However even Deep Learning has a very long way to go: 1. Each of the advisers or modules has only learned and trained so much. They are not infallible 2. More complex decisions and problems have many and complex aspects in the decision process and as a team the different layers of advisers or modules can be trained together only so much as the cases they have worked on together. In real life curve balls are the norm and "intelligence" is not merely about dealing with the known. It is most definitely about dealing with the unknown.

Hah, I was wondering if someone would do this. One interesting application is not just classification of the disease (or nutrient deficiencies), but quantification of disease. One struggle for a lot of geneticists is quantifying how infected a plant is. I feel like CV is a good approach here.

The IDLF project can be used by application developers, cloud service providers, academic researchers, or anyone seeking the best performance on the full spectrum of Intel platforms for Deep Learning. Support for additional hardware platforms can be added over time in a completely API-agnostic manner allowing for maximum code reuse. This is an active open source project distributed under the BSD 3-clause open source license. Intel is the leading contributor to IDLF, enabling application and system developers to make the most of Intel Xeon and Xeon Phi processors as well as Intel Iris Pro graphics.

Here's What We Can Expect from Deep Learning in 2016 and Beyond

The idea that humans are on the verge of developing an artificial intelligence whose abilities far outstrip our own is ridiculous, said Chris Bishop, Microsoft's director of research at Cambridge, highlighting the many limitations of AI systems today. "This is a good moment for a little reality check," he told a public discussion hosted by The Royal Society in London this week. While recent breakthroughs in machine learning have allowed computers to become as adept as the average person at recognising faces and objects and to make huge strides in areas such as voice recognition, Bishop cautioned against assuming that machines are outstripping human performance across the board. "Yes, deep learning has achieved human-level performance in object recognition but what does that mean? It means the machine makes about the same number of errors as the human. "The reason the machine is as good as the human at this is because it can distinguish between 157 varieties of mushroom, whereas it makes all kinds of stupid mistakes that humans wouldn't make." Even some of the most celebrated examples of machine intelligence, such as a Google DeepMind system beating a world champion in the notoriously complex game of Go, need to be understood in context of the time and effort that went into building the system, he said. In 2015, GE inaugurated a new, Multi-Modal manufacturing facility in Chakan, India. If the company's ambitions for the space are realized, it could drive a massive change in global manufacturing. "[Take] the Go example, where the machine has just about crept ahead of the best human.

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.

During the Deep Learning in Healthcare Summit in London last week we hosted the'Shaping Tomorrow' startup session to showcase innovative startups applying cutting-edge deep learning algorithms and tools to advance healthcare and medicine. Daria Danilina, an MBA student from London Business School, attended the event and kindly summarised the startup presentations. Key take-away: Humans are trained to identify certain patterns. However, we tend to overlook things which we do not expect to see or are not trained to detect. In addition to this, anomalies exist that are impossible to identify for human eyes, such as tumours composed of soft tissue.

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.

As well as his new upcoming book "The Economic Singularity" that Calum is nice enough to be sharing a review copy of. So what is "Singularity" you ask? Well there are many types Wikipedia puts it this way... Because the capabilities of such a superintelligencemay be impossible for a human to comprehend, the technological singularity is the point beyond which events may become unpredictable or even unfathomable to human intelligence. "I don't like the term'singularity' when applied to technology. A singularity is a state where physical laws no longer apply because some value or metric goes to infinity, such as the curvature of space-time at the center of a black hole. No one can predict what happens at a singularity."

For artificial intelligence, 2016 has been called "like 2015 on steroids." Want to learn more about what that really means? Follow these 10 twitter users for an insider's take on the latest developments in AI. The brains behind Google's AI platform DeepMind, Hassabis is arguably one of the most important voices in the AI world today. AlphaGo, created by DeepMind, has surpassed expectations, winning in the game of Go ten years before experts predicted.