Information Technology


Why Unsupervised Machine Learning is the Future of Cybersecurity

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As we move towards a future where we lean on cybersecurity much more in our daily lives, it's important to be aware of the differences in the types of AI being used for network security. Over the last decade, Machine Learning has made huge progress in technology with Supervised and Reinforcement learning, in everything from photo recognition to self-driving cars. However, Supervised Learning is limited in its network security abilities like finding threats because it only looks for specifics that it has seen or labeled before, whereas Unsupervised Learning is constantly searching the network to find anomalies. Machine Learning comes in a few forms: Supervised, Reinforcement, Unsupervised and Semi-Supervised (also known as Active Learning). Supervised Learning relies on a process of labeling in order to "understand" information.


Apple's Acquisition of Xnor.ai Aims to Deliver TinyML to Edge Devices

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The News: Last week, Apple's acquisition of Xnor.ai was reported, no doubt aiming to deliver TinyML to edge devices. Xnor.ai, a Seattle startup specializing in low-power, edge-based artificial intelligence (AI) tools. Spun off from the Allen Institute for Artificial Intelligence, the three-year-old startup's technology embeds AI on the edge, enabling facial recognition, natural language processing, augmented reality, and other ML-driven capabilities to be executed on low-power devices rather than relying on the cloud. Analyst Take: Developers of AI applications for edge deployment are doing their work in a growing range of frameworks and deploying their models to myriad hardware, software, and cloud environments. This complicates the task of making sure that each new AI model is optimized for fast inferencing on its target platform, a burden that has traditionally required manual tuning.


Symphony RetailAI Names Chris Koziol CEO

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Seasoned software executive brings full suite of global retail and technology leadership experience to drive Symphony RetailAI's focus on worldwide customer success, AI-powered innovation and growth Symphony RetailAI, a leading global provider of AI-powered platforms and customer-centric solutions for customer-centric merchandising, marketing and supply chain solutions that deliver profitable growth for retailers and CPG manufacturers, announced the appointment of Chris Koziol as CEO and a member of the company's board of directors. Dr. Pallab Chatterjee, who previously served as CEO, has retired after a long career in technology, including several years at Symphony Technology Group. Koziol has an extensive background in retail and enterprise software and brings with him over 35 years of executive experience and success in the software technology sector, including 20 years in CEO, president and chief operating officer positions for mid-size and billion-dollar businesses. He most recently served as president and CEO of Aspect Software where he helped reposition the company as a $330M cloud-based software company. Prior to that, Koziol was COO of JDA Software and was instrumental in JDA's rapid growth during his tenure, presiding over its expansion into supply chain optimization and planning solutions through a combination of acquisitions and organic growth.


Symphony RetailAI Names Chris Koziol CEO

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Seasoned software executive brings full suite of global retail and technology leadership experience to drive Symphony RetailAI's focus on worldwide customer success, AI-powered innovation and growth Symphony RetailAI, a leading global provider of AI-powered platforms and customer-centric solutions for customer-centric merchandising, marketing and supply chain solutions that deliver profitable growth for retailers and CPG manufacturers, announced the appointment of Chris Koziol as CEO and a member of the company's board of directors. Dr. Pallab Chatterjee, who previously served as CEO, has retired after a long career in technology, including several years at Symphony Technology Group. Koziol has an extensive background in retail and enterprise software and brings with him over 35 years of executive experience and success in the software technology sector, including 20 years in CEO, president and chief operating officer positions for mid-size and billion-dollar businesses. He most recently served as president and CEO of Aspect Software where he helped reposition the company as a $330M cloud-based software company. Prior to that, Koziol was COO of JDA Software and was instrumental in JDA's rapid growth during his tenure, presiding over its expansion into supply chain optimization and planning solutions through a combination of acquisitions and organic growth.


