was neural networks, their power, potential, and promise. A dozen distinguished professors and researchers presented informative and entertaining talks to an audience of technically minded business executives and industrial researchers who subscribe to MIT's popular series of symposia offered through their Industrial Liaison Program. The Massachusetts Institute of Technology (MIT) Industrial Liaison Program, "Networks and Learning," was held 15-16 November 1989 at MIT. A dozen distinguished professors and researchers presented informative and entertaining talks to an audience of technically minded business executives and industrial researchers who subscribe to MIT's popular series of symposia offered through its Industrial Liaison Program.


Machine Learning, Machine Vision, and the Brain

AI Magazine

The problem of learning is arguably at the very core of the problem of intelligence, both biological and artificial. In this article, we review our work over the last 10 years in the area of supervised learning, focusing on three interlinked directions of research--(1) theory, (2) engineering applications (making intelligent software), and (3) neuroscience (understanding the brain's mechanisms of learnings)--that contribute to and complement each other. Because seeing is intelligence, learning is also becoming a key to the study of artificial and biological vision. In the last few years, both computer vision--which attempts to build machines that see--and visual neuroscience--which aims to understand how our visual system works--are undergoing a fundamental change in their approaches. Visual neuroscience is beginning to focus on the mechanisms that allow the cortex to adapt its circuitry and learn a new task.