We present a new framework called KorraAI for conceiving and building embodied conversational agents (ECAs). Our framework models ECAs' behavior considering contextual information, for example, about environment and interaction time, and uncertain information provided by the human interaction partner. Moreover, agents built with KorraAI can show proactive behavior, as they can initiate interactions with human partners. For these purposes, KorraAI exploits probabilistic programming. Probabilistic models in KorraAI are used to model its behavior and interactions with the user. They enable adaptation to the user's preferences and a certain degree of indeterminism in the ECAs to achieve more natural behavior. Human-like internal states, such as moods, preferences, and emotions (e.g., surprise), can be modeled in KorraAI with distributions and Bayesian networks. These models can evolve over time, even without interaction with the user. ECA models are implemented as plugins and share a common interface. This enables ECA designers to focus more on the character they are modeling and less on the technical details, as well as to store and exchange ECA models. Several applications of KorraAI ECAs are possible, such as virtual sales agents, customer service agents, virtual companions, entertainers, or tutors.

Conversational recommender systems (CRS) aim to timely trace the dynamic interests of users through dialogues and generate relevant responses for item recommendations. Recently, various external knowledge bases (especially knowledge graphs) are incorporated into CRS to enhance the understanding of conversation contexts. However, recent reasoning-based models heavily rely on simplified structures such as linear structures or fixed-hierarchical structures for causality reasoning, hence they cannot fully figure out sophisticated relationships among utterances with external knowledge. To address this, we propose a novel Tree structure Reasoning schEmA named TREA. TREA constructs a multi-hierarchical scalable tree as the reasoning structure to clarify the causal relationships between mentioned entities, and fully utilizes historical conversations to generate more reasonable and suitable responses for recommended results. Extensive experiments on two public CRS datasets have demonstrated the effectiveness of our approach.

A typical benchmark dataset for recommender system (RecSys) evaluation consists of user-item interactions generated on a platform within a time period. The interaction generation mechanism partially explains why a user interacts with (e.g.,like, purchase, rate) an item, and the context of when a particular interaction happened. In this study, we conduct a meticulous analysis on the MovieLens dataset and explain the potential impact on using the dataset for evaluating recommendation algorithms. We make a few main findings from our analysis. First, there are significant differences in user interactions at the different stages when a user interacts with the MovieLens platform. The early interactions largely define the user portrait which affect the subsequent interactions. Second, user interactions are highly affected by the candidate movies that are recommended by the platform's internal recommendation algorithm(s). Removal of interactions that happen nearer to the last few interactions of a user leads to increasing difficulty in learning user preference, thus deteriorating recommendation accuracy. Third, changing the order of user interactions makes it more difficult for sequential algorithms to capture the progressive interaction process. Based on these findings, we further discuss the discrepancy between the interaction generation mechanism that is employed by the MovieLens system and that of typical real world recommendation scenarios. In summary, models that achieve excellent recommendation accuracy on the MovieLens dataset may not demonstrate superior performance in practice for at least two kinds of differences: (i) the differences in the contexts of user-item interaction generation, and (ii) the differences in user knowledge about the item collections.

When designing a new API for a large project, developers need to make smart design choices so that their code base can grow sustainably. To ensure that new API components are well designed, developers can learn from existing API components. However, the lack of standardized methods for comparing API designs makes this learning process time-consuming and difficult. To address this gap we developed API-Miner, to the best of our knowledge, one of the first API-to-API specification recommendation engines. API-Miner retrieves relevant specification components written in OpenAPI (a widely adopted language used to describe web APIs). API-miner presents several significant contributions, including: (1) novel methods of processing and extracting key information from OpenAPI specifications, (2) innovative feature extraction techniques that are optimized for the highly technical API specification domain, and (3) a novel log-linear probabilistic model that combines multiple signals to retrieve relevant and high quality OpenAPI specification components given a query specification. We evaluate API-Miner in both quantitative and qualitative tasks and achieve an overall of 91.7% recall@1 and 56.2% F1, which surpasses baseline performance by 15.4% in recall@1 and 3.2% in F1. Overall, API-Miner will allow developers to retrieve relevant OpenAPI specification components from a public or internal database in the early stages of the API development cycle, so that they can learn from existing established examples and potentially identify redundancies in their work. It provides the guidance developers need to accelerate development process and contribute thoughtfully designed APIs that promote code maintainability and quality. Code is available on GitHub at https://github.com/jpmorganchase/api-miner.

'Let's just say this is a very significant redesign of society,' said Mo Gawdat, the former chief business officer at Google's secretive R&D wing, Google X. The convergence of these technologies, as Gawdat explained on a recent podcast interview, may lead to sex dolls that seem'alive' or dating apps filled with AI'avatars.' 'If we think a few years further and think of Neuralink and other ways of connecting directly to your nervous system,' Gawdat speculated, 'why would you need another being in the first place?' Speaking on the YouTube channel for the show Impact Theory with Tom Bilyeu, Gawdat pointed out that technologists, policymakers and society at large often focus too tightly on philosophical questions that big business interests will not. 'We get lost in those conversations of'Are they alive?

