It may be Tesla's Elon Musk who most often invites comparison to Marvel's superhero Iron Man – the alter ego of billionaire inventor Tony Stark – but it is Mark Zuckerberg who might be the first to bring Stark's technology to life. Memorably, the Facebook CEO sets himself annual goals such as learning Mandarin in 2010, eating only meat from animals he killed himself in 2011, or reading two books a month in 2015. In January, the Facebook founder said that his 2016 challenge would be to build an artificial intelligence-based personal assistant for his home. In his Facebook post announcing his aim, Zuckerberg said that "You can think of it kind of like Jarvis in Iron Man." In a Facebook conversation on Thursday, Zuckerberg invited suggestions for who should voice his real-life Jarvis (which, in the Iron Man and Avengers movies, stands for Just A Rather Very Intelligent System). Suggestions included actors Morgan Freeman, Benedict Cumberbatch and Paul Bettany – who voices Jarvis in the movies – as well as astrophysicist Neil deGrasse Tyson.

What Artificial Intelligence Can and Can't Do Right Now The SpaceNet Challenge: help us to harness machine learning to make maps more current and ... Stay up-to-date on the topics you care about. We'll send you an email alert whenever a news article matches your alert term. It's free, and you can add new alerts at any time.

Can you spot the lone dugong in the image above? Now do that with 45,000 more, and you'll have a general idea of the population of these endangered critters. If that sounds tedious, then perhaps you, like researchers at Murdoch University, would prefer to delegate the duty to a specially-trained computer. Amanda Hodgson, of the school's Cetacean Research Unit, has been using UAVs to capture images of marine animals for years, but the data piles up fast, and there are only so many grad students. Hodgson worked with computer scientist Frederic Maire, of the Queensland University of Technology, to automate the process.

Today, consumers have access to modern wearables and smartphones as well as AI and machine learning-powered platforms like BioBeats Hear and Now. Consumers can take it upon themselves to reduce their stress levels and improve their overall quality of life. To be clear, stress management and other types of healthcare platforms/applications are not intended to be replacements for regular visits to a primary care physician (PCP). There are often cases where high blood pressure, obesity, heart disease, and other stress-related health issues not only require regular visits to a PCP, but also specialized treatments and medications. With that said, consumers can use AI and machine learning-powered stress management platforms to be proactive about stress; changing behaviors and reducing stress levels on their own using focused techniques such as clinically validated breathing exercises, biometric feedback, mindfulness exercises, and meditation.

Some of the smartest minds in the UK are being lured away from their research positions at Oxford and Cambridge by DeepMind -- a London-based AI lab that was acquired by Google for £400 million in 2014. More than a dozen AI researchers have left the academic powerhouses over the last couple of years for what are likely to be better-paid roles at DeepMind, according to LinkedIn. Steven Cave, the director of Cambridge University's new Centre for the Future of Intelligence, believes that the exodus of talent from academia to corporates is something of a problem. "The best people are being offered huge sums of money to go and work at these tech companies," Cave told Business Insider in Cambridge last week. "You find that you're talking to someone and they're expressing a great deal of interest in a research project and then they're snapped up. We understand that ambitious young people want to work at these big name companies and earn lots of money and that's fine. But at the same time we hope that there will be enough bright young things who are motivated by the intellectual challenge of the issues we're working on and by the sense of wanting to do something good that makes a difference for the world."

Published at: Nov. 9, 2016, 4:40 p.m. Author: Piotr Płoński Dyslexia is reading disorder - characterized by trouble with reading despite normal intelligence. We used ML methods to find brain regions responsible for this! Check out the paper: "Multi-Parameter Machine Learning Approach to the Neuroanatomical Basis of Developmental Dyslexia" in Human Brain Mapping journal with cooperation with fantastic neurobiologists. We have a dataset with Magnetic Resonance Images from 236 kids from Poland, France and Germany. Among them 130 were with dyslexia.

We recently launched SpaceNet on AWS, an open corpus of training data established with the goal of enabling advancements in machine learning using satellite imagery. To accelerate this initiative, we're thrilled to announce The SpaceNet Challenge in collaboration with CosmiQ Works and NVIDIA, which is being facilitated by Topcoder. This is the first in a series of recurring open innovation competitions focused on developing next generation computer vision algorithms for automated mapping. With $34,500 of prizes, the first challenge is to tackle the automated extraction of 2D building footprints from imagery. The competition officially starts on 11/14, but you can pre-register today.

When it comes to artificial intelligence (AI), perhaps very few people can claim they fathered a huge part of it. One such man is Jürgen Schmidhuber. Schmidhuber is considered the"father of very deep learning," and the pioneer of deep learning neural networks. In fact, he built the foundations for many of the AI systems we find in our smartphones today. If anyone can predict how far AI will go in the next couple years, it's him. During a talk at WIRED2016, Schmidhuber presented the future of AI as something beyond just taking over jobs.

Like most sectors, the solar industry is rapidly embracing ways to analyze and crunch data in order to lower the cost of solar energy and to open up new markets for their technology. The rise of data tools--algorithms, machine learning, sensors--are driving investments in, and acquisitions of, solar startups, while entrepreneurs are launching new companies that are using data to solve various solar industry problems. Meanwhile, big companies are spending money on tracking, monitoring and evaluating data from solar projects worldwide, helping to lower the cost of generating energy from the sun. It shouldn't come as a surprise that the solar sector is the latest to embrace the value of data. Other traditionally non-digital sectors, like the auto industry, oil and gas, and agriculture are turning to managing data as a necessity to keep their technology competitive and their companies in business.

The MogIA artificial intelligence (AI) platform has successfully predicted its fourth US election in a row, paving the way for the potential of this new technology in the future. This election in particular tested the system's ability to predict the results of a presidential race as its prediction was the opposite of what many pre-election polls indicated, leading some to believe that it would lose its streak of successful predictions. MogIa makes its predictions by utilising 20 million data points from a number of online services including Google, Twitter and YouTube. It then analyses the data it collects related to public engagement from social media posts before making a decision as to who will win. Sanjiv Rai, the founder of the Indian startup Genic.ai that developed MogIA, explained how the system operates: "While most algorithms suffer from programmers' /developers' biases, MogIA aims at learning from her environment, developing her own rules at the policy layer and developing expert systems without discarding any data."