Artificial intelligence (AI) is a common topic of science fiction. Science fiction sometimes emphasizes the dangers of artificial intelligence, and sometimes its positive potential. The general discussion of the use of artificial intelligence as a theme in science fiction and film has fallen into three broad categories including AI dominance, Human dominance, and Sentient AI. The notion of advanced robots with human-like intelligence has been around for decades. Samuel Butler was the first to raise this issue, in a number of articles contributed to a local periodical in New Zealand and later developed into the three chapters of his novel Erewhon that compose its fictional Book of the Machines.

Isaac Asimov's Three Laws of Robotics, first codified in his 1942 short story "Runaround" (http://bit.ly/1AAkKhW), Robots were never to harm humans, or by inaction allow them to come to harm. Within months of the elucidation of these laws, however, an extremely primitive robot, the Norden bombsight, was being put to lethal use by the U.S. Army Air Corps. The bombsight combined a computer that calculated various factors affecting an aircraft's arrival over a target with an autopilot. Touted for its accuracy from high altitude, the Norden nevertheless tended to miss the aim point by an average of a quarter-mile.

In Davos right now, the world's best and best-performing economic minds are gathered for their annual bout of elite networking. You know you're not invited because a ticket costs $35,000, and that's before the cost of membership, which is also required, and even more expensive. But we get news reports from the proceedings and the most interesting one today concerns the World Economic Forum's recent report which claims the biggest risk in 2017 is people losing their jobs to robots. The word out of Davos is we have nothing to fear. If you don't believe them, you might find some comfort in a story about Donald Trump that's been kicking around for a couple of years that is, well, intriguing. The next Leader of the Free World has never used a computer. It's great fun (Matt Novak has tenaciously taken up the baton at Gizmodo), and not at all as far-fetched as you might be thinking right now. We know Trump tweets, badly. But it is actually surprisingly difficult to find evidence of him looking comfortable behind a MacBook.

Australian big businesses are spending an average of $6 million a year on artificial intelligence (AI) technologies, according to research released at the World Economic Forum. From driverless tractors on farms to banks using machine learning to predict investors, developments in AI can lead to better business outcomes, but they also raise potential concerns around ethics and opportunity. So what does this mean for startups and emerging entrepreneurs? "For startups, the popularity of artificial intelligence amongst big businesses presents a fantastic opportunity from a technology perspective," Infosys Australia and New Zealand vice-president and regional head Andrew Groth tells StartupSmart. Infosys, which conducted the study, is a $US10 billion ($13 billion) Indian-founded multinational firm specialising in new technologies.

Orange is a component based data mining and machine learning software suite written in the Python language. It is an Open source data visualization and analysis for novice and experts. Data mining can be done through visual programming or Python scripting. It has components for machine learning. There are add ons for bioinformatics and text mining.

Google uses "deep learning" to generate captions for images, Facebook uses it to recognize faces and Tesla uses it to train self-driving cars. Now astronomers have caught on to deep learning, a form of machine learning in which a computer can be trained to identify or classify particular objects in images. The newest telescopes, such as the Dark Energy Survey, which uses a 4-meter telescope in northern Chile and covers about one quarter of the southern sky, take millions of images of a variety of celestial objects. These often include visual distortions, cosmic rays and satellite trails that make them difficult to interpret. Deep learning could help process this deluge of data quickly.

Deloitte Global predicts that over 300 million smartphones, or more than one fifth of units sold in 2017, will have machine learning capabilities within the device in the next 12 months. Mobile devices will be able to perform machine learning tasks even without connectivity which will significantly alter how humans interact with technology across every industry, market and society, notes Deloitte Global in its 16th Technology, Media & Telecommunications (TMT) Predictions. Over time, machine learning on-the-go will not just be limited to smartphones, the report states. These capabilities are likely to be found in tens of millions (or more) of drones, tablets, cars, virtual or augmented reality devices, medical tools, Internet of Things (IoT) devices and unforeseen new technologies. "Machine learning is fascinating as it will revolutionise how we conduct simple tasks like translating content, but it also has major security and health consequences that can improve societies around the world," says Paul Sallomi, Deloitte Global TMT industry leader, in a statement.

The World Economic Forum's "Global Risks Report 2017″ says tech firms must take responsibility for the disruption caused by their innovations. A new report by the World Economic Forum lists IoT hacking and weaponized AI among what they say will be the top security threats in 2017. The Global Risks Report 2017 surveyed business executives in the US, Germany, Japan, Switzerland, Australia, Singapore, Malaysia, UEA and other countries across the globe. Those executives cited cyberattacks as the biggest risk facing their businesses. The report pointed out the risks involved with letting decision-making be taken over by AI applications and said both AI and robotics can be serious risks without proper regulation and governance.

The objective of this paper is to present the process of building a Deep Learning Model for optimising the output for a Production Process from a Training sample using Weka Multilayer Perceptron. The scope is limited to implementation only and does not cover the theory behind Artificial Neural Networks. This work is the outcome of a comprehensive prototyping and proof-of-concept exercise conducted at Turing Point (http://www.turing-point.com/) a consulting company focused on providing genuine Enterprise Machine Learning solutions based on highly advanced techniques such as 3D discrete event simulation, deep learning and genetic algorithms. Predictive Analytics is the process of extracting information from the data for predicting future trends. There are a number of Machine Learning approaches available to model the behaviour.

MEPs have warned European countries must "seriously" consider introducing a general basic income to prepare for wide scale unemployment that could come as a result of robots taking over manual jobs. A draft report, tabled by a socialist MEP Mady Delvaux-Stehres, warns preparations must be made for what it describes as the "technological revolution" currently taking place, including provisions for the "possible effects on the labour market of robotics". The report, which passed by 17 votes to two and will be put in front of the entire European Parliament in February, urges member states to consider a general basic income in preparation for robots taking over people's jobs. It states: "In the light of the possible effects on the labour market of robotics and AI a general basic income should be seriously considered, and invites all Member States to do so." The resolution also suggests that a system of reporting on how robotics are affecting the economic results of companies should be established "for the purpose of taxation and social security contributions".