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) …
The holidays are upon us, and with them this time of year brings joy, excitement, sugar, and chaos. Not to mention, lots and lots of lights. And between running from one event to another, managing your holiday shopping list, and ensuring your house is decorated to seasonal perfection, you're juggling more than your fair share of festive balls in the air. Which is why the idea of smart Christmas lights might be appealing. Look, you've probably already embraced the wonder of a smart home.
Machine learning is making inroads into every aspect of business life and asset management is no exception. Here are six ways in which machine learning has transformed the field – from the feel of the trading floor to the ideal skillset. Most flow trading done by banks has already been fully automated. While 20 years ago such products as cash equities or foreign exchange were mostly traded by humans, often with hundreds of traders occupying the trading floors, shouting "buy" or "sell" orders, currently most market makers rely on the algorithmic execution and automated inventory management. In fact, many institutional orders are not executed by hand either; they are routinely sent to the algorithms ensuring optimal execution that would minimize their market impact or trading costs.
Researchers have designed multiplayer games occupants of autonomous vehicles can play with other players in nearby self-driving cars. A new study, led by researchers from the University of Waterloo details three games created for level three and higher semi-autonomous vehicles. The researchers also made suggestions for many exciting types of in-car games for future exploration. Level three and higher semi-autonomous vehicles are those that have, at minimum, environmental detection capabilities and can make informed decisions for themselves. "As autonomous vehicles start to replace conventional vehicles, occupants will have much more free time than they used to," said Matthew Lakier, a PhD student in Waterloo's School of Computer Science.
'We can use artificial intelligence and machine learning to be able to pick up nuances that are much more difficult to apply through the traditional classical tools that are available in machine vision.' -- Harry Kekedjian, controls engineering manager at the Advanced Manufacturing Center of Ford Motor Co.
The neural net framework in the Wolfram Language enables powerful and user-friendly network training tools for Audio objects. This example trains a net to classify spoken digits. The dataset is comprised of recordings of the digits from 0 to 9. It is essentially an audio equivalent to the MNIST digit dataset. You can start by deciding how a recording will be transformed into something that a neural network can use. The "AudioMFCC" net encoder is used, where the signal is split into overlapping partitions and some processing is applied to each to reduce the dimension while preserving information that is important for understanding the signal.
Sitecore Symposium 2019, Orlando, Fla. – Nov. 5, 2019 -- Sitecore, the global leader in digital experience management software, today announced Sitecore AI, a new machine learning framework that empowers marketers with predictive insights to automate the delivery of personalized digital experiences. Automated Personalization -- an add-on for the Sitecore Experience PlatformTM (XP) 9.0 and above -- is the first of the next-generation services within Sitecore AI, providing one of the industry's first solutions powered by Microsoft Azure to automatically identify visitor trends, create customer segments, and modify page elements to deliver personalized experiences. Sitecore AI amplifies the power of Sitecore XP to deliver individualized, contextual experiences that guide visitors along their journey with content that is useful and actionable. It can analyze a customer's behavior to understand their interests and intent, enabling marketers to learn about where each person is in their journey -- without the guesswork -- and determines the best content for each customer while continuously optimizing their experience, nurturing them toward conversion. "Sitecore AI helps us solve the content crisis for marketers by giving them the intelligent insights they need to optimize content creation and personalized experience delivery at massive scale," said Desta Price, executive vice president of product, Sitecore.
Finally, artificial intelligence and machine learning are moving past hype and into the tools you actually depend on to keep operations humming. And now that machine learning has arrived in SolarWinds products, we know you've got some questions – probably a lot of questions. Join SolarWinds Distinguished Engineer Karlo Zatylny and SolarWinds Head Geek Thomas LaRock, along with Microsoft Senior Cloud Advocate Anthony Bartolo, for an in-depth whiteboard discussion to strip away AI/ML hype and explain what machine learning really is. Learn how to use ML to solve real-world challenges, like simplifying monitoring and resolving issues faster. As a Sr. Cloud Advocate for Microsoft, Anthony takes great pride in architecting and conducting "science experiments" to incorporate Microsoft technology and services to address a customer problem or opportunity.
Cortex just released V 0.10, which includes their new Predictor Interface for serving models. It lets you take models from any framework and implement them in simple Python, before deploying them with a single terminal command. V 0.10 also still includes out-of-the-box support for TensorFlow Serving and ONNX Runtime.
In the recent update of GraphVite, we release a new large-scale knowledge graph dataset, along with new benchmarks of knowledge graph embedding methods. The dataset, Wikidata5m, contains 5 million entities and 21 million facts constructed from Wikidata and Wikipedia. Most of the entities come from the general domain or the scientific domain, such as celebrities, events, concepts and things. To facilitate the usage of knowledge graph representations in semantic tasks, we provide a bunch of pre-trained embeddings from popular models, including TransE, DistMult, ComplEx, SimplE and RotatE. You can directly access these embeddings by natural language index, such as "machine learning", "united states" or even abbreviations like "m.i.t.".