Artificial intelligence has made some great developments toward speeding up cancer diagnosis so far in 2017. Last month it was announced that AI from Sophia Genetics was helping to accelerate patient diagnosis across Latin America. Earlier this year researchers at Stanford University developed a deep learning algorithm that can analyse skin cancer as accurately as a human doctor. Now, Israel-based company, Medial EarlySign has announced the ability of its AI tool to identify the top 1% at highest risk of undiagnosed colorectal cancer (CRC). The machine learning developer announced the first-year results of its implementation with Maccabi Healthcare Services (MHS), for ColonFlag, a tool developed in collaboration with MHS to identify individuals with a high probability of having CRC.

Researchers from the D'Or Institute for Research and Education have used machine learning to train a computer to identify what song a participant is listening to by analyzing brain activity. The study, published in Scientific Reports, aims to advance brain decoding for future communication with patients without spoken words. A total of six volunteers listened to 40 pieces of classical, rock, pop and jazz music while undergoing magnetic resonance imaging (MRI). The MRI identified the neural fingerprint of each song in a participant's brain while a computer simultaneously learned the specific patterns occurring during each song. The computer included tonality, dynamics, rhythm and timbre in its analysis for an improved recall.

The sound of music can speak to one's soul in myriad ways and evidently is also true in regards how listening to different musical genres impact the brain. Functional magnetic resonance imaging (fMRI) data and computational algorithms were used in new research published in Scientific Reports on Feb. 2 to demonstrate that music genre can be identified through observing neurological responses to certain characteristics associated with that particular genre. "Our approach was capable of identifying musical pieces with improving accuracy across time and spatial coverage," said lead researcher Sebastian Hoefle, a doctoral candidate at the Federal University of Rio de Janeiro in Brazil. "Specifically, we showed that the distributed information in auditory cortices and the entropy of musical pieces enhanced overall identification accuracy up to 95 percent." Researchers investigated fMRI brain responses of six participants who listened to 40 musical pieces of various genres, including rock, pop, jazz, classical and folk without lyrics.

The Pentagon's new #AI is already hunting terrorists. Just a couple of the many ways #Deep-learning will add value to every enterprise. Signal: Is the US losing Latin America?

Mind-reading technology may be closer to reality than you might think. An international team of scientists, including researchers from the D'Or Institute for Research and Education in Rio de Janeiro, have used a Magnetic Resonance (MR) machine to read people's minds and identify what song they were listening to. Six participants listened to 40 different pieces of music ranging from classical to rock, pop, jazz and other types while being monitored by the MR machine, which was linked to a computer. The computer was fitted with special software which learned to identify the'neural fingerprint' of each song, or the specific brain patterns associated with it. It did this by searching for musical features such as rhythm, tonality, dynamics and timbre.

Before i start to write what i'm supposed to, sorry for the bad english, i'm far from being fluent. So, i'm a software engineer student from Brazil and recently i had an idea to apply my machine learning knowledge into a personal project. I thought to myself: "What about applying machine learning algorithms to predict the competitive matches results?" Which features winning teams have in common? Which type of compositions have more win ratio above others?

The present work shows the application of transfer learning for a pre-trained deep neural network (DNN), using a small image dataset ($\approx$ 12,000) on a single workstation with enabled NVIDIA GPU card that takes up to 1 hour to complete the training task and archive an overall average accuracy of $94.7\%$. The DNN presents a $20\%$ score of misclassification for an external test dataset. The accuracy of the proposed methodology is equivalent to ones using HSI methodology $(81\%-91\%)$ used for the same task, but with the advantage of being independent on special equipment to classify wheat kernel for FHB symptoms.

I come from physics and computer engineering. I studied both in Venezuela, and then I did a Master in Physics in Mexico. But I consider myself a Data Scientist. So even though I have a good and extensive background in math, calculus and statistics, it was not easy to get started with machine learning and then deep learning. This subjects are not new, but the way we study them, how we build software and solutions that use them, and also the way we program or interact with them has changed dramatically.

A decade ago, Pablo Graiver was working as a VP at Kayak, the online airfare aggregator, when he sat down to dinner with an old friend--a heart surgeon from his home country of Argentina. The talk turned to how tech was doing more to save folks a few bucks on a flight to Rome than to save people's lives. Right now, the US has exactly 19,816 clinical trials open and ready to recruit patients--trials of promising new therapeutics to fight everything from HIV to cancer to Alzheimer's. About 18,000 of them will get stuck on the tarmac because they won't get enough people enrolled. And a third of those will never get off the ground at all, for the same reason. So where are all the patients?

Artificial Intelligence is a central topic in the computer science curriculum. From the year 2011 a project-based learning methodology based on computer games has been designed and implemented into the intelligence artificial course at the University of the Bio-Bio. The project aims to develop software-controlled agents (bots) which are programmed by using heuristic algorithms seen during the course. This methodology allows us to obtain good learning results, however several challenges have been founded during its implementation. In this paper we show how linguistic descriptions of data can help to provide students and teachers with technical and personalized feedback about the learned algorithms. Algorithm behavior profile and a new Turing test for computer games bots based on linguistic modelling of complex phenomena are also proposed in order to deal with such challenges. In order to show and explore the possibilities of this new technology, a web platform has been designed and implemented by one of authors and its incorporation in the process of assessment allows us to improve the teaching learning process.