If you are looking for an answer to the question What is Artificial Intelligence? and you only have a minute, then here's the definition the Association for the Advancement of Artificial Intelligence offers on its home page: "the scientific understanding of the mechanisms underlying thought and intelligent behavior and their embodiment in machines."
However, if you are fortunate enough to have more than a minute, then please get ready to embark upon an exciting journey exploring AI (but beware, it could last a lifetime) …
Uber is a cab service provider for people wanting to travel from one place to another. Here, I have taken an Uber request dataset from Kaggle to try and perform analysis using the visualization libraries such as seaborn and matplotlib. At the end of this article, I have given a link to my Kaggle notebook where I have performed a detailed analysis of this Uber dataset. Let us jump right into the analysis and see what can be understood to make relevant conclusions. Before moving on to understanding the fields/observations in the data, let us import the required python libraries required for this analysis.
Previously, facial recognition technology was reserved for the movies and was a thing of fiction. However, much like other biometric solutions that have seen improvement and progress, facial recognition technology also steadily became a reality. Over the past decade, it has not only been developed and perfected; it is being deployed around the world as well. However, not as rapidly as other biometric technologies did – which include fingerprint, iris recognition, hand geometry, and DNA. Before we discuss the history and gradual evolution of facial recognition technology, there is a need to have an understanding of how this technology works and why there was a need for it in the first place?
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."
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.
Financially strapped airlines are pushing an idea intended to breathe new life into the travel industry: coronavirus tests that passengers can take before boarding a flight. Several airlines, including United, American, Hawaiian, JetBlue and Alaska, have announced plans to begin offering testing -- either kits mailed to a passenger's home or rapid tests taken at or near airports -- that would allow travelers to enter specific states and countries without having to quarantine. The tests will cost fliers $90 to $250, depending on the airline and the type of test. At Los Angeles International Airport, a design company has announced plans to convert cargo containers into a coronavirus testing facility with an on-site lab that can produce results in about two hours. On Thursday, Tampa International Airport began offering testing to all arriving and departing passengers on a walk-in basis. It's an idea that has gone global, with a trade group for the world's airlines calling on governments to create a testing standard for airline passengers as a way to fight the COVID-19 pandemic instead of using travel restrictions and mandatory quarantines.
From autonomous vehicles to virtual assistants, artificial intelligence is becoming increasingly present in our daily lives, and yet we are really just at the beginning of the curve. A powerful, transformative technology though it is, dealing with vast amounts of data, applications are already triggering unease in the public and the continued adoption of the game-changing technology must be balanced with heightened scrutiny towards policy, regulation, and ethics. The need for more stringent oversight is demonstrated by the increasing reliance we place on this technology in our daily lives -- in the case of driverless cars, we'd be placing our lives in the hands of AI. But it's also demonstrated in use by businesses and organizations. In the case of law enforcement, Flaws, or incompleteness in the data used by facial recognition systems in law enforcement, for example, can lead to racial profiling or misidentification of suspects, or add to the sense of an invasive surveillance culture at best.
In 2003, the SARS outbreak took the world by surprise. "For me, the SARS outbreak was an eye-opening event," says Dr. Kamran Khan, infectious disease physician, professor of medicine and public health at the University of Toronto, and founder and CEO of BlueDot. "I recognized that we'd never seen anything like it before, but there would be more outbreaks like this again in the future." Khan spent the next 10 years studying infectious disease spread, looking for a way to better detect and respond to threats like SARS and the ones that followed. By 2013, machine learning technology had advanced to the point where he was able to put his vision of a digital global warning system into action -- and BlueDot was born.
The Federal Aviation Administration (FAA) will soon be evaluating several drone detecting systems for airports, the agency has announced. It will be testing at least 10 technologies and systems developed not just to detect unmanned aerial systems, but also to mitigate the potential safety risks they pose. The tests are part of the agency's Airport Unmanned Aircraft Systems Detection and Mitigation Research Program and are expected to begin later this year. The first tests will be conducted at FAA's William J. Hughes Technical Center, which is right next to the Atlantic City International Airport in New Jersey. After that, the agency expects to expand its tests to four additional airports in the US.
Health screenings might become part of the touchless airport experience, too. Most people have seen images of passengers getting their temps taken with handheld thermometer wands at gates or security checkpoints. But increasingly, airports are opting for (or testing out) walk-through thermal-screening cameras, which operate by detecting heat emanating from a person's body and then estimating its core temperature. The idea with both devices is to detect people with fevers who might be infected with COVID-19. Airlines have asked the U.S. government for temperature screenings at airports to keep passengers safer and make them more confident about flying.
The accuracy and flexibility of facial recognition technology has seen it securing everything from smartphones to Australia's airports, but a team of security researchers is warning of potential manipulation after finding a way to trick the systems using deepfake images. Researchers within the McAfee Advanced Threat Research (ATR) team have been exploring ways that'model hacking' – also known as adversarial machine learning – can be used to trick artificial intelligence (AI) computer-vision algorithms into misidentifying the content of the images they see. This approach has previously been used to show how autonomous-car safety systems, which can read speed-limit signs and adjust the car's speed accordingly, could be tricked by modifying street signs with stickers that were misread by the systems. Subtle modifications to the signs would be picked up by the computer-vision algorithms but might be indiscernible to the human eye – an approach that the McAfee team has now successfully turned towards the challenge of identifying people from photos, as in the screening of passports. Starting with photos of two people – called A and B – ATR researchers used what they described as a "deep learning-based morphing approach" to generate large numbers of composite images that combined features from both.