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.

In a modern company like Amazon, almost all human activity is directed by computer programs. They not only monitor workers' actions but are used to choose who should be employed. Yet it emerged last week that the company had scrapped an attempt to use artificial intelligence to select workers on the basis of their CVs, since the results consistently discriminated against women. This is a welcome decision that illuminates two important facts about machine learning, the most widely used technique of AI at the moment. The technical or operational point is that these programs, no matter how fast they learn, can only learn from the data presented to them.

Alexa is no longer "new." The smart-tech revolution is now in full swing, and Amazon Echo is at the heart of it. According to Edison Research, nearly 40 million people own voice-activated speakers, which is about one in six U.S. adults. Echo changed the game, and millions of households have integrated Alexa into their daily lives. For many technophiles, it's not a question of whether to invest in smart technology, but what kind.

Global defence giant Thales is set to create up to 100 jobs at a new £1million base in Plymouth where it will test robotic boats for dealing with sea-borne mines. The French multinational has opened a trials and training centre at Turnchapel Wharf, creating 20 high-skilled jobs, but with an ambition to grow, possibly to as many as 100. The investment is part of a "major commitment" to developing autonomous and unmanned technology for use in the air and on sea – in other words, robotic vessels. Thales, which reported global sales of €14 billion in 2015, is creating two new UK centres, the other being in Wales. The company said that only through experimentation with new and disruptive technologies will the UK military be able to stay ahead and maintain an advantage over other forces.

Artificial Intelligence is a field of computer science that consists of the construction of intelligent machines that are put into operation through computer programs. The purpose of building these gadgets or robots is to replace human intelligence to a certain extent by doing more than one action. AI is a new area under development and has a particular focus on the banking system and the way it operates. Of the most influential sectors are customer service, financial services, and fraud detection. Artificial Intelligence can make banking services automate and thus perform much faster than people.