Science-fiction can sometimes be a good guide to the future. In the film Upgrade (2018) Grey Trace, the main character, is shot in the neck. His wife is shot dead. Trace wakes up to discover that not only has he lost his wife, but he now faces a future as a wheelchair-bound quadriplegic. He is implanted with a computer chip called Stem designed by famous tech innovator Eron Keen – any similarity with Elon Musk must be coincidental – which will let him walk again.

This is essential reading for those interested in incorporating robotics into their organization! Jonathan Padgett, VP, UiPath, talks to ReadITQuik about the many fascinating aspects of Robotic Process Automation (RPA) and how RPA can improve processes and drive efficiencies. Learn about how to select the best RPA provider and RPA best practices in this geektastic interview on one of the latest IT trends. Robotic Process Automation (RPA) technology integrates with the workforce to not only improve execution but also to tackle routine business processes. Think of a UiPath bot as a teleworker or virtual employee to which your employees can delegate repetitive (although necessary) tasks to free up your most valuable resources (your people) to focus on more strategic, creative and interpersonal work.

Imagine being alive when the first commercial airline flight was flown or remember the first time you encountered an ATM. At first, these new technologies were cause for caution, and perhaps seemed a bit daunting and maybe even dangerous. After all, they were highly disruptive innovations that dramatically changed how we traveled and accessed our money but eventually, society recognized their benefits. We live in an era when another disruptive tool is on the cusp of transforming our world. Artificial intelligence has shown the potential to be the greatest workforce disruptor since the first industrial revolution.

AR and thermal imaging in the Qwake C-Thru mask could help firefighters better navigate burning buildings. With smoke, flames and a claustrophobic mask on, running into a burning building is a leap of faith. Firefighters are taught never to leave the wall, because they could become disoriented, run out of air and die. "The way we used to look for people was almost as if you were blind," said Harold Schapelhouman, fire chief of the Menlo Park Fire Protection District. That could change with technology like Qwake's C-Thru.

The recent surveys, studies, forecasts and other quantitative assessments of the health and progress of AI estimated the impact on productivity of human-machine collaboration, the number of jobs that could be automated in major U.S. cities, and the size of the future AI in retail and healthcare markets; and found AI optimism among the general population, algorithms outperforming (again) pathologists, and that our very limited understanding of how our brains learn may improve machine learning. Do you think securing your devices and personal data will become more or less complicated over the next 12 months? DeepMind has developed a machine learning model that can label most animals at Tanzania's Serengeti National Park at least as well as humans while shortening the process by up to 9 months (it normally takes up to a year for volunteers to return labeled photos) [Engadget] In a simulation, biological learning algorithms outperformed state-of-the-art optimal learning curves in supervised learning of feedforward networks, indicating "the potency of neurobiological mechanisms" and opening "opportunities for developing a superior class of deep learning algorithms" [Scientific Reports] The AI in retail market is estimated to reach $4.3 billion by 2024 [P&S Intelligence] [e.g., Nike acquires Celect, August 6, 2019] The AI in healthcare market is estimated to reach $12.2 billion by 2023 [Market Research Future] [e.g., BlueDot has raised $7 million in Series A funding, August 7, 2019] AI companies funded in the last 3 months: 417 for total funding of $8.7 billion Data is eating the world quote of the week: "Although it is fashionable to say that we are producing more data than ever, the reality is that we always produced data, we just didn't know how to capture it in useful ways"--Subbarao Kambhampati, Arizona State University AI is eating the world quote of the week: "We advocate for a new perspective for designing benchmarks for measuring progress in AI. Unlike past decades where the community constructed a static benchmark dataset to work on for the next decade or two, we propose that future benchmarks should dynamically evolve together with the evolving state-of-the-art"--Keisuke Sakaguchi, Ronan Le Bras, Chandra Bhagavatula, Yejin Choi, Allen Institute for Artificial Intelligence and the University of Washington

Driverless cars and mobility-as-a-service is expected to grow exponentially in the next 30 years but how will programmers today determine the best way to program A.I. to make the ethical decisions that humans make every day when behind the wheel? By 2050, driverless cars and mobility as a service will be estimated to grow to $7 trillion all across the world. From 2035 to 2045 it's expected that consumers will regain up to 250 million hours of free time that otherwise would be spent on driving. But if people are no longer behind the wheel, how will A.I. make those decisions that we make every time we are on the road? And even if it can decide these decisions, can they do it in an ethically acceptable way? "Driverless cars'must decide quickly, with incomplete information, in situations that programmers often will not have considered, using ethics that must be encoded all too literally" said Noah J. Goodall, the Senior Research at the Virginia Transportation Research Council.

Intel revealed the broad outlines of its new Nervana Neural Network Processor for Inference, of NNP-I for short, that comes as a modified 10nm Ice Lake processor that will ride on a PCB that slots into an M.2 port (yes, an M.2 port that is normally used for storage), at an event in Haifa, Israel two months ago. Today, the company provided further deep-dive details of the design here at Hot Chips 31, the premier venue for leading semiconductor vendors to detail their latest microarchitectures. Intel is working on several different initiatives to increase its presence in the booming AI market with its'AI everywhere' strategy. The company's broad approach includes GPUs, FPGAs, and custom ASICs to all tackle different challenges in the AI space, with some solutions designed for compute-intensive training tasks that create complex neural networks for object recognition, speech translation, and voice synthesis workloads, to name a few, and separate solutions for running the resulting trained models as lightweight code in a process called inference. Intel's Spring Hill Nervana Neural Network Processor for inference (NNP-I) 1000, which we'll refer to as the NNP-I, tackles those lightweight inference workloads in the data center.

Researchers from the University of Maryland have figured out how to reliably create such questions through a human-computer collaboration, developing a dataset of more than 1,200 questions that, while easy for people to answer, stump the best computer answering systems today. The system that learns to master these questions will have a better understanding of language than any system currently in existence. The work is described in an article published in the 2019 issue of the journal Transactions of the Association for Computational Linguistics. "Most question-answering computer systems don't explain why they answer the way they do, but our work helps us see what computers actually understand," said Jordan Boyd-Graber, associate professor of computer science at UMD and senior author of the paper. "In addition, we have produced a dataset to test on computers that will reveal if a computer language system is actually reading and doing the same sorts of processing that humans are able to do."

The rapid progress in artificial intelligence, smart devices, and smart cities promises to revolutionise the way we work, live, and connect. However, recent scandals surrounding the handling of user data have prompted a wave of privacy concerns. The smarter a city gets, the more it can keep tabs on our every move. Likewise, with connected home devices and digital assistants picking up our daily activities and queries, the potential for privacy breaches are endless. Europe's pioneering General Data Privacy Regulation (GDPR) is one of several attempts by governments to mitigate widespread shortfalls in customer data protection, for both companies and governments. Other countries, and even US states like California, have followed.

An FBI agent displays seized firearms from a gang investigation. Digital facial recognition helped the bureau track down an MS-13 member wanted in connection with murder. An FBI agent displays seized firearms from a gang investigation. Digital facial recognition helped the bureau track down an MS-13 member wanted in connection with murder. Walter Yovany-Gomez evaded authorities for years before the FBI put him on its Ten Most Wanted Fugitives list.