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AI-Decision Making: State Of Play And What's Next - The Innovator

#artificialintelligence

FinnAir, an airline that dominates domestic and international air traffic in Finland, thought it could use AI to manage airport congestion. AI alone was not up to the job so Finland's largest airline instead implemented a hybrid system that uses AI to make predictions about air traffic and allows the humans-in-the-loop to make better decisions, explains Tero Ojanpera, CEO of Silo.ai, a Finnish AI lab that specializes in bringing cutting-edge AI talent to corporations around the world. Getting the FinnAir project to that point was not a question of plug and play. It required a complex multi-step modeling process to help the organization become more AI literate. Finnair's experience neatly illustrates the current state of play. AI is not fully ready to make the kind of decision-making corporates expect it to make and even if it were corporate teams and networks are not fully ready to implement and reap the full benefits of AI.


Inspection drones buzz this airport (and the FAA is cool with it)

ZDNet

Since September 2018, FedEx has been inspecting its aircraft at a busy international airport using drones that normally wouldn't be allowed anywhere near the facility. Strict regulations prohibit drones from sharing airspace with planes, but a novel FAA pilot that includes FedEx, as well as drone companies such as DJI and Asylon, could change that in the future. Drone inspection has long been a hot area for enterprise drones, including in unexpected spaces, but this program is a real watershed in the FAA's evolving approach to drone regulation. I reached out to Joel Murdock, managing director at FedEx Express, for insights about the company's airport drone operations and what it means for the future of enterprise drones in sensitive areas, and he's optimistic. "We believe drones could help improve efficiencies around aircraft inspections and maintenance at our World Hub at Memphis International Airport," says Murdock, "and other airports around the country. We also believe drones can be used to supplement our existing airport perimeter surveillance and runway/taxiway FOD detection activities."


Explainable AI: Making Sense of the Black Box

#artificialintelligence

The Black Square is an iconic painting by Russian artist Kazimir Malevich. The first version was done in 1915. The Black Square continues to impress art historians even today, however it did not impress the then Soviet government and was kept in such poor conditions that it suffered significant cracking and decay. Complex machine learning algorithms can be mathematical work of art, but if these black box algorithms fail to impress and build trust with the users, They might be ignored like Malevich's black square. Dramatic success in machine learning has led to a surge of Artificial Intelligence (AI) applications.


Avius Launches Gestures – Touchless Customer Feedback Technology

#artificialintelligence

Customers who wish to provide feedback at business no longer need to physically touch a survey screen. Avius, a leading tech company that provides real-time customer feedback solutions, launched Gestures, a touchless AI-powered thumbs up/thumbs down survey experience. The innovative technology has launched at Hartsfield-Jackson Atlanta International Airport (ATL), the busiest airport in the world, and at LEGOLAND Florida. The pandemic has quickly created a new operating environment for businesses who are now more than ever embracing touchless technology. Avius predicted early on during the pandemic that touchless would play an important role in society moving forward.


Global Model Interpretability Techniques for Black Box Models

#artificialintelligence

This article was published as a part of the Data Science Blogathon. There is no mathematical equation for model interpretability. 'Interpretability is the degree to which a human can consistently predict the model's result' An interpretable model that makes sense is far more trustworthy than an opaque one. There are two reasons for this. First, the business users do not make million-dollar decisions just because a computer said so.


Amazon's Latest Gimmicks Are Pushing the Limits of Privacy

WIRED

At the end of September, amidst its usual flurry of fall hardware announcements, Amazon debuted two especially futuristic products within five days of each other. The first is a small autonomous surveillance drone, Ring Always Home Cam, that waits patiently inside a charging dock to eventually rise up and fly around your house, checking whether you left the stove on or investigating potential burglaries. The second is a palm recognition scanner, Amazon One, that the company is piloting at two of its grocery stores in Seattle as a mechanism for faster entry and checkout. Both products aim to make security and authentication more convenient--but for privacy-conscious consumers, they also raise red flags. Amazon's latest data-hungry innovations are not launching in a vacuum.


Global Artificial Intelligence in Aviation Market : Industry Analysis and Forecast (2019-2026) _ by Offering (Hardware, Software), by Technology (Natural Language Processing (NLP), Context Awareness Computing, and Others), by Application, and by Geography - re:Jerusalem

#artificialintelligence

Artificial Intelligence in Aviation Market has been segmented on the basis of technology, offering, application, and geography. Based on offering market is split into Hardware & Software. Technology is divided into Natural Language Processing (NLP), Context, Awareness Computing, Machine Learning, and Computer Vision. Application of the market is Flight Operations, Smart Maintenance, Training, Virtual Assistants, Surveillance, Dynamic Pricing, and Manufacturing. Artificial intelligence in aviation sort the information and provide the pilot with the best possible options for operation, which is impossible for human being to perform.


Pilotless planes will take off 'within a decade' as British companies team up to create new AI

Daily Mail - Science & tech

Two British companies are teaming up to create cutting-edge artificial intelligence that will allow aeroplanes to be flown without a human pilot by 2030. Isle of Wight-based Britten-Norman has announced it intends to have just one pilot in its planes by 2025, and no pilots at all by 2030. To achieve these lofty heights as the first pilotless commercial aircraft, it has teamed up with Blue Bear, a British autonomous flight specialist. Britten-Norman's Islander plane will be the focal point of the project and specialises in short-haul flights, currently operating between Scottish islands Isle of Wight-based Britten-Norman has announced it intends to have just one pilot in its planes by 2025, and no pilots at all by 2030. Britten-Norman's Islander plane will be the focal point of the project and specialises in short-haul flights, currently operating between Scottish islands.


Pilotless planes to fly passengers by 2030 as AI breakthrough announced

#artificialintelligence

Pilotless passenger planes are planned to fly by 2030, a company has sensationally promised. Manufacturer Britten-Norman is to roll out single-piloted aircraft in five years and hopes to go fully pilot-free within a decade. The Isle of Wight-based company said it will allow operators to offer "uncrewed and piloted" flights. Britain's only independent commercial aircraft manufacturer said its ultimate goal of optional full automation "should be realised within this decade". But pilotless planes will need regulator approval and will likely scare off passengers, the British Airline Pilots' Association said.


Ramp Activity Expert System for Scheduling and Coordination at an Airport

AI Magazine

In this project, we have developed the ramp activity coordination expert system (races) to solve aircraft-parking problems. By user-driven modeling for end users and near-optimal knowledge-driven scheduling acquired from human experts, races can produce parking schedules for about 400 daily flights in approximately 20 seconds; human experts normally take 4 to 5 hours to do the same. Scheduling results in the form of Gantt charts produced by races are also accepted by the domain experts. After daily scheduling is completed, the messages for aircraft change, and delay messages are reflected and updated into the schedule according to the knowledge of the domain experts. By analyzing the knowledge model of the domain expert, the reactive scheduling steps are effectively represented as the rules, and the scenarios of the graphic user interfaces are designed.