Physical caregiving robots hold promise for improving the quality of life of millions worldwide who require assistance with feeding. However, in-home meal assistance remains challenging due to the diversity of activities (e.g., eating, drinking, mouth wiping), contexts (e.g., socializing, watching TV), food items, and user preferences that arise during deployment. In this work, we propose FEAST, a flexible mealtime-assistance system that can be personalized in-the-wild to meet the unique needs of individual care recipients. Developed in collaboration with two community researchers and informed by a formative study with a diverse group of care recipients, our system is guided by three key tenets for in-the-wild personalization: adaptability, transparency, and safety. FEAST embodies these principles through: (i) modular hardware that enables switching between assisted feeding, drinking, and mouth-wiping, (ii) diverse interaction methods, including a web interface, head gestures, and physical buttons, to accommodate diverse functional abilities and preferences, and (iii) parameterized behavior trees that can be safely and transparently adapted using a large language model. We evaluate our system based on the personalization requirements identified in our formative study, demonstrating that FEAST offers a wide range of transparent and safe adaptations and outperforms a state-of-the-art baseline limited to fixed customizations. To demonstrate real-world applicability, we conduct an in-home user study with two care recipients (who are community researchers), feeding them three meals each across three diverse scenarios. We further assess FEAST's ecological validity by evaluating with an Occupational Therapist previously unfamiliar with the system. In all cases, users successfully personalize FEAST to meet their individual needs and preferences. Website: https://emprise.cs.cornell.edu/feast

Maximizing the spectral gap through graph rewiring has been proposed to enhance the performance of message-passing graph neural networks (GNNs) by addressing over-squashing. However, as we show, minimizing the spectral gap can also improve generalization. To explain this, we analyze how rewiring can benefit GNNs within the context of stochastic block models. Since spectral gap optimization primarily influences community strength, it improves performance when the community structure aligns with node labels. Building on this insight, we propose three distinct rewiring strategies that explicitly target community structure, node labels, and their alignment: (a) community structure-based rewiring (ComMa), a more computationally efficient alternative to spectral gap optimization that achieves similar goals; (b) feature similarity-based rewiring (FeaSt), which focuses on maximizing global homophily; and (c) a hybrid approach (ComFy), which enhances local feature similarity while preserving community structure to optimize label-community alignment. Extensive experiments confirm the effectiveness of these strategies and support our theoretical insights.

Real-time AI/ML use cases such as fraud prevention and recommendations are on the rise, and feature stores play a key role in deploying them successfully to production. According to popular open source feature store Feast, one of the most common questions users ask in their community Slack is: how scalable / performant is Feast? This is because the most important characteristic of a feature store for real-time AI/ML is the feature serving speed from the online store to the ML model for online predictions or scoring. Successful feature stores can meet stringent latency requirements (measured in milliseconds), consistently (think p99) and at scale (up to 100Ks of queries per second and even million of queries per second, and with gigabytes to terabytes sized datasets) while at the same time maintaining a low total cost of ownership and high accuracy. As we will see in this post, the choice of online feature store as well as the architecture of the feature store play important roles in determining how performant and cost effective it is.

As AI begins to play a much larger role in our daily lives, informing healthcare decisions, making recommendations, helping us resolve customer service issues, talking with us as companion bots, making financial decisions, driving autonomous cars, and helping employees make more informed, faster decisions, it becomes more important that ethics and morality are built into AI applications. AI applications are making decisions that affect people's privacy, health, finances, jobs, criminal justice, safety, and overall happiness. Ethical AI is no longer an afterthought -- it must be built into the fabric of AI from this point forward. This article will look at the ways that ethics and diversity are being built into AI and the importance of doing so. To ensure that AI is ethical, it must be transparent and explainable.

Emerging technologies meet both advocates and resistance as users weigh the potential benefits with the potential risks. To successfully implement new technologies, we must start small, in a few simplified forms, fitting a small number of use cases to establish proof of concept before scaling usage. Artificial intelligence is no exception, but with the added challenge of intruding into the cognitive sphere, which has always been the prerogative of humans. Only a small circle of specialists understand how this technology works -- therefore, more education to the broader public is needed as AI becomes more and more integrated into society. I recently connected with Josh Feast, CEO and cofounder of Boston-based AI company Cogito, to discuss the role of AI in the new era of work.

Where does your enterprise stand on the AI adoption curve? Take our AI survey to find out. Emerging technologies meet both advocates and resistance as users weigh the potential benefits with the potential risks. To successfully implement new technologies, we must start small, in a few simplified forms, fitting a small number of use cases to establish proof of concept before scaling usage. Artificial intelligence is no exception, but with the added challenge of intruding into the cognitive sphere, which has always been the prerogative of humans.

Feature store applications are fairly new product technology domain that allows for the development, maintaining, and monitoring of data features used by machine learning algorithms in artificial intelligence systems around us. Basically, a feature store is a data management layer used for saving and repurposing data features specifically designed for machine learning use cases. It is a measurable data property of an entity or representation of a object (e.g. The cores system capabilities for a feature store comprises of the abilities to support feature engineering (feature creation), a storage layer for both online and offline feature storage, a serving layer (via API, SDK), with a registry that features can be discovered with historical lineage that is trackable and lastly monitoring (and alerting) of features being used in understanding data drift with anomalies detection. The benefits are ten fold in having a feature store.

Machine learning has now entered its business heyday. Almost half of CIOs were predicted to have implemented AI by 2020, a number that is expected to grow significantly in the next five years. Because creating a machine learning model and putting it into operation in an enterprise environment are two very different things. The biggest challenge for companies looking to use AI is operationalizing machine learning, the same way DevOps operationalized software development in the 2000's. Simplifying the data science workflow by providing necessary architecture and automating feature serving with feature stores are two of the most important ways to make machine learning easy, accurate, and fast at scale.

Although conceptually simple, feature extraction is one of those areas that ends up consuming incredibly large amounts of time in machine learning implementations.

When being empathetic is your full-time job, burning out is only human. Few people are more aware of this than customer service representatives, who are tasked with approaching each conversation with energy and compassion -- whether it's their first call of the day or their 60th. It's their job to make even the most difficult customer feel understood and respected while still providing them accurate information. But over the last few years, an unlikely aide has come forward: artificial intelligence tools designed to help people tap into and maintain "human" characteristics like empathy and compassion. One of these tools is a platform called Cogito, named for the famous Descartes philosophy Cogito, ergo sum ("I think, therefore I am").