When Amazon introduced its 2nd-generation Echo Buds in 2021, the company redesigned them to address nearly every issue with its first attempt. They had true active noise cancellation (ANC), better sound and a smaller size. Amazon took a much different approach for its third version, opting to move from mid-range to a low-cost model with an all-new design that covers most of the basic features you'd expect. As always, the Alexa faithful will get the most out of the new $50 Echo Buds, but in a lot of ways you could argue a budget model that does well with the essentials is where Amazon should've been all along. While the first two iterations of the Echo Buds had similar designs, Amazon completely changed things up for the third generation.

In this paper, we propose a new constraint, called shift-consistency, for solving matrix/tensor completion problems in the context of recommender systems. Our method provably guarantees several key mathematical properties: (1) satisfies a recently established admissibility criterion for recommender systems; (2) satisfies a definition of fairness that eliminates a specific class of potential opportunities for users to maliciously influence system recommendations; and (3) offers robustness by exploiting provable uniqueness of missing-value imputation. We provide a rigorous mathematical description of the method, including its generalization from matrix to tensor form to permit representation and exploitation of complex structural relationships among sets of user and product attributes. We argue that our analysis suggests a structured means for defining latent-space projections that can permit provable performance properties to be established for machine learning methods.

Deep recommendation systems (DRS) heavily depend on specialized HPC hardware and accelerators to optimize energy, efficiency, and recommendation quality. Despite the growing number of hardware errors observed in large-scale fleet systems where DRS are deployed, the robustness of DRS has been largely overlooked. This paper presents the first systematic study of DRS robustness against hardware errors. We develop Terrorch, a user-friendly, efficient and flexible error injection framework on top of the widely-used PyTorch. We evaluate a wide range of models and datasets and observe that the DRS robustness against hardware errors is influenced by various factors from model parameters to input characteristics. We also explore 3 error mitigation methods including algorithm based fault tolerance (ABFT), activation clipping and selective bit protection (SBP). We find that applying activation clipping can recover up to 30% of the degraded AUC-ROC score, making it a promising mitigation method.

Presently, knowledge graph-based recommendation algorithms have garnered considerable attention among researchers. However, these algorithms solely consider knowledge graphs with single relationships and do not effectively model exercise-rich features, such as exercise representativeness and informativeness. Consequently, this paper proposes a framework, namely the Knowledge-Graph-Exercise Representativeness and Informativeness Framework, to address these two issues. The framework consists of four intricate components and a novel cognitive diagnosis model called the Neural Attentive cognitive diagnosis model. These components encompass the informativeness component, exercise representation component, knowledge importance component, and exercise representativeness component. The informativeness component evaluates the informational value of each question and identifies the candidate question set that exhibits the highest exercise informativeness. Furthermore, the skill embeddings are employed as input for the knowledge importance component. This component transforms a one-dimensional knowledge graph into a multi-dimensional one through four class relations and calculates skill importance weights based on novelty and popularity. Subsequently, the exercise representativeness component incorporates exercise weight knowledge coverage to select questions from the candidate question set for the tested question set. Lastly, the cognitive diagnosis model leverages exercise representation and skill importance weights to predict student performance on the test set and estimate their knowledge state. To evaluate the effectiveness of our selection strategy, extensive experiments were conducted on two publicly available educational datasets. The experimental results demonstrate that our framework can recommend appropriate exercises to students, leading to improved student performance.

This research article introduces AIOptimizer, a prototype for a software performance optimisation tool based on cost reduction. AIOptimizer uses a recommendation system driven by reinforcement learning to improve software system efficiency and affordability. The paper highlights AIOptimizer's design factors, such as accuracy, adaptability, scalability, and user-friendliness. To provide effective and user-centric performance optimisation solutions, it emphasises the use of a modular design, data gathering techniques, continuous learning, and resilient integration. The article also investigates AIOptimizer features such as fault identification, cost optimisation recommendations, efficiency prediction, and cooperation. Furthermore, it explores several software development life cycle models and introduces AIOptimizer uses a reinforcement learning-based recommendation engine for cost optimisation. The purpose of this research study is to highlight AIOptimizer as a prototype that uses advanced optimisation techniques and smart recommendation systems to continually enhance software performance and save expenses. The research focuses on various software development life cycle models, such as the Waterfall model, Iterative model, Spiral model, V-Model, Big Bang model and Agile Model. Each model has advantages and disadvantages, and their usefulness is determined by the project's specifications and characteristics. The AIOptimizer tool is a theoretical prototype for such software performance optimizers.