I explain Artificial Intelligence terms and news to non-experts. Two years ago, I saw my first research paper ever. I remember how old it looked and how discouraging the mathematics inside was. It really did look like what the researchers worked on in movies. To be fair, the paper was from the 1950s, but it hasn't changed much since then.

David Cox, the head of a prestigious artificial intelligence lab in Cambridge, Massachusetts, was scanning an online computer science bibliography in December when he noticed something odd--his name listed as an author alongside three researchers in China whom he didn't know on two papers he didn't recognize. At first, he didn't think much of it. The name Cox isn't uncommon, so he figured there must be another David Cox doing AI research. "Then I opened up the PDF and saw my own picture looking back at me," Cox says. It isn't clear how prevalent this kind of academic fraud may be, or why someone would list as a coauthor someone not involved in the research.

Are you an AI enthusiast who wants to keep abreast of the latest developments in space but do not know where to begin? This Mumbai-based computer science engineer may have an answer for you. With the number of papers and publications that are published each week growing exponentially, one of the biggest challenges for the AI and machine learning enthusiasts is to pick the papers that are trending in the space. There are very few dedicated platforms that host the archives of the technical papers and even fewer websites that surface and suggest top trending papers in AI, ML, computer vision and related domains. In fact, 42papers is one of a kind initiative that lets tech enthusiasts pick from the top trending papers.

At the world's top computer-vision conference last June, Google and Apple sponsored an academic contest that challenged algorithms to make sense of images from twin cameras collected under varied conditions, such as sunny and poor weather. Artificial intelligence software proficient at that task could help the US tech giants with money-making projects such as autonomous cars or augmented reality. But the winner was an institution with very different interests and allegiances: China's National University of Defense Technology, a top military academy of the People's Liberation Army. That anecdote helps illustrate China's broad ambitions in AI and recent prominence on the field's frontiers. In 2017 the country's government announced a new artificial intelligence strategy that aims to rival the US in the crucial technology by 2020.

At the world's top computer-vision conference last June, Google and Apple sponsored an academic contest that challenged algorithms to make sense of images from twin cameras collected under varied conditions, such as sunny and poor weather. Artificial intelligence software proficient at that task could help the US tech giants with money-making projects such as autonomous cars or augmented reality. But the winner was an institution with very different interests and allegiances: China's National University of Defense Technology, a top military academy of the People's Liberation Army. That anecdote helps illustrate China's broad ambitions in AI and recent prominence on the field's frontiers. In 2017, the country's government announced a new artificial intelligence strategy that aimed to rival the US in the crucial technology by 2020.

The popular narrative around artificial intelligence research is that it's mainly a war between China and the United States. Not so fast, says Europe. New data released today (Dec. The data was assembled from Scopus, a citation database owned by scientific publishing company Elsevier. If the current trend continues, China will soon overtake Europe in the number of papers published.

We trust in science because we can verify the accuracy of its claims. We test and verify that accuracy by repeating the scientist's original experiments. What happens when those tests fail, particularly in a field that has the potential to create billions of dollars of revenue? In 2016, Nature surveyed more than 1,500 scientists and found that more than 70% of them had tried and failed to reproduce experiments by other scientists published in scientific journals. More than half couldn't even reproduce their own work.

Google is miles and miles ahead of Apple in AI. But really that should be expected as Google has been doing it a lot longer than Apple and spent way, way more money. Plus Google has so much more data than Apple. But hopefully Apple will really get serious and close the gap over the next couple of year. Think the biggest reason Apple fell so far behind is they really did not focus on AI like Google did and now sounds like Apple is getting more serious about it. In comparisons Apple has done very poorly to Google.

Apple has stayed true to its promise and published its first academic paper on artificial intelligence. The world's most valuable company has traditionally kept its AI research private but earlier this month Ruslan Salakhutdinov, director of AI research at Apple, made a pledge to start being more open. The new Apple paper -- published December 22 and titled "Learning from simulated and unsupervised images through adversarial training" -- gives an insight into some of the techniques that Apple is using to develop AI. In the study, which was published through the Cornell University Library, Apple researchers explain a technique that can be used to improve how an algorithm learns to "see" what is in an image. The paper's six authors state that using synthetic images (such as those seen in a video game), as opposed to real-world images, can be more efficient when it comes to training AI models known as neural networks, which are designed to think in the same way as the human brain. Because synthetic image data is already labelled and annotated while real-world images aren't.

Apple has stayed true to its promise and published its first academic paper on artificial intelligence. The world's most valuable company has traditionally kept its AI research private but earlier this month Ruslan Salakhutdinov, director of AI research at Apple, made a pledge to start being more open. The new Apple paper -- published December 22 and titled "Learning from simulated and unsupervised images through adversarial training" -- gives an insight into some of the techniques that Apple is using to develop AI. In the study, which was published through the Cornell University Library, Apple researchers explain a technique that can be used to improve how an algorithm learns to "see" what is in an image. The paper's six authors state that using synthetic images (such as those seen in a video game), as opposed to real-world images, can be more efficient when it comes to training AI models known as neural networks, which are designed to think in the same way as the human brain. Because synthetic image data is already labelled and annotated while real-world images aren't.