Google announced Friday that it has achieved state-of-the-art results in using artificial intelligence to identify breast cancer. The findings are a reminder of the rapid advances in artificial intelligence, and its potential to improve global health. Google used a flavor of artificial intelligence called deep learning to analyze thousands of slides of cancer cells provided by a Dutch university. Deep learning is where computers are taught to recognize patterns in huge data sets. With 230,000 new cases of breast cancer every year in the United States, Google (GOOGL, Tech30) hopes its technology will help pathologists better treat patients.

Machine Learning (ML) has revolutionized the world of computers by allowing them to learn as they progress forward with large datasets, thus mitigating many previous programming pitfalls and impasses. Machine Learning builds algorithms, which when exposed to high volumes of data, can self-teach and evolve. When this unique technology powers Artificial Intelligence (AI) applications, the combination can be powerful. We can soon expect to see smart robots around us doing all our jobs – much quicker, much more accurately, and even improving themselves at every step. Will this world need intelligent humans anymore or shall we soon be outclassed by self-thinking robots? What are the most visible 2017 Machine Learning trends?

In an era of maturing artificial intelligence technology, what does the future of the corporation look like? Will the rise of robots help us do our jobs better, or harm them? This dynamic has become a mainstay of the dialogue around AI, with voices from technology visionaries such as Bill Gates and Stephen Hawking weighing in. But at Fortune's Most Powerful Women International Summit in Hong Kong on Tuesday, leaders at two of the world's most powerful tech giants pushed back on those concerns. AI is intended to help--not hinder--the human workforce, they said.

BARCELONA, Spain--IBM has come up with a way to show off the more artistic side of its artificial intelligence capabilities while honoring one of Spain's most famous architects. For this year's Mobile World Congress in Barcelona, IBM tasked its Watson super computer with the understanding Antoni Gaudí's greatest works such as La Pedrera and la Sagrada Família to help create a unique sculpture for the annual trade show. Using its visual-recognition technology, Watson reviewed hundreds of images of Gaudí's buildings, along with additional examples of local architecture to understand trends in composition and inspirations. IBM then tapped Watson's AlchemyLanguage software to read documents about Gaudí and other architects, along with song lyrics and other documents about the city's history and culture. "What we were really trying to do at the essence was figure out if we can programmatically start to understand what the features of a particular style or architect are," said Jeff Arn, a Watson manager at IBM. Watson identified key elements of Gaudí's Catalan modernist style--known for its dream-like colors and shapes and odes to the natural world.

The highly intelligent machines that will be unleashed in the near future won't be coming for our lives. They'll be coming for our jobs. Being rendered obsolete by technology has been a concern among the flesh-and-blood set for hundreds of years -- cars put many in the horse industry out of work, for example -- but the speed and types of recent advances are about to give the issue an exceptional urgency. Previously, it was repetitive blue-collar jobs that were at risk, such as those in manufacturing. In the near future, however, the leaps in artificial intelligence will soon make it possible for machines to do all sorts of jobs, including those that require thinking skills we once believed beyond the reach of machines.

The all- new test vehicle will be used to explore a full range of autonomous driving capabilities. Toyota's work on autonomous vehicles in the United States began in 2005 at its technical center in Ann Arbor, Mich.-- The company secured its first U.S. patents in the field in 2006.-- According to a report last year by the Intellectual Property and Science division of Thomson Reuters, Toyota holds more patents in the field than any other company. "This new advanced safety research vehicle is the first autonomous testing platform developed entirely by TRI, and reflects the rapid progress of our autonomous driving program," said TRI CEO Gill Pratt.

The leader of one of the world's largest automobile producers expects that cars will soon drive themselves and sync to the world around them -- but don't count out the human behind the wheel just yet. Carlos Ghosn, the chief executive and chairman of an alliance that includes Nissan, Renault and Mitsubishi, said Thursday that humans will remain involved in the operation of vehicles for the foreseeable future, even as cars with self-driving technology enter the market in the next five years. You will push a button to activate the car's autonomous driving feature, he said, but it will encounter everyday scenarios it cannot compute and that require human assistance. "Artificial intelligence is still way below the creativity of the human brain," Ghosn said. Imagine a self-driving car coming upon a broken-down vehicle in the road, but there is a solid line to either side of it, Ghosn said.

Geologists have long been able to work out the approximate risk of an earthquake. Their approach is to work out when the fault moved in the past and use any periodicity to predict the future. The most famous example involves the Parkfield segment of the San Andreas Fault in California, one of the most carefully studied faults on the planet. Earthquakes occurred here in 1857, 1881, 1901, 1922, 1934, and 1966, suggesting a pattern in which quakes occur every 22 years give or take a few years. Geologists therefore predicted that a quake would occur between 1988 and 1993, but they had to wait until 2004 for their temblor.

The data features that we use to train our machine learning models have a huge influence on the performance we can achieve. Irrelevant or partially relevant features can negatively impact model performance. Feature selection is a process where we automatically select those features in our data that contribute most to the prediction variable or output in which we are interested. Having irrelevant features in our data can decrease the accuracy of many models, especially linear algorithms like linear and logistic regression. We can learn more about feature selection with scikit-learn in the article Feature selection.

Abstract: Designing a practical, low complexity, close to optimal, channel decoder for powerful algebraic codes with short to moderate block length is an open research problem. Recently it has been shown that a feed-forward neural network architecture can improve on standard belief propagation decoding, despite the large example space. In this paper we introduce a recurrent neural network architecture for decoding linear block codes. Our method shows comparable bit error rate results compared to the feed-forward neural network with significantly less parameters. We also demonstrate improved performance over belief propagation on sparser Tanner graph representations of the codes.