And you probably notice countless other details as well. A machine sees none of this; an image is encoded as an array of numbers representing color values for each pixel, as in the second photo, the one on the right. So how do we enable machine vision to go from pixels to a deeper understanding of an image?

But a machine sees none of this; to a machine, it's just a bunch of pixels. It's up to computer-vision technology like the one developed at FAIR to segment each object out. Considering that real-world objects come in so many shapes and sizes as well as the fact that photos are subject to varying backgrounds and lighting conditions, it's easy to see why visual recognition is so complex. The answer, Dollar writes, lies in deep convolutional neural networks that are "trained rather than designed." The networks essentially learn from millions of annotated examples over time to identify the objects. "The first stage would be to look at different parts of the image that could be interesting," he says. "The second step is to then say, 'OK, that's a sheep,' or'that's a dog.' "Our whole goal is to get at all the pixels, to get at all the information in the image," he says.

Assigning people the tedious task of manually reviewing and manipulating documents is a sure-fire way to miss errors and introduce inaccuracies as blurry-eyed employees move between programs and cut-and-paste data. Although this is standard practice at many companies, it's a problem that Robotic Process Automation (RPA) technology is now able to solve. RPA can mimic a human's ability to gather data and put data into Excel spreadsheets. It can access disparate systems (ERP, sales, tax, etc.), review tens of thousands of documents in an hour or two (instead of several days), do so without error, and put the data in a standardized format. But RPA's advantages go far beyond imitating keystrokes and avoiding errors; RPA's speed, accuracy and formatting abilities make it an essential foundational technology for a more automated, intelligent enterprise.

The elimination of poverty worldwide is the first of 17 UN Sustainable Development Goals for the year 2030. To track progress towards this goal, we require more frequent and more reliable data on the distribution of poverty than traditional data collection methods can provide. In this project, we propose an approach that combines machine learning with high-resolution satellite imagery to provide new data on socioeconomic indicators of poverty and wealth. Check out the short video below for a quick overview and then read the paper for a more detailed explanation of how it all works.

What is machine learning, when and how is it used? Why is it such a buzzword? Listen to Josefin Rosén explaining and giving examples how machine learning make sense of all data we have and how to create value of it.

The Department of Pathology at the Icahn School of Medicine at Mount Sinai has established the Center for Computational and Systems Pathology to revolutionize pathology practice, using advanced computer science and mathematical techniques coupled with cutting-edge microscope technology and artificial intelligence. The goal of this new academic research facility is to explore efforts to more accurately classify diseases and guide treatment using computer vision and machine learning techniques. The Center for Computational and Systems Pathology will be a hub for the development of new diagnostic, predictive, and prognostic tests and will partner with Mount Sinai-based "Precise Medical Diagnostics" (Precise MDTM), which has been under development for more than three years by a team of physicians, scientists, mathematicians, engineers, and programmers. Carlos Cordon-Cardo, MD, PhD, will oversee the new center, located at Mount Sinai St. Luke's, and will continue his role as Chair of the Department of Pathology at the Mount Sinai Health System and Professor of Pathology, Genetics and Genomic Sciences, and Oncological Sciences at the Icahn School of Medicine. Gerardo Fernandez, MD, Associate Professor of Pathology, and Genetics and Genomic Sciences at the Icahn School of Medicine at Mount Sinai, will be the Center's Medical Director.

Cooperation is one of the hallmarks of being human. We are extremely social compared to other species. On a regular basis, we all enter into helping others in small but important ways, whether it be letting someone out in traffic or giving a tip for good service. We do this without any guarantee of payback. Donations are made at a small personal cost but with a bigger benefit to the recipient.

As machine-learning becomes increasingly embedded into our everyday lives, Dr David Bray, a 2015 Eisenhower Fellow, Visiting Executive In-Residence at the Harvard Kennedy School and CIO at the US Federal Communications Commission, explores both the possible positive opportunities and potential cyber-security challenges. "The exponential changes being driven by digital technology are typically painted in a highly positive light. Just look at the average Internet minute during 2013: more than 204 million emails were sent, more than 4 million Google search results were conducted, more than 2.4 million items of Facebook content were uploaded and more than 72 hours of video were uploaded to YouTube. But it's worth remembering that technology itself is amoral: it's how we humans choose to use it that decides whether it's good or bad. The more challenging side of the exponential era is evident elsewhere: in 2013, cyber-security technology company McAfee identified 200 new threat vectors ...

For the second installment of our series exploring the future of human augmentation, we bring you a story by the Transhumanist Party's presidential candidate (and occasional Motherboard columnist), Zoltan Istvan. Though he's spent most of the last year traveling the nation in a coffin-shaped bus, spreading the gospel of immortality and H, he's no stranger to fiction. His novel, The Transhumanist Wager, is about the impact of evolving beyond this mortal coil. This story is even bolder. He struggled to open his eyes. 'Who the hell is calling me in the middle of the night?' he thought. He rolled out of bed and walked naked to his desk to see. His phone showed it was his secretary.

By 2025 Australians may well see the application of artificial intelligence in the preparation of tax returns, according to a report by KPMG. The report, 2025: People, economy and the future of tax, suggests a future world in which service industry professionals such as accountants will be replaced by robots and the size of revenue authorities such as the Australian Taxation Office will shrink. "The technological changes ... are likely to mean few mid-skilled jobs will be required," the report says. Tax in 2025 could involve the application of artificial intelligence to the preparation of tax returns. "This is beyond complex automation, which would involve picking up items in the general ledger and mechanistically using that data in the tax return," it says.