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) …
We are in the midst of a deep learning revolution. Unprecedented success is being achieved in designing deep neural network models for building computer vision and Natural Language Processing (NLP) applications. State-of-the-art benchmarks are disrupted and updated on a regular basis in tasks like object detection, language translation, and sentiment analysis. It's a great time to work with deep learning! The application of deep learning in Information Security (InfoSec) is still very much in its nascent stages.
Over the last few years, artificial intelligence and machine learning have increasingly come up in conversations about enterprise search. As artificial intelligence (AI) and its cousin, machine learning (ML), increased in accuracy and ease of integration, instances of them being directly integrated with or running alongside of search to improve results increased as well. But chances are you remember when search relevancy was based on simple metrics like term frequency -- the document with the largest number of instances was ranked highest, and documents with fewer instances ranked lower. You were able to provide stop words like "the" and "of" whose frequent use typically added no value in retrieving relevant documents. The only content really useful to the search engine was the terms in the user query.
Wave Computing, the Silicon Valley company accelerating artificial intelligence (AI) from the datacenter to the edge, announced its new TritonAI 64 platform, which integrates a triad of powerful technologies into a single, future-proof intellectual property (IP) licensable solution. Wave's TritonAI 64 platform delivers 8-to-32-bit integer-based support for high-performance AI inferencing at the edge now, with bfloat16 and 32-bit floating point-based support for edge training in the future. Wave's TritonAI 64 platform is an industry-first solution, enabling customers the ability to address a broad range of AI use cases with a single platform. The platform delivers efficient edge inferencing and training performance to support today's AI algorithms, while providing customers with flexibility to future-proof their investment for emerging AI algorithms. Features of the TritonAI 64 platform include a leading-edge MIPS 64-bit SIMD engine that is integrated with Wave's unique approach to dataflow and tensor-based configurable technology.
Microsoft Research Asia (MSRA) has achieved eight top places in the recent machine translation challenge organized by the 2019 fourth Conference on Machine Translation (WMT19), out of the eleven tasks it undertook. Overall, there are nineteen machine translation categories in WMT this year. MSRA achieved first place in machine translation tasks for Chinese-English, English-Finnish, English-German, English-Lithuanian, French-German, German-English, German-French and Russian-English. Three other tasks were placed second in their respective categories, which included English-Kazakh, Finnish-English and Lithuanian-English. As one of the leading machine translation competition globally, WMT is a platform for leading researchers to demonstrate their solutions, as well as to understand the continuous evolvement of machine translation technology.
It takes just 10 minutes and no coding or programming. It is available on 12 different platforms - WhatsApp, Messenger, Kik, Telegram, Line, Viber, Skype, Slack, Website, etc. The platform offers voice feature, contextual, intelligent paths, training, analytics, private labelling, and more. Leverage the power of machine learning, NLP & NLU to design your very own chatbot. Get started with Engati today!
As part of a corporate-wide digital transformation, BP is embracing artificial intelligence (AI) to change the way the company works. Using Microsoft Azure Machine Learning service and its automated machine learning capabilities, BP scientists can now explore the potential of new energy deposits. They can also build more finely tuned, accurate models in dramatically less time, helping them better gauge available hydrocarbon reserves.
German automaker Mercedes-Benz just showed off a concept car with a super power that's straight out of Inspector Gadget: a Roomba-like robotic safety triangle that can deploy itself from the vehicle's rear bumper after a car crash, like a smart traffic cone. The tech could end up saving lives, especially if the vehicle's occupants aren't in a condition to warn oncoming drivers of an accident and other road hazards. The "Warning Triangle 4.0" was designed for Mercedes-Benz's autonomous Experimental Safety Vehicle 2019 concept, a showcase that's focused on what car safety technologies could soon look like in the age of self-driving. A deployable ramp built into the rear bumper of the car lowers the adorable bot to the ground, according to Car and Driver. The robot then can roll out and warn oncoming drivers of the accident.
Successfully deploying conversational artificial intelligence (AI) is like no other digital business-process upgrade. In fact, it's not an IT upgrade in the conventional sense; conversational AI does nothing less than usher sophisticated robotics into the front office. The surest route to project failure would be taking this fact for granted. Where these cross-channel AI systems--designed to interact naturally and fluidly with internal users and/or customers in text or verbal conversation--are most like traditional business systems is in how short-sighted decisions can doom development and hobble future productivity. What should you keep in mind when deploying conversational AI?
To help curb AI sprawl and make smarter decisions about implementation, organizations should devote more thought to defining the value they are hoping to get from implementing AI solutions. With AI, teams often lean first toward the build option, believing it gives them the most control over what will hopefully prove to be a competitive differentiator. More often than not, however, buy or partner makes more sense. Small businesses, for example, usually use APIs to access data from companies such as Visa on credit card transactions, data that can be used in AI solutions. If the needed data or expertise do not exist internally, companies need to determine who they can partner with to obtain them relatively quickly.
Wolfram Research today announced free access to the engine that powers its technology stack. The Wolfram Engine is available to developers for free, assuming it is used for non-production development. Wolfram Research is best known for creating the modern technical computing system Mathematica and the computational knowledge engine Wolfram Alpha (stylized Wolfram Alpha). Founded by computer scientist Stephen Wolfram, the company celebrated the 10-year anniversary of Wolfram Alpha just last week. "The Wolfram Engine is the heart of all our products," Stephen Wolfram explains.