Existing state-of-the-art symbolic music generation models predominantly adopt autoregressive or hierarchical autoregressive architectures, modelling symbolic music as a sequence of attribute tokens with unidirectional temporal dependencies, under the assumption of a fixed, strict dependency structure among these attributes. However, we observe that using different attributes as the initial token in these models leads to comparable performance. This suggests that the attributes of a musical note are, in essence, a concurrent and unordered set, rather than a temporally dependent sequence. Based on this insight, we introduce Amadeus, a novel symbolic music generation framework. Amadeus adopts a two-level architecture: an autoregressive model for note sequences and a bidirectional discrete diffusion model for attributes. To enhance performance, we propose Music Latent Space Discriminability Enhancement Strategy(MLSDES), incorporating contrastive learning constraints that amplify discriminability of intermediate music representations. The Conditional Information Enhancement Module (CIEM) simultaneously strengthens note latent vector representation via attention mechanisms, enabling more precise note decoding. We conduct extensive experiments on unconditional and text-conditioned generation tasks. Amadeus significantly outperforms SOTA models across multiple metrics while achieving at least 4$\times$ speed-up. Furthermore, we demonstrate training-free, fine-grained note attribute control feasibility using our model. To explore the upper performance bound of the Amadeus architecture, we compile the largest open-source symbolic music dataset to date, AMD (Amadeus MIDI Dataset), supporting both pre-training and fine-tuning.

Artificial intelligence needs no introduction, although it might need a definition. Amazon is as good a place as any to start. It defines AI as "the field of computer science dedicated to solving cognitive problems commonly associated with human intelligence, such as learning, problem solving, and pattern recognition." Amazon's definition also categorises machine learning and deep learning as "derived from the discipline of AI" which synchs nicely with IBM's framing of AI as the "general concept that machines can be'taught' to mimic human decision-making". Other subsets of AI include logic, natural language processing and robotics.

At Amadeus, we're heavily invested in AI and machine learning (ML) in many of our solutions, including revenue management (RM). We're also driving innovation in this area with participation at key industry events like AGIFORS and our partnerships with universities to foster PhD researchers. These elements are enabling our airline partners to embrace the amount of data they have and use it to make informed business decisions. As airlines service more passengers with different profiles, while offering more travel options and more types of services and ways to combine them, AI and ML backed solutions will be key in helping them make better decisions. Forecasting future passenger demand has always been one of the most challenging tasks for revenue management systems.

Airport hubs increasingly are embracing technology in their operations. As part of a three-day Airport IT conference in Munich, Amadeus head of airport IT product management Holger Mattig outlined the future of airport management and said that aviation hubs will witness more use of wearables, internet of things (IoT) applications and predictive analysis in the future. Talking about how the IoT has impacted the aviation industry, Mattig said that computing devices are already exchanging data between each other. "If you look at the apron, all of the devices that go on there – the push back tractors, the de-icing elements, all of these are actually able to talk to each other and give data about every stage of activity," he said. "In terms of flight handling, we now have technologies from companies like Assaia who can make prediction through videos generated by machine learning, and technologies like geofencing, where you can manage drones and improve safety. "We have the same for indoor where there are a lot of initiatives that are used to engage with the mobile phones of passengers in events of potential disruptions." While aviation companies are increasingly using technologies such as IoT and machine learning, Mattig said that going forward, airport and airline companies will start using wearable technology to improve efficiency. He added that employees could start wearing devices such as "smart sunglasses" and "smart bracelets" to track passenger activity, and that monitoring how passengers prefer to shop, eat and spend their time in an airport could help authorities to understand consumer behaviour. "Airports must start to build what I would call airport-centric visible analytics by implementing CRM solutions with the aim to look at the profile of passengers.

Amadeus is launching a new set of artificial intelligence (AI) Application Programming Interfaces (APIs) as part of the Amadeus for Developers programme, which the global travel technology firm claims will help in "empowering startups and independent developers to gain an edge". These APIs will allow developers to build solutions that can predict travel intent, traveller behaviour, and flight delays, among others – without needing any prior background in AI or data. Amadeus claimed that it is the first time in the travel industry that AI capabilities are made available to startups and independent developers via open APIs. The firm is providing ready-to-implement predictive models based on insights and functionalities fed by its sources of travel data. These APIs enable travel innovators to create AI-based apps with brand new features and disruptive business models that can transform the travel experience, said the company.

Artificial Intelligence is here to stay. AI systems are set to provide a $14 trillion boost to the global economy by 2035.

Airline passengers are suffering major disruption at airports around the world after a computer programme which handles passenger check in systems crashed. Queues formed at check-in desks worldwide this morning after the computer system used by more than 100 airlines crashed. Problems have been reported at London's Heathrow and Gatwick airports, as well as Charles de Gaulle Paris, Washington DC, Baltimore, Melbourne, Changi in Singapore, Johannesburg and Zurich. The check-in system which went down is run by Amadeus Altea, which services 64% of the Star Alliance flights, 75% of One World and 53% of the Sky Team, including BA, AirFrance, KLM and Lufthansa. The company behind the programme confirmed a'network issue' is causing the problems, but insisted'services are gradually being restored'.

Artificial intelligence is expected to transform a wide range of industries, as simple tasks are automated and carried out by machines. The IT sector is no different, with machine learning algorithms increasingly being targeted at automating and improving data centre operations. A notable example has been Google, which recently revealed that it is using its own DeepMind technology to manage power consumption at its huge server farms, reducing the amount of electricity needed by 40 percent. There is also potential for AI technology to automate functions carried out by IT operations teams. Machine learning offers a way to manage infrastructure and react quickly to faults without human intervention.

In the age of the customer, human IT operators will be inferior and a new rulebook for IT operations will be needed. IT will need to change from supporting back-office processes to interacting directly with consumers and users, who will have far higher expectations of IT systems than traditional business users do. This email address is already registered. By submitting my Email address I confirm that I have read and accepted the Terms of Use and Declaration of Consent. By submitting your personal information, you agree that TechTarget and its partners may contact you regarding relevant content, products and special offers.

Artificial intelligence (AI), which many experts predict will have an enormous impact on the travel industry, is becoming a reality not in the form of blockbuster apps but as a slow, steady trickle of apps, features and technological innovations. The pace of its progress was measured recently by a London School of Economics study, which identified AI and big data as "key disruptive factors shaping the travel distribution industry over the next decade." But the resulting report also noted that those factors have yet to spark major changes. More and more companies are developing and using AI technology today, and experts agree that a recognizable impact isn't too far off; it will begin trickling into agents' workflows over the next several months. "It's definitely not going to be like a flip switch -- one day there's no AI and the next day there's AI," said Paul English, co-founder of the travel agency Lola, where agents use AI to augment their workflows on a daily basis.