With driverless cars already on California roads, it is not too soon to ask whether we can program a machine to act ethically.

Today, at Build 2016, Microsoft revealed a new AI tool for developers called the Bot Framework. The Bot Framework allows developers to write software bots that interact with people (and other bots). Essentially this makes it easy to create a Conversational User Interface (CUI), ushering in a new era that makes us think more about what a user interface should be. Beyond a visual interface, User Experience (UX) will take on new meaning. The bot will need to lead the user through a series of steps that make sense, yet be able to adjust so as not to be too rigid and lead them down a path that might not make sense for the situation.

We examine top Python Machine learning open source projects on Github, both in terms of contributors and commits, and identify most popular and most active ones.

I have an image database containing raw medical images (lung x-ray image) and their corresponding binary masks indicating blood vessels. I would like to apply machine learning techniques suck as GLM on these training data to build a model. So I can use this model to generate binary blood vessle mask for other lung x-ray images. I need some help for a machine learning workflow/pseudo code for this project.

ARTIFICIAL INTELLIGENCE: Neural Networks: What Are They, and Why Is the Tech Industry Obsessed With Them? John Brownlee Fast Company "Combine them with conversational interfaces, and neural networks can make true artificial intelligence finally possible--a revolution that would have a knock-on effect in the way we pretty much do everything. Designers in the future won't just use neural networks; neural networks may very well be designers themselves." COMPUTING: Why a Chip That's Bad at Math Can Help Computers Tackle Harder Problems Tom Simonite MIT Technology Review "In a simulated test using software that tracks objects such as cars in video, Singular's approach was capable of processing frames almost 100 times faster than a conventional processor restricted to doing correct math--while using less than 2 percent as much power." CULTURE & TECHNOLOGY: How Early Computer Games Influenced Internet Culture Adrienne LaFrance The Atlantic "Games also shaped people's understanding of what computers are for--and what humankind's relationship with such machines could be like...Gaming is the first form of computational technology most of us ever handled…Games taught us principles of interaction and screen responsiveness, about coordination between hand and eye, how to type, how to sit, how to look at a screen."

It is hard to think of the words "artificial intelligence" without conjuring up Doomsday images of The Matrix and The Terminator where man and highly intelligent machine are pitched into battle. Even a step further back from that science-fiction precipice conflates the term with massive job losses and the eventual irrelevance – or liberation – of humankind from labour as we know it. Artificial intelligence or AI is, of course, all around us already in obvious ways – Apple's voice recognition service Siri or Google's increasingly reliable search results – or in more obscure ones such as better weather forecasting and lower levels of spam e-mail in your inbox. There is nothing new about the concept of AI which started to gain traction in the 1950s when Alan Turing explored the notion of machines that could think. J.C.R. Licklider's paper Man-Computer Symbiosis from 1960 may have sounded like something penned by sci-fi writer Philip K. Dick, but was instead a formative paper on how the world would move beyond programmable computers to one where computers "facilitate formative thinking".

If you spent any amount of time online in the past few days, you'll have noticed plenty of chatter about chat bots, tiny little computer programs that live inside messaging platforms like Facebook Messenger, Skype, or Slack that are designed to spit out useful bits of data like weather forecasts or the latest headlines using an interface--chat!--that requires little or no training to understand. It's a nice idea, maybe, but you know what's the single most obvious thing to me in the days since Mark Zuckerberg took the stage in San Francisco to proclaim the beginning of the Chat Bot Era? Brands and developers are feverishly trying to figure out in real time, right before our very eyes, where this whole chat bot thing is going. "There's no question that bots are more suited for some kinds of interactions than others," Sam Mandel, CEO of weather app Poncho, which released a weather bot for Facebook Messenger earlier this week, told Motherboard. "We think of it as really a conversation: When we built Poncho we wanted him to be your friend, and that's why we think weather is just a great starting point in the sense that that's a conversation that everyone has with their friends." Unlike my friends, however, Poncho doesn't quite understand the nuances of the Attitude Era: As Mandel explains it, Poncho decided to go all-in with a Facebook Messenger bot because people "have already voted with their time."

While 3D artist Moritz Reichartz was creating a virtual rendition of Ai Weiwei's Stools in Mashup Between the Clouds, he was also nearing completion of an automated 3D animation inspired by artificial intelligence.Titled Hands Off [A.I.], Reichartz created the animated video over a year of research and development. In describing the animations as "automated," Reichartz means that no keyframes were used whatsoever for any of the clips--he merely "designed and guided the self-driving movements." In this sense the animated virtual objects and spaces, which are equally surreal, beautiful and alien, are types of A.I. Reichartz considers Hands Off [A.I.] to be an artistic answer to Tim Urban's essay "The AI Revolution: The Road to Superintelligence." The artist says the article gave him "heavy mental vertigo" and might have even scared him. "I was always interested about automation in computer usage," Reichartz tells The Creators Project.

I think that's quite a misleading title. More appropriate to say "Adversarial images for humans". What makes adversarial examples for deep learning "intriguing" is that there are indistinguishable from the original image. We want to have behavior similar to humans and how to achieve it is an open research problem.

Since the inception of artificial intelligence (AI), computer scientists have used our biological systems to model and design predictive analytic systems. The design of a neural network is roughly modeled against the way our brain works: with sensory inputs, neurons, and synapses. Genetic algorithms are designed to mimic the way our genes combine to optimally adapt to our surroundings. This approach has its flaws: we don't completely understand the way we work; even current technology can't come close to the volume and speed with which we process information; and let's face it--we're not perfect. So in some ways humans aren't a great paragon for an advanced analytic system, which is why current-day data scientists often ignore the way our memory system works.