Robots in the work place can perform hazardous or even 'impossible' tasks; e.g., toxic waste clean-up, desert and space exploration, and more. AI researchers are also interested in the intelligent processing involved in moving about and manipulating objects in the real world.
Despite what people might think, the biggest challenge to the fully-autonomous future is not technical or scientific, but in fact, societal. According to a survey from the American Automobile Association (AAA), 88% of the American adult population say that they would never trust riding in a self-driving car. It's easy to dismiss this lack of trust as ignorance on the part of the American consumer, but it's crucial that the automotive industry addresses consumer concerns with new developments in technology. Driving is quite safe overall--the US sees about one death per 100 million miles travelled. Some estimates suggest that in order to directly compare autonomous vehicle safety with that of non-autonomous vehicles, one would need to test drive self-driving cars for billions of miles, a prohibitively costly endeavour.
Waymo conducted 1.45 million miles' worth of autonomous vehicle testing in California last year, the company reported to the state Department of Motor Vehicles. Tesla vehicles drove a total of 12.2 autonomous miles, to record what it called a "demo run" around its Palo Alto headquarters. Tesla has argued that it "has a fleet of hundreds of thousands of customer-owned vehicles that test autonomous technology in'shadow-mode' during their normal operation," constantly improving through billions of miles of real-world driving. Shadow Mode allows it to test some of those automated features without actually activating them in the real world.
The World Economic Forum, or WEF, said robotics and machinery will eliminate tens of millions of jobs over the next five years, but create just as many – perhaps more -- through the emergence of new technologies. The WEF, an international nongovernmental organization based in Geneva, Switzerland, said in a survey-based report that accelerating automation will wipe out 85 million jobs across 15 industries and 26 economies by 2025 – but concurrently create 97 million new jobs, particularly in the fields of data, artificial intelligence, content creation, the "green" economy and cloud computing. Still, the WEF conceded that such a dramatic disruption of labor markets could initially increase inequality and pressure companies around the world to quickly retrain workers in order to compete. "[Jobs] in areas such as data entry, accounting and administrative support are decreasing in demand as automation and digitization in the workplace increases," WEF said. "[Approximately] 50% of employers are expecting to accelerate the automation of some roles in their companies."
Machine Learning is an algorithmic approach of creating computer models with the ability to learn and adapt from a given data-set, these models can then be used to make useful predictions of results against similar but never-seen-before data. It is often referred to as a subset of Artificial Intelligence and forms the very base on which AI models are created. The concept of Machine Learning is based on the idea that whether machines can be designed to imitate human behaviour of learning, adapting skills, and applying where necessary. Just like all living beings who learn from every experience in life and take future decisions, similarly, the Machine Learning approach creates models that are first trained to'learn' on a data-set distribution. The trained models predict results by applying the knowledge learned during training with reasonable high accuracy.
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Two Princeton researchers, architect Stefana Parascho and engineer Sigrid Adriaenssens, dreamed of using robots to simplify construction, even when building complex forms. "We want to use robots to build beautiful architecture more sustainably," said Adriaenssens, an associate professor of civil and environmental engineering and the director of the Form Finding Lab. So the professors partnered with architecture and engineering firm Skidmore, Owings and Merrill (SOM) to create a striking and unique installation for the SOM exhibition "Anatomy of Structure" in London last March. They used two industrial robots provided by U.K.-based Global Robots to build a breathtaking vault, 7 feet tall, 12 feet across and 21 feet long, constructed of 338 transparent glass bricks from Poesia Glass Studio. The nearly finished central arch awaits its last brick.
The spacesuited figure on the cover makes it look like science fiction, the opening liability disclaimer would fit right into to an end-user licence agreement, the index is sandwiched between a 40-page bibliography and a handy list of abbreviations, from CAPTCHA to YOLO, that you probably know but might like an reminder of, and the whole book is covered by a Creative Commons licence. So how does this unusual approach to publishing stand up? The introduction to Exponential Progress purports to be written at the back end of the 21st century, reframing the usual'outhouse to zero-gravity toilet in 70 years' reference to the speed of progress as a 300-year jump from no electricity to'outer-terrestrial colonies' (along with hints about a civilisation of AIs). By promising to explain to fictional readers some eight decades in the future how we got there, author Farabi Shayor gets licence to cover the state of the art across a range of current and bleeding-edge technologies -- virtual reality, electric and self-driving cars, AI (both software and'brain-like' chips), the singularity, brain-computer interfaces, CRISPR and synthetic biology -- for a general audience. Although the book promises to explore the dangers of emerging technology and whether the pace of innovation is beyond human control, the writing is often unstintingly optimistic.
People who work behind a computer screen all day take it for granted that everyone's work will be tracked and accessible when they collaborate with others. But if your job takes place out in the real world, managing projects can require a lot more effort. In construction, for example, general contractors and real estate developers often need someone to be physically present on a job site to verify work is done correctly and on time. They might also rely on a photographer or smartphone images to document a project's progress. Those imperfect solutions can lead to accountability issues, unnecessary change orders, and project delays.
Amateurs talk strategy, professionals talk logistics. In the 2020s, that old chestnut should probably be updated: "Professionals talk about the network." And boy are they talking. The U.S. government will be one of the biggest spenders on private 5G infrastructure, and the Department of Defense leads the pack. DoD's growing network demands include connecting in-field technology as well as supporting day-to-day base operations and force training.