Stephen Shankland of CNet recently reported, “Google updated its Hangouts app on Wednesday so the multipurpose communication tool can detect when people are trying to find each other and make it easier to connect. The updated app, available for Google’s Android mobile operating system first but submitted to Apple for approval on iOS, also lets people express themselves with stickers and video filter effects, Bradley Horowitz, vice president of product at Google, said at the LeWeb conference here.”
Posts Tagged ‘Machine Learning’
Serdar Yegulalp of InfoWorld recently wrote, “Over the last year, as part of the new enterprise services that IBM has been pushing on its reinvention, Watson has become less of a “Jeopardy”-winning gimmick and more of a tool. It also remains IBM’s proprietary creation. What are the chances, then, of creating a natural-language machine learning system on the order of Watson, albeit with open source components? To some degree, this has already happened — in part because Watson itself was built in top of existing open source work, and others have been developing similar systems in parallel to Watson. Here’s a look at four such projects.” Read more
Conner Forrest of Tech Republic reports, “AppZen, a Sunnyvale, California-based startup, is leveraging natural language processing and machine learning to automate the expense report process. The company bills itself as an ambient expense manager, and it works in the background by tracking your expenses as they happen and creates a report for you. The process begins with the mobile app that runs on Android or iOS, which is able to track an employee’s calendars, itineraries, and credit card charges to build out an expense report as it happens.” Read more
Want to keep your high-risk customers from heading out the door? Well, Framed Data wants to give you a hand.
The company, which today announced that it has raised $2 million from Google Ventures, Innovation Works, Jotter, and NYU Innovation Fund as well as a number of angels, applies machine learning to ending subscription churn. It’s focused especially on B2B SaaS companies, uncovering less than obvious behavioral traits of churned users and applying that knowledge for future use, says director of marketing Tim Wu.
“Churn is the biggest pain we can tackle,” he says, providing the company a way to distinguish itself in an increasingly commoditized data analytics space. And in the B2B SaaS market, “the lifetime value of customers is really clear – they know that if customers leave, they lose x dollars.”
Yandex is going beyond web search and into the enterprise. This week it announced a new venture, the Yandex Data Factory, which will apply its machine-learning products and algorithms – which power more than 70 percent of its own products and applications – to business’ Big Data issues.
Using a client’s pre-existing data, the press release notes, Yandex creates an algorithmic model, which it then applies to the client’s new data to predict what will happen next in various scenarios. “This is exactly what is happening every second on Yandex’s services when we personalize search suggestions, recommend music, recognize speech or images, or target ads,” the release notes.
The model cases for Yandex Data Factory include: churn prediction by running segmentation and micro-segmentation algorithms on the data to find patterns in customer behavior that indicate they’re heading for the exit or that possible fraud activity is underway; personalizing cross-sell and up-sell recommendations based on user profiles built upon the searches they made, links or ads they clicked, videos they watched, and other activities; using its speech-to-text technology to analyze call center or other support call speech streams and detect anomalies in interactions to drive employee interaction quality and improve skills.
It also uses history-based prediction technology and its own computer vision and image recognition technologies to enables businesses to analyze large volumes of images and videos to spot anomalies, find recurring objects or events, and other things that will help them assess conditions and assure productivity.
Daniela Hernandez of Wired recently wrote that Quoc Le “works on the Google Brain, the search giant’s foray into ‘deep learning,’ a form of artificial intelligence that processes data in ways that mimic the human brain—at least in some ways. Le was one of the main coders behind the widely publicized first-incaration of the Google Brain, a system that taught itself to recognize cats on YouTube images, and since then, the 32-year-old Vietnam-native has been instrumental in helping to build Google systems that recognize your spoken words on Android phones and automatically tag your photos on the web, both of which are powered by deep-learning technology.” Read more
Skytree, “The Machine Learning Company,” has published a technology brief entitled, “Machine Learning on Natural Language Text and Log Data.”
The author of the brief is Nick Pendar, PhD, who serves as NLP Data Scientist for the company.
Pendar states, “Critical business information is often in the form of unstructured and semi-structured data that can be hard or impossible to interpret with legacy systems. In this brief, discover how you can use machine learning to analyze both unstructured text data and semi-structured log data, providing you with the insights needed to achieve your business goals.”
Open Systems Technologies is looking for a Research Scientist – Machine Learning in New York, NY. According to the post, “Ideal candidates have exceptional core software engineering skills, experience with Natural Language Processing, and a familiarity with Machine Learning and Statistical Modeling techniques. A desire to learn, and a strong motivation to succeed are an integral part of our team, and we expect everyone to be a force for shaping the long term direction of our products. The team is primarily engineering-focused, but participates actively in research collaborations with world-class institutions, attends and presents at conferences, publishes papers, and releases open-source software to push forward the state-of-the-art. We believe in each engineer owning the full product life-cycle, from research to implementation and maintenance.” Read more
The MIT Technology Review recently wrote, “Translating one language into another has always been a difficult task. But in recent years, Google has transformed this process by developing machine translation algorithms that change the nature of cross cultural communications through Google Translate. Now that company is using the same machine learning technique to translate pictures into words. The result is a system that automatically generates picture captions that accurately describe the content of images. That’s something that will be useful for search engines, for automated publishing and for helping the visually impaired navigate the web and, indeed, the wider world.” Read more
Some people say that reading “Harry Potter and the Sorcerer’s Stone” taught them the importance of friends, or that easy decisions are seldom right. Carnegie Mellon University scientists used a chapter of that book to learn a different lesson: identifying what different regions of the brain are doing when people read. Read more
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