Since the first computer-managed elements entered service in a General Motors auto manufacturing plant in 1961, almost every service and manufacturing industry in the world has benefited from increased automation provided -- to a greater or lesser degree -- by robotics. And, as industries become more deeply interconnected as a result of the demands of globalization and ubiquitous connectivity, so the very nature of robots will also evolve. However, increased proliferation of robots will bring as many new or accentuated risks as benefits, heightening the need for control over our creations. Today, there are many different types of robots in commercial and private use, with form factors varying considerably from the static to the fully mobile, from the microscopic to the truly huge and from the single function-specific design to the multi-function, modular types popularised by science fiction. Risks and threats posed by robots will also vary considerably.

Creating a new business and optimising an existing one are fundamentally different management challenges. It's doing both simultaneously that is the real challenge for business leaders, innovation guru Vijay Govindarajan tells Kanika Datta The three-box paradigm sets out an ideal for management. What goes wrong in practice? The Three-Box Solution essentially covers everything an organisation should be doing. Box 1 involves managing the business at peak profitability, which addresses the efficiency angle.

Experts are warning that skilled jobs will soon start disappearing because of the rise of artificial intelligence. So far, robots have mainly been replacing manual labor, performing routine and intensive tasks. But smarter machines are putting more skilled professions at risk. Robots are likely to be performing 45% of manufacturing tasks by 2025, versus just 10% today, according to a study by Bank of America. And the rise of artificial intelligence will only accelerate that process as the number of devices connected to the Internet doubles to 50 billion by 2020.

Apache Spark is increasingly thought of as the new jack-of-all-trades distributed platform for big data crunching – what with everything from traditional MapReduce-like workloads, streaming, graph computation, statistics, and machine learning all in one package. Except for Spark Streaming, with its micro-batches, Spark is focused for the most part on higher-latency, rich/complex analytics workloads. What about using Spark as an embedded, web-speed / low-latency query engine? This post will dive into using Apache Spark for low-latency, higher concurrency reporting / dashboard / SQL-like applications - up to hundreds of queries a second! Launching Spark applications on a cluster, or even on localhost, has a pretty high overhead.

The internet, and more importantly how we consume data from the web, has evolved at an incredible pace in recent years. One thing that has been steadily growing, and is only now really starting to make the headlines is Machine Learning. "Artificial intelligence would be the ultimate version of Google. The ultimate search engine that would understand everything on the Web. It would understand exactly what you wanted, and it would give you the right thing. However, we can get incrementally closer to that, and that is basically what we work on."

Well, there is a little bit difference between machine learning and data mining although I don't see any difference between them. See the Stackexchange debate on the difference between machine learning and data mining. At the end, it is about training the machine to recognize the data, and the predict the future (or unknown variables) with the training. Please, feel free to challenge me if I am wrong. I have been in search for the better explanation.

Google is known for experimenting heavily with new technologies, but it's becoming clear that machine learning is becoming a real value proposition for the search giant. On Thursday, at the Google Cloud Platform Next conference, Google's Julia Ferraioli broke down some of the key announcements that Google made around machine learning the day prior, and how developers could take advantage of them. Machine learning has struggled with a formal definition, but Ferraioli said that Google views it as how developers can add intelligence to their applications. And, using that data and intelligence is growing in importance. "How well you use your data can determine the degree of your success," Ferraioli said. Machine learning has long been the realm of a few specialized individuals and used to require education at the PhD level to implement.

The tech world has been voraciously chattering about "chatbots," and Microsoft CEO Satya Nadella has already declared them the new apps, the next big thing. But what tends to get lost in the discussion is the reason why these chatbots could represent such a power shift in the industry, and how they could fundamentally change the idea of apps, or distinct pieces of software in general. First, though, when we talk about chatbots, what are we actually talking about? The basic idea is "conversation as a platform." "Bots" -- as people have begun to shorten them -- are virtual assistants, software programs that you can talk to in order to get stuff done.

More than 20 private companies working to advance artificial intelligence technologies have been acquired in the last 3 years by corporate giants competing in the space, including Google, Amazon, Apple, IBM, Yahoo, Facebook, Intel, and, more recently, Salesforce. There have been 4 major acquisitions already in 2016. Google has been the most prominent global player, with 5 key acquisitions under its belt. In 2013, the corporate giant picked up deep learning and neural network startup DNNresearch from the computer science department at the University of Toronto. This acquisition reportedly helped Google make major upgrades to its image search feature.

We have lots of cutting-edge projects for you being discussed in the Gitter channels, ranging from mapping brain activity at scale, computer vision, neural networks, data visualisation to natural language processing. Check out the list below and don't hesitate to join the conversation. Did we miss a particular community in this category, or do you want us to feature your channel? Drop us a line in the Gitter HQ and we will add it to the list.