Don't look now but Apache Spark is about to turn 10 years old. The open source project began quietly at UC Berkeley in 2009 before emerging as an open source project in 2010. For the past five years, Spark has been on an absolute tear, becoming one of the most widely used technologies in big data and AI. Let's take a look at Spark's remarkable run up to this point, and see where it might be headed next. Apache Spark is best known as the in-memory replacement for MapReduce, the disk-based computational engine at the heart of early Hadoop clusters.

Tonya Hall interviews executives, authors, and thought leaders for our sister site ZDNet, and we're running a selection of some of her most viewed videos. The following is an edited transcript of her conversation with Byron Reese, publisher of Gigaom, author, futurist, and host of the podcast Voices in AI. To watch more of her videos, check out The Tonya Hall Show on ZDNet's YouTube channel. Tonya Hall: Robots will not take your job. At least that's what my guest says, and he's got some pretty compelling evidence to back it up. Most people recognize Gigaom as a leading technology research publication, but you've written a book, The Fourth Age: Smart Robots, Conscious Computers, and the Future of Humanity.

The online programing services, such as Github,TopCoder, and EduCoder, have promoted a lot of social interactions among the service users. However, the existing social interactions is rather limited and inefficient due to the rapid increasing of source-code repositories, which is difficult to explore manually. The emergence of source-code mining provides a promising way to analyze those source codes, so that those source codes can be relatively easy to understand and share among those service users. Among all the source-code mining attempts,program classification lays a foundation for various tasks related to source-code understanding, because it is impossible for a machine to understand a computer program if it cannot classify the program correctly. Although numerous machine learning models, such as the Natural Language Processing (NLP) based models and the Abstract Syntax Tree (AST) based models, have been proposed to classify computer programs based on their corresponding source codes, the existing works cannot fully characterize the source codes from the perspective of both the syntax and semantic information. To address this problem, we proposed a Graph Neural Network (GNN) based model, which integrates data flow and function call information to the AST,and applies an improved GNN model to the integrated graph, so as to achieve the state-of-art program classification accuracy. The experiment results have shown that the proposed work can classify programs with accuracy over 97%.

In this article, we'll take a gander at the different regions of tech, how much interest exists for every ability, and where to go to begin your learning venture. Right away, here are 10 tech aptitudes sought after in 2019– in addition to where to get the online specialized preparing you have to wind up a professional. A subset of Artificial Intelligence (AI), Machine Learning (ML) is the territory of computational science that centers around investigating and deciphering examples and structures in information to empower getting the hang of, thinking, and basic leadership outside of human association. Basically, AI enables the client to encourage a PC calculation a massive measure of information and have the PC dissect and settle on information driven suggestions and choices dependent on just the information. On the off chance that any remedies are distinguished, the calculation can consolidate that data to improve its future basic leadership.

Is the age of intelligent machines bringing gender equality nearer or turning back the clock? Gemma Lloyd, co-founder of Work180, an Australia-based international jobs network for women, is proud of her engineering team in which women outnumber men. She just wishes there were more female engineers generally. "If there aren't enough women in the mix, the products won't be as good as they could be, and they certainly won't be what society wants -- because women are 50 per cent of society," she says. The lack of female technologists -- only 22 per cent of artificial intelligence professionals globally are female, for instance -- is a frustration for many gend er equality advocates.

Every sibling relationship has its clichés. In the Microsoft family of social-learning chatbots, the contrasts between Tay, the infamous, sex-crazed neo-Nazi, and her younger sister Zo, your teenage BFF with #friendgoals, are downright Shakespearean. When Microsoft released Tay on Twitter in 2016, an organized trolling effort took advantage of her social-learning abilities and immediately flooded the bot with alt-right slurs and slogans. Tay copied their messages and spewed them back out, forcing Microsoft to take her offline after only 16 hours and apologize. A few months after Tay's disastrous debut, Microsoft quietly released Zo, a second English-language chatbot available on Messenger, Kik, Skype, Twitter, and Groupme.

Google, which already dominates India's smartphone, search, and online video market, today launched a learning app for primary school children in the country as part of an effort to cement its grip on the world's fastest-growing internet market. The Android app, called Bolo, aims to help young kids improve their reading comprehension and vocabulary skills in Hindi and English. Bolo (the Hindi word for "speak"), features a range of games and tasks, and it rewards kids as they progress. Bolo, which is powered by Google's speech recognition and text-to-speech technology, first asks kids to read sentences. The app then listens to the efforts and reviews them, and an animated voice assistant -- called Diya -- suggests pronunciation and vocabulary corrections wherever applicable.

Today, AI has turned into reality what used to be the stuff of sci-fi novels. For decades, scholars from diverse disciplines have been predicting how AI and robotics are about to change the way we think, work and live. Although, not everyone is on the same page when it comes to AI, there is no denying that it is already demonstrating its positive potential in many industries. One area where AI is expected to play a huge role is education. However, in India, the education sector is still seeking ways to respond to the advent of this technology.

We may not be far into 2019, but the appetite for reports exploring the future of work shows little sign of abating. At the start of this year, the World Bank published a report that explored the changing nature of work. The report comes in at around 130 pages, so covers a lot of ground, from the changing nature of both work and firms to the need to strengthen social protection and support social inclusion. As many of my previous articles on this topic have looked at human capital, and especially lifelong learning, it was the section on that which caught my attention however. Recently I covered research highlighting the importance of creativity for many roles in the future of work, and the World Bank report echoes this, claiming that specific cognitive skills, such as problem solving, and sociobehavioral skills, such as creativity, are not only likely to be crucial skills to have, but are largely transferable across jobs.

Want to build skills in artificial intelligence (A.I.) and deep learning? Udacity and Google are launching a free introductory course on the subject, which naturally leans into TensorFlow, the open-source library for deep learning software developed by Google. "Intro to TensorFlow for Deep Learning" is a two-month course, and now open to enrollment. Its goal is to help developers build A.I. applications that can scale (using TensorFlow, of course). It's the second TensorFlow-based collaboration between the two firms; in 2016, Udacity and Google launched a TesnorFlow course that taught students the basics of the platform.