recognition


Operationalizing Machine Learning

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Machine Learning (ML) powers an increasing number of the applications and services that we use daily. For organizations who are beginning to leverage datasets to generate business insights -- the next step after you've developed and trained your model is deploying the model to use in a production scenario. That could mean integration directly within an application or website, or it may mean making the model available as a service. As ML continues to mature the emphasis starts to shift from development towards deployment. You need to transition from developing models to real world production scenarios that are concerned with issues of inference performance, scaling, load balancing, training time, reproducibility and visibility.


Cloudy With A Chance Of AI - Disruption Hub

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Cloud computing first began as a way to access cheap computing power and data storage, allowing users to rent space in data centres via the internet. Now, the market is undergoing a transition. In China, Alibaba, Tencent and Amazon are just a few of the companies vying to dominate the scene. In the West, Google and Microsoft are attempting to move in on Amazon Web Services. To make their cloud services more competitive, tech leaders are developing new cloud computing tools that can be used by developers.


Google AI Creates Its Own Offspring

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The most exciting work in artificial intelligence (AI) is happening in the field of machine learning using a technique known as deep learning. This process enables computers to learn by running massive amounts of data through a complex set of algorithms. The system is inspired by the biology of the human brain, and is called a neural network. Building these systems requires a team of scientists and engineers who painstakingly design the set of rules and methods necessary to achieve the desired result. With the current AI boom underway, experts with the skills required to create these systems are in short supply, with only an estimated 10,000 people worldwide proficient in the task.


Microsoft's Seeing AI app for the blind now reads handwriting

Engadget

Artificial intelligence took center stage at Microsoft's AI Summit in San Francisco on Wednesday. Aside from announcing AI smarts for a range of software -- from Bing to Office 365 -- the tech titan is also ramping up its Seeing AI app for iOS, which uses computer vision to audibly help blind and visually impaired people to see the world around them. According to Microsoft, it's nabbed 100,000 downloads since its launch in the US earlier this year, which convinced the tech titan to bring it to 35 countries in total, including the EU. It's also getting a bunch of new features. The app now boasts more currency recognition, adding British pounds, US dollars, Canadian dollars, and Euros to its tally.


Microsoft announces new AI-powered search features for Bing

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Today, Microsoft announced a series of artificial intelligence-driven features for its Bing search engine to make it more conversational and nuanced. The news, unveiled at an event in San Francisco, means that Bing will make better use of object recognition, so-called machine reading (for parsing text and extracting meaning), and other techniques tuned and improved using AI training methods. Search results will now show both multiple perspectives and multiple sources, culled from a list of pre-approved news sources, to show Bing users different sides of issues ranging from the benefits and downsides of kale to the pros and cons of contentious political issues. This builds on an earlier feature, announced back in September, in which Bing added fact checks to search results in an effort to cut down on misinformation, fake news, and other distorted stories from manipulative information sources. In a new partnership with social news site Reddit, Bing will also surface information from subreddits right in search results by using algorithms to read and analyze the user-generated text across Reddit's many communities.


Google wants to solve new AI problems: Jeffrey Dean

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Tokyo: Jeffrey (Jeff) Dean is a Google senior fellow in a research group at Google where he leads the company's artificial intelligence (AI) project called Google Brain. Along with his team, Dean, who joined Google in 1999, is currently implementing the company's vision as articulated by chief executive Sundar Pichai--to build an "AI-first" world. In an interview on the sidelines of a "Google #MadewithAI" event, held recently in Tokyo, Dean explains what this vision encompasses and the challenges involved in implementing it. What are the major steps involved in this process of implementing the Google strategy of building an AI-first world? The steps involve making products that are useful, help others innovate and solve humanity's big challenges.


10 AI terms every marketer should know

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As artificial intelligence (AI) becomes more prevalent, marketers need to keep up with how this technology affects the marketing world. To do so, it's necessary to speak the language and have a basic understanding of how AI works. From chatbots for customer service to self-driving cars, AI is quickly becoming a major part of the world. Sentiment analysis, social media tracking, and media intelligence are all examples of AI-powered services which can assist marketers with their day-to-day responsibilities. The media update team has compiled a list of AI-related terms that every marketer should be familiar with: 1. Algorithm A set of steps that runs on a computer program and is designed to solve a problem.


Gfycat says it'll use machine learning to make more high-res GIFs

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Embedding a GIF into your blog post is often a hilarious way to illustrate your reactions, but the GIFs themselves can be low in resolution, like text running across an image that's too blurry to read. Gfycat is offering an AI-powered solution to improve GIF quality with machine learning, according to a press release. "Human curation doesn't take into consideration people's tastes and mood, so we've created our own AI tools," CEO Richard Rabbat told The Verge in a statement. Because the company loves cats, it has named each of its three parts of the solution after a feline. The first, called Project Angora, involves searching online for a GIF video source, then swapping out the GIF with a higher-res version.


Gfycat wants to fix your low-fidelity GIFs with machine learning

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There can be plenty of copies of the same video clips as a GIF, or maybe it's just difficult to capture and upload, but Gfycat hopes that it can be solved at a technical level. Gfycat is now making a big push on the technical front to make those GIFs look better and more discoverable as creators look to continue to upload content, regardless of what kind of quality or fidelity they are. And it's more of a video problem than an image recognition problem, CEO Richard Rabbat said. "We have scaled [through] creators through word of mouth, and they are just getting excited about Gfycat and [creating] content," Rabbat said. "In many cases, what we're building from an AI and machine learning perspective are additional tools to support their excitement.


Medical Imaging Drives GPU Accelerated Deep Learning Developments

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Although most recognize GE as a leading name in energy, the company has steadily built a healthcare empire over the course of decades, beginning in the 1950s in particular with its leadership in medical X-ray machines and later CT systems in the 1970s and today, with devices that touch a broad range of uses. Much of GE Healthcare's current medical device business is rooted in imaging hardware and software systems, including CT imaging machines and other diagnostic equipment. The company has also invested significantly in the drug discovery and production arena in recent years--something the new CEO of GE, John Flannery (who previously led the healthcare division at GE), identified as one of three main focal points for GE's financial future. According to Flannery, the company's healthcare unit has one million scanners in service globally, which generate 50,000 scans every few moments. As one might imagine, this kind of volume will increasingly require more processing and analysis capabilities cooked in--something the company is seeking to get ahead with in today's partnership with Nvidia.