In his general session at 19th Cloud Expo, Manish Dixit, VP of Product and Engineering at Dice, discussed how Dice leverages data insights and tools to help both tech professionals and recruiters better understand how skills relate to each other and which skills are in high demand using interactive visualizations and salary indicator tools to maximize earning potential. As the leader of the Product, Engineering and Data Sciences team at Dice, he takes a metrics-driven approach to management. His experience in building and managing high performance teams was built throughout his experience at Oracle, Sun Microsystems and SocialEkwity. Under his direction Dice continues to lead the recruitment category in skills mapping and leveraging data science to both advise companies as they examine their internal resources and help tech pros maximize their earning potential by understanding the value of their skills. Manish has over 20 years of experience developing products and leading technical / engineering teams.

For decades Hollywood has treated computers as magic boxes from which endless plot points could be conjured, in denial of all common sense. TV and movies depicted data centers accessible only through undersea intake valves, cryptography that can be cracked through a universal key, and e-mails whose text arrives one letter at a time, all in caps. "Hollywood hacker bullshit," as a character named Romero says in an early episode of Mr. Robot, now in its second season on the USA Network. "I've been in this game 27 years. Not once have I come across an animated singing virus."

"HI!" says the chat window as users converse with the person on the other side of the screen to help them complete important but mundane HR tasks like requesting leave or even calling in sick. With every line of the chat sounding natural, you may think that the person responding to your request is a human. Chatbots are now becoming a mainstay in enterprises, as they help companies to save cost and be more efficient in delivering personalised data and experience to their employees and customers. How does it help companies achieve better performance? A recent webinar, organised by Microsoft and Ramco, revealed a few insights into the phenomenon that is gaining traction in the enterprise world.

While running a digital marketing agency, Neerav Parekh regularly updated his clients on their campaign performance with reports and charts that were carefully put together. However, the clients were quickly snowed under the blizzard of data, and inevitably demanded that account managers personally visit them and take them through these reports. This was a laborious process and, rather than plod through it repeatedly, Parekh, a serial entrepreneur, turned to artificial intelligence (AI), the science of trying to make computers think and act like humans, for a solution. His product, Phrazor, is aimed at automating the process of interpreting data and communicating insights. Having used Phrazor for his agency, Parekh has now sought to extend the reach of his product.

More and more, 67-year-old Washington resident Lon Coleman feels like he's wandering through a fog. He walks into the living room and forgets why, or makes a phone call only to blank on whose number he dialed. An author of three books who once wrote up to five poems a day, now the lines that spring to his mind often slip away as soon as he puts pencil to paper. Sometimes the fog clears, and when his memory comes back, "it's amazing," he says. "Sometimes it doesn't, I have to admit."

Not sure what colour car to buy? Lexus'screen on wheels' has 41,999 programmable LEDs - and it'll even sync up with music and hand gestures If you're stumped about what colour Lexus to buy, then the custom LIT SI sedan may be the car for you.he Japanese car maker has wrapped the vehicle in 41,999 programmable LEDs, transforming it into a screen on wheels that is capable of broadcasting glittering graphics and multi-coloured animations. LIT SI was created by Lexus employees placing 41,999 LEDs on the body of the car by hand. There are three modes drivers can use to power the LEDs. Attract mode displays a loop of colourful graphics on the car.

The 7th Internet of @ThingsExpo will take place on June 6-8, 2017, at the Javits Center in New York City, New York. Chris Matthieu is the co-founder and CTO of Octoblu, a revolutionary real-time IoT platform recently acquired by Citrix. Octoblu connects things, systems, people and clouds to a global mesh network allowing users to automate and control design flows, processes and sensor data, and analyze/react to real-time events and messages as well as big data trends and anomalies. Prior to co-founding Octoblu, Chris was the founder of Nodester, an open-source Node.JS PaaS which was acquired by AppFog and the founder of Teleku, a communications-as-a-service cloud platform which was acquired by Voxeo. Earlier Chris served as the Tech Chair of SYS-CON's WebRTC Summit.

Uber is creating a new AI research lab dedicated to exploring the frontiers of machine learning and applying key advances to its business. The lab will be based in Silicon Valley and will be led by Gary Marcus, a professor at NYU and the CEO of Geometric Intelligence, a company Uber is acquiring for an undisclosed sum. The Uber AI lab will also employ another big-name AI researcher, Zoubin Ghahramani, who will retain a part-time post as a professor at the University of Cambridge in the U.K. The company's other cofounders are Ken Stanley, an associate professor at the University of Central Florida, and Doug Bemis, a recent NYU graduate with a PhD in neurolinguistics. The new lab will have 15 founding members, and it will explore a range of fundamental challenges, including developing forms of machine learning that need less data; training AI systems using not only data but also explicit rules; and designing machine-learning systems that explain their decisions. Advances in these areas could be vital to self-driving cars but might also help improve Uber's existing business by, for instance, helping route cars or match customers in an Uber pool more efficiently.

Creating aesthetically pleasing pieces of art, including music, has been a long-term goal for artificial intelligence research. Despite recent successes of long-short term memory (LSTM) recurrent neural networks (RNNs) in sequential learning, LSTM neural networks have not, by themselves, been able to generate natural-sounding music conforming to music theory. To transcend this inadequacy, we put forward a novel method for music composition that combines the LSTM with Grammars motivated by music theory. The main tenets of music theory are encoded as grammar argumented (GA) filters on the training data, such that the machine can be trained to generate music inheriting the naturalness of human-composed pieces from the original dataset while adhering to the rules of music theory. Unlike previous approaches, pitches and durations are encoded as one semantic entity, which we refer to as note-level encoding. This allows easy implementation of music theory grammars, as well as closer emulation of the thinking pattern of a musician. Although the GA rules are applied to the training data and never directly to the LSTM music generation, our machine still composes music that possess high incidences of diatonic scale notes, small pitch intervals and chords, in deference to music theory.

Generative adversarial learning is a popular new approach to training generative models which has been proven successful for other related problems as well. The general idea is to maintain an oracle $D$ that discriminates between the expert's data distribution and that of the generative model $G$. The generative model is trained to capture the expert's distribution by maximizing the probability of $D$ misclassifying the data it generates. Overall, the system is \emph{differentiable} end-to-end and is trained using basic backpropagation. This type of learning was successfully applied to the problem of policy imitation in a model-free setup. However, a model-free approach does not allow the system to be differentiable, which requires the use of high-variance gradient estimations. In this paper we introduce the Model based Adversarial Imitation Learning (MAIL) algorithm. A model-based approach for the problem of adversarial imitation learning. We show how to use a forward model to make the system fully differentiable, which enables us to train policies using the (stochastic) gradient of $D$. Moreover, our approach requires relatively few environment interactions, and fewer hyper-parameters to tune. We test our method on the MuJoCo physics simulator and report initial results that surpass the current state-of-the-art.