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.

I am a transitional research fellow in the Digital Creativity Hub at the University of York working on Artificial Intelligence (AI), data mining and machine learning for digital games and interactive media. I am also a member of the Artificial Intelligence and Games groups, in the Department of Computer Science. My research is focussed on using games to push boundaries in the capabilities of modern AI and making state of the art methods accessible to the industry to encourage a new generation of intelligent games.

The "photonic time stretch" was invented by Professor Barham Jalali, who holds a patent for this technology, and its use in microscopes is just one of many possible applications. It works by taking pictures of flowing blood cells using laser bursts in the way that a camera uses a flash. This process happens so quickly – in nanoseconds, or billionths of a second – that the images would be too weak to be detected and too fast to be digitised by normal instrumentation. The new microscope overcomes those challenges using specially designed optics that boost the clarity of the images and simultaneously slow them enough to be detected and digitised at a rate of 36 million images per second. It then uses deep learning to distinguish the cancer cells from healthy white blood cells. Deep learning is a form of artificial intelligence that uses complex algorithms to extract meaning from data, with the goal of achieving accurate decision making.

For both humans and machines, understanding the visual world requires relating new percepts with past experience. We argue that a good visual representation for an image should encode what makes it similar to other images, enabling the recall of associated experiences. Current machine implementations of visual representations can capture some aspects of similarity, but fall far short of human ability overall. Even if one explicitly labels objects in millions of images to tell the computer what should be considered similar--a very expensive procedure--the labels still do not capture everything that might be relevant. This thesis shows that one can often train a representation which captures similarity beyond what is labeled in a given dataset.

A recent acquisition looks to bring artificial intelligence and deep learning capabilities to the CRM powerhouse. Marketers using Salesforce products may soon find artificial intelligence–enabled features rolling out across the CRM platform due to the company's acquisition of AI and deep learning technology service MetaMind. MetaMind CEO Richard Socher announced the merger through a company blog post. The acquisition will ostensibly infuse Salesforce's existing services with MetaMind's natural language and deep machine learning software, which can reportedly analyze images, as well as text and sentiment. "At MetaMind, we've always been excited to bring breakthrough AI to high impact use cases. I can't think of a better place to have impact with AI than Salesforce and its many existing and future products," Socher said in a separate statement.