Alphabet Inc. (GOOGL - Analyst Report) owned Google on Monday unveiled a new A.I.-based digital assistance tool Springboard that can search across Google Apps like Gmail, Docs, Calendar, Drives and Contacts for particular information or documents. According to the company, Springboard is a work-focused digital tool that can assist users with useful and actionable information as well as recommendations. Prabhakar Raghavan, vice president of engineering for Google Apps, said in a blog post: "As we continue to invest in applying technologies like machine intelligence to power our web search engine, we're working on a new way to bring all of this power to the enterprise." The company also said that it will update Google Sites with website building features, an effort that we think will help boost Springboard's functionality. The updated version will allow business to create and design internal websites.

Artificial intelligence can now cook you a meal, chat to you on your phone, and beat you in a game of rock, paper, scissors. But Andy Rubin, who co-founded Android, believes that AI in the future will go one step further - being so powerful that it underpins every connected device. Mr Rubin said a combination of quantum computing and AI advancements could yield a conscious intelligence that would control every internet-enabled gadget. According to recent studies, certain jobs are already at risk of being replaced by automated workers. Accountants are 99 per cent likely to lose their jobs to machines.

Artificial intelligence (AI) keeps making news. It was front and center at the recent Code Conference and discussions since have covered everything from how quickly the technology will develop to what sectors will be impacted first--and whether we have anything to fear. With respect to that, concerns about who will control AI and how it will be used are coupled with calls for transparency. A new take on the man-versus-machine chess match, a contest that's symbolized the evolution of machine learning ever since Garry Kasparov beat IBM's Deep Blue twenty years ago, got me reconsidering what transparency in AI really means. "The Thinking Machine 6" is the latest iteration of a project that was introduced in 2002.

That's the age of one participant in a recent study looking at ways to take the fingerprints of infants. "The pattern is there at birth," says Anil Jain at Michigan State University in East Lansing. But it is hard to capture. Now Jain and his colleagues are developing a device that could be up to the task. Taking fingerprints from very young children – even newborns – is part of a drive in developing countries to monitor the health of infants, who often lack other forms of identification.

This post, from Rubedo: Big Data and Machine Learning, is made up of a collection of 10 Github repositories consisting in part, or in whole, of IPython (Jupyter) Notebooks, focused on transferring data science and machine learning concepts. They go from introductory Python material to deep learning with TensorFlow and Theano, and hit a lot of stops in between. To read the original article and see the 5 other IPython Tutorials, click here.

Machine learning has become a central part of our life – as consumers, customers, and hopefully as researchers and practitioners! Whether we are applying predictive modeling techniques to our research or business problems, I believe we have one thing in common: We want to make "good" predictions! Fitting a model to our training data is one thing, but how do we know that it generalizes well to unseen data? How do we know that it doesn't simply memorize the data we fed it and fails to make good predictions on future samples, samples that it hasn't seen before? And how do we select a good model in the first place?

Job Description - Experience and Background: 8-10 years of overall experience 4 years of Solution Designer for case analysis, requirements structuring, data analysis and data modelling to support Enterprise wide decision support systems. Minimum 6 months of experience in Hadoop/Big Data platform Architecture Solution design for Automation based solution specifically using machine or cognitive learning. Areas of Expertise Expertise: in handling huge data loads and optimizing the system to accommodate ever increasing data in the data warehouse/ data store. Rich Data Design & Data Governance Experience Diverse experience in Requirement Capture, Architecture, Solution Design & Delivery Strong Domain experience preferably in Banking, Aviation, Healthcare, Telecom (Either one of them will suffice). Concepts of solution hosted in cloud infrastructure Cognitive or Machine learning based algorithms and implementation Knowledge of concepts like data mining, text mining, data classification, pattern matching, pattern recognition etc.

The folks at [Design I/O] have come up with a way for you to play the world's tiniest violin by rubbing your fingers together and actually have it play a violin sound. For those who don't know, when you want to express mock sympathy for someone's complaints you can rub your thumb and index finger together and say "You hear that? It's the world's smallest violin and it's playing just for you", except that now they can actually hear the violin, while your gestures control the volume and playback. The first is Google's Project Soli, a tiny radar on a chip. Project Soli's goal is to do away with physical controls by using a miniature radar for doing touchless gesture interactions.

In the movie Transcendence, Johnny Depp plays Dr Will Caster, a researcher in artificial intelligence at Berkeley trying to build a sentient computer. Stuart Russell is Will Caster's real life equivalent. He works on artificial intelligence at the University of California at Berkeley, and is co-author of the definitive textbook on AI. He has also been very vocal about the risks of research in AI succeeding. Earlier this year, Google's DeepMind taught a computer program to play a wide variety of Atari video games at a superhuman level in a matter of hours.

The recent progress of machine learning is impressive, and the applications seem endless. Neural networks are incredible tools allowing us not just to analyze but also manipulate and generate images, movies and music. Over the last 18 months, the Lab at the Cultural Institute has invited artists and creative technologists to explore ways to apply machine learning to culture and the arts. We'll present some of our results with some onstage technical demos and a few special guests. See all the talks from Google I/O 2016 here: https://goo.gl/olw6kV