There's no way of knowing when the machines will take over, but scientists have a prediction about the breakthrough that would have to occur in order for that to happen: the development of an artificial intelligence (AI) system that rivals our own brains. There's an interesting reason why such a system would almost certainly overtake human intelligence and precipitate the rise of machines than are smarter than us - not just equally smart. As director of the Search for Extraterrestrial Intelligence Institute (SETI), Seth Shostak ends up thinking a lot about AI. He predicts we'll find AI in the universe before we will be able to find the biological beings that might have created it, since computers and various devices can travel great distances much more easily than living beings (just think of the rovers we've sent to Mars). Here on Earth, as well as on other planets, Shostak thinks the exponential rise of computers will eventually allow them to outsmart us.

This is not a fiction, this is not just an idea, this is the future. Join us to understand the limitless possibilities when geospatial big data meets deep learning and how it will shape a smarter world.

Scientists at Insilico Medicine have developed a new drug-discovery engine that they say is capable of predicting therapeutic use, toxicity, and adverse effects of thousands of molecules, and they plan to reveal it at the Re-Work Machine Intelligence Summit in Berlin, June 29–30. Drug discovery takes decades, with high failure rates. Among the reasons: irreproducible experiments with poor choice of animal models and inability to translate the results from animal models directly to humans, the wide variety of diseases, and communication difficulties between scientists, managers, venture capitalists, pharmaceutical companies and regulators. And perhaps the biggest problem: the slow-paced, bureaucratic culture in the pharmaceutical industry, the researchers note. Insilico Medicine says it aims to address these reasons by developing "multimodal deep-learned and parametric biomarkers," as well as multiple drug-scoring pipelines for drug discovery and drug repurposing, and hypothesis and lead generation.

AT&T has also applied its virtual network applications to directly improve consumer relations. Using large-scale machine learning systems that pull data from contacts, chat, and customer service voice operations, the system learned how to make predictions about customer sentiment. AT&T is able to provide this big-data-based intelligence to managers and supervisors, who can look and monitor patterns to identify anomalies and ask a range of important questions, such as: 'Were my customers happy or not?' 'If we put them on hold, did that make them unhappy?' 'Did my agent solve their problem the first time?' 'Why did the customer call in the first place, and are they likely to call again?'

If you've ever seen a live robot manipulation demo, you've almost certainly noticed that the robot probably spends a lot of time looking like it's not doing anything. It's tempting to say that the robot is "thinking" when this happens, and that might even be mostly correct: odds are that you're watching some poor motion-planning algorithm try and figure out how to get the robot's arm and gripper to do what it's supposed to do without running into anything. This motion planning process is both one of the most important skills a robot can have (since it's necessary for robots to "do stuff"), and also one of the most time and processor intensive. At the RSS 2016 conference this week, researchers from the Duke Robotics group at Duke University in Durham, N.C., are presenting a paper about "Robot Motion Planning on a Chip," in which they describe how they can speed up motion planning by three orders of magnitude while using 20 times less power. How? Rather than using general purpose CPUs and GPUs, they instead developed a custom processor that can run collision checking across an entire 3D grid all at once.

Machine learning is at the top of the "hype curve" for emerging technologies and is one of the top 10 strategic technology trends for 2016 according to Gartner. A method of data analysis that automates analytical model building, machine learning can benefit almost any business that gathers data with the intention of acting upon it. It's safe to say that most of us are already interacting with machine learning applications on a daily basis, whether it's Apple's Siri, Facebook's face detection, Amazon and Netflix personalized recommendations, or iOS's autocorrect. What can we learn from this growing trend? What can we do with such a widely applicable technology and how can we maximize its potential?

The intelligence exhibited by machine also known as Artificial Intelligence is an area that very many large technology companies are investing into these days. Apple made its mark in artificial intelligence by introducing its digital assistant Siri, google and Microsoft also followed. Social media giants; Facebook and amazon have shown indications of investing in Artificial Intelligence. The interest of these companies in this field of computer science is an indicator that Artificial Intelligence is an area that offers a lot of promise. The country has tried to use AI in some areas for example KCCA introduced the smart traffic lights at Wandegeya.

Tomorrow's companies are going to rely on algorithms more than ever before, the firm said in its latest research note, which could lead to business models that morph and change automatically. It all sounds like something from science fiction, but what does it really mean? An algorithm is simply a set of instructions to follow when completing a process. Every piece of software is algorithmic, meaning that business has been using algorithms since the launch of the LEO I, so we might well ask why the term is being bandied about so breathlessly now. Whereas businesses used software logic in discrete ways to automate certain processes, in the new model algorithms become a more central part of the business, making decisions that couldn't easily be reached without them, and then even taking action on them automatically.

This is a list of various books, courses and other resources for robotics. It's an attempt to gather useful material in one place for everybody who wants to learn more about the field. ROS The Robot Operating System (ROS) is a flexible framework for writing robot software. It is a collection of tools, libraries, and conventions that aim to simplify the task of creating complex and robust robot behavior across a wide variety of robotic platforms. RobWork RobWork is a collection of C libraries for simulation and control of robot systems.

This year especially, artificial intelligence (AI) has had a renaissance -- Tesla pushed their self-driving autopilot out to all eligible cars, and Google and Facebook have both announced large investments in AI research. The latest human jobs to be taken by robots include video game playing and trading stocks. In the near future, robots might even become your best friend. Where will these technologies take us next? Well to know that we should determine what's the best of the best now.