Intelligent Machines and the Future of Recruitment The impact of AI, Big Data and semantic technologies on the labour market, talent acquisition and the way we work. The rise of machine learning, search engines, semantic technologies and the large amounts of available information are changing the labour market as we know it now. What is the state-of-the-art in these technologies and what can we expect in the near future? How will we use it to define policies? How will it change the way we hire and how we work?

These tools allow machines to read and understand English text (such as text you type into a browser to do a Google search). And the Parsey McParseface program implements SyntaxNet in English (it learned from an annotated collection of old newswire stories called The Penn Treebank Project). Here's an example of how it parses and analyzes an English sentence:Using deep neural networks, SyntaxNet is implemented in Google's TensorFlow (see Google open-sources its TensorFlow machine learning system). On a standard benchmark consisting of randomly drawn English newswire sentences ("Penn Treebank"), Parsey McParseface recovers individual dependencies between words with over 94% accuracy, Google says.

Google has just released two powerful natural language understanding tools for free, open-source use by anyone. These tools allow machines to read and understand English text (such as text you type into a browser to do a Google search). SyntaxNet is a "syntactic parser" -- it allows machines to parse, or break down, sentences into their component parts of speech and identify the underlying meaning). And the Parsey McParseface program implements SyntaxNet in English (it learned from an annotated collection of old newswire stories called The Penn Treebank Project). Here's an example of how it parses and analyzes an English sentence:Using deep neural networks, SyntaxNet is implemented in Google's TensorFlow (see Google open-sources its TensorFlow machine learning system).

The Internet of Things (IoT) is accelerating at an impressive speed, forecasters predict 25 billion devices will be online by 2020, creating over 300 billion in opportunities for companies involved. Even with this considerable growth coming in the next five years, most enterprise folks still don't understand or aren't invested in the IoT revolution. Management consulting firm Bain & Company believe that's due to misrepresentation on the definition of IoT. In a new report, Bain & Company cut the IoT pie into five slices, which it calls the "major emerging battlegrounds" that will define the industry: Apple, Google, Samsung, and other mobile leaders will extend their reach to customers by launching new products in the autonomous, robotic, and smart home categories. We are already seeing the battleground emerge, Samsung acquired SmartThings, Google acquired Nest Labs, and Apple launched HomeKit.

There is a downside to the rise of AI beyond the irrational "rise of the machines" fear. A prime example of this is the resistance to driverless cars as a practical transportation tool. This exemplifies why it will never be possible for us to allow full artificial intelligence to blossom and take over control. Microsoft's AI chatbot is also an example of this failure to relinquish control. For true AI to develop completely, we would have to allow it to truly build and develop personality, emotion and perhaps most critically, a point of view that could drastically differ from our own.

Artificial intelligence sounds cool in theory, and as Google CEO Sundar Pichai said at the Google I/O event on Wednesday, the company wants to "help you get things done" with AI. But one example that Google used to showcase its AI at Google I/O on Wednesday was anything but exciting. Using its new messaging app called Allo, Google showed how easy it is to find restaurants and make reservations, or find a movie and buy tickets. Allo is designed so you can do those things by having a conversational texting session, as you would with a friend, with a bot called @google that uses the company's new AI platform called Google Assistant. There were some other examples, like recognizing the context of messages and pictures you send between your friends and coming up appropriate short replies so you don't have to come up with the response yourself.

Google I/O was full of announcements about upcoming products and enhancements. We're in a seminal moment, said Google chief executive (CEO) Sundar Pichai kicking off the company's annual I/O Conference in San Francisco. Looking back at the past 17(!?) years, Pichai discussed Google's evolution to the live audience of 7,000. As technology gets more sophisticated, he sees artificial intelligence (AI) playing a huge role in the company's next 17 years. "Leveraging our state-of-the-art capabilities in machine learning and AI, we truly want to take the next step in being more assistive to our user. Today, we are announcing the Google Assistant," said Pichai, one of the only people in the world who's allowed to use the "L" word on Search Engine Watch.

The possibility that a malevolent artificial intelligence might pose a serious threat to humankind has become a hotly debated issue. Various high profile individuals from the physicist Stephen Hawking to the tech entrepreneur, Elon Musk, have warned of the danger. Which is why the field of artificial intelligence safety is emerging as an important discipline. Computer scientists have begun to analyse the unintended consequences of poorly designed AI systems, of AI systems created with faulty ethical frameworks or ones that do not share human values. But there's an important omission in this field, say independent researcher Federico Pistono and Roman Yampolskiy from the University of Louisville in Kentucky. "Nothing, to our knowledge, has been published on how to design a malevolent machine," they say.

Years of work down the drain, the convolutional neural network is a step change in image classification accuracy. Image Analyst Dr Mike Pound explains what it does. AI's Game Playing Challenge: https://youtu.be/5oXyibEgJr0 This video was filmed and edited by Sean Riley.

Software engineering is about developing programs or tools to automate tasks. Instead of "doing things manually," we write programs; a program is basically just a machine-readable set of instructions that can be executed by a computer. Let's consider a classic example: e-mail spam filtering. Assuming that we have access to the source code of our e-mail client and know how to handle it, we could come up with an instinctive set of rules that may help us with our spam problem. For example: if not "sender in contacts": if "subject line contains BUY!: e-mail spam folder:" else if ... It is intuitive to say that coming up with these rules is a pretty tedious task.