The Internet of Things (IoT) is poised to fundamentally change the way a wide range of industries approach the procurement, processing, and distribution of raw materials and finished products. New efficiencies based on the introduction of intelligent sensors, mission-critical communications, automation, and robotics will optimize industries ranging from mining and shipping to manufacturing verticals including electronics, automotive and petrochemical products. This emerging megatrend is alternatively called the Fourth Industrial Revolution and Industry 4.0, although these aren't interchangeable terms. Let's take a look at both. The First Industrial Revolution, which started in Britain around 1760 and ran until between 1820 and 1840, saw the mechanization of the textile industry via a transition from hand tools to machine tools.

Researchers have warned that millions of human workers in the US will be replaced by robots over the next few decades, leaving Americans to wonder what areas are at the highest risk. Now, a new map has shown where the most'automatable' jobs are in the nation - and almost every metropolitan area is set to experience a robot takeover. However, it is the low-wage cities like Las Vegas, Nevada, El Paso, Texas and San Bernardino, California that will be hit the hardest – robots are predicted to take more than 60% of jobs in these cities by 2035. A new map has shown where the most'automatable' jobs are in the nation - and almost every metropolitan area is set to experience a robot takeover. The bubble size shows the number of workers employed in the metropolitan areas in December 2016.

Once you start poking around in the muck of consciousness studies, you will soon encounter the specter of Sir Roger Penrose, the renowned Oxford physicist with an audacious--and quite possibly crackpot--theory about the quantum origins of consciousness. He believes we must go beyond neuroscience and into the mysterious world of quantum mechanics to explain our rich mental life. No one quite knows what to make of this theory, developed with the American anesthesiologist Stuart Hameroff, but conventional wisdom goes something like this: Their theory is almost certainly wrong, but since Penrose is so brilliant ("One of the very few people I've met in my life who, without reservation, I call a genius," physicist Lee Smolin has said), we'd be foolish to dismiss their theory out of hand. Penrose, 85, is a mathematical physicist who made his name decades ago with groundbreaking work in general relativity and then, working with Stephen Hawking, helped conceptualize black holes and gravitational singularities, a point of infinite density out of which the universe may have formed. He also invented "twistor theory," a new way to connect quantum mechanics with the structure of spacetime. His discovery of certain geometric forms known as "Penrose tiles"--an ingenious design of non-repeating patterns--led to new directions of study in mathematics and crystallography. The breadth of Penrose's interests is extraordinary, which is evident in his recent book Fashion, Faith and Fantasy in the New Physics of the Universe--a dense 500-page tome that challenges some of the trendiest but still unproven theories in physics, from the multiple dimensions of string theory to cosmic inflation in the first moment of the Big Bang. He considers these theories to be fanciful and implausible.

The Modular Advanced Armed Robotic System is an unmanned ground vehicle for reconnaissance, surveillance, and target acquisition missions. From April 11–15, 2016, at the United Nations Office at Geneva, the Convention on Certain Conventional Weapons (CCW) conducted a third year of informal meetings to hear expert testimony regarding a preemptive ban on lethal autonomous weapons systems (LAWS). A total of 94 states attended the meeting, and at the end of the week they agreed by consensus to recommend the formation of an open-ended Group of Government Experts (GGE). A GGE is the next step in forging a concrete proposal upon which the member states could vote. By the end of 2016 a preemptive ban has been called for by 19 states. Furthermore, meaningful human control, a phrase first proposed by advocates for a ban, has been adopted by nearly all the states, although the phrase's meaning is contested. Thus a ban on LAWS would appear to have gained momentum. Even the large military powers, notably the U.S., have publicly stated that they will support a ban if that is the will of the member states.

