In this paper, we offer a simple argument for the conclusion that the outputs of large language models (LLMs) are meaningless. Our argument is based on two key premises: (a) that certain kinds of intentions are needed in order for LLMs' outputs to have literal meanings, and (b) that LLMs cannot plausibly have the right kinds of intentions. We defend this argument from various types of responses, for example, the semantic externalist argument that deference can be assumed to take the place of intentions and the semantic internalist argument that meanings can be defined purely in terms of intrinsic relations between concepts, such as conceptual roles. We conclude the paper by discussing why, even if our argument is sound, the outputs of LLMs nevertheless seem meaningful and can be used to acquire true beliefs and even knowledge.

You may not have to fumble with your cellphone in the boarding area very much longer. As the travel industry embraces facial recognition technology, phones are beginning to go the way of paper tickets at airports, cruise terminals and theme parks, making checking in more convenient, but raising privacy and security concerns, too. "Before Covid it felt like a future thing," said Hicham Jaddoud, a professor of hospitality and tourism at the University of Southern California, describing the way contactless transactions have become common since the pandemic. That includes facial recognition, which is "now making its way into daily operations" in the travel industry, Dr. Jaddoud said. Facial recognition systems are already being expanded at some airports.

Cross-modal contrastive learning in vision language pretraining (VLP) faces the challenge of (partial) false negatives. In this paper, we study this problem from the perspective of Mutual Information (MI) optimization. It is common sense that InfoNCE loss used in contrastive learning will maximize the lower bound of MI between anchors and their positives, while we theoretically prove that MI involving negatives also matters when noises commonly exist. Guided by a more general lower bound form for optimization, we propose a contrastive learning strategy regulated by progressively refined cross-modal similarity, to more accurately optimize MI between an image/text anchor and its negative texts/images instead of improperly minimizing it. Our method performs competitively on four downstream cross-modal tasks and systematically balances the beneficial and harmful effects of (partial) false negative samples under theoretical guidance.

The meltdowns at Southwest and the FAA, just weeks apart, were because of weaknesses in systems scheduled for upgrades--underscoring the urgent need to give priority to efforts to modernize those systems, as well as the consequences of waiting to do so, the consultants said. While starting over wholesale with new information-technology infrastructure is likely unrealistic, consultants said, the sector should take advantage of cloud-based tools that can integrate the fire hose of real-time data driving airline operations. Newer, cloud-based infrastructure and databases can scale horizontally--meaning they can take advantage of distributed computing resources across the internet as needed. This design allows information to flow more freely, reducing the likelihood of glitches that cascade into systemwide shutdowns. Older, legacy systems are limited to the amount of computing power available.

IATA sees RFID (radio frequency identification) as one of the keys to transforming the baggage handling process. SITA worked with IATA back in 2017 on a detailed business case, estimating that RFID could reduce the number of mishandled bags by an extra 25% and could potentially save the air transport industry $3 billion in baggage mishandling costs. Airlines and airports are now proactively working together to boost their baggage handling efforts as part of IATA's Resolution 753, which requires airlines to "maintain an accurate inventory of baggage by monitoring the acquisition and delivery of baggage". RFID tagging is now 99.98% accurate, according to IATA. Within the next four years most baggage systems will be RFID enabled, which is a huge improvement on barcodes alone.

My last blog said we should stand by for the rise of AI in aviation. I underlined that as an industry we'd better fasten our seatbelts, as AI's traction in aviation is becoming overwhelming! So why is that the case (particularly for an industry with a reputation for being change and risk adverse)? Well, we're clearly seeing three major triggers at play: Forward-looking airlines, airports and ground handlers are embracing AI and embarking on the'road to optimization' so they can leverage their data assets and remain relevant and competitive. This will help to ensure passengers will still have a seamless journey, despite the upcoming congestion.

What many people call airports, you like know as that one huge queue. From curb to gate, zig zagging between retractable barriers, from one pinch point to the next--in industry parlance, this is your travel ribbon, flowing, or jamming, through the terminal. Check in, bag drop, security, the coffee shop, the lounge, the boarding gate, the halting march down the aisle. Now, imagine a future free of security gates, where you walk from the curb to your plane as easily as you unlock your phone, and without needing to worry about the dangers that come with air travel in the 21st century. Such is the promise of airports taking advantage of biometric data, using facial recognition and other AI-powered techniques to recognize, authorize, and screen you from afar.

SITA's Intelligent Tracking: A Baggage Management Revolution paper, published today, notes that more than 4.5 billion bags are handled by industry baggage systems each year but airlines and airports will have to cope with twice that number with passenger numbers set to double over the next 20 years. This will be a tough ask of the industry, despite the huge inmprovement in its baggage handling performance over the last decade. Indeed, improvements to technology and processes have halved the industry's annual mishandling cost over the past decade from $4.22bn to $2.1 billion. However, every mishandled bag is one too many and the industry continues to seek ways to reduce the number further. Ilya Gutlin, president of SITA Air Travel Solutions, says: "We at SITA believe that harnessing data and AI in a meaningful way will revolutionise how we manage the air transport industry in the next decade. "SITA has a unique role to play in realising the potential of data and baggage management is one area that will benefit.

Airlines and airports are embracing new technologies as they turn to artificial intelligence (AI), making it more than just a chatbot technology. Today, AI is able to assist travelers by boosting conversations to either upgrade a seat or help find special offers for their trip or even help manage mishandled baggage. According to SITA, a specialist in air transport communications and information technology, airlines and airports are increasingly adopting AI to manage baggage handling. Over 4.5 million bags are being handled annually by industry baggage systems, a number which is set to double in tandem with the growth in passenger numbers over the next 20 years. But AI is set to speed up operations for the aviation sector, research indicates.

The smart use of technologies such as artificial intelligence is expected to revolutionize the management of baggage over the next decade, promising to make mishandled bags an increasingly rare event for passengers globally. This is according SITA's Intelligent Tracking: A Baggage Management Revolution paper published today. The paper notes that more than 4.5 billion bags1 are handled by industry baggage systems each year but airlines and airports will have to cope with twice that number with passenger numbers set to double over the next 20 years. Already, through improvements to technology and processes, the air transport industry has halved its annual mishandling cost over the past decade from US$4.22bn to US$2.1bn. However, every mishandled bag is one too many and the industry continues to seek ways to reduce the number further.