In 2018 the Reddit community r/deepfakes gained international attention thanks to a piece of investigative journalism by Samantha Cole, deputy editor at VICE. Members of the forum had been using a burgeoning technology to superimpose celebrities' faces onto pornographic videos. For the general public – and no doubt the unwitting stars – it was a shock. Most were unaware this technology existed. Very few believed it was possible to produce such realistic footage.

But what if the final assembly was the only assembly, with the whole plane built out of a large array of tiny identical pieces, all put together by an army of tiny robots? That's the vision that graduate student Benjamin Jenett, working with Professor Neil Gershenfeld in MIT's Center for Bits and Atoms (CBA), has been pursuing as his doctoral thesis work. It's now reached the point that prototype versions of such robots can assemble small structures and even work together as a team to build up a larger assemblies. The new work appears in the October issue of the IEEE Robotics and Automation Letters, in a paper by Jenett, Gershenfeld, fellow graduate student Amira Abdel-Rahman, and CBA alumnus Kenneth Cheung SM '07, PhD '12, who is now at NASA's Ames Research Center, where he leads the ARMADAS project to design a lunar base that could be built with robotic assembly. "What's at the heart of this is a new kind of robotics, that we call relative robots," Gershenfeld says.

Verizon and NEC used artificial intelligence (AI)-infused software and sensors as part of a recent proof-of-concept (PoC) trial that allowed already installed fiber infrastructure to monitor traffic and road conditions. The PoC used NEC-developed optical sensor technology with the AI software running alongside existing wavelength division multiplexing (WDM) communication channels on the same fiber lines. The mixture turned the cables into distributed optical sensors that collected information on traffic patterns, road conditions, road capacity, and vehicle classification information. The AI tools used included convolutional neural networks (CNN) and software vector machines that were able to tap into distributed intelligent traffic informatics (DITI). Those tools were plugged into a single integrated monitor that was able to detect back-scattering light traveling through the fiber optic streams to detect static strain, dynamic strain, acoustics, vibrations, and temperatures for each fiber segment.

Powerful new technologies have the potential to radically transform both science and society. In science, as Douglas Robertson describes in Phase Change (2003), a new technology like the microscope, the telescope, and the calculus can profoundly alter the questions we ask, and advance our ability to better understand nature. Society, visibly, can also be transformed by technology, as we've seen with examples ranging from the steam engine and the telegraph to automation and the internet. The catch is, this transformation doesn't occur overnight – far from it. The remarkable and often maddening aspect of innovation (as I've discussed here, here) is the exceptionally long time it takes between the time a technology is originally invented and the time when people figure out how to use it most effectively.

Ever since its origin in post-war research, AI has been subject to profound hyperbole, rapturous prognostications, and projected nightmares. In 2019, things have once again reached fever pitch in what science board co-chair and External Professor Melanie Mitchell wryly notes is a hype cycle that routinely ripples through her fellow computer scientists and those who fund them. Her illuminating new book, Artificial Intelligence: A Guide for Thinking Humans, lays bare the inner workings of these potent tools, exposing their realistic limits and patiently detailing our deployment errors. It is a solid history of how we got from pocket calculators to facial recognition and self-driving cars, a lucid tour of how these systems operate, and a tempered read on just how far we have to go before we're obsolete. Mitchell, a professor of computer science at Portland State University, has spent decades studying AI and writes with the measured understanding of someone who has lived on the volcano.

Over the last few weeks, Computer Weekly has looked at how a number of organisations are combining automation and artificial intelligence (AI) to deliver measurable business benefits. During the Microsoft Future Decoded event in London, the use of Microsoft tools, and Thoughtonomy's intelligent automation platform at East Suffolk and North Essex NHS Foundation Trust, were used to demonstrate how AI and automation can combine to deliver benefits in the public sector. In logistics, Canandian transportation company Polaris Transportation is using AI and automation in a project to streamline the handling of scanned-in customs paperwork, enabling it to reduce many hours of manual work. The company used the WorkFusion intelligent automation platform to scan and "read" customs paperwork associated with cross-border shipping documents. According to Cindy Rose, CEO of Microsoft UK, more advanced organisations are accelerating their use of AI, which has enabled them to see its benefits on their bottom line.

At its Made By Google event yesterday, the search giant announced a number of new products and services to build on its "Helpful Home" smart home concept. AI-powered systems like Google Assistant are at the heart of this strategy, tying together a range of tools and services from first- and third-party brands to create new means of controlling the home and to allow Google to automate and anticipate the wants and needs of consumers at home. Here's what Google announced for the smart home: Despite the event being nominally about its hardware, the overriding takeaway from these announcements is that Google's underlying AI is far more central to its home strategy, especially relative to Amazon. Google's portfolio of first-party hardware for smart speakers and the smart home pales in comparison to Amazon, its chief rival in the space in the US and Europe. That's because Google seems intent to use its hardware to be smarter about getting inputs and information from users, rather than surrounding the user with a flood of devices. Google's approach emphasizes to the consumer that the AI is doing more of the heavy lifting and "inhabiting" the home in unseen ways.

The Sino-American relationship has been quite a roller coaster this year, courtesy of the belligerent occupant of the White House. With its technical and operational superiority in 5G mobile networks (the vital infrastructure for technologies like A.I. and the Internet of Things), Huawei might be an avatar for China itself: ambitious, future-focused, and a serious threat to U.S. exceptionalism. The deluge of American complaints about Huawei being a national security risk (due to its links to the Chinese state) should be recognized for what it is: cover for the United States to engage in economic warfare, throwing its significant weight around to help ensure Huawei is blacklisted across the globe. The desperation of the U.S. efforts reflects a cold truth about the international competition in technology, and A.I. in particular: China is opening up a lead. Following their public commitment in 2017 to develop world leadership in A.I. by 2030, China has backed up its strategy with several billion dollars' worth of funding and a cohesive bureaucratic effort to manage the plan's execution.

Facial-recognition software from major tech companies is apparently ill-equipped to work on transgender and non-binary people, according to new research. A recent study by computer-science researchers at the University of Colorado Boulder found that major AI-based facial analysis tools--including Amazon's Rekognition, IBM's Watson, Microsoft's Azure, and Clarifai--habitually misidentified non-cisgender people. They eliminated instances in which multiple individuals were in the photo, or where at least 75% of the person's face wasn't visible. The images were then divided by hashtag, amounting to 350 images in each group. Scientists then tested each group against the facial analysis tools of the four companies.

It is true that since the start of developing AI, it has replaced certain occupations and has the potential to seriously disrupt labour. However, seeing that AI is aiming to replace all jobs instantaneously of labour from humans to machines is a tremendous over-simplification. There have been certain transformations of employment since nineteenth century and there has been a number of occupations since the rapid development of population which has generally been consistent. Regardless of what is being said, there is exceptionally little proof to propose any mass unemployment or widespread redundancy of human workforce's is likely. It is just as possible that a more productive and beneficial economy can take place increasing the effectiveness and reduction of waste from automation promises, this in turn can grant more alternatives in investing time on productive and profitable pursuits.