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) …
In contrast to the more standard uses of neural networks as regressors or classifiers, Variational Autoencoders (VAEs) are powerful generative models, now having applications as diverse as from generating fake human faces, to producing purely synthetic music. This post will explore what a VAE is, the intuition behind why it works so well, and its uses as a powerful generative tool for all kinds of media. When using generative models, you could simply want to generate a random, new output, that looks similar to the training data, and you can certainly do that too with VAEs. But more often, you'd like to alter, or explore variations on data you already have, and not just in a random way either, but in a desired, specific direction. This is where VAEs work better than any other method currently available.
Surex Direct, Canada's fastest growing online insurance brokerage, continues to propel the artificial intelligence and machine learning movement – from a broker's perspective – as evident by their latest feature in CI Top Broker magazine. Already incorporating AI to automate and optimize billing processes, Surex Direct is focused on researching and developing AI and machine learning processes in-house. ''We take pride in being a trailblazer in the online insurance space,'' Lance Miller, CEO of Surex Direct, said. Aside from the development and deployment of AI, Surex Direct plans to license products, allowing other brokerages to increase their efficiencies. ''We're fully invested in the AI and machine learning movement,'' Matthew Alston, COO of Surex Direct, said.
Rivers of digital ink have been spilled on the new opportunities of artificial intelligence and machine learning. A lot of the coverage has been thought-provoking pieces on the long-term possibilities for "cognitive computing," which allows computers to reason and simulate human thought processes. In the meantime, robust machine learning algorithms are proving their worth in three ways that can easily be implemented in your business today. As a pattern-recognition engine, machine learning enables more automation in existing business processes. These "boring" uses of machine learning are by far the biggest real opportunity for business value today.
Most people are not very familiar with the concept of artificial intelligence (AI). As an illustration, when 1,500 senior business leaders in the United States in 2017 were asked about AI, only 17 percent said they were familiar with it.1 A number of them were not sure what it was or how it would affect their particular companies. They understood there was considerable potential for altering business processes, but were not clear how AI could be deployed within their own organizations. Despite its widespread lack of familiarity, AI is a technology that is transforming every walk of life. It is a wide-ranging tool that enables people to rethink how we integrate information, analyze data, and use the resulting insights to improve decisionmaking. Our hope through this comprehensive overview is to explain AI to an audience of policymakers, opinion leaders, and interested observers, and demonstrate how AI already is altering the world and raising important questions for society, the economy, and governance. In this paper, we discuss novel applications in finance, national security, health care, criminal justice, transportation, and smart cities, and address issues such as data access problems, algorithmic bias, AI ethics and transparency, and legal liability for AI decisions. We contrast the regulatory approaches of the U.S. and European Union, and close by making a number of recommendations for getting the most out of AI while still protecting important human values.2 Although there is no uniformly agreed upon definition, AI generally is thought to refer to "machines that respond to stimulation consistent with traditional responses from humans, given the human capacity for contemplation, judgment and intention."3 According to researchers Shubhendu and Vijay, these software systems "make decisions which normally require [a] human level of expertise" and help people anticipate problems or deal with issues as they come up.4 As such, they operate in an intentional, intelligent, and adaptive manner. Artificial intelligence algorithms are designed to make decisions, often using real-time data. They are unlike passive machines that are capable only of mechanical or predetermined responses. Using sensors, digital data, or remote inputs, they combine information from a variety of different sources, analyze the material instantly, and act on the insights derived from those data. With massive improvements in storage systems, processing speeds, and analytic techniques, they are capable of tremendous sophistication in analysis and decisionmaking.
Global business value derived from artificial intelligence (AI) is projected to total $1.2 trillion in 2018, an increase of 70 percent from 2017, according to Gartner. AI-derived business value is forecast to reach $3.9 trillion in 2022. The Gartner AI-derived business value forecast assesses the total business value of AI across all the enterprise vertical sectors covered by Gartner. There are three different sources of AI business value: customer experience, new revenue, and cost reduction. Customer experience is a necessary precondition for widespread adoption of AI technology to both unlock its full potential and enable value.
Indian President Ram Nath Kovind, speaking at the 17th convocation of the Shri Lal Bahadur Shastri Rashtriya Sanskrit Vidyapeetha, said the ancient language of Sanskrit was the most suitable language for writing algorithms and developing artificial intelligence. Why it matters: President Kovind was addressing a crowd of academics and specifically talking about Sanskrit's usefulness. He spoke about the rich history of Sanskrit as an ancient language used for early Indian mathematics and science. Many scholars believe that the grammar of Sanskrit, which is rule-bound, formula-bound and logical, is the most appropriate to write algorithms, or to be used in machine learning and even artificial intelligence. Reading between the lines: Keeping the venue in mind, the President's words were meant to inspire and celebrate, not spark controversy over which language is'best' for machine learning.
This post explains why artificial intelligence (AI) is the key to building anti-abuse defenses that keep up with user expectations and combat increasingly sophisticated attacks. This is the first post of a series of four posts dedicated to provide a concise overview of how to harness AI to build robust anti-abuse protections. The remaining three posts will delve into the top 10 anti-abuse specific challenges encountered while applying AI to abuse fighting, and how to overcome them. Following the natural progression of building and launching an AI-based defense system, the second post covers the challenges related to training, the third will delve into classification issues and the last one will look at how attackers attempt to attack AI-based defenses. This series of posts is modeled after the talk I gave at RSA 2018.
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Apache Thrift The Thrift stack is a common class hierarchy implemented in each language that abstracts out the tricky details of protocol encoding and network communication 26. Chukwa A data collection system for monitoring large distributed systems; Provides flexible/powerful toolkit to display, monitor, and analyze results; Architecture: Agents - run on each machine and emit data; Collectors - receive data from the agent and write it to stable storage; MapReduce jobs - parsing and archiving the data; Hadoop Infrastructure Care Center - a web-portal style interface.
The Conference Series Team is organizing an international conference on Machine Learning which is themed as "Machine Learning: Discovering the New Era of Intelligence". The conference aims to expand it's coverage in the areas of Machine Learning and Deep Learning where in the experts from the industry will be giving presentations on the subject. World Machine Learning and Deep Learning Congress is bringing the most innovative minds, experts,practitioners, and thinkers to inspire and present to the delegates new innovative ways to work and innovate through their data. Machine Learning is a process of teaching the intelligent computers as to hot to perform and carry out complex tasks that cannot be easily described or processed by humans.It is a combination of Mathematical Optimization and statistics. On the other hand, Deep Learning forms a part of Machine Learning that focuses even more narrowly like neuron to solve any problem.