Stewart Butterfield (left), CEO of Slack, talks about the future of work with Box CEO Aaron Levie. "We need self-driving Ubers, like now," said Levie, 30, referencing the ride-sharing company's Thursday announcement that it would soon begin testing autonomous Volvos in Pittsburgh. Apparently, Levie's Uber driver got lost en route to this lunchtime session Friday on the future of work, a discussion organized for a few journalists featuring Levie and Stewart Butterfield, 43, CEO of messaging platform Slack. Over the next hour, the two startup entrepreneurs discussed how workplace expectations have been upended by increasingly easy access to information; how work-life balance rules may need to be rewritten; and how artificial intelligence will be able to bail us out of information overload. "I'd say right now (workplace tools) are a net cultural positive, but we're also going to have to learn how to handle it," said Butterfield, who added that his company instituted a Do Not Disturb feature to help create a work-life divide.

Businesses are increasingly adopting connected technologies such as wearables and office sensors to streamline operations and boost productivity, and as a result, the Internet of Things (IoT) industry is on the up. By 2020, there'll be more than 50 billion internet-connected objects in use, and the industry will be turning over trillions in profit annually, according to Cisco. At the same time, these devices will be generating large amounts of data. Clearly, the industry is entering a golden age. But while there's major potential here, there are fears that cybercriminals are eyeing up IoT as a lucrative opportunity.

This post is authored by Jennifer Marsman, Principal Software Development Engineer at Microsoft. The first Microsoft Data Science Summit will take place on Sept 26-27, 2016 in Atlanta, GA, at the same time as Ignite. This means that you have access to the amazing Ignite Expo hall, in addition to the fabulous speakers and content catered just to data scientists, machine learning developers, and big data engineers. For more information on how the co-location with Ignite will work and what you have access to, please see here. I will be speaking on the Microsoft Cognitive Services.

September 8, 1966 was the day Science Fiction on television changed forever. It was the day Star Trek entered our public consciousness. In many ways, Star Trek was the right show, at the right time, this despite the fact that only after its initial run of 79 episodes, and a legendary letter writing campaign by its fans to try to save it, it was canceled by NBC for poor ratings. It came at a moment in our country's history when we were enjoying unprecedented technological advances and yet were experiencing social and political turmoil that threatened to rip apart the fabric of our nation and also potentially destroy our way of life as we knew it. This disparity between technology and humanity were themes that would define Star Trek in its various incarnations for the next fifty years -- and perhaps, if its fans see to make it so, for decades more.

The German robot company Magazino is has signed a deal to see three robots to logistics company Fiege. Fiege is one of the largest logistics companies in the world with a turnover last year of 1.4bn. The company is starting small with its new contract for three of the perception controlled robots that Fiege will use in its shoe supplying subsidiary. Established in 1873 and still family run, Fiege has 10,500 employees in 160 offices in 15 countries. It has 2.7 square metres of warehouse space.

Deep Learning is one of the key parts of data science. As data becomes increasingly important and accessible, today's biggest companies are rapidly investing in deep learning. In fact, it is considered to be so vital to future technologies that many are sharing their own results and discoveries with the public. Researchers have been playing with the idea of deep learning for decades, but it has only blossomed in recent years. With companies like Facebook and Google pouring funds and resources into research, consumers are finally seeing the results of deep learning for themselves.

This article is the fifth part of the tutorial on Clustering with DPMM. In the previous posts we covered in detail the theoretical background of the method and we described its mathematical representationsmu and ways to construct it. In this post we will try to link the theory with the practice by introducing two models DPMM: the Dirichlet Multivariate Normal Mixture Model which can be used to cluster Gaussian data and the Dirichlet-Multinomial Mixture Model which is used to cluster documents. Update: The Datumbox Machine Learning Framework is now open-source and free to download. Check out the package com.datumbox.framework.machinelearning.clustering to see the implementation of Dirichlet Process Mixture Models in Java.

We offer a contextual authentication API that really has one job and that's to tell you if a user is who you think they are. The answer we return must be on point as our customers will use it to make decisions, allow access, or process transactions within their applications. A question that comes up often is "how well does your system work with limited data?". In other words, how do we confirm identity and return a risk score for a user on the very first time we've seen them? A reliable answer to this question enables better user experiences, improved security, and assists in detecting fake or fraudulent new account registrations.

Abstract: With the mission "leave no valuable data behind", we developed techniques for knowledge fusion to guarantee the correctness of the knowledge. This talk starts with describing a few crazy ideas we have tested. The first, known as "Knowledge Vault", used 15 extractors to automatically extract knowledge from 1B Webpages, obtaining 3B distinct (subject, predicate, object) knowledge triples and predicting well-calibrated probabilities for extracted triples. The second, known as "Knowledge-Based Trust", estimated the trustworthiness of 119M webpages and 5.6M websites based on the correctness of their factual information. We then present how we bring the ideas to business in filling the gap between the knowledge at Google Knowledge Graph and the knowledge in the world.

Amazon's boosting the features of Fire TV and Fire TV Stick with several convenient tweaks designed to make it easier to find the shows you actually want to watch. On Wednesday, the company announced you will soon be able to find content with your voice on more than 75 apps and channels. This includes big name services such as Netflix and HBO Go. HBO Now support is coming soon. Perhaps even more handy than the new voice search are new interface convenience features for Fire TV devices.