After announcing a slew of new updates to its smart home, VR and mobile products, Google unveiled the latest feature coming to its core function -- the search engine. In the next few weeks, users in the US will be able to look for job listings on Google.com This function will make it easier to discover jobs close to you, as well as positions that have been traditionally more difficult for existing portals to find and classify (like in retail and service). According to Google, "almost half of U.S. employers say they still have issues filling open positions," while job seekers aren't necessarily aware of listings available near them. The search giant says this is because high turnover, low traffic and inconsistency related to job posts make them difficult for engines to classify.

Google wants to play matchmaker, but for job seekers and talent hunters. Google announced on Wednesday a new job-oriented search tool that will use the company's advanced "machine learning" technology to provide personalized results. Google teased the new tool as a good way for candidates to find jobs, but also for companies to find the right people. With Google for Jobs, we'll use machine learning to help people find jobs, and make it easier for companies to find talent. The new tool will be integrated into Google's existing search engine and is meant to use contextual details like location to help surface relevant job listings at the top of searches.

Google provided a look at its latest digital offerings, with a heavy focus on its efforts to extend artificial intelligence features into more of its apps and services. CEO Sundar Pichai unveiled Google Lens, a set of vision-based computing capabilities that can understand what you are looking at. It will first be available as part of Google's voice-controlled digital assistant -- which bears the straightforward name "Google Assistant" -- and Photos app. In the real world, that means you could, for instance, point your phone camera at a restaurant and get reviews for it. Pinterest has a similar tool.

It may sound like the fictional fish used to translate languages in'The Hitchhiker's Guide to the Galaxy', but a German startup has brought the Babel fish to life with earbuds. Called Dash Pro tailored by Starkey, these high-tech earbuds are capable of integrating with the iTranslate app, providing face-to-face conversational language translation in nearly 40 different languages. While wearing the custom earbuds, users simply activate the app and carry on a conversation that will be translated into their native tongue in real-time. Bragi has unveiled two new products to the family – The Dash Pro tailored by Starkey and The Dash Pro, which is a'reengineered sequel to The Dash that continues to press innovation forward,' the firm explained. The Dash Pro tailored by Starkey integrates with the iTranslate app, which will translate the conversations into the wearer's native tongue.

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The data that tracks our behavior feeds into machine-learning algorithms that make judgments about us. When used for advertising, they can reproduce our own prejudiced behavior. Latanya Sweeney, the director of the Data Privacy Lab at Harvard University, found that Google searches for black-sounding names more often resulted in ads for arrest records compared to searches for white-sounding names, likely a result of the algorithm learning to predict what users are likely to click on. Marketers can also use machine learning to figure out your unique quirks--do you respond better to words or to pictures? Do you make impulsive shopping decisions?--to

Historically, most of the data businesses have analyzed for decision-making has been of the structured variety--easily entered, stored, and queried. In the digital age, that universe of potentially valuable data keeps expanding exponentially. Most of it is unstructured data, coming from a wide variety of sources, from websites to wearable devices. As a recent McKinsey Global Institute report noted: "Much of this newly available data is in the form of clicks, images, text, or signals of various sorts, which is very different than the structured data that can be cleanly placed in rows and columns." At the same time, we have entered an era when machine learning can theoretically find patterns in vast amounts of data to enable enterprises to uncover insights that may not have been visible before.

Five years ago, researchers made a sudden leap in the accuracy of software that can interpret images. The technology behind it, artificial neural networks, underpins the recent boom in artificial intelligence (see "10 Breakthrough Technologies 2013: Deep Learning"). Yann LeCun, director of Facebook's AI research group and a professor at New York University, helped pioneer the use of neural networks for machine vision. That's what would allow them to acquire common sense, in the end.

Microsoft is making some bold promises with Story Remix, its recently announced app for the Windows 10 Fall Creators update. Together with the company's deep learning technology, it can automatically craft your photos and videos into short films. Story Remix resembles Apple Clips and Google's Photo Assistant, but it goes a bit farther with the ability to analyze everything on a pixel level-basis to detect people, objects and the overall setting. If it works as advertised, it could be a transformational app for consumers fed up with their ever-growing libraries of digital media. It's the latest attempt by Microsoft to make your life easier by predicting what you want.

Data Preparation for Machine Learning in Vertica Posted on Monday, May 8th, 2017 at 1:05 pm. This blog post was authored by Vincent Xu. Introduction Machine learning (ML) is an iterative process. From understanding data, preparing data, building models, testing models to deploying models, every step of the way requires careful examination and manipulation of the data. This is especially true at the beginning of this cycle where the raw data must be cleaned and prepared for modelling.