If you are looking for an answer to the question What is Artificial Intelligence? and you only have a minute, then here's the definition the Association for the Advancement of Artificial Intelligence offers on its home page: "the scientific understanding of the mechanisms underlying thought and intelligent behavior and their embodiment in machines."
However, if you are fortunate enough to have more than a minute, then please get ready to embark upon an exciting journey exploring AI (but beware, it could last a lifetime) …
India is currently the hotbed for emerging technologies like AI & machine learning. But the need of the hour is "Deep Tech" which fundamentally is a connection of different types of technologies, not just AI & ML but also computer vision, image processing, blockchain, and AR/VR, to come up with a solution that is not trivial but a revolutionary one. However, there are a few limitations such as the skills of the engineering graduates in India to meet this demand, the quality of data in India continues to be an issue because of the diversity of demographics, languages etc. in the Indian market which could slow the momentum. With a passion for solving such issues in India, Bangalore-based Dhiyo is utilising AI and machine learning to help the folks to solve job market inefficiencies for the blue-collar sector by building a quick prototype using Google Assistant. Founded by Santhosh SS in 2018, Dhiyo is a Conversational AI interface for the job seekers in the informal sector who can create their profile or resume just by speaking to their smartphone through a series of voice commands in their own native language.
Machine Learning might be a department of computer science pointed at empowering computers to memorize unused behaviors based on experimental data. The objective is to plan the algorithms that allow a computer to show the behavior learned from past encounters, instead human interaction. Now we will examine applications of machine learning in cybersecurity and see how the machine learning algorithms offering assistance to us for battle with cyber-attacks. Machine learning (without human interaction) can collect analyze and prepare data. In cybersecurity, this innovation makes a big difference to analyze past cyber-attacks and create individual defense reactions.
The social media giant Facebook has always been at the forefront of AI advancement. Amid all the controversies and roadblocks in its strive to attain AI leadership, the company is moving forward with innovation and tech developments. These developments are a major result of its acquisitions; small but significant. Facebook's M&A activities are proving to be quite beneficial in its AI journey. Recently, the company acquired Scape Technologies which is a London-based computer vision startup working on location accuracy beyond the capabilities of GPS.
Employees toil at lines, doing the same task, repeatedly, in order to assemble a final product. A line stoppage or bottleneck can cost a fortune. What if the manufacturer could see what was going on, in real time, and fix any issues before they become real problems? Or come up with ways to make the process run smoother and more efficiently? That's the basic idea behind Drishti Technologies, a four-year-old startup cofounded by Prasad Akella, a 57-year-old Indian entrepreneur who's best known for leading the General Motors team that developed collaborative robots in the 1990s.
Dave Aitel is the founder and CTO of Immunity. You can follow him @daveaitel. Export control on AI and machine learning algorithms is becoming a more important part of national security strategy as the world moves to a great-power competition landscape and technological changes force accommodation and rapid change to many national interests. However, like security software before it, AI presents unique challenges to how export control has traditionally worked, and these should be considered before being codified into international regulatory frameworks. As an example, on January 6, 2020, The Bureau of Industry and Security (BIS) in the U.S. Department of Commerce released the following rule, which imposed a license requirement on a particular kind of software useful for automatically identifying objects from drone or other imagery: "Geospatial imagery "software" "specially designed" for training a Deep Convolutional Neural Network to automate the analysis of geospatial imagery and point clouds, and having all of the following: Technical Note: A point cloud is a collection of data points defined by a given coordinate system. A point cloud is also known as a digital surface model."
Spark is a powerful, general purpose tool for working with Big Data. Spark transparently handles the distribution of compute tasks across a cluster. This means that operations are fast, but it also allows you to focus on the analysis rather than worry about technical details. In this course you'll learn how to get data into Spark and then delve into the three fundamental Spark Machine Learning algorithms: Linear Regression, Logistic Regression/Classifiers, and creating pipelines. With this background you'll be ready to harness the power of Spark and apply it on your own Machine Learning projects!
This study demonstrates that it is possible to generate a highly accurate model to predict inpatient and emergency department utilization using data on socioeconomic determinants of care. ABSTRACT Objectives: To determine if it is possible to risk-stratify avoidable utilization without clinical data and with limited patient-level data. Study Design: The aim of this study was to demonstrate the influences of socioeconomic determinants of health (SDH) with regard to avoidable patient-level healthcare utilization. The study investigated the ability of machine learning models to predict risk using only publicly available and purchasable SDH data. A total of 138,115 patients were analyzed from a deidentified database representing 3 health systems in the United States.
Word embeddings enable knowledge representation where a vector represents a word. This improves the ability for neural networks to learn from a textual dataset. Before word embeddings were de facto standard for natural language processing, a common approach to deal with words was to use a one-hot vectorisation. Each word represents a column in the vector space, and each sentence is a vector of ones and zeros. As a result, this leads to a huge and sparse representation, because there are many more zeros than ones.
The Oil and Gas industry has seen volatile times and is affected by its own set of unique challenges ranging from commodity price fluctuations, a potential supply crunch, geo-political events, and energy policies including energy transition. Moreover, the challenges and requirements are distinct at different stages of operations – upstream, midstream and downstream. The industry has been an early adopter of a few emerging technologies and is looking to leverage them to remain competitive and better employee management. Oil and Gas companies are having to clean up old processes, as the market gets increasingly competitive. Ecosystm research finds that the top business priorities for Oil and Gas companies do not stop at cost reduction and revenue growth.
Digital images are a momentous part of today's digital communication. It is very easy to manipulate digital images for hiding some useful information by image rendering tools such as Adobe Photoshop, Microsoft Paint etc. The common image forgery which is easy to carry out is copy-move in which some part of an image is copied and pasted on another part of the same image to hide the important information. In this paper we propose an algorithm to spot the copy-move forgery based on exact match block based technique. The algorithm works by matching the regions in image that are equivalent by matching the small blocks of size b b.