Transportation


AI to drive GDP gains of $15.7 trillion with productivity, personalisation improvements

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Healthcare: Data-driven diagnostic support: Pandemic identification: Imaging diagnostics (radiology, pathology) Automotive: Autonomous fleets for ride sharing; Smart cars/driver assist; Predictive and autonomous maintenance Financial services: Personalised financial planning; Fraud detection and anti-money laundering; Transaction automation Retail: Personalised design and production; Customer insights generation; Inventory and delivery management Technology, communications and entertainment: Media archiving and search; Content creation (marketing, film, music, etc.); Personalized marketing and advertising Manufacturing; Enhanced monitoring and auto-correction; Supply chain and production optimisation; On-demand production Energy: Smart metering; More efficient grid operation and storage; Intelligent infrastructure maintenance Transport and logistics; Autonomous trucking and delivery: Traffic control and reduced congestion; Enhanced security Methodology: To estimate AI impact, our team conducted a dual-phased top-down and bottom-up analysis combining a detailed assessment of the current and future use of AI and an exploration of the economic impact in terms of new jobs, new products, and other secondary effects. Healthcare: Data-driven diagnostic support: Pandemic identification: Imaging diagnostics (radiology, pathology) Automotive: Autonomous fleets for ride sharing; Smart cars/driver assist; Predictive and autonomous maintenance Financial services: Personalised financial planning; Fraud detection and anti-money laundering; Transaction automation Retail: Personalised design and production; Customer insights generation; Inventory and delivery management Technology, communications and entertainment: Media archiving and search; Content creation (marketing, film, music, etc.); Personalized marketing and advertising Manufacturing; Enhanced monitoring and auto-correction; Supply chain and production optimisation; On-demand production Energy: Smart metering; More efficient grid operation and storage; Intelligent infrastructure maintenance Transport and logistics; Autonomous trucking and delivery: Traffic control and reduced congestion; Enhanced security Healthcare: Data-driven diagnostic support: Pandemic identification: Imaging diagnostics (radiology, pathology) Automotive: Autonomous fleets for ride sharing; Smart cars/driver assist; Predictive and autonomous maintenance Financial services: Personalised financial planning; Fraud detection and anti-money laundering; Transaction automation Retail: Personalised design and production; Customer insights generation; Inventory and delivery management Technology, communications and entertainment: Media archiving and search; Content creation (marketing, film, music, etc.


An Artificial Intelligence Roadmap For Contact Centers - Brand Quarterly

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From being able to offer an omnichannel customer experience across multiple channels to internet-enabled devices connecting directly to contact centers to provide proactive service, there is no doubt that the days of the single channel call center are long gone. An intelligent routing solution can rout interactions from multiple channels, including voice, email, chat, social, mobile, and more. AI technology is also being used now to create smart customer care solutions that mimic customer care agents with humanlike recommendations and high precision search. Virtual contact center assistants make it fast and efficient for customers to obtain the help they need.


Self-driving cars can be even safer with connected technology

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The test facility is 32 acres of simulated urban and suburban environment where researchers can test potentially dangerous scenarios that self-driving cars must master before they're road-ready. "In the last year, we've added infrastructure connectivity, the ability to use augmented reality to create a more robust testing environment, and the Michigan Traffic Lab, which is Mcity's traffic control center," said Mcity Director Huei Peng, who is the Roger L. McCarthy Professor of Mechanical Engineering. Peng led the Lincoln demo, which involved vehicle-to-infrastructure communication, as well as vehicle-to-vehicle communication and augmented reality technology. The data came from the Ann Arbor Connected Vehicle Test Environment, one of the world's largest operational, real-world deployments of connected vehicles and infrastructure.


