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.

Welcome to the "mom report" (Hi mom!); if jargon and mumbo jumbo are more your style then maybe this is what you're after, otherwise enjoy! I'm already counting the days (four, at the moment) until Term 2 begins and trying to decide the best way to sustain my momentum, starting with this here recap of Project 5 -- Vehicle Detection. The interesting thing to me about this project, in particular, was that it sort of occupied the middle ground between the first and fourth projects and the second and third projects. The first and fourth projects used old-school computer vision techniques and explicitly defined steps to produce an output (highlighting the location of lane lines), whereas the second and third projects employed deep learning's hot-ass newness (I might have to trademark that) to sort of let the program figure out the rules on its own based on a ton of examples. The goal of the Vehicle Detection project was to identify vehicles in dashcam video. While there are already deep learning implementations (e.g.

In the last few months, I have had several people contact me about their enthusiasm for venturing into the world of data science and using Machine Learning (ML) techniques to probe statistical regularities and build impeccable data-driven products. However, I have observed that some actually lack the necessary mathematical intuition and framework to get useful results. This is the main reason I decided to write this blog post. Recently, there has been an upsurge in the availability of many easy-to-use machine and deep learning packages such as scikit-learn, Weka, Tensorflow, R-caret etc. Machine Learning theory is a field that intersects statistical, probabilistic, computer science and algorithmic aspects arising from learning iteratively from data and finding hidden insights which can be used to build intelligent applications. Despite the immense possibilities of Machine and Deep Learning, a thorough mathematical understanding of many of these techniques is necessary for a good grasp of the inner workings of the algorithms and getting good results. The main question when trying to understand an interdisciplinary field such as Machine Learning is the amount of maths necessary and the level of maths needed to understand these techniques.

Caffe is a deep learning framework made with expression, speed, and modularity in mind. It is developed by the Berkeley Vision and Learning Center (BVLC) and by community contributors. Yangqing Jia created the project during his PhD at UC Berkeley. Caffe is released under the BSD 2-Clause license. Expressive architecture encourages application and innovation.

Jessica is a business and finance writer, focusing on impact investing, social entrepreneurship and economic development. She previously reported for financial publications covering the global private equity, real estate and insurance markets. "Quality education will always require active engagement by human teacher," write researchers from the Stanford One Hundred Study on Artificial Intelligence. But artificial intelligence will inform the teaching processes of the future as pressure builds on educators to "contain costs while serving a larger number of students and moving students through school more quickly." This week, ImpactAlpha is extracting nuggets from Stanford's century-long effort to understand AI's long-term possibilities and dangers.