Tesla CEO Elon Musk's 1bn non-profit artificial intelligence (AI) research firm OpenAI has announced its major technological goals which include creating a household robot, a natural language processing chatbot and an intelligent agent capable of winning any game. Announced in a blog post on 20 June, the firm says "robotics is a good testbed for many challenges in AI". "OpenAI's mission is to build safe AI, and ensure AI's benefits are as widely and evenly distributed as possible," the company says in the blog post authored by Ilya Sutskever, Greg Brockman, Sam Altman, and Elon Musk. "We're trying to build AI as part of a larger community, and we want to share our plans and capabilities along the way. We're also working to solidify our organisation's governance structure and will share our thoughts on that later this year."

Suranga Chandratillake: In the past, computer programmers needed to tell computers exactly what to do. The computers would then follow these instructed steps over and over. Machine Learning aims to help computers learn about the world for themselves, by creating a more generic algorithm. These algorithms can be shown a lot of data, and they then figure out how to react to, or process the data for themselves. SC: There are a number of different approaches to creating these self-learning algorithms.

Unlike Google, Facebook, and Amazon, Apple appears to be sitting out the massive data suck revolution. Marco Arment, bless him, has the ability to distill popular sentiment down to it's essence and post it in a way that makes every sensationalist site on the web attention-link it with self-serving abandon. Unfortunately, his point often gets lost in the pile-on that follows. This time, his point is about Artificial Intelligence, Machine Learning, and whether companies unfettered by concerns over privacy and security have a lead Apple will find tough to follow. Today, Amazon, Facebook, and Google are placing large bets on advanced AI, ubiquitous assistants, and voice interfaces, hoping that these will become the next thing that our devices are for.

The school's Computer Science and Artificial Intelligence Lab made one of its "neural networks" watch 600 hours of shows like "Desperate Housewives" and "The Office." The deep-learning program predicted the correct action more than 43% of the time when the video was paused one second before the real action. "We wanted to show that just by watching large amounts of video, computers can gain enough knowledge to consistently make predictions about their surroundings," said Carl Vondrick, a PhD student in computer science and artificial intelligence. Even though MIT's algorithms aren't accurate enough yet for real-world application, the study is another example of how technologists are trying to improve artificial intelligence.

"Ex Machina" stars Oscar Isaac (left) and Domhnall Gleeson (right). The UK's AI scene is once again the talk of the town following the sale of Magic Pony Technology to Twitter for a reported 150 million ( 102 million). Evi was acquired by Amazon for a reported 18 million in 2013, DeepMind was bought by Google for around 400 million in 2014, VocalIQ was acquired by Apple for an unknown amount in 2015, and SwiftKey was bought by Microsoft for 175 million in 2016. The Oxbridge-London triangle is playing an important role in the creation of the UK's best AI companies, according to LocalGlobe investor Saul Klein. Oxford, Cambridge, Imperial, and UCL all have deep expertise in applied mathematics, computer science, and machine learning.

Given rising pressure from demographic change and shrinking finances; the public sector is having to look for new ways to support and manage demand. In parallel, there is an on-going explosion in sources and volumes of data available (and a reduction in the technical cost to make sense of this data) to understand and predict future demand. Consequently, there is a growing interest in using this data (and related Big Data technologies) to build algorithms that support more timely and accurate decision making – whether this is to support strategic commissioning decisions or the targeting of interventions. Ethical challenges are often premised on the assumption that algorithms will be implemented as an autonomous system. Although there are a few examples in the private sector (ie financial credit scoring systems) it is highly unlikely that this type of autonomous implementation would occur in the public sector.

Ole Winther received a Ph.D. degree from The Niels Bohr Institute at the University of Copenhagen (KU) in 1998. From 1998 to 2001 Ole Winther was post doc at Lund University, Sweden and at Center for Biological Sequence Analysis, Technical University of Denmark (DTU), from 2001 associate professor at DTU and from 2006 group leader in gene regulation at Bioinformatics, KU, part time. Currently, Ole Winther is a professor in Data Science and Complexity at Cognitive Systems, DTU. His main research area is machine learning. Machine learning combines statistical modelling and artificial intelligence.

Editor's Note: While Andrew Ng, chief scientist at Baidu was delivering his ISC keynote this morning on how HPC is supercharging AI, his colleague Greg Diamos, research scientist at Baidu's Silicon Valley AI Lab, was preparing to present a paper on GPU-based deep learning at the 33rd International Conference on Machine Learning in New York. Greg Diamos, senior researcher, Silicon Valley AI Lab, Baidu, is on the front lines of the reinvigorated frontier of machine learning. Before joining Baidu, Diamos was in the employ of NVIDIA, first as a research scientist and then an architect (for the GPU streaming multiprocessor and the CUDA software). Given this background, it's natural that Diamos' research is focused on advancing breakthroughs in GPU-based deep learning. Ahead of the paper he is presenting, Diamos answered questions about his research and his vision for the future of machine learning.

So I'm often getting asked "What is Machine Learning, and how is it different than Predictive Analytics?" As with any hot technology trend, Machine Learning has started to become a buzzword that is both misused and misunderstood. The right question is not "How is Machine Learning different than Predictive Analytics?", but rather "How can Machine Learning be used FOR Predictive Analysis, and how is it different than classical statistics?" In other words, Predictive Analytics is a use and Machine Learning is a technique. To answer this question, let's break down this question into it's component parts.

These FAQ's oftentimes raise more questions than they answer, so that the correct answer is often not found. With Automated Chat you can anticipate in a fast and accurate way Every website visitor can communicate with a virtual assistant (chatbot). All answers are pre-entered in a knowledge management system. This system is made so every question and answer thinkable can be recognised through sentence or word recognision. With a chatbot you can provide your website visitors with information or advice in a personal way, 24 hours a day.