Driverless vans will now deliver groceries in London

Mashable

Autonomous delivery vehicles are making drop-offs in London as part of a trial program and study spearheaded by University of Oxford self-driving spin-off Oxbotica, as well as Ocado Technologies, a developmental division of the UK-based, online-only supermarket service. SEE ALSO: Car rental companies are nervous about driverless cars, so they're doing something about it The CargoPod runs on Oxbotica's Selenium autonomous control system, which was designed for multiple vehicle types. The UK requires that autonomous test vehicles have someone to take control if anything goes wrong, like most areas that allow autonomous trials in the United States. The team behind the project is also focused on observing how such a system might impact cities and fit into a residential neighborhood, along with how real-world customers react to a driverless vehicle pulling up to their door with their groceries.


Ocado trials driverless delivery van in London

BBC News

"We have chosen it to work specifically in this type of environment, where bigger vehicles are not allowed," said Graeme Smith, chief executive of robotics company Oxbotica, which developed the vehicle. The CargoPod trial was part of a broader £8m research project into driverless technology, using the Greenwich area as a test location. Chief executive Paul Clark said driverless delivery was "a natural stage in the progression of our transport technologies". While Amazon is developing a drone delivery service, Ocado had no immediate plans to follow suit, Mr Clark said.


GE's research scientists are learning to meld AI with machines

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So far, nearly 400 employees from across the company have completed GE's certification program for data analytics, and about 50 scientists have moved into digital analytics jobs of the kind Nichols has taken on. They enable GE to track wear and tear on its aircraft engines, locomotives, gas turbines, and wind turbines using sensor data instead of assumptions or estimates, making it easier to predict when they will need maintenance. What's more, if data is corrupted or missing, the company fills in the gaps with the aid of machine learning, a type of AI that lets computers learn without being explicitly programmed, says Colin Parris, GE Global Research's vice president for software research. Parris says GE pairs computer vision with deep learning, a type of AI particularly adept at recognizing patterns, and reinforcement learning, another recent advance in AI that enables machines to optimize operations, to enable cameras to find minute cracks on metal turbine blades even when they are dirty and dusty.


These drone racing goggles could spark the sport's digital era

Engadget

Fat Shark has been the go-to maker of racing drone goggles for several years, and it's about to double down on digital, which in turn could be the nudge toward dropping analog feeds that the sport needs. It's still not uncommon to see a racing drone held together by tape or cable ties sporting a shoddily 3D-printed GoPro mount, and for the most part, that's fine. We've seen drones like UVify's Draco and Amimon's Falcore try and sex-up racing drones, and introduce digital video features -- but most of the sport hasn't committed to going digital just yet. Seemingly something the company was aware of, so it's dropped the shark logo (kinda), and given itself a visual makeover.


The Real Threat of Artificial Intelligence

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Imagine the gains to a loan company that could issue 30 million loans a year with virtually no human involvement. We are thus facing two developments that do not sit easily together: enormous wealth concentrated in relatively few hands and enormous numbers of people out of work. It seems American businesses will dominate in developed markets and some developing markets, while Chinese companies will win in most developing markets.


In the Auto Industry, the Future Is Software--Not Machinery - Column

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But this was an autonomous Volvo, part of a small test fleet Uber operated in Pittsburgh, San Francisco, and Arizona. The Cal DMV had revoked the registrations for Uber's 16 test vehicles, and if the bureaucrats were motivated by the fear of a couple tons of undercooked technology circulating among the driving public, those fears seem to have been vindicated by the photos of the capsized Volvo. Note that around 17.5 million light-duty vehicles were sold last year, swelling the national fleet to more than 240 million vehicles, and only the most infinitesimal percentage of them has any autono mous ability what soever. A friend who works in so-called big data told me recently that the digital information generated by these test cars meas ures out in petabytes per day, a petabyte being 1 million gigabytes.


cope-open-source-data-age-artificial-intelligence

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When data replaces code as the secret sauce for analytics, it should come as no surprise that an open data movement exists, which like the open source movement, seeks to ensure useful big data sets are freely available to all. MK: Uber deserves major kudos for releasing rides data. MK: Companies that offer goodwill gestures in terms of releasing data must be careful to not inadvertently violate customer privacy. AOL Research made a similar goodwill gesture to Uber's in 2006, when they released their search logs to better help researchers tune their search algorithms.