On this cheerful Tuesday morning, Angela Merkel is at peace with herself and her country. She is standing in a factory loft in central Berlin and listening to Rami Rihawi, a refugee from Syria, who, in his blue suit and only slightly accented German, looks as though he has just jumped out of a glossy brochure on successful integration. After fleeing his homeland to Germany, Rihawi attended a school for computer experts, the site of Merkel's visit. He then received an internship at steel retailer Klöckner before being offered a fulltime job at the company. "We were extremely happy that Rami accepted our job offer," says Klöckner CEO Gisbert Rühl, who is standing proudly next to Rihawi.

As manufacturing processes become increasingly automated, so should tool condition monitoring (TCM) as it is impractical to have human workers monitor the state of the tools continuously. Tool condition is crucial to ensure the good quality of products: Worn tools affect not only the surface quality but also the dimensional accuracy, which means higher reject rate of the products. Therefore, there is an urgent need to identify tool failures before it occurs on the fly. While various versions of intelligent tool condition monitoring have been proposed, most of them suffer from a cognitive nature of traditional machine learning algorithms. They focus on the how to learn process without paying attention to other two crucial issues: what to learn, and when to learn. The what to learn and the when to learn provide self regulating mechanisms to select the training samples and to determine time instants to train a model. A novel tool condition monitoring approach based on a psychologically plausible concept, namely the metacognitive scaffolding theory, is proposed and built upon a recently published algorithm, recurrent classifier (rClass). The learning process consists of three phases: what to learn, how to learn, when to learn and makes use of a generalized recurrent network structure as a cognitive component. Experimental studies with real-world manufacturing data streams were conducted where rClass demonstrated the highest accuracy while retaining the lowest complexity over its counterparts.

You have a weapon and one last chance: automate, or die! If your job is process oriented, for example, calculating make-goods for improperly filled orders, your whole department will be automated soon. Don't let the pink slip surprise you. If you analyze numbers in Excel, craft a narrative about them, and move them around spreadsheets, your employable days are numbered. Any white-collar job you can learn to do in a few days is threatened – even if it takes a lifetime to master.

India, the second-most populous country in the world with over 1.2 billion people, boasts one of the fastest-growing economies bolstered by the youngest workforce in the world. While millions of dollars are spent every year on tackling malnutrition, 3,000 children die every day due to hunger. In a bid to combat this problem, Accenture Labs has teamed up with Akshaya Patra, the world's largest NGO-run mid-day meal program, to use disruptive technologies such as the blockchain, the Internet of Things (IoT) and artificial intelligence (AI) to boost the number of meals served to children in schools in India that are run and aided by the government. Since 2000, Akshaya Patra has been working toward reaching more children to provide them with wholesome food every single school day. When it first started it was serving 1,500 schoolchildren in five schools.

An "emotional chatting machine" has been developed by scientists, signalling the approach of an era in which human-robot interactions are seamless and go beyond the purely functional. The chatbot, developed by a Chinese team, is seen as a significant step towards the goal of developing emotionally sophisticated robots. The ECM, as it is known for short, was able to produce factually coherent answers whilst also imbuing its conversation with emotions such as happiness, sadness or disgust. Prof Björn Schuller, a computer scientist at Imperial College London who was not involved in the latest advance, described the work as "an important step" towards personal assistants that could read the emotional undercurrent of a conversation and respond with something akin to empathy. "This will be the next generation of intelligence to be met in daily experience, sooner rather than later," he said.

Years from now, machines may be able to diagnose diseases like cancer simply by analyzing a patient's smell. Several international companies are using analytics and artificial intelligence to create new diagnostic tools that can pinpoint diseases using odor analysis, according to The New York Times. Developers in England and Israel have begun testing their diagnostic tools in the clinical setting with some success--including one professor who recently published a study showing that his tool could accurately diagnose 17 different conditions based on breath samples. In the U.S., researchers at the University of Pennsylvania are working to develop a prototype that uses odors in blood plasma to recognize ovarian cancer. A study published in January by researchers at Northeastern University indicated that a new class of medical devices known as "e-noses" is gaining traction in the medical community.