SpaceX and Tesla CEO Elon Musk was in Dubai to launch Tesla in the United Arab Emirates at the World Government Summit Monday. The South African billionaire inventor was interviewed onstage and gave his opinion on aliens, artificial intelligence, universal basic income and the future of humanity. Universal basic income will solve the economic problems caused by automation. However, the existential problems that come from a world with few workers will be harder to solve, Musk said. "There will be fewer and fewer jobs that a robot cannot do better. These are not things I wish will happen; these are things I think probably will happen. And if my assessment is correct and they probably will happen, than we have to think about what are we going to do about it? I think some kind of universal basic income is going to be necessary. The output of goods and services will be extremely high. With automation there will come abundance. Almost everything will get very cheap. I think we'll end up doing universal basic income. It's going to be necessary. The much harder challenge is, how are people going to have meaning? A lot of people derive their meaning from their employment. That's a much harder problem to deal with."

Artificial intelligence can now outperform humans in a number of advanced tasks, but when it comes to pondering life's greatest mysteries, they're still just as lost as we are. Google researchers have developed a chatbot that can carry out a natural conversation with a human, even demonstrating common sense reasoning. The system can generate solutions in an IT helpdesk scenario and even weigh in on the meaning of life – but with responses like'to live forever' and'to find out what happens when we get to the planet earth,' it doesn't quite have all the answers yet. Google researchers have developed a chatbot that can carry out a natural conversation with a human, even demonstrating common sense reasoning. In one conversation, the human participant asks the machine, 'What is the purpose of life?' A stock image is pictured Machine: to find out what happens when we get to the planet earth .

I discuss here off-the-beaten-path beautiful, even spectacular results from number theory: not just about prime numbers, but also about related problems such as integers that are sum of two squares. The connection between these numbers and prime numbers will appear later in this article. A few important unsolved mathematical conjectures are presented in a unified approach, and some new research material is also introduced, especially an attempt at generalizing and unifying concepts related to data set density and limiting distributions. The approach is very applied, focusing on algorithms, simulations, and big data, to help discover fascinating results. Even though some of the most exciting topics of mathematics are discussed here (including fundamental, century-old problems still unresolved as well as brand new hypotheses), most of the article can be understood by the layman. Among other things, you will learn some new ways to estimate Pi based on non-traditional experiments, or how a conjecture for prime numbers somehow generalizes to apply to Fibonacci numbers as well.

It's difficult for humans to identify phase transitions, or exotic states of matter that come about through unusual transitions (say, a material becoming a superconductor). They might not have to do all the hard work going forward, however. Two sets of researchers have shown that you can teach neural networks to recognize those states and the nature of the transitions themselves. Similar to what you see with other AI-based recognition systems, the networks were trained on images -- in this case, particle collections -- to the point where they could detect phase transitions on their own. They're both very accurate (within 0.3 percent for the temperature of one transition) and only need to see a few hundred atoms to identify what they're looking at.

That's the size of the workforce dedicated to a business that's thrived for the past three decades on a growth model of labour arbitrage. In the process, it's matured into a industry worth almost $150 billion. The multi-billion dollar question today is, where does it go from here, as it comes head to head with a series of challenges. Consider: New Bills being introduced by the Trump administration in the US Congress seek to make outsourcing not only tougher but more expensive. This comes at a time when protectionist sentiments that call for onsite hiring of locals are spreading globally. All this, even as traditional IT services are moving towards the digital economy.

A machine learning algorithm designed to teach computers how to recognize photos, speech patterns, and hand-written digits has now been applied to a vastly different set of data: identifying phase transitions between states of matter. This new research, published today in Nature Physics by two Perimeter Institute researchers, was built on a simple question: could industry-standard machine learning algorithms help fuel physics research? To find out, former Perimeter Institute postdoctoral fellow Juan Cassasquilla and Roger Melko, an Associate Faculty member at Perimeter and Associate Professor at the University of Waterloo, repurposed Google's TensorFlow, an open-source software library for machine learning, and applied it to a physical system. Melko says they didn't know what to expect. "I thought it was a long shot," he admits. Using gigabytes of data representing different state configurations created using simulation software on supercomputers, Carrasquilla and Melko created a large collection of "images" to introduce into the machine learning algorithm (also known as a neural network).

Do you think you can tell a piece of music composed by artificial intelligence (AI) from one created by a human composer? Before you read any further, let's find out. The following audio consists of two fragments, one written by AI, the other by a human. TNW Conference won best European Event 2016 for our festival vibe. See what's in store for 2017.

Chinese search engine giant Baidu is one of the latest brands in the Asia-Pacific region to transform its customer experience through artificial intelligence (AI) technology: In October, the Chinese search engine launched Melody, an AI-powered chatbot that lives inside the search engine's existing Doctor app. The app collects users' medical information to help doctors make more informed diagnostic decisions. While the concept of chatbots–a technology that simulates human conversation–isn't new, its potential is being transformed thanks to advancements in AI. This, along with adoption by legacy brands, will drive the refinement of chatbot use to effectively engage customers in a more personalised way. Growing Use In APAC People are becoming so accustomed to chat application interfaces that chatbot use is pegged to explode.

From web to mobile to natural language interfaces, tools emerge that allow companies to create chatbots and voicebots that interact with users. These tools make it simple to create experiences, integrate and distribute to different messaging platforms, and later improve and analyze the end product. API.AI, recently acquired by Google, is a great example of such a tool, focused on NLU capabilities. We will have Ilya Gelfenbeyn (former Co-Founder and CEO) and now Product Manager of API.AI present the company's products, the tool's capabilities, demo how to create an experience and integrate/distribute to messaging channels (including the recent integration with Google Home!). We will hold the event on Monday 2/27 at 6pm at Galvanize, 44 Tehama Street, San Francisco.

Hoping to go to Google's annual developer conference this spring? If you are, mark your calendar for Feb. 22. That's the day when you can first apply for tickets to Google I/O. Google announced on its events page that the window for submitting ticket applications is between Feb. 22 at 1 p.m. ET and Feb. 27 at 8 p.m. ET. The conference is being held May 17-19 at the Shoreline Amphitheatre in Mountain View, Calif. This is the second year that the event will be held in Mountain View.