In recent years, deep neural networks have demonstrated increasingly strong abilities to recognize objects and activities in videos. However, as video understanding becomes widely used in real-world applications, a key consideration is developing human-centric systems that understand not only the content of the video but also how it would affect the wellbeing and emotional state of viewers. To facilitate research in this setting, we introduce two large-scale datasets with over 60,000 videos manually annotated for emotional response and subjective wellbeing. The Video Cognitive Empathy (VCE) dataset contains annotations for distributions of fine-grained emotional responses, allowing models to gain a detailed understanding of affective states. The Video to Valence (V2V) dataset contains annotations of relative pleasantness between videos, which enables predicting a continuous spectrum of wellbeing. In experiments, we show how video models that are primarily trained to recognize actions and find contours of objects can be repurposed to understand human preferences and the emotional content of videos. Although there is room for improvement, predicting wellbeing and emotional response is on the horizon for state-of-the-art models. We hope our datasets can help foster further advances at the intersection of commonsense video understanding and human preference learning.

In recent years, deep neural networks have demonstrated increasingly strong abilities to recognize objects and activities in videos. However, as video understanding becomes widely used in real-world applications, a key consideration is developing human-centric systems that understand not only the content of the video but also how it would affect the wellbeing and emotional state of viewers. To facilitate research in this setting, we introduce two large-scale datasets with over 60,000 videos manually annotated for emotional response and subjective wellbeing. The Video Cognitive Empathy (VCE) dataset contains annotations for distributions of fine-grained emotional responses, allowing models to gain a detailed understanding of affective states. The Video to Valence (V2V) dataset contains annotations of relative pleasantness between videos, which enables predicting a continuous spectrum of wellbeing.

In recent years, deep neural networks have demonstrated increasingly strong abilities to recognize objects and activities in videos. However, as video understanding becomes widely used in real-world applications, a key consideration is developing human-centric systems that understand not only the content of the video but also how it would affect the wellbeing and emotional state of viewers. To facilitate research in this setting, we introduce two large-scale datasets with over 60,000 videos manually annotated for emotional response and subjective wellbeing. The Video Cognitive Empathy (VCE) dataset contains annotations for distributions of fine-grained emotional responses, allowing models to gain a detailed understanding of affective states. The Video to Valence (V2V) dataset contains annotations of relative pleasantness between videos, which enables predicting a continuous spectrum of wellbeing.

As the academic consortia members of the EU Horizon project SONATA ("Situation-aware OrchestratioN of AdapTive Architecture"), we respond to the workshop call for "Office Wellbeing by Design: Don't Stand for Anything Less" by proposing the "Adaptive Workplace" concept. In essence, our vision aims to adapt a workplace to the ever-changing needs of individual occupants, instead of that occupants are expected to adapt to their workplace.

The rapid advancement and adoption of generative AI (GenAI) technologies like ChatGPT signify the dawn of "The Age of AI." (Gates, 2023; Kissinger, Schmidt, & Huttenlocher, 2021) These developments mark a significant leap in the capabilities and adoption of AI systems. However, for many people, the swift and disorienting integration of AI into daily life raises many issues (Cugurullo & Acheampong, 2023; Fietta, Zecchinato, Stasi, Polato, & Monaro, 2022; Qasem, 2023). Concerns include the potential impacts on employment, privacy, and inequality, along with broader societal implications like human rights, mental health, and the preservation of democratic norms (Future of Life Institute, 2023; Prabhakaran, Mitchell, Gebru, & Gabriel, 2022; Shahriari & Shahriari, 2017; Stray, 2020). This article argues for the importance of wellbeing as a key objective in AI and for human-centered design (HCD) as a key methodology. Based on this framing, it shares a set of key challenges that will face designers of AI for wellbeing, or Positive AI. The idea that AI should support wellbeing is not uncommon. In 2018, Zuckerberg (2018) (CEO of Meta, previously Facebook) publicly stated that wellbeing should be the goal of AI. Further, in an interview Jan Leike (Wiblin, n.d.) (head of the'Superalignment' research lab at OpenAI) said AI optimization should align to "flourishing."

Recent advances in Automated vehicle (AV) technology and micromobility devices promise a transformational change in the future of mobility usage. These advances also pose challenges concerning human-AV interactions. To ensure the smooth adoption of these new mobilities, it is essential to assess how past experiences and perceptions of social interactions by people may impact the interactions with AV mobility. This research identifies and estimates an individual's wellbeing based on their actions, prior experiences, social interaction perceptions, and dyadic interactions with other road users. An online video-based user study was designed, and responses from 300 participants were collected and analyzed to investigate the impact on individual wellbeing. A machine learning model was designed to predict the change in wellbeing. An optimal policy based on the model allows informed AV actions toward its yielding behavior with other road users to enhance users' wellbeing. The findings from this study have broader implications for creating human-aware systems by creating policies that align with the individual state and contribute toward designing systems that align with an individual's state of wellbeing.

