I am writing this article while my baby daughter sleeps. Like all new parents, her dad and I have spent the last few months in a joy-filled, sleepy haze of getting to know her and imagining what her future might look like. This brings a new intensity, and a little more trepidation, to my role advising on the future of work. What will work look like for this generation of young women, especially as more and more of our roles are being automated -- or even replaced -- by artificial intelligence (AI)? And how can leaders ensure that AI does not lead to gender bias in their organizations? Recent research is beginning to answer these questions, and the outlook is mixed: on the one hand, women may be spared from the job disruptions men will face in the longer-term.

Bengaluru: The fifth edition of TechGig's coding competition – Geek Goddess 2019 – this year will encourage participants to build solutions on development platforms such as Automation Anywhere and JetBrains. The competition will be held in Bengaluru on November 8. Geek Goddess 2019 will host six themes, based on technologies such as Artificial Intelligence (AI), Machine Learning (ML), Robotic Process Automation (RPA) and Cloud. For instance, Cloudify Everything would need participants to provide a data science-based solution leveraging Azure ML services or AWS Sagemaker. This hackathon theme has got more than 4,403 registrations. TechGig, a Times Internet-backed platform for IT professionals, conducts the annual coding challenge to find the best women coders in the country, in a bid to improve representation of women in the field of technology, it said in a statement.

The NEXT Future Transportation module is a far cry from the sleek visions of self-driving cars designed by Tesla or Mercedes. With an average cruising speed of 20 kilometers per hour, the electric pods are unlikely to set pulses racing. But perhaps the most crucial distinction is that this self-driving vehicle is passenger ready. Following trials in 2018, several NEXT units are expected to be in action at the Expo 2020 site in Dubai, providing short-distance rides for some of the estimated 25 million visitors attending the six-month world fair. The Ruler of Dubai, Sheikh Mohammed bin Rashid Al Maktoum, has set the demanding target of having 25% of journeys in the city to be made through driverless transport by 2030 -- going beyond the existing driverless metro and monorail systems.

Ongoing Brexit tensions have cut the productivity of UK companies and created a crucial need for increased productivity – which Robotic Process Automation (RPA) could help provide. New technologies like RPA are becoming commonplace in offices and increasing the efficiencies of businesses processes as a result. That's because they execute rules-based office tasks, freeing people up for more meaningful, strategic tasks, and helping boost engagement, productivity and happiness in turn. They are also ushering in a new world of work: according to the World Economic Forum, 65% of children now entering primary school will hold jobs that currently don't exist. Research conducted with Forrester has revealed that both businesses and workers have already started to benefit from the adoption of RPA. According to Gallup, 87% of employees worldwide are not engaged in their roles, whilst disengaged employees cost the UK £52-70 billion per year in lost productivity.

Enterprises of the previous decade have transformed from transactional to digital. Today, digital enterprises use machine learning pipelines with humans-in-loop. However, the enterprises of tomorrow will be aiming for end-to-end AI-driven core business solutions, or intelligent enterprises. To address these demands, Google this week announced TensorFlow Enterprise at the ongoing TensorFlow World conference. TensorFlow, one of the most popular machine learning frameworks, was open sourced by Google in 2015.

The ACM Symposium on Operating Systems Principles (SOSP) has a long history and a great reputation in Operating Systems (OS) research. This year SOSP was held in Huntsville, a charming town located in lake country, some 200km north of Toronto. On a rainy Sunday, Synced visited Huntsville to check out the SOSP AI System Workshop. The growing and widespread deployment of AI has motivated OS researchers to develop novel system engineering for AI. The SOSP AI System Workshop explored these efforts to advance research in AI and operating systems.

It can take years of birdwatching experience to tell one species from the next. But using an artificial intelligence technique called deep learning, Duke University researchers have trained a computer to identify up to 200 species of birds from just a photo. The real innovation, however, is that the A.I. tool also shows its thinking, in a way that even someone who doesn't know a penguin from a puffin can understand. The team trained their deep neural network -- algorithms based on the way the brain works -- by feeding it 11,788 photos of 200 bird species to learn from, ranging from swimming ducks to hovering hummingbirds. The researchers never told the network "this is a beak" or "these are wing feathers."

A few years ago, Paul Allen, the co-founder of Microsoft, published the results of something called the Great Elephant Census, which counted all the savanna elephants in Africa. What it found rocked the conservation world: In the seven years between 2007 and 2014, Africa's savanna Elephant population decreased by about a third and was on track to disappear completely from some African countries in as few as 10 years. To reverse that trend, researchers landed on a technology that is rewriting the rules for everything from our household appliances to our cars: artificial intelligence. AI's ability to find patterns in enormous volumes of information is demystifying not just Elephant behavior but human behavior -- specifically poacher behavior -- too. "AI can process huge amounts of information to tell us where the elephants are, how many there are," said Cornell University researcher Peter Wrege.

People from across the TensorFlow community came together in Santa Clara, California for TensorFlow World. Below you'll find links to highlights from the event. Jeff Dean explains why Google open-sourced TensorFlow and discusses its progress. Get a free trial today and find answers on the fly, or master something new and useful. Theodore Summe offers a glimpse into how Twitter employs machine learning throughout its product.

The project lead says that future troops may be deployed with watches or chest straps that could predict when they will get sick and how long it would take to recover. When U.S. Service members get ill at the last minute, it could cause serious consequences in regards to executing critical duties. To get ahead of the issues, the Defense Threat Reduction Agency (DTRA), leading health technology company Royal Phillips and the Defense Innovation Unit (DIU), launched a project to develop a technology that could predict whether a service member is getting sick 48 hours in advance. The project was launched 18 months ago and announced its completion on Oct 22. "By coupling large-scale data, with our experience in AI and remote patient monitoring with DTRA's drive for innovation, we were able to develop a highly predictive early-warning algorithm based on non-invasively collected biomarkers," Joe Frassica, chief medical officer and head of research for Philips North America, said in the release. Using 165 distinct biomarkers across 41,000 cases, the Phillips team created the Rapid Analysis of Threat Exposure (RATE) algorithm which is the "first large-scale empirical exploration of prediction of pre-symptomatic infection in humans."