Industry


Amazon's new Echo lineup targets Google, Apple and Sonos

Engadget

It wouldn't be an Amazon event without a slew of new Echo devices, and this time is no different. The company announced not one, not two, but seven new Echo products today at an event today in Seattle. Not only are there now new versions of the Echo Dot, the Echo Plus and the Echo Show, Amazon also introduced several new Echo companions that you can use to bring Alexa to every room in your house -- and even your car. Like the new Echo Show, the new Echo Dot and Echo Plus look a lot better this year than last. The Dot, for example, no longer looks like an oversized hockey puck.


Technology: What's Going To Make A Difference In 2019?

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The following article has been republished with permission from Process Excellence (PEX) Network. Have you ever wondered what the other people in the industry are saying? The PEX Network often publishes surveys, and sometimes the results deserve a wider audience. The Technology Excellence (Europe) Survey 2018 was designed to get a snapshot of the industry, and while responses were weighted towards banking, financial services and insurance (24%) and manufacturing (16%), plenty of other industries were represented: gaming, logistics, and software development to name but three. Occasionally, the PEX Network throws out a broad question to get the sort of answers that you can't put on a spreadsheet -- and in particular, it likes to ask the Network to make bold predictions.


r/MachineLearning - [D] Question Regarding LSTM Input

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I'm trying to train an LSTM to generate song lyrics. For my input data, I downloaded a bunch of song lyrics (1-D list where each entry is one line of a song) and used Keras Tokenization w one-hot. This is is where I'm having trouble setting up the structure. Once I have converted the lyric-lines to one-hot, what should the input to the LSTM look like? When I fit the model, what should I use for my target?


r/MachineLearning - [N] Stable-Baselines v2.0.0 Released

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Has anyone tried to use Stable-Baselines? How does it compare to the official Baselines from OpenAI in your experience? Stable Baselines is a set of improved implementations of reinforcement learning algorithms based on OpenAI Baselines. You can read a detailed presentation of Stable Baselines in the Medium article. These algorithms will make it easier for the research community and industry to replicate, refine, and identify new ideas, and will create good baselines to build projects on top of.


Northern Trust gears up for GDPR through data governance solutions - SiliconANGLE

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Mention data governance to many corporate executives and you might get eye rolling and a quick change of subject. Business leaders are more focused on revenue rather than meeting regulatory demands. But the rise in enterprise data has also brought government-driven demands for privacy and protections that are forcing companies across the world to adopt tools to meet new requirements. "Governance is a very abstract concept. Most people want to run away from anything close to governance," said Sanjay Saxena (pictured), senior vice president of enterprise data governance at Northern Trust Corp. Saxena visited theCUBE, SiliconANGLE's mobile live-streaming studio, and answered questions from hosts Dave Vellante (@dvellante) and James Kobielus (@jameskobielus) during IBM Fast Track Your Data in Munich, Germany.


Researchers built AI-powered gliders that learn how to fly just like birds

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It took mankind untold eons to learn how to fly, but now artificial intelligence is doing something similar and in a fraction of the time. No, there's no robots constructing planes like the Wright brothers, but some AI-powered gliders are indeed learning how to cruise through the air just like birds, and they're getting pretty good at it. Researchers equipped a glider with an advanced algorithm and control system that allows it to navigate wind currents in the same way that birds to. By finding updrafts which help it stay aloft, the glider can slip through the air indefinitely, much like birds to when trying to minimize their energy output. The research, which was published in Nature, describes how these wind currents are used by birds.


Top 5 Customer Experience (CX) Predictions for 2020

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The customer-centric organizations of today are already keenly aware of the digital revolution, which has transformed conventional business models while empowering customers. Over the last decade, customer experience (CX) has drastically changed with the introduction of several opportunities for customers to interact, engage and transact with brands at their convenience across multiple channels. Digital 2.0 is the next phase, where the plain and simple customer experience of old will make space for intuitive, contextual and practical engagement across different customer touchpoints. By 2020, digital technologies like AI, biometrics, machine and deep learning and robotic automation will revolutionize the way consumers interact with organizations and brands. According to Gartner, 2020 is going to witness 20 billion'things' connected to the Internet.


How artificial intelligence can help brokers close sales

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Artificial intelligence could help a brokerage figure out how likely a prospective client is to actually buy insurance. At least that's what officials with Surex Direct are hoping. Magreth, Alta.-based Surex Direct places home, auto and commercial lines and says it can deliver 10 quotes online in 10 minutes or less. If a person gives Surex information to generate a home or auto quote, the brokerage defines that person as a lead, said Matt Alston, co-founder and chief operating officer of Surex. "We are trying to build out sophistication in scoring leads," Alston said in an interview.


Artificial Intelligence Edge Device Shipments to Reach 2.6 Billion Units Annually by 2025

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Artificial intelligence (AI) processing today is mostly done in a cloud-based data center. The majority of AI processing is dominated by training of deep learning models, which requires heavy compute capacity. In the last 6 years, the industry has experienced a 300,000X growth in compute requirements, with graphics processing units (GPUs) providing most of that horsepower. According to a new report from Tractica, however, as the diversity of AI applications grows, an increasing amount of AI processing will be handled within edge devices rather than in a centralized, cloud-based environment. Tractica forecasts that AI edge device shipments will increase from 161.4 million units in 2018 to 2.6 billion units worldwide annually by 2025.


Approach Intelligently - How to Make Using AI a Success

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"TensorFlow is by far the most popular tool among our respondents, with Keras in second place, and PyTorch in third. Other frameworks like MXNet, CNTK, and BigDL have growing audiences as well" As if businesses today didn't already have enough to worry about, then along comes a new wave of game-changing technologies that they must master quickly if they are not to fall behind their competitors, with pressure mounting to start using AI. . Artificial Intelligence is the most visible of these technologies – and arguably the most important. Open a newspaper, and it might seem as if every business is making great strides towards developing and using AI applications that will transform their operations and enable them to deliver new products and services to their customers. It's easy for businesses yet to achieve success by using AI – or even to get started on their journey – to get despondent about the lead they perceive their competitors to have.