Researchers at the National Robotarium, hosted by Heriot-Watt University and the University of Edinburgh, are developing an artificial intelligence (AI) companion that will aid memory recollection, boost confidence and combat depression in people living with Alzheimer's disease and other types of dementia. The idea for the ground-breaking'Agent-based Memory Prosthesis to Encourage Reminiscing' (AMPER) project originated from Dr. Mei Yii Lim, a co-investigator of the project and an experienced memory modelling researcher. Memory loss in people with Alzheimer's disease occurs in reverse chronological order, with pockets of long-term memory remaining accessible even as the disease progresses. While most current rehabilitative care methods focus on physical aids and repetitive reminding techniques, AMPER's AI-driven user-centred approach will focus on personalised storytelling to help bring a patient's memories back to the surface. Dr. Lim explains "AMPER will explore the potential for AI to help access an individual's personal memories residing in the still viable regions of the brain by creating natural, relatable stories. These will be tailored to their unique life experiences, age, social context and changing needs to encourage reminiscing."

The estimates for the economic costs of inventory mismanagement in the non-grocery retail sector in the US in 2018 amounted to USD 300Bn and the retail fashion sector have been destroying surplus inventory! This is both a financial and environmental wastage that application of Machine Learning for better forecasting product demand, recommendation algorithms to better target and match supply with demand and supply chain optimisation may assist with. A report undertaken by PWC and commissioned by Microsoft set out that applying AI to four key sectors of the economy (Energy, Transportation, Agriculture and Water) alone would result in material reductions of Green House Gas Emissions, whilst also driving economic growth and substantial increase in jobs. More specifically, the report set out that by 2030 AI applied to the four sectors could enable the creation of 38 million jobs, $5.2 Trillion of GDP growth and 2.4 Gigatons of Carbon Dioxide emissions (or a 4% reduction). These are vast numbers and align the benefits of economic growth with climate goals. Accenture Strategy forecast that standalone 5G network technology may create 3 million jobs across the US and $500Bn of GDP growth. More recently a BCG Study forecast that 5G may drive the addition of approximately 4.5 Million Jobs and an increase of About $1.5 Trillion in US GDP Over this decade in the US alone! Standalone 5G networks alongside AI will enable the AIoT across the Edge of the network and a whole new era of innovation ranging from 5G enabled smart glasses for the Metaverse to other next generation wearables and dynamically responsive intelligent agents across our homes and workplaces. Intelligence here is not defined as AGI at the level of the human brain and rather ranging from Narrow AI (ANI) and increasingly Broad AI (Artificial Broad Intelligence, ABI) that may multitask but not quite match the capabilities of the human brain.

It's time to reset, re-create and collaborate on a new paradigm where Compassion and Kindness are the prevailing norms, one where technology is a tool for making humans more humane and creating an Abundant world for the majority. Join us to turn this vision into reality. Let's look into the'White Mirror' … Inspired by Black Mirror (Netflix series) - 'White Mirror' (holding name while we devise a suitable one) provides an immersive flash forward (glimpse/vision) of our Utopian future. In uncertain times (like now), technological disruption and impactful stories can change our mental worldview - our perceptions and eventually our reality. Black Mirror is a powerful show, depicting a dystopian future caused in part by misused evolving technologies.

The Economic Action Plan (EAP) has set the direction for a broader and more balanced approach to economic development with a shift towards a focus on place and making communities stronger and more resilient. The EAP places a greater emphasis on tackling inequality and signals a shift to a'something for something' relationship with business. Promoting inclusive growth through a new focus on the foundational economy sits alongside the other three pillars of our Economic Contract; supporting business investment that future-proofs the economy through Calls to Action; a regional approach to investing in the skills people need to enter, remain and progress in work; and the infrastructure communities need to be connected and vibrant. The foundational economy approach offers the chance to reverse the deterioration of employment conditions, reduce the leakage of money from communities and address the environmental cost of extended supply chains.With join-up across portfolio responsibilities we are driving a greater synergy between the Valleys Taskforce, Better Jobs Closer to Home programmes and maximising the social value of procurement with what may be described as mainstream Government economic interventions. A Ministerial Advisory Board Task and Finish Group on the Foundational Economy has been established to provide advice to Welsh Ministers on current and future interventions and best practice; support wider engagement with stakeholders in the foundational economy; and promote join-up of relevant government and non-governmental initiatives.