Ambient intelligence (AmI) is a computing paradigm in which physical environments are embedded with sensing, computation, and communication so they can perceive people and context, decide appropriate actions, and respond autonomously. Realizing AmI at global scale requires sixth generation (6G) wireless networks with capabilities for real time perception, reasoning, and action aligned with human behavior and mobility patterns. We argue that Generative Artificial Intelligence (GenAI) is the creative core of such environments. Unlike traditional AI, GenAI learns data distributions and can generate realistic samples, making it well suited to close key AmI gaps, including generating synthetic sensor and channel data in under observed areas, translating user intent into compact, semantic messages, predicting future network conditions for proactive control, and updating digital twins without compromising privacy. This chapter reviews foundational GenAI models, GANs, VAEs, diffusion models, and generative transformers, and connects them to practical AmI use cases, including spectrum sharing, ultra reliable low latency communication, intelligent security, and context aware digital twins. We also examine how 6G enablers, such as edge and fog computing, IoT device swarms, intelligent reflecting surfaces (IRS), and non terrestrial networks, can host or accelerate distributed GenAI. Finally, we outline open challenges in energy efficient on device training, trustworthy synthetic data, federated generative learning, and AmI specific standardization. We show that GenAI is not a peripheral addition, but a foundational element for transforming 6G from a faster network into an ambient intelligent ecosystem.

This paper introduces the Ambient Intelligence Rehabilitation Support (AIRS) framework, an advanced artificial intelligence-based solution tailored for home rehabilitation environments. AIRS integrates cutting-edge technologies, including Real-Time 3D Reconstruction (RT-3DR), intelligent navigation, and large Vision-Language Models (VLMs), to create a comprehensive system for machine-guided physical rehabilitation. The general AIRS framework is demonstrated in rehabilitation scenarios following total knee replacement (TKR), utilizing a database of 263 video recordings for evaluation. A smartphone is employed within AIRS to perform RT-3DR of living spaces and has a body-matched avatar to provide visual feedback about the excercise. This avatar is necessary in (a) optimizing exercise configurations, including camera placement, patient positioning, and initial poses, and (b) addressing privacy concerns and promoting compliance with the AI Act. The system guides users through the recording process to ensure the collection of properly recorded videos. AIRS employs two feedback mechanisms: (i) visual 3D feedback, enabling direct comparisons between prerecorded clinical exercises and patient home recordings and (ii) VLM-generated feedback, providing detailed explanations and corrections for exercise errors. The framework also supports people with visual and hearing impairments. It also features a modular design that can be adapted to broader rehabilitation contexts. AIRS software components are available for further use and customization.

It is hard to make robots (including telerobots) that are useful, and harder still to make autonomous and collaborative robots that are robust and general. Current smart robots are created using manual programming, mathematical models, planning frameworks, and reinforcement learning. These methods do not lead to the leaps in performance and generality seen with deep learning, generative AI, and foundation models (FMs). Today's robots do not learn to provide home care, to be nursing assistants, or to do household chores nearly as well as people do. Addressing the aspirational goals of service robots requires improving how they are created. The high cost of bipedal multi-sensory robots ("bodies") is a significant obstacle for both research and deployment. A deeper issue is that mainstream FMs ("minds") are not created by agents sensing, acting, and learning in context in the real world. They do not lead to robots that communicate well or collaborate. They do not lead to robots that learn by experimenting, by asking others, and by imitation learning as appropriate. In short, they do not lead to robots that are ready to be deployed widely in human service applications. This paper focuses on what human-compatible service robots need to know. It recommends developing "robotic" FMs based on diverse experiential data.

Previous researchers have proposed intelligent systems for therapeutic monitoring of cognitive impairments. However, most existing practical approaches for this purpose are based on manual tests. This raises issues such as excessive caretaking effort and the white-coat effect. To avoid these issues, we present an intelligent conversational system for entertaining elderly people with news of their interest that monitors cognitive impairment transparently. Automatic chatbot dialogue stages allow assessing content description skills and detecting cognitive impairment with Machine Learning algorithms. We create these dialogue flows automatically from updated news items using Natural Language Generation techniques. The system also infers the gold standard of the answers to the questions, so it can assess cognitive capabilities automatically by comparing these answers with the user responses. It employs a similarity metric with values in [0, 1], in increasing level of similarity. To evaluate the performance and usability of our approach, we have conducted field tests with a test group of 30 elderly people in the earliest stages of dementia, under the supervision of gerontologists. In the experiments, we have analysed the effect of stress and concentration in these users. Those without cognitive impairment performed up to five times better. In particular, the similarity metric varied between 0.03, for stressed and unfocused participants, and 0.36, for relaxed and focused users. Finally, we developed a Machine Learning algorithm based on textual analysis features for automatic cognitive impairment detection, which attained accuracy, F-measure and recall levels above 80%. We have thus validated the automatic approach to detect cognitive impairment in elderly people based on entertainment content.

