For those that wonder what Google is actually aiming for, Larry Page offers clear direction. He said recently, "Artificial intelligence would be the ultimate version of Google. The ultimate search engine that would understand everything on the Web. It would understand exactly what you wanted, and it would give you the right thing." The key components of the quote are that this AI-based uber-Google would not only know "everything" on the web (basically the sum of all human knowledge) but would also have an insight into the specific needs of each user, presumably even before the need was stated (hence the use of the term "what you wanted"). These two differences are central to the AI leap – essentially aimed at helping Google make the move from a highly sophisticated database search engine that compares text to a system that has a level of "understanding" of the context.

SEOUL--Humanity has fallen to artificial intelligence in checkers, chess, and, last month, Go, the complex ancient Chinese board game. But some of the world's biggest nerds are confident that machines will meet their Waterloo on the pixelated battlefields of the computer strategy game StarCraft. A key reason: Unlike machines, humans are good at lying. StarCraft, created in 1998, is one of the world's most popular computer game franchises. It pits three races against one another: the humanlike Terrans, the slimy insectoid Zerg and a mystical race with psionic powers called the Protoss.

Autonomous cars use a variety of technologies like radar, lidar, odometry and computer vision to detect objects and people on the road, prompting it to adjust its trajectory accordingly. But these tools can drive up the cost of driverless cars, and still aren't as effective as the human brain in visually distinguishing some objects from pedestrians. To tackle this problem, electrical engineers from University of California, San Diego used powerful machine learning techniques in a recent experiment that incorporated so-called deep learning algorithms in a pedestrian-detection system that performs in near real-time, using visual data only. "We're aiming to build computer vision systems that will help computers better understand the world around them," said Nuno Vasconcelos, an electrical engineering professor at the University of California San Diego who led the study, quoted in a story posted by UC San Diego's Jacobs School of Engineering. The findings, which were presented at the International Conference on Computer Vision in Santiago, Chile, are an improvement over current methods of pedestrian detection, which uses something called cascade detection.

Facebook's new chatbots for the Messenger app are slow, annoying and not ready for public consumption, argues #TalkingTech host Jefferson Graham. But how do the rest of this week's panelists feel? Tune in to hear their verdict.

AI may have trounced humanity in the ancient game of Go, but it remains untested in countless other gaming arenas. Case in point: Doom, which, it turns out, will be the technology's next big challenge. Launched in 1993, Doom is widely considered a landmark title in the video-game industry for popularizing the first-person shooter genre. Now, artificial-intelligence researchers will have a chance to pit their creations against others in a contest based on the game at the IEEE Computational Intelligence and Games conference in September. To create their bots, competitors in the Visual Doom AI competition will use ViZDoom, a platform based on the game that's used for research in machine learning.

SAN FRANCISCO -- The cloud may be the future, but the specter of the PC lingers. Microsoft is the latest tech giant whose earnings say that loud and clear. Microsoft on Thursday posted substantial drops in revenue and earnings as it continues to navigate from its legacy PC business into emerging technologies -- a day after chipmaker Intel announced a 11% workforce reduction. The Redmond, Wash.-based company reported a 6% decline in fiscal third-quarter revenue to 20.5 billion. Earnings of 3.8 billion, or 47 cents per share, fell 25%in the same quarter a year ago.

I want to develop a machine learning model in R that I can deploy in Java. I want to describe what I've tried, and how it failed, and what my next iteration is so every one here can help guide me. We have a system where a human will examine raw text and assign it meta-data where each piece of meta-data is a separate category. For example, say we had String 1 and available categories A-Z and a human assigned String 1 categories A, B, C, and F. There is a large amount of human QA that happens afterwards to ensure that String 1 either received all the categories it should have and didn't receive any categories it shouldn't have, for example String 1 should not have received the f category. I am tasked with developing a way to automatically detect if a String needs QA after meta-data has been assigned to it.

Google is beginning to look beyond search to tap into some of the most lucrative and promising businesses in the tech industry: artificial intelligence and cloud computing. The company, the largest and most significant part of Alphabet Inc., has grown to mammoth proportions off the back of its search-based advertising division. But those revenues are starting to slow. The cloud allows companies to manage and sell server space and software that lives inside its data centers, like AI, to other large companies. That type of service-based business is fast becoming the new way to reap profits in the tech industry.

Actually I've known about MXnet for weeks as one of the most popular library / packages in Kaggler, but just recently I heard bug fix has been almost done and some friends say the latest version looks stable, so at last I installed it. I think that the most important feature of MXnet is its implementation of not only Deep Neural Network (DNN) but also Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN) in R, because as far as I've known there has been no R packages implementing CNN (and/or RNN). In the original post of my blog, I tried a CNN {mxnet} R package with a short version of MNIST handwritten digit datasets whose maximum accuracy may be less than 0.98 for its small sample size. As a result, CNN of {mxnet} performed accuracy 0.976: this is better than Random Forest (0.951), Xgboost (0.953) or DNN by {h2o} (0.962). MXnet is a framework distributed by DMLC, the team also known as a distributor of Xgboost.

The following interview is one of many included in the report. As part of my ongoing series of interviews surveying the frontiers of machine intelligence, I recently interviewed Yoshua Bengio. Bengio is a professor with the department of computer science and operations research at the University of Montreal, where he is head of the Machine Learning Laboratory (MILA) and serves as the Canada Research Chair in statistical learning algorithms. The goal of his research is to understand the principles of learning that yield intelligence. Yoshua Bengio: I have been researching neural networks since the '80s.