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5 Top AI Trends

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Here are 5 significant artificial intelligence trends to look forward to that will affect myriad industries on an international scale led by giant tech companies that are now investing huge sums in artificial intelligence research. Last year, implementations of AI rose significantly in so many platforms, tools and applications around the world, impacting healthcare, education and other industries as more and more people are opting for e-solutions based on AI and machine learning. Then there's the automotive industry with self-driving cars, the agricultural sector opting for intelligent robots to tackle the sowing as well as insecticide spraying on crops; the list goes on. As tech industry giants, including Google, Facebook and Amazon, invest billions now in AI and machine learning research, let's explore how 2019 is unfolding on this front. Major chip manufacturers including Intel, Nvidia, AMD and ARM aim to produce AI-powered chips to speed up the operations of applications that run on AI.


Advanced Topics in Deep Convolutional Neural Networks

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Throughout this article, I will discuss some of the more complex aspects of convolutional neural networks and how they related to specific tasks such as object detection and facial recognition. This article is a natural extension to my article titled: Simple Introductions to Neural Networks. I recommend looking at this before tackling the rest of this article if you are not well-versed in the idea and function of convolutional neural networks. Due to the excessive length of the original article, I have decided to leave out several topics related to object detection and facial recognition systems, as well as some of the more esoteric network architectures and practices currently being trialed in the research literature. I will likely discuss these in a future article related more specifically to the application of deep learning for computer vision.


The Amazing Ways Hitachi Uses Artificial Intelligence And Machine Learning

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Since its founding in 1910, Japanese company Hitachi has been at the forefront of innovation with a philosophy to contribute to society through "the development of superior, original technology and products." Today, Hitachi is a multinational conglomerate that offers operational products and services as well as IT-related digital technologies such as artificial intelligence and big data analysis. Its artificial intelligence and machine learning technologies are impacting not only their own services and products but how other industries such as healthcare, shipping, finance operate. Announced in 2015, H is Hitachi's solution for a generalized artificial intelligence technology that can be applied to many applications rather than just built for a specific application. H supports a wide range of applications and can generate hypotheses from the data itself and select the best options given to it by humans.


How Machine Learning Helps Improve Cybersecurity

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Cyberattacks have increased on an unprecedented scale. The main reason obviously is our increasing dependence on computing devices (computers, smartphones etc) and the internet for our day-to-day needs. The technology that we depend on today has interconnectedness as one of its salient features. This, plus our habit of using unsecured networks and devices (like, for example, public Wi-Fi) for convenience's sake, too has proven to be the cause for an unprecedented increase in cyberattacks. Of the various technologies that we use today to prevent cyberattacks and to ensure cybersecurity, machine learning deserves special mention.


Can crime be predicted with AI/ML?

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For many years, AI/ML has been used to establish the identity of perpetrators, the perpetrators' whereabouts at the time of a criminal act and their actions and whereabouts prior to and following a criminal act. By hand, these are arduous tasks but AI categorization sifting through massive amounts of visual data along with ML behavior scripts AI/ML algorithms can eliminate human errors especially in witness identification and therefore increasing arrest accuracy. "Predictive policing" is the practice of identifying the date, times and locations where specific crimes are most likely to occur, then scheduling officers to patrol those areas in hopes of preventing crimes from taking place, therefore keeping neighborhoods safer. After much research and input from major police departments in cooperation with software suppliers, predictive analytic models have been continuously refined. A profile matrix can be constructed from a database containing known associates, possible DNA found at the scene, gunshot detection, etc.


Using mid-infrared spectroscopy and supervised machine-learning to identify vertebrate blood meals in the malaria vector, Anopheles arabiensis

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The Global Technical Strategy for Malaria Elimination 2016–2030 [1] recommends that countries should integrate effective surveillance as a core intervention in their malaria policies. As such, the World Health Organization (WHO) recently provided guidelines to support measurements of the most important parasitological and entomological indicators [2]. Effective entomological surveillance requires detailed quantitative understanding of key biological attributes which influence overall potential of vector populations to transmit Plasmodium to humans [3]. Such attributes may include the likelihood with which specific Anopheles populations bite humans as opposed to the other available vertebrate hosts, i.e. the human blood indices (HBI), defined as proportion of all mosquito blood meals obtained from humans [4, 5]. Other attributes include parasite infection rates, i.e. the proportion of females infected with Plasmodium [6], survivorship, i.e. whether the mosquitoes can live long enough to allow complete sporogonic development of Plasmodium inside them [7], mosquito susceptibility to insecticides commonly used to control them [8], and the location of mosquito biting, i.e. indoors or outdoors, and how it overlaps in space and time with humans [9–12].


Detecting and preventing cyberattacks with anomaly detection and machine learning

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The Gartner Security & Risk Management Summit is just a few days away, and I'm delighted to have the opportunity to chat with attendees about how anomaly detection and machine learning can help give your organization a more proactive security posture. You don't need to have been in the cybersecurity space for long to be bewildered by and unsure about vendor claims around artificial intelligence, machine learning, and analytics. At Interset (acquired by Micro Focus in February of this year), we have regular conversations with security professionals who struggle to understand which techniques and tools are effective in boosting breach defense in the real world. Ultimately, these conversations lead to an important question for us: How can you implement user and entity behavioral analytics (UEBA) in a way that will enable an efficient security operations center (SOC)? There are multiple factors that go into an effective UEBA implementation, but it's helpful to start with ensuring that the math and machine learning powering the solution are suitable for your security objectives.


9 Indicators Of The State Of Artificial Intelligence (AI), May 2019

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US federal government contract obligations and AI-related investments grew almost 75% to nearly $700 million between fiscal 2016 and 2018 [Federal News Network]. Of those that have adopted an AI-driven marketing solution, 74% reported using AI in an "assistive" fashion, which surfaces insights for marketers to consider during manual decision making. Only 26% of marketers reported using autonomous AI, which can act on its own insights and work collaboratively with marketers (without adding manual work) [Albert and Forrester]. Notable growth came in areas like food and consumer goods (48%), plastics and rubber (37%), life sciences (31%), and electronics (22%) [Robotic Industry Association]. Nearly eight out of 10 enterprise organizations currently engaged in AI and ML report that projects have stalled, and 96% of these companies have run into problems with data quality, data labeling required to train AI, and building model confidence; only half of enterprises have released AI/ML projects into production; 78% of their AI/ML projects stall at some stage before deployment; 81% admit the process of training AI with data is more difficult than they expected; 76% combat this challenge by attempting to label and annotate training data on their own; 63% go so far as to try to build their own labeling and annotation automation technology; 71% report that they ultimately outsource training data and other ML project activities [Alegion and Dimensional Research].


Computer Vision -- An Introduction

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In the previous blog, I discussed Visual Perception and its both biological and computational aspects. This blog is specifically about computational Visual Perception, also known as Computer Vision. Computer vision has been around for more than 50 years, but recently, we see a major resurgence of interest in how machines'see' and how computer vision can be used to build products for consumers and businesses. The key driving factor behind all these is Computer Vision. In the simplest terms, Computer Vision is the discipline under a broad area of Artificial Intelligence which teaches machines to see.


Facial Expression Recognition on FIFA videos using Deep Learning: World Cup Edition

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Few Hearts were broken few still live. No matter who wins, the game will still make me thrill. Fifa world cup 2018 has become one of the highest goal scoring world cups in history. No matter which country is playing, the moment those 11 players step on the field, people get connected to them emotionally. While watching them we share their joy, fear, and excitement through the expression conveyed by them.