In this paper an agent-based simulation is developed in order to evaluate an AmI scenario based on agents. Many AmI applications are implemented through agents but they are not compared to any other existing alternative in order to evaluate the relative benefits of using them. The proposal simulation environment developed in Netlogo analyse such benefits using two evaluation criteria: First, measuring agent satisfaction of different types of desires along the execution. Second, measuring time savings obtained through a correct use of context information. So, here, a previously suggested agent architecture, an ontology and a 12-steps protocol to provide AmI services in airports, is evaluated using a NetLogo simulation environment. The present work uses a NetLogo model considering scalability problems of this application domain but using FIPA and BDI extensions to be coherent with our previous works and our previous JADE implementation of them. The NetLogo model presented simulates an airport with agent users passing through several zones located in a specific order in a map: passport controls, check-in counters of airline companies, boarding gates, different types of shopping. Although initial data in simulations are generated randomly, and the model is just an approximation of real-world airports, the definition of this case of use of Ambient Intelligence through NetLogo agents opens an interesting way to evaluate the benefits of using Ambient Intelligence, which is a significant contribution to the final development of them.

Whatever academics like me thought artificial intelligence was, or what it might become, one thing is now undeniable: It is no longer ours to control. As a computer science professor at Stanford, it had been a private obsession of mine--a layer of thoughts that superimposed itself quietly over my view of the world. By the mid-2010s, however, the cultural preoccupation with AI had become deafeningly public. Billboards along Highway 101 on the California coast heralded the hiring sprees of AI start-ups. I'd hear fragments of conversation about AI on my car radio as I changed stations. The little red couch in my office, where so many of the projects that had defined our lab's reputation had been conceived, was becoming the place where I'd regularly plead with younger researchers to keep some room in their studies for the foundational texts upon which our science was built. I'd noticed, first to my annoyance and then to my concern, how consistently those texts were being neglected as the ever-accelerating advances of the moment drew everyone's attention to more topical sources of information.

It's an ominous-looking disc that sits on a night table, resembling a tiny satellite dish. It uses radar to monitor your movements while you sleep, combining that data with information about your bedroom--temperature, humidity, and brightness--to measure the quality of your sleep. Around the time you've set the alarm--at the instant it senses your sleep has passed out of the deepest stages--it brightens its semicircle of soft LED light to ease you gently from your slumbers. And this most intimate companion is made by Amazon, one of the world's biggest--and to some scariest--companies. Meet Halo Rise, the latest contribution to Amazon's mission of creating a persistent yet almost undetectable computational cocoon that monitors, listens, and fulfills your every whim and need.

When I spoke to iRobot's Colin Angle earlier this summer, he said iRobot OS -- the latest software operating system for its robot vacuums and mops -- would provide its household bots with a deeper understanding of your home and your habits. This takes on a whole new meaning with the news today that Amazon has bought iRobot for $1.7 billion. From a smart home perspective, it seems clear Amazon wants iRobot for the maps it generates to give it that deep understanding of our homes. The vacuum company has detailed knowledge of our floor plans and, crucially, how they change. It knows where your kitchen is, which your kids' rooms are, where your sofa is (and how new it is), and if you recently turned the guest room into a nursery.

We are excited to bring Transform 2022 back in-person July 19 and virtually July 20 - 28. Join AI and data leaders for insightful talks and exciting networking opportunities. Many, if not most, AI experts maintain that artificial general intelligence (AGI) is still many decades away, if not longer. And the AGI debate has been heating up over the past couple of months. However, according to Amazon, the route to "generalizable intelligence" begins with ambient intelligence. And it says that future is unfurling now.

Microsoft has completed its acquisition of Siri backend creator Nuance in a bumper deal that it says will usher in a "new era of outcomes-based AI". "Completion of this significant and strategic acquisition brings together Nuance's best-in-class conversational AI and ambient intelligence with Microsoft's secure and trusted industry cloud offerings," said Scott Guthrie, Executive Vice President of the Cloud AI Group at Microsoft. "This powerful combination will help providers offer more affordable, effective, and accessible healthcare, and help organisations in every industry create more personalised and meaningful customer experiences. I couldn't be more pleased to welcome the Nuance team to our Microsoft family." Nuance became a household name (in techie households, anyway) for creating the speech recognition engine that powers Apple's smart assistant, Siri.