Generative RNNs are now widely popular, many modeling text at the character level and typically using unsupervised approach. Here we show how to generate contextually relevant sentences and explain recent work that does it successfully. Deep learning has risen to prominence, both delighting and enraging computer scientists, following a number of breakthrough results on difficult classification tasks. Convolutional neural networks demonstrate an unprecedented ability to recognize objects in images. A variety of neural networks have similarly revolutionized the field of speech recognition.

Recently I wrote about a new service being offered by tech giant IBM to utilize machine learning in the field of radiology. The service, called Avicenna, hopes to identify anatomical features and abnormalities in medical images, and by taking this analysis and cross-referencing it with the patient's medical record provide support for a successful diagnosis. The aim is for the service to make the work of cardiologists and radiologists quicker and more effective. It is still very much in the early stages of development and is being trained on anonymized images in cardiology and breast radiology areas. A British startup is now taking a similar approach to detecting the onset of dementia.

Within the rail industry, anything which helps keep trains moving, avoiding operational delays and improves customer experience, is worth pursuing. Many OEMs are now investing significant resources into one of the most valuable and potentially rewarding currencies in business: Big Data. In rail, and specifically when it comes to rolling stock maintenance, big data is synonymous with Condition Based Maintenance (CBM) and Predictive Maintenance (PM). Thanks to the rapidly expanding scale of manufacturing and asset maintenance industries, they are now adapting to the wider applications of advanced algorithms on consumer generated big data. Though CBM and PM are commonly adopted practices in rail industry, the scope of CBM is far wider than that of PM.

With the lopsided 4-1 rout by Google's AlphaGo over Go grandmaster Lee Sedol, the easy takeaway is that artificial intelligence (AI) has achieved another milestone against humans, raising the specter that machines may eventually replace people, even managers. But by winning even in such convincing fashion, AlphaGo has revealed that AI still has a number of shortcomings, particularly when it comes to machine-made intuition. Google acquired DeepMind, the developer of AlphaGo, in 2014, in a 500 million bid to expand its burgeoning AI portfolio. AlphaGo's deep-learning algorithm allows both a "policy network" and a "value network" to store not only millions of past games played by the masters but also those played against tweaked versions of itself. The naming of the two networks is managerial-sounding and is aimed at promoting efficiency, not just raw computing power.

Image recognition is a hot area of research using artificial intelligence, and now IBM offers an online demo to let anyone test out the capabilities offered by its Watson cognitive computing system. Six sample photos are provided for illustration, or you can upload your own and ask Watson to analyze them.

It was nearly 30 years ago that I first got infatuated with Artificial Intelligence (AI) and I ended up focusing both my undergraduate and graduate engineering research on applications of Artificial Neural Networks (ANNs). My first two jobs after graduate school stayed in the same groove; over 6 years I developed AI and machine learning techniques to address real world problems that ranged from recognizing human speech and natural language, to converting handwriting to searchable digitized text, and to streamlining maintenance procedures in nuclear reactor cores. So it is with a mix of amazement and amusement that I am soaking up the resurgence of AI and machine learning as the buzzword-du-jour: "Deep Learning". Deep Learning is very visible in the high hopes we hold for driverless cars and in the triumph of machines over chess champions. It is less conspicuously and more frequently used in the form of Apple's Siri, Amazon's Echo, playlists generated on Spotify, that auto-tag feature on Facebook Photos, the voice assistant that answers the phone when you call your bank, or when your fingerprint is recognized by a machine.

This helps to validate DeepMind's machine learning techniques and the neural network construction behind it. Having proven their mettle in Go, the DeepMind team could now have the confidence (and funding) to tackle more complex AI challenges. ARTIFICIAL INTELLIGENCE (AI) just overcame a new hurdle: learning to play Go, a game considered thousands of times more complex than chess--well enough to beat the greatest human player at his own game. South Korean national Lee Se-dol, one of the world's top Go players, won only one of the five matches against Google's AlphaGo, missing out on the 1-million prize up for grabs in a recent'challenge' held in Seoul. AlphaGo, an AI system developed by Google DeepMind, just bested the best Go-playing human currently alive. This was not supposed to happen.

The World Science Festival Brisbane's'Shared Space Bots' performance demonstrations at Queensland University of Technology used pint-sized, futuristic floor robots to reveal research into the technologies that will allow humans to communicate with driverless cars, and allowed audience members a chance to'test drive' the systems safely. Shared Space Bots were demonstrated by internationally acclaimed technologist Christopher Lindinger from Austrian R&D company Ars Electronica Futurelab. They have been developed as part of the ongoing research cooperation between Mercedes-Benz and Ars Electronica Futurelab on the topic of future mobility.

Artificial intelligence is breathing down our necks: Software built by Google startled the field last week by easily defeating the world's best player of the Asian board game Go in a five-game match. Go resembles chess in the deep, complex problems it poses but is even harder to play and has resisted AI researchers longer. It requires mastery of strategy and tactics while you conceal your own plans and try to read your opponent's. Mastering Go fits well into the ambitious goals of AI research. It shows us how much has been accomplished and forces us to confront, as never before, AI's future plans.

As the nonplayable characters (NPCs) of squad-based shooter computer games share a common goal, they should work together in teams and display cooperative behaviors that are tactically sound. Our research examines genetic programming (GP) as a technique to automatically develop effective squad behaviors for shooter games. GP has been used to evolve teams capable of defeating a single powerf.ul This paper is an extension of our paper presented at the 2008 Conference on Artificial Intelligence and Interactive Digital Entertainment (AIIDE'08). Its aim is to explore the effects of shared perception on the evolution of effective squad behaviors.