London Tech Week is finishing up this weekend and though it's not specifically aimed at travel, there's no doubt that technology is going to play a huge role in how we move around the globe in coming years. At the moment it can often feel as if technology hasn't really lived up to its promise. Airline apps are, for the most part, underwhelming and limited in what they can do. Communications about delays and disruptions certainly haven't gotten any better. Navigating a day of travel can sometimes feel more like an ongoing battle against technology (a temperamental check-in kiosk, for example) rather than a sign of how far we've come.
These days, nearly all the artificial intelligence-based products in our lives rely on "deep neural networks" that automatically learn to process labeled data. For most organizations and individuals, though, deep learning is tough to break into. To learn well, neural networks normally have to be quite large and need massive datasets. This training process usually requires multiple days of training and expensive graphics processing units (GPUs) -- and sometimes even custom-designed hardware. But what if they don't actually have to be all that big, after all?
Everyone's had that coworker, the one who never asks for help even when fully out of their depth, unaware of their own incompetence. But what happens when your colleague isn't a human suffering from Dunning-Kruger but artificial intelligence? That's a question Vishal Chatrath has had to consider as the CEO and co-founder of Prowler.io, an AI platform for generalised decision making for businesses that aims to augment human work with machine learning. "The decision-making process can be quite similar [across different businesses], if abstracted at a low-enough level," he says. "In some cases, the decisions are fully automated, in some cases, there's a human in the loop. Keeping a human as part of the process is partially because of a lack of trust in machine-based decision making, but it's also an admission by Chatrath that we remain in the early years of AI. Such systems aren't perfect, and likely never will be, and one failing of AI is it doesn't inherently understand its own competency. If a human worker needs help, they can ask for it -- but how do you build an understanding of personal limitations into code? "In both crashes, the commonality was that the autopilot did not understand its own incompetence," Chatrath says. Prowler.io built an awareness of incompetence into its system, teaching its AI to not only understand its limitations but to forecast when it's going to reach a situation where it has no experience or background. "Then it gently taps the human on the shoulder, so to speak, for the human to take control," he says. The system can learn from those interactions, and after enough training may eventually be able to stop asking for help. Such limits to AI could be placed by regulators, as is the case in the financial industry where levels of risk are carefully weighed, or by the business itself. The fourth consideration is how are we even sure the AI is asking the right questions. "There is no cookie cutter answer to these," he says. If there's a 10 per cent chance a logistics scheduler is wrong, and a lorry is therefore a bit late, that's okay. If there's a 10 per cent chance that shape in front of a driverless car is a human, the car should stop -- the risk are too high for any uncertainty. "Rather than doing stupid things like running someone over, it brings the human into the [process]," Chatrath explains, as it's been told when the risks are too high for it to screw up. That's important, says Taha Yasseri, a researcher at the Oxford Internet Institute and the Alan Turing Institute for Data Science, because while we can delegate decision making to machines, we can't delegate responsibility. "The ultimate responsibility in implementing the decisions made by machines are on us," he says. In practice, whenever the expected accuracy of a human is higher than a machine, it is practically justified to use human judgment to overlook machine decisions."
A new tool launched by privacy activists offers to help travelers avoid increasingly invasive facial recognition technologies in airports. Activist groups Fight for the Future, Demand Progress and CREDO on Wednesday unveiled a new website called AirlinePrivacy.com, The site also helps customers to directly book flights with airlines that don't use facial recognition technologies. Airlines' use of facial recognition technology is raising fresh questions about privacy and data security, advocates have argued. Instead of verifying passengers' details by scanning a boarding pass, the technology – which is provided by government agencies – scans passengers' face and sends that information to border control to verify identity and flight details.
In our upcoming webinar, we're taking a closer look at machine learning, a technology that has the potential to transform how travel brands interact with their customers and deliver more personalized experiences. Register now to attend on Wednesday, June 19 from 1-2 p.m. EDT. Machine learning is taking the travel industry by storm. Experts suggest this game-changing technology could fundamentally transform significant portions of the travel business, whether that's marketing, pricing decisions, loyalty programs, customer service, supply chain management, operations, or beyond. One 2018 study by McKinsey estimated that artificial intelligence and predictive analytics, two technologies closely associated with machine learning, will have an $800 billion economic impact on the travel industry.
Artificial intelligence (AI) can deliver significant business value. But to maximize the benefits of AI, you need to focus on creating solutions to real business challenges rather than on the technology itself. AI can help you solve business challenges in virtually every industry. At the recent Gartner ITxpo, my colleague Anne-Sophie Lotgering, Orange Business Services CMDO, talked about how the most successful companies will augment human workforces with AI. In fact, Gartner predicts that AI augmentation will generate $2.9 trillion in business value and recover 6.2 billion hours of worker productivity by 2021.
NEC Corp. gave a demonstration of its facial recognition system at its headquarters in Tokyo on Friday that it says will help passengers board planes faster without having to present passports or boarding passes. Narita will be the first airport in the country to deploy the system, called OneID, ahead of an expected spike in foreign arrivals for the 2020 Tokyo Olympic and Paralympic Games. In the media presentation, NEC showed how the system streamlined the boarding procedure. First, at the self check-in machine, passenger consent was obtained, then their passport was scanned and a barcode on their smartphone screen provided the flight details. A camera was used to capture their facial image during the process.
Companies understand the importance of artificial intelligence and machine learning, especially since it's become an increasingly important competitive differentiator, and are eager to jump in. But as always, the question stands: Once you've identified the potential of AI for your business, do you buy, or do you build? That's one of the big questions we'll be tackling at this year's Transform: Accelerating Your Business With AI. Spoiler alert: Lyft, Uber, Airbnb, and LinkedIn, featuring prominent speakers at this year's event, have come down firmly on the side of building their own AI solutions. And they've ended up with some dramatically successful -- and very cool -- results.
From screening patients for clinical trials to assessing the emotional state of drivers, we dive in to how facial recognition technology is shaping the future. Download the free report to get a break down of which industries facial recognition is disrupting and how. The biometric software behind facial recognition applications can identify facial structures, contours, and expressions, making it a no-brainer for security and identification purposes. But it can also lead to creative applications that serve a different purpose. Listerine, for example, created an app that uses facial recognition to notify people who are blind that they were being smiled at. While the technology is still developing, many companies (including Amazon) are banking on it as a disruptive force in a myriad of markets.
In this Oct. 31, 2018, file photo, a man, who declined to be identified, has his face painted to represent efforts to defeat facial recognition during a protest at Amazon headquarters over the company's facial recognition system, "Rekognition," in Seattle. San Francisco is on track to become the first U.S. city to ban the use of facial recognition by police and other city agencies. These days, with facial recognition technology, you've got a face that can launch a thousand applications, so to speak. Sure, you may love the ease of opening your phone just by facing it instead of tapping in a code. But how do you feel about having your mug scanned, identifying you as you drive across a bridge, when you board an airplane or to confirm you're not a stalker on your way into a Taylor Swift concert?