The problem of building an autonomous robot has traditionally been viewed as one of integration: connecting together modular components, each one designed to handle some portion of the perception and decision making process. For example, a vision system might be connected to a planner that might in turn provide commands to a low-level controller that drives the robot's motors. In this talk, I will discuss how ideas from deep learning can allow us to build robotic control mechanisms that combine both perception and control into a single system. This system can then be trained end-to-end on the task at hand. I will show how this end-to-end approach actually simplifies the perception and control problems, by allowing the perception and control mechanisms to adapt to one another and to the task.

In TensorFlow, during the training process of the models, the parameters are maintained in the memory as variables. When a variable is created, you can use a tensor defined as a parameter of the function as an initial value, which can be a constant or a random value.

In this special guest feature, Dave O'Flanagan, CEO and co-founder of Boxever, outlines how airlines are leveraging big data and predictive capabilities to transform how they engage with customers. Dave is the CEO and co-founder of Boxever, a data science and omni-channel personalization platform for travel companies. The subprime financial crisis revealed that our data is only as good as our ability to analyze and understand it. AI will be necessary to helping prevent the next crisis before it happens. Marco Scirea, a PhD student at the IT University of Copenhagen, won the best paper award at the EvoMUSART conference for his research on music composition using artificial intelligence.

This is the latest installment in a regular series of conversations with William McDonough (@billmcdonough), designer, architect, author and entrepreneur. Joel Makower: Tell me about the innovation future roundtable you recently convened. Bill McDonough: I have been working with companies that are looking at the future of mobility in India, and designing factories and other things for them. The chairman said he would like to connect to some of the advanced thinking across many sectors and integrate that with some conversations that he could participate in. The first person I thought of for that was Jack Hidary.

Actor and writer Wil Wheaton wants to "add an A to the STEM acronym and make it STEAM." He'll be speaking at the USA Science and Engineering Festival April 16-17 in Washington about why he thinks the arts should be represented in the acronym commonly used when referring to the science, technology, engineering and math fields. Wheaton, 43, best known for his role as Wesley Crusher on "Star Trek: The Next Generation" in the 1980s and '90s and more recently as a fictionalized version of himself on "The Big Bang Theory," says that he has always been fascinated by science and technology, and has made it a goal of his to ensure that kids get the encouragement they need to pursue those fields. Wheaton spoke with U.S. News by phone about why he got involved in the festival, how science fiction and fact have shaped his life and career and why he thinks it should be "science, technology, engineering, arts and math." How did you get involved with the USA Science and Engineering Festival?

With the evolution of digital technology, the definitions of AI and Machine Learning are quite distinct from what they were before. Add to that the complexity of people using these terms interchangeably or to mean different things, and we have a prime example of our confusions about modern technology. Ask a random guy at Times Square, "what do you think is meant by AI?" and he's sure to think of aliens, Men In Black or Halo. However, the fact is that artificial intelligence is quite a common part of our everyday lives – at least in the 21st century. What does it basically mean?

DigitalGenius is announcing its Human AI customer service platform today, along with a 4.1 million seed investment. The work is to augment the process, while still keeping the human element decidedly at the center of things. It's interesting to note that Salesforce was part of the deal, as that could conceivably help the startup scale quickly in this space, thanks to Salesforce's massive distribution network and its suite of automation products ripe for AI. I talked to DigitalGenius chief strategy officer Mikhail Naumov to clarify what AI is and isn't. "It's important to decipher between Hollywood AI and practical AI you can use today," he said.

Demis Hassabis has a modest demeanour and an unassuming countenance, but he is deadly serious when he tells me he is on a mission to "solve intelligence, and then use that to solve everything else". Coming from almost anyone else, the statement would be laughable; from him, not so much. Hassabis is the 39-year-old former chess master and video-games designer whose artificial intelligence research start-up, DeepMind, was bought by Google in 2014 for a reported 625 million. He is the son of immigrants, attended a state comprehensive in Finchley and holds degrees from Cambridge and UCL in computer science and cognitive neuroscience. A "visionary" manager, according to those who work with him, Hassabis also reckons he has found a way to "make science research efficient" and says he is leading an "Apollo programme for the 21st century". He's the sort of normal-looking bloke you wouldn't look twice at on the street, but Tim Berners-Lee once described him to me as one of the smartest human beings on the planet. Artificial intelligence is already all around us, of course, every time we interrogate Siri or get a recommendation on Android. And in the short term, Google products will surely benefit from Hassabis's research, even if improvements in personalisation, search, YouTube, and speech and facial recognition are not presented as "AI" as such. "It's just stuff that works.") In the longer term, though, the technology he is developing is about more than emotional robots and smarter phones.

Mauro Maggioni has been named the Bloomberg Distinguished Professor of Data Intensive Computation at Johns Hopkins in the Krieger School of Arts and Sciences' Department of Mathematics and the Whiting School of Engineering's Department of Applied Mathematics and Statistics. He will join Johns Hopkins from Duke University, where in 2012 he was promoted from assistant professor to full professor of mathematics, electrical and computer engineering, and computer science. Maggioni is the 20th Bloomberg Distinguished Professor appointed across Johns Hopkins. The professorships are supported by a 350 million gift to the university by Johns Hopkins alumnus, philanthropist, and three-term New York City Mayor Michael R. Bloomberg. The majority of this gift is dedicated to creating 50 new interdisciplinary professorships, galvanizing people, resources, research, and educational opportunities to address major world problems.

DigitalGenius is announcing a Human AI customer service platform today with a 4.1 million seed investment. The platform integrates with existing customer service software suites -- like Salesforce, Zendesk, and Oracle -- to automate the most repetitive parts of customer service through AI and machine learning-powered chat bots. The work to augment the process, while still keeping the human element decidedly at the center of things. It's interesting to note that Salesforce was part of the deal, as that could conceivably help the startup scale quickly in this space, thanks to Salesforce's massive distribution network and its suite of automation products ripe for AI. I talked to DigitalGenius' chief strategy officer Mikhail Naumov to clarify what AI is and isn't.