For centuries, humans have been fretting over "technological unemployment" or the loss of jobs caused by technological change. Never has this sentiment been accentuated more than it is today, at the cusp of the next industrial revolution. With developments in artificial intelligence continuing at a chaotic pace, fears of robots ultimately replacing humans are increasing. However, while AI continues to master an increasing number of tasks, we're still decades away from human jobs going extinct. With AI finding its way into more and more domains, the demand for tech talent is growing.

Chia-Chiunn Ho was eating lunch inside Facebook headquarters, at the Full Circle Cafe, when he saw the notice on his phone: Larry Zitnick, one of the leading figures at the Facebook Artificial Intelligence Research lab, was teaching another class on deep learning. Ho is a 34-year-old Facebook digital graphics engineer known to everyone as "Solti," after his favorite conductor. He couldn't see a way of signing up for the class right there in the app. So he stood up from his half-eaten lunch and sprinted across MPK 20, the Facebook building that's longer than a football field but feels like a single room. "My desk is all the way at the other end," he says. Sliding into his desk chair, he opened his laptop and surfed back to the page.

For centuries, humans have been fretting over "technological unemployment" or the loss of jobs caused by technological change. Never has this sentiment been accentuated more than it is today, at the cusp of the next industrial revolution. With developments in artificial intelligence continuing at a chaotic pace, fears of robots ultimately replacing humans are increasing. We're inviting 250 to exhibit at TNW Conference and pitch on stage! However, while AI continues to master an increasing number of tasks, we're still decades away from human jobs going extinct.

For centuries, humans have been fretting over "technological unemployment" or the loss of jobs caused by technological change. Never has this sentiment been accentuated more than it is today, at the cusp of the next industrial revolution. With developments in artificial intelligence continuing at a chaotic pace, fears of robots ultimately replacing humans are increasing. We've teamed up with Product Hunt to offer you the chance to win an all expense paid trip to TNW Conference 2017! However, while AI continues to master an increasing number of tasks, we're still decades away from human jobs going extinct.

Too often, engineers are brainwashed into thinking they can create an impeccable artificial intelligence (AI) model -- a blank slate they release into the wild for independent learning. They think: "If I create flawless math on top of the right infrastructure, I'll have the perfect model." Train the algorithm, let it run free, and that's the end of the story, right? Just like human intelligence, artificial intelligence requires continuous learning to advance its expertise. Training a commercially applied AI is not a one-and-done exercise.

This week the World Government Summit took place in Dubai attracting considerable global interest. Though not immediately associated with excitement and anticipation, trends in government are receiving considerable attention. By 2020, 60 percent of the world's population will be living in cities, putting a huge strain on existing government operations. Increasingly it is clear that governments across the board have to overhaul their approach to be more streamlined, effective and dynamic. What had begun as a niche academic reference, "The future of government" is fast becoming a school in itself. With the development of cognitive technologies such as computer learning, natural language processing, robotics and speech recognition -- the need to build government capabilities to use such developments is clear.

Recently, deep learning (DL) methods have been introduced very successfully into human activity recognition (HAR) scenarios in ubiquitous and wearable computing. Especially the prospect of overcoming the need for manual feature design combined with superior classification capabilities render deep neural networks very attractive for real-life HAR application. Even though DL-based approaches now outperform the state-of-the-art in a number of recognitions tasks of the field, yet substantial challenges remain. Most prominently, issues with real-life datasets, typically including imbalanced datasets and problematic data quality, still limit the effectiveness of activity recognition using wearables. In this paper we tackle such challenges through Ensembles of deep Long Short Term Memory (LSTM) networks. We have developed modified training procedures for LSTM networks and combine sets of diverse LSTM learners into classifier collectives. We demonstrate, both formally and empirically, that Ensembles of deep LSTM learners outperform the individual LSTM networks. Through an extensive experimental evaluation on three standard benchmarks (Opportunity, PAMAP2, Skoda) we demonstrate the excellent recognition capabilities of our approach and its potential for real-life applications of human activity recognition.

For centuries, humans have been fretting over "technological unemployment" or the loss of jobs caused by technological change. Never has this sentiment been accentuated more than it is today, at the cusp of the next industrial revolution. With developments in artificial intelligence continuing at a chaotic pace, fears of robots ultimately replacing humans are increasing. Gary Vaynerchuk was so impressed with TNW Conference 2016 he paused mid-talk to applaud us. However, while AI continues to master an increasing number of tasks, we're still decades away from human jobs going extinct.

For centuries, humans have been fretting over "technological unemployment" or the loss of jobs caused by technological change. Never has this sentiment been accentuated more than it is today, at the cusp of the next industrial revolution. With developments in artificial intelligence continuing at a chaotic pace, fears of robots ultimately replacing humans are increasing. However, while AI continues to master an increasing number of tasks, we're still decades away from human jobs going extinct. With AI finding its way into more and more domains, the demand for tech talent is growing.

For centuries, humans have been fretting over "technological unemployment" or the loss of jobs caused by technological change. Never has this sentiment been accentuated more than it is today, at the cusp of the next industrial revolution. With developments in artificial intelligence continuing at a chaotic pace, fears of robots ultimately replacing humans are increasing. TNW Conference won best European Event 2016 for our festival vibe. See what's in store for 2017.