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.

Isn't it cool if we could predict cyber attacks before it happens? Predicting cyber attacks before it happens can help to prevent it. A Scientist team at Massachusetts Institute of Technology have developed an Artificial Intelligence system that can detect and stop almost 85% of cyber attacks with a little human help. This Advanced intelligent system is known as AI2. Researchers from MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL) and the machine-learning startup ParrernEx have demonstrated an artificial intelligence platform knows AI2.

Retired Army Col. Lawrence Wilkerson says the decentralization and advancements of 3-D printing, artificial intelligence, and nanotechnology are the future of warfare, and may enable the rise of modernized private robotic armies. Wilkerson's statements were made during an exclusive interview with Rick Wiles of TRUNEWS on Thursday, while discussing the possibility that billionaires like George Soros could bring rise to a modern version of the East India Company. "As were developing these new technologies particularly 3-D printing, nanotechnology, nano engineering, artificial intelligence and robotics, as were developing these now, we are reducing enormously the costs for some of the most sophisticated weapons to be in the world," Wilkerson said. These advancements, Wilkerson noted, are already being placed into conceptual practice. "With 3-D printing we have recently produced, in less than 16 hours, a drone that underwater went to the coast of France and back to the Eastern coast of the United States, underwater. You produce this drone with 3-D printing almost overnight, you hang some smart weapons on it like submarine killing torpedoes or smart mines, you take it out there and you kill a 4 billion Ohio class submarine. This is the future and if you make these kinds of weapons available to almost anyone in the world, at a reasonable price, I mean you can make this drone for about 100,000, its going to kill a 4 billion submarine, thats quite a price exchange there."

As the world celebrated Earth Day on Friday, a team led by an Indian-origin researcher has found a way to use artificial intelligence (AI) to protect the Earth's endangered animals and forests by outwitting poachers with technology. With support from the US National Science Foundation (NSF) and the US Army Research Office, researchers are using AI and game theory to solve poaching, illegal logging and other problems worldwide, in collaboration with researchers and conservationists in the US, Singapore, the Netherlands and Malaysia. "This research is a step in demonstrating that AI can have a really significant positive impact on society and allow us to assist humanity in solving some of the major challenges we face," said Milind Tambe, professor of computer science and industrial and systems engineering at the University of Southern California (USC). "In most parks, ranger patrols are poorly planned, reactive rather than pro-active and habitual," said Fei Fang, PhD candidate from the University of Southern California (USC). Fang is part of an NSF-funded team at USC led by Tambe who is also director of the Teamcore Research Group on Agents and Multiagent Systems.