An "evolving" artistic tribute to pioneering scientist Alan Turing is to be unveiled just a few miles from his west London birthplace. The work, called Message from the Unseen World, uses artificial intelligence to light up a wall with words and phrases from a poem written in honour of the scientist, who was played by Benedict Cumberbatch in the 2014 Oscar-winning The Imitation Game. Turing was a pioneering mathematician who is now hailed for his work as part of the war-time team tasked with cracking Nazi codes. He is widely credited as the father of modern computing, but died a broken man in 1954, aged 41, after chemical castration following a conviction for homosexual activity. The installation on Bishop's Bridge Road, near the entrance to Paddington Tube station, is based on a poem by Nick Drake which imagines the scientist looking back on his life.

In the last couple of years, we have examined how deep learning shops are thinking about hardware. From GPU acceleration, to CPU-only approaches, and of course, FPGAs, custom ASICs, and other devices, there are a range of options--but these are still early days. The algorithmic platforms for deep learning are still evolving and it is incumbent on hardware to keep up. Accordingly, we have been seeing more benchmarking efforts of various approaches from the research community. This week yielded a new benchmark effort comparing various deep learning frameworks on a short list of CPU and GPU options.

I've spent many years referencing Wikipedia's list of cognitive biases whenever I have a hunch that a certain type of thinking is an official bias but I can't recall the name or details. It's been an invaluable reference for helping me identify the hidden flaws in my own thinking. Nothing else I've come across seems to be both as comprehensive and as succinct. However, honestly, the Wikipedia page is a bit of a tangled mess. Despite trying to absorb the information of this page many times over the years, very little of it seems to stick.

IBM Watson Artificial Intelligence XPRIZE -Using AI in new ways to solve the World's biggest problems #Gsummit At the Singularity University Global Summit 2016, Nextbigfuture interviewed Amir Banifatemi who is managing the IBM Watson AI XPRIZE. Amir Banifatemi is a founder and managing partner at K5 Ventures. He focuses on working with startups and growth-oriented companies on products and initiatives that could trigger significant breakthrough with strong economic and societal impact. He has a special emphasis on machine learning and predictive systems, IoT, knowledge sharing and crowdsourcing, Education, and digital health. The IBM Watson AI XPRIZE is a 5 million AI and cognitive computing competition challenging teams globally to develop and demonstrate how humans can collaborate with powerful AI technologies to tackle the world's grand challenges.

A model takes a selfie with CHiQ TV in the background at the launch of the device in Beijing. Watching television in the digital era has just got a lot easier. Sichuan Changhong Electronic Co Ltd, a manufacturer of TVs, has ushered in the era of intelligent TVs. Combining its expertise in sensor modules, speech recognition chips, artificial intelligence and big data, Changhong has produced a TV that promises to eliminate desperate browsing of hundreds of live channels during prime time to figure out what's on and what to watch. Freedom from the struggle with multiple remote-controller buttons is nigh.

Machine learning and artificial intelligence are finding their way into an insane amount of products and services these days, some more conventionally useful than others. You would be hard-pressed to find a company in the tech sphere that isn't involved in AI in some form, whether by using somebody's product that leverages AI, or by developing their own solutions. This push is not a fad or just another random happening in the tech world; it's something that has been long in the making, but the technology just hasn't been up to snuff until now. Specifically, neural networking in computers and other methods of machine learning have been researched since the 1970s, though they spent a while on the shelf, since people recognized after a while that the technology just wasn't ready. Now it is, however, and the race is in full swing to find the next great AI platform for all of the services of tomorrow to run on.

As machine learning increasingly affects domains protected by anti-discrimination law, there is much interest in the problem of algorithmically measuring and ensuring fairness in machine learning. Across academia and industry, experts are finally embracing this important research direction that has long been marred by sensationalist clickbait overshadowing scientific efforts. This sequence of posts is a sober take on the subtleties and difficulties in engaging productively with the issue of fairness in machine learning. Prudence is necessary, since a poor regulatory proposal could easily do more harm than doing nothing at all. In this first post, I will focus on a sticky idea I call demographic parity that through its many variants has been proposed as a fairness criterion in dozens of papers.

The goal of Explainable AI (XAI) is to create a suite of new or modified machine learning techniques that produce explainable models that, when combined with effective explanation techniques, enable end users to understand, appropriately trust, and effectively manage the emerging generation of AI systems.

Apple's Siri upgrades will let you dictate LinkedIn messages, verbally search for shoe photos on Pinterest, and hail an Uber, hands-free. When Apple unveils its new iPhone Wednesday, it's expected to push hard on software improvements available for its next mobile operating system, iOS 10, available for current iPhones and a predicted new iPhone model. The iPhone's voice-activated digital assistant Siri will be a key part of this upgrade, say tech analysts and developers. Siri, primarily used for voice commands to dial phone numbers, set reminders and get traffic information, has been opened to third-party developers after five years of existence. Apple, via a spokeswoman, said app developers are creating "completely new ways for users to interact" within apps.

Neural Nets are quite powerful models in machine learning which are used for learning the behavior of high dimensional data. Typically, data is not always present in its purest form, the signal. Most of the data that is made available for machines during training or runtime prediction do have some amount of noise. During training, the Neural Nets can get highly sensitive to the noise in the input and can start overfitting the learning to the noise in the input. In the previous post titled "Algorithms to Improve NeuralNetwork Accuracy", we learnt about the overfitting problem in Neural Nets and how to break it using L1/L2 regularizers, Weight penalties decay and constraints.