IBM (NYSE: IBM) today introduced AI OpenScale, a new technology platform that addresses key challenges of artificial intelligence (AI) adoption, such as concerns over how AI applications make decisions, the global shortage of AI skills and the complexities of working with disparate AI tools from multiple vendors. IBM's new technology platform is the first of its kind. It will enable companies to manage AI transparently throughout the full AI lifecycle, irrespective of where their AI applications were built or in which environment they currently run. AI OpenScale can detect and address bias across the spectrum of AI applications, as those applications are being run. As part of AI OpenScale, IBM also will debut NeuNetS, a major scientific breakthrough in which AI builds AI – making it possible to create complex, deep-neural networks from scratch.

Two Artificial Intelligence-driven Internet paradigms may emerge in the near future. One will be based on logic, smart enterprises and human merit while the other may morph into an Orwellian control tool. Even former Google CEO Eric Schmidt has foreseen a bifurcation of the Internet by 2028 and China's eventual triumph in the AI race by 2030. In the meantime, the US seems more interested in deflecting the smart questions of today than in building the smart factories of tomorrow. Nothing embodies this better than the recent attempt by MIT's Computer Science and Artificial Intelligence Lab (CSAIL) and the Qatar Computing Research Institute (QCRI) to create an AI-based filter to "stamp out fake-news outlets before the stories spread too widely."

A rectangular black box that sits in your entertainment center: It sure does look like another streaming box competitor, but it's important to understand that the Caavo Control Center isn't that at all. It's a universal remote control that takes a wholly different approach than those used by Logitech. This is actually Caavo's second run at this concept. The first was a wildly innovative (and pricy) crowd-funded system designed to be NORAD for your entertainment system; you plugged everything into the Caavo, then controlled it all with its custom remote. With eight HDMI ports, two USB ports, and more, the $400 device was clad in steel and decked out in fancy wood, built to be an eye-catching showstopper. The system, which launched in very limited quantities in early 2018, met mostly with resistance and confusion, which sent Caavo back to the drawing board to come up with a more approachable and affordable concept.

Two Artificial Intelligence-driven Internet paradigms may emerge in the near future. One will be based on logic, smart enterprises and human merit while the other may morph into an Orwellian control tool. Even former Google CEO Eric Schmidt has foreseen a bifurcation of the Internet by 2028 and China's eventual triumph in the AI race by 2030. In the meantime, the US seems more interested in deflecting the smart questions of today than in building the smart factories of tomorrow. Nothing embodies this better than the recent attempt by MIT's Computer Science and Artificial Intelligence Lab (CSAIL) and the Qatar Computing Research Institute (QCRI) to create an AI-based filter to "stamp out fake-news outlets before the stories spread too widely."

In addition to visual inspection by employees, several small cameras are installed directly in the presses. They evaluate the captured images with the help of image-recognition software. This process will soon be replaced by an ML procedure. Software based on a complex artificial neural network operates in the background of this innovative procedure. The software detects the finest cracks in sheet metal with the utmost precision and reliably marks the spot.

Growing up, one of my favorite movies was Steven Spielberg's Minority Report. I was fascinated by the idea that a crime could be prevented before it occurred. More interesting to me at the time was the futuristic role that'super intelligent' technology – something depicted as more sophisticated and advanced than humans – could play in doing this accurately. Recently, the role that pre-crime and artificial intelligence can play in our world has been explored in episodes of the popular Netflix TV show Black Mirror, focusing on the debate between free will and determinism. Working in counter-terrorism, I know that the use of artificial intelligence in the security space is fast becoming a reality.

We discuss deep reinforcement learning in an overview style. We draw a big picture, filled with details. We discuss six core elements, six important mechanisms, and twelve applications, focusing on contemporary work, and in historical contexts. We start with background of artificial intelligence, machine learning, deep learning, and reinforcement learning (RL), with resources. Next we discuss RL core elements, including value function, policy, reward, model, exploration vs. exploitation, and representation. Then we discuss important mechanisms for RL, including attention and memory, unsupervised learning, hierarchical RL, multi-agent RL, relational RL, and learning to learn. After that, we discuss RL applications, including games, robotics, natural language processing (NLP), computer vision, finance, business management, healthcare, education, energy, transportation, computer systems, and, science, engineering, and art. Finally we summarize briefly, discuss challenges and opportunities, and close with an epilogue.

Deep reinforcement learning enables algorithms to learn complex behavior, deal with continuous action spaces and find good strategies in environments with high dimensional state spaces. With deep reinforcement learning being an active area of research and many concurrent inventions, we decided to focus on a relatively simple robotic task to evaluate a set of ideas that might help to solve recent reinforcement learning problems. We test a newly created combination of two commonly used reinforcement learning methods, whether it is able to learn more effectively than a baseline. We also compare different ideas to preprocess information before it is fed to the reinforcement learning algorithm. The goal of this strategy is to reduce training time and eventually help the algorithm to converge. The concluding evaluation proves the general applicability of the described concepts by testing them using a simulated environment. These concepts might be reused for future experiments.

Artificial intelligence (AI) is changing the world around us. From automated factories that build everything without human intervention, to computer systems capable of beating world masters at some of the most complex games, AI is powering our society into the future – but what happens when this artificial intelligence becomes greater than ours? Should we fear automated weapon turning on us, or Hollywood-style "skull-stomping robots"? We spoke to Max Tegmark, an MIT professor and co-founder of the Future of Life Institute, about his book, Life 3.0, in which he answers some of the key questions we need to solve to make the future of artificial intelligence one that benefits all of humankind. Can you describe your book in a nutshell?

TLDR: No! Your Machine Translation Model is not "prophesying", but let's look at the six major issues with neural machine translation (NMT). So I saw a Twitter thread today with the editor-in-chief of Motherboard tweeting, "Google Translate is popping out bizarre religious texts and no one is sure why". I am going to spend a little time on the "why" part (folks who work in MT know why), but mostly focus on actual problems with neural machine translation. The choice of headlines, the promotion tweet, and the tone of the article reminds me of all the irresponsible writing that went around the famous "Facebook Frankenstein" experiment. I would not be surprised if other media outlets picked up this Motherboard piece and ran ridiculous stories about machine translation conspiracy theories.