Apple's self-driving car system could use voice, gesture guidance - Roadshow

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Apple has its eye on self-driving car tech. Interacting with a future self-driving car could be a lot like working with some future interpretation of Apple iOS with voice, gesture and touch-enabled commands at your disposal. It's the overarching view gathered after reading through an Apple patent application filed last August and published last week for a self-driving car voice and gesture guidance system. CEO Tim Cook said in 2017 that Apple was working on an autonomous car system, rather than a car itself, as had been previously rumored. At its core, the system described in the patent application gives passengers three ways to give the autonomous car directions and input, and much of the described system is incredibly similar to commands we're used to today.


AI, machine learning and deep learning: What's the difference? - IBM IT Infrastructure Blog

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It's not unusual today to see people talking about artificial intelligence (AI). When I was a kid in the 1980s, AI was depicted in Hollywood movies, but its real-world use was unimaginable given the state of technology at that time. While we don't have robots or androids that can think like a person or are likely to take over the world, AI is a reality now, and to understand what we mean when we talk about AI today we have to go through a -- quick, I promise -- introduction on some important terms. Simply put, AI is anything capable of mimicking human behavior. From the simplest application -- say, a talking doll or an automated telemarketing call -- to more robust algorithms like the deep neural networks in IBM Watson, they're all trying to mimic human behavior.


Getting Started with TensorFlow and Keras – Maker.io Digi-Key Electronics

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In this tutorial, we show you how to configure TensorFlow with Keras on a computer and build a simple linear regression model. If you have access to a modern NVIDIA graphics card (GPU), you can enable tensorflow-gpu to take advantage of the parallel processing afforded by CUDA. The field of Artificial Intelligence (AI) has been around for quite some time. As we move to build an understanding and use cases for Edge AI, we first need to understand some of the popular frameworks for building machine learning models on personal computers (and servers!). These models can then be deployed to edge devices, such as single-board computers (like the Raspberry Pi) and microcontrollers.


Fish Detection Using Deep Learning

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Recently, human being's curiosity has been expanded from the land to the sky and the sea. Besides sending people to explore the ocean and outer space, robots are designed for some tasks dangerous for living creatures. Take the ocean exploration for an example. There are many projects or competitions on the design of Autonomous Underwater Vehicle (AUV) which attracted many interests. Authors of this article have learned the necessity of platform upgrade from a previous AUV design project, and would like to share the experience of one task extension in the area of fish detection. Because most of the embedded systems have been improved by fast growing computing and sensing technologies, which makes them possible to incorporate more and more complicated algorithms. In an AUV, after acquiring surrounding information from sensors, how to perceive and analyse corresponding information for better judgement is one of the challenges. The processing procedure can mimic human being's learning routines. An advanced system with more computing power can facilitate deep learning feature, which exploit many neural network algorithms to simulate human brains. In this paper, a convolutional neural network (CNN) based fish detection method was proposed.


Solutions Engineer, Data Management - IoT BigData Jobs

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Location: US, Central Region OR Toronto, Canada Talend is a 600 employee, recent IPO, big data integration software company with deep open source roots. Well-funded, with over $100 million raised to date and continued rapid growth, Talend is the second largest independent open source company in the world. We are hiring Pre Sales Engineers to continue to build a proactive, customer-facing organization that ensures customers are getting value from Talend's products and solutions. We are seeking Engineers to join the sales team and support the increasing demand from our direct sales. Our portfolio of products has expanded from purely Data Integration to include Data Quality (DQ), Master Data Management (MDM), Enterprise Service Bus (ESB) and Big Data.


AI-startup CloudMinds Slashes Workforce by Over 200

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Cloud robotics and AI firm CloudMinds is slashing its global workforce, reports Reuters. The SoftBank-backed start-up is laying off around 175-225 employees from its 700-strong workforce in China. Following the layoffs, CloudMinds will remain with only a nominal presence in the US and Japan. The Silicon Valley office will be closed, and a small number of remaining staff will be moved to an office in Irvine, California. The company is reducing its global workforce as it burns through cash after repeated attempts to list on the stock market, said the report.