Follow us on Twitter: https://twitter.com/JourneymanVOD Computer scientist Alan Turing created a conversation test for robots that has been a benchmark for over sixty years. But as humanoid robots develop every day, can any yet fool us into thinking they are human? From robots like Atlas at Boston Dynamics, built to withstand physical bullying, to the uncanny silicon and hair humanoids developed at Hanson robotics, we take a look at some of the most advanced androids ever created. Yet imitating simple conversation can still be a test too far, as Microsoft discovered after the widely publicised failure of their Twitter robot, Tay. Alistair Charlton, senior tech reporter for the International Business Times, argues that the industry has more successful options to pursue.

Google is planning to bring artificial intelligence and machine learning tools to the Raspberry Pi. The low-cost credit-card sized computer is widely used by schools and the maker community for programming devices. Google has asked makers to complete a survey about what smart tools would be "most helpful". And it suggests tools to aid face and emotion recognition, speech-to-text translation, natural language processing and sentiment analysis. Google has previously developed a range of tools for machine learning, internet of things devices, wearables, robotics and home automation.

When people hear about artificial intelligence, they have one of two responses: they are terrified of a Skynet dystopia, or they are excited for the new possibilities afforded by machine learning and robotics. While 2017 will not be the year that humanoid, Westworld-esque robots work alongside us or take over all of our jobs, we will definitely be seeing an even smarter circulation of "alternative facts". We will see greater capabilities from AI in facilitating business processes such as services, software delivery and IT infrastructure changes. Google has built a hub for chatbots to fetch information from the net, Freshdesk acquired Chatimity to strengthen its customer service chatbot capabilities, and Microsoft has had another bash at its AI chatbot with Zo. In process flow scenarios, ChatOps bots will be more fluid in enabling processes using simple commands. You could write something like "I need help with ticket 6876 from network, database, and payment processing," and all necessary information would be pulled for you, from across all relevant systems.

Three years ago, artificial intelligence research firm DeepMind was acquired by Google for a reported £400m. As part of the acquisition, Google agreed to set up an ethics and safety board to ensure that its AI technology is not abused. The existence of the ethics board wasn't confirmed at the time of the acquisition announcement, and the public only became aware of it through a leak to industry news site The Information. But in the years since, senior members of DeepMind have publicly confirmed the board's existence, arguing that it is one of the ways that the company is trying to "lead the way" on ethical issues in AI. But in all that time DeepMind has consistently refused to say who is on the board, what it discusses, or publicly confirm whether or not it has even officially met. The Guardian has asked DeepMind and Google multiple times since the acquisition on 26 January 2014 for transparency around the board, and received just one answer on the record.

Machine learning is cresting the fresh wave of 2017 HR trends. Gartner research predicts algorithms will positively alter the behavior of over one billion global workers by 2020, while over 3 million people can look forward to "roboboss" supervisors. Yvonne Bauer, Head of Predictive Analytics at SAP SuccessFactors, sees machine learning becoming more widespread this year as part of HR's steady progression from art to data-driven science. "More companies will look into machine learning, moving from individual projects to actual products built into HCM suites," she said. "Conversational interfaces like chat bots and natural language processing will emerge this year, allowing companies to change how workers interact with the system and derive insights from those activities, including what people are working on and how engaged they are."

DeepTraffic is a gamified simulation of typical highway traffic. Your task is to build a neural agent – more specifically design and train a neural network that performs well on high traffic roads. Your neural network gets to control one of the cars (displayed in red) and has to learn how to navigate efficiently to go as fast as possible. The car already comes with a safety system, so you don't have to worry about the basic task of driving – the net only has to tell the car if it should accelerate/slow down or change lanes, and it will do so if that is possible without crashing into other cars. The page consists of three different areas: on the left you can find a real time simulation of the road, with different display options, using the current state of the net.

Brand-new global information infrastructure design doesn't come along every day. The internet of things (IoT) presents a rare opportunity to build global information infrastructure from scratch -- a new, more expansive, and capable set of functions on the periphery of what already exists. But the truly new IoT that's expected is only now emerging. At the moment, what's being developed is really just a nervous system of sorts -- protocols, communications pathways, local processing and storage, endpoints embedded in various places, and ways for things to talk to and interpret one another. But what would the things say to each other?

The emerging technologies of the Fourth Industrial Revolution (4IR) will inevitably transform the world in many ways – some that are desirable and others that are not. The extent to which the benefits are maximized and the risks mitigated will depend on the quality of governance – the rules, norms, standards, incentives, institutions, and other mechanisms that shape the development and deployment of each particular technology. Too often the debate about emerging technologies takes place at the extremes of possible responses: among those who focus intently on the potential gains and others who dwell on the potential dangers. The real challenge lies in navigating between these two poles: building understanding and awareness of the trade-offs and tensions we face, and making informed decisions about how to proceed. This task is becoming more pressing as technological change deepens and accelerates, and as we become more aware of the lagged societal, political and even geopolitical impact of earlier waves of innovation.

The market of voice-controlled assistants is on fire at the moment. Siri, Cortana, Alexa, Google Assistant, just to name a few, are the most well-known digital assistants that are currenlty dominating the market. Their cornerstone lies in an aim to provide a seamless and hands-free experience, which empowers us and eases our daily lives. From playing music to ordering pizza, voice assistants seem to be taking the market by storm. In 2015 1.7 million voice-first devices have been shipped, and 6.5 million in 2016, that excluding the mobile-built in voice services. VoiceLabs, a voice software start-up, foresees that by the end of 2017 more than 24 million devices will find their new families and homes, totalling at 33 million voice-first gadgets in circulation.

AI has already been applied to customer engagement in various forms, and there's an evolution underway. First AI appeared in tools and appliances used for customer engagement. More recently chatbots have emerged. "Chatbots are very good at taking natural language and extracting intent from that: 'Play the music for me. Tell me the weather,'" Sutton said.