After demonstrating discontinuous jumps in image recognition performance and defeating Korean grandmaster Lee Se-dol at Go, a game long resistant to computer mastery, deep learning has kicked up a swirling cloud of hype. On the one hand, serious folks are studying how to prevent a recursively self-improving super intelligence from seizing Earth's reins from humanity. On the other, IBM's "cognitive" marketing claims are rightly being called out as hyperbolic. I think much of the excitement derives from the tremendous strides deep learning has recently made in processing less-structured input, like images and voice, that relate to the way we perceive the world. From a business perspective the challenge is to soberly assess the usefulness of new machine learning techniques for application in the near future.

In addition, the built-in artificial intelligence will be able to predict situations, producing ready-made proposals for the border protection. "The system is fully based on domestic policy decisions that ensure protection of information resources against data loss, hackers and other unauthorized interventions," the press service quoted the deputy director of OPK Sergei Skokov as saying. The developers also noted that the new system is intended not only to collect different types of information, but also contains elements of artificial intelligence which will allow for analysis and forecasting of the situation and work out proposals for the protection of borders, by calculating steps and routes that offenders may take, as well as the necessary measures to prevent malicious acts, including the assessment of possible risks. The state borders need protection due to ever rising threats. Since the beginning of this year in the Rostov region, more than 60 "wanted" persons were found and arrested.

A version of this essay was originally published at Tech.pinions, a website dedicated to informed opinions, insight and perspective on the tech industry. At the recent Code Conference, Jeff Bezos made a rather provocative statement when he said that when we talk about technology, we are on "the edge of a golden era." When it comes to artificial intelligence, Bezos said, "It's probably hard to overstate how big of an impact it's going to have on society over the next 20 years." He also said that Amazon has 1,000 people working on its Alexa platform, which powers the company's popular voice-controlled Echo device. Of course, Bezos is hardly alone with this line of thinking about artificial intelligence and its impact.

Stephen Hawking made headlines at the end of 2014 when, in a BBC interview, he said that we should be very wary of developing "full artificial intelligence" as it "could spell the end of the human race." His doomsday musings were hardly original. SpaceX's Elon Musk had said the same thing earlier that year, warning that AI is "potentially more dangerous than nukes." The worrisome idea of computers possessing greater than human intelligence, coupled with a sudden independent consciousness, was first termed "The Singularity" back in 1993, in a paper by the computer scientist Vernor Vinge. And while his initial predictions about vast computer improvements merely mirrored the foresight of others – like the expected frequent doubling in computer power envisioned by Intel co-founder Gordon Moore in 1965 – Vinge believed it would lead to "change comparable to the rise of human life on Earth." As we all know, computers already control and facilitate much of our daily life from banking to robotic automobile assembly, and no one wants to return to the old days of manual drudgery for menial tasks like repetitive spot welding.

I'm working on a project in the e-commerce field involving product entity matching (given 2 product listings from 2 different marketplaces, such as Amazon and eBay, determine if they are the same product) I'm a real newbie in ML but I am deeply excited by it, and I was thinking of several ways we could potentially utilize ML to improve our matching algorithm. This one is maybe far-fetched, but perhaps we can use Deep Learning to do practically all the work by itself? I'm really clueless about how I would train for these purposes, would I need to manually create a gigantic dataset (especially for no. I'd love to hear feedback from you guys if I'm just talking nonesense or is there any potential to what I'm thinking?

Hewlett Packard Enterprise's Haven OnDemand offers businesses and developers a self-service, cognitive API toolkit to create data rich applications. Haven OnDemand runs on the HPE Helion cloud and enables customers to analyse all forms of data, including business data, machine data, and unstructured, human information. Since its beta launch in 2014, thousands of developers worldwide have leveraged the platform to create a vast range of applications. The Haven OnDemand Developer Community now has over 12,000 members and the Hewlett Packard Enterprise continue to offer the service as a freemium model, where developers can build and test for free, making it accessible to early-stage start ups. HPE now offers over 70 cutting-edge machine learning APIs, allowing businesses to harness the true business value of their data through a comprehensive set of use-cases including anomaly detection and insights from text, audio, video and images.

No paper today, instead a short piece to tee-up the next mini-series of papers I'll be covering… The whole process is about augmenting the capabilities of the team to make them more effective, not about replacing people with AI systems. I've been using this model with one of the startups I work with, Atomist, who are using a bot as the primary interface to the system they are building. What does all this have to do with The Morning Paper? In order to separate the hype from the reality, the practical from the pipe-dream, it's very useful to have a good handle on the capabilities of the underlying technologies. The WildML blog recently posted a great high-level overview of Deep Learning for ChatBots (and I'm looking forward to the next instalments in the series).

Computer vision has been having a moment. No more does an image recognition algorithm make dumb mistakes when looking at the world: these days, it can accurately tell you that an image contains a cat. But how it pulls off the party trick may not be as familiar to humans as we thought. Most computer vision systems identify features in images using neural networks, which are inspired by our own biology and are very similar in their architecture--only here, the biological sensing and neurons are swapped out for mathematical functions. Now a study by researchers at Facebook and Virginia Tech says that despite those similarities, we should be careful in assuming that both work in the same way.

But this idea of superintelligence is fallacious because it assumes that the brain is "like a computer", i.e. a biological information processing machine. Our brain is not neither a computer nor a "machine". It does not "store memories" and does not "process information". We use such words to describe the brain metaphorically, not literarily. Musk and Hawking confuse metaphor with reality, and they do so systematically. Indeed, Hawking has repeatedly said that he believes that in the future humans will be able to "download their consciousness" in a computer and live forevermore.