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."

"It hurts to love someone and not be loved in return, but what is the most painful is to love someone and never find the courage to let the person know

Artificial intelligence is one of those tech terms that seems to inevitably conjure up images (and jokes) of computer overlords running sci-fi dystopias -- or, more recently, robots taking over human jobs. But AI is already here: It's powering your voice-activated digital personal assistants and Web searches, guiding automated features on your car and translating foreign texts, detecting your friends in photos you post on social media and filtering your spam. But as practical uses of AI have exploded in recent years, one critical element remains missing: an industrywide set of ethics standards or best practices to guide the growing field. Now, the industry heavyweights are partnering to fill that gap. Called the Partnership on Artificial Intelligence to Benefit People and Society, the group consists of Amazon, Facebook, Google, Microsoft and IBM. Apple is also in talks to join.

In recent years, there has been a staggering surge in interest in intelligent systems as applied to everything from customer support to curing cancer. Simply sprinkling the term "AI" into startup pitch decks seems to increase the likelihood of getting access to funding. The media continuously reports that AI is going to steal our jobs, and the U.S. government seems as worried about the prospect of super-intelligent killer robots as it is about addressing the highest wealth disparity in the country's history. Comparatively, there has been very little discussion of what artificial intelligence is, and where we should expect it to actually affect business. When people talk about AI, machine learning, automation, big data, cognitive computing, or deep learning, they're talking about the ability of machines to learn to fulfill objectives based on data and reasoning.

Digital tools using machine learning to analyze a person's spoken or written words could be instrumental in aiding mental health clinicians in assessments determining whether that person is suicidal, researchers have found. A new study published in the journal Suicide and Life-Threatening Behavior found machine learning is 93 percent accurate in correctly identifying a suicidal person, and is 85 percent accurate in determining differential diagnosis of mental illness. The study, led by researchers at the Cincinnati Children's Hospital Medical Center, looked at 379 patients who were recruited from three different sites – two academic medical centers and a rural community hospital. "Death by suicide demonstrates profound personal suffering and societal failure," writes lead author Dr. John Pestian, who is also a professor of biomedical informatics and psychiatry at Cincinnati Children's. "While basic sciences provide the opportunity to understand biological markers related to suicide, computer science provides opportunities to understand suicide thought markers."

Simply put, any algorithm that has the ability to learn on its own, given a set of data, without having to program the rules of the domain explicitly, falls under the ambit of Machine Learning. This is different from Data Analytics or Expert systems where, rules, logic, propositions or activities has to be manually coded by an expert programmer. Systems which has ability to learn on its own and progress towards a pre-defined goal, without much of human intervention can be broadly termed as Intelligent Systems. The quality of intelligence can range from an amoeba, algae, ant, armadillo all the way to chimps, humans or beyond. As an example, systems which interact with humans in natural language cannot be built by coding the rules and conversational logic of human language.