Netflix will roll out a new TikTok-inspired featured that specifically targets its younger viewers this week, according to Bloomberg. The streaming giant is reportedly launching "Kids Clips" on its iOS app, which will show short video clips from its library of children's programming to help young viewers find something to watch. Bloomberg says the feature builds upon Fast Laughs, the comedy feed it launched earlier this year. Unlike Fast Laughs, however, Kids Clips videos will be horizontal instead of vertical and will take over the entire screen. In addition, kids will only be able to view 10 to 20 clips at any one time.

This paper presents a comprehensive framework for run-time self-checking of logical agents, by means of temporal axioms to be dynamically checked. These axioms are specified by using an agent-oriented interval temporal logic defined to this purpose. We define syntax, semantics and pragmatics for this new logic, specifically tailored for application to agents. In the resulting framework, we encompass and extend our past work.

For most organizations, the bifurcation of Artificial Intelligence has been as stark as it's been simplistic. AI was either machine learning or rules-based approaches (the former of which outnumbered the latter), supervised or unsupervised learning, computer vision or natural language technologies. Due to a number of developments in the past year around ModelOps, composite AI, and neuro-symbolic AI, there's currently a growing awareness throughout the enterprise that AI--and its ROI--not only involves each of the foresaid dimensions, but does so optimally when they operate in conjunction with each other to pare the costs, difficulty, and time they otherwise require. CTO Marco Varone, "There are situations where you can get better results combining the different approaches; there are situations where you can use both and it's not too different, and there are situations where it's better with one approach." By incorporating the full AI spectrum into their toolkits, organizations can not only deploy the most appropriate method for their cognitive computing tasks, but also exploit surrounding areas of opportunity like intellectual property for machine learning models, cloud or Internet of Things use cases, and explainable AI. "The future is what we call a hybrid or composite approach where you use all the techniques that are available and you put them together in a way that the end user or data scientist trying to solve a specific problem can take different techniques and decide to use the ones giving the best results," Varone predicted.

Internet companies are increasingly using machine learning models to create personalized policies which assign, for each individual, the best predicted treatment for that individual. They are frequently derived from black-box heterogeneous treatment effect (HTE) models that predict individual-level treatment effects. In this paper, we focus on (1) learning explanations for HTE models; (2) learning interpretable policies that prescribe treatment assignments. We also propose guidance trees, an approach to ensemble multiple interpretable policies without the loss of interpretability. These rule-based interpretable policies are easy to deploy and avoid the need to maintain a HTE model in a production environment.

Overweight and obesity remain a major global public health concern and identifying the individualized patterns that increase the risk of future weight gains has a crucial role in preventing obesity and numerous sub-sequent diseases associated with obesity. In this work, we use a rule discovery method to study this problem, by presenting an approach that offers genuine interpretability and concurrently optimizes the accuracy(being correct often) and support (applying to many samples) of the identified patterns. Specifically, we extend an established subgroup-discovery method to generate the desired rules of type X -> Y and show how top features can be extracted from the X side, functioning as the best predictors of Y. In our obesity problem, X refers to the extracted features from very large and multi-site EHR data, and Y indicates significant weight gains. Using our method, we also extensively compare the differences and inequities in patterns across 22 strata determined by the individual's gender, age, race, insurance type, neighborhood type, and income level. Through extensive series of experiments, we show new and complementary findings regarding the predictors of future dangerous weight gains.

Although the state-of-the-art traditional representation learning (TRL) models show competitive performance on knowledge graph completion, there is no parameter sharing between the embeddings of entities, and the connections between entities are weak. Therefore, neighbor aggregation-based representation learning (NARL) models are proposed, which encode the information in the neighbors of an entity into its embeddings. However, existing NARL models either only utilize one-hop neighbors, ignoring the information in multi-hop neighbors, or utilize multi-hop neighbors by hierarchical neighbor aggregation, destroying the completeness of multi-hop neighbors. In this paper, we propose a NARL model named RMNA, which obtains and filters horn rules through a rule mining algorithm, and uses selected horn rules to transform valuable multi-hop neighbors into one-hop neighbors, therefore, the information in valuable multi-hop neighbors can be completely utilized by aggregating these one-hop neighbors. In experiments, we compare RMNA with the state-of-the-art TRL models and NARL models. The results show that RMNA has a competitive performance.

In spoken dialogue systems, we aim to deploy artificial intelligence to build automated dialogue agents that can converse with humans. Dialogue systems are increasingly being designed to move beyond just imitating conversation and also improve from such interactions over time. In this survey, we present a broad overview of methods developed to build dialogue systems over the years. Different use cases for dialogue systems ranging from task-based systems to open domain chatbots motivate and necessitate specific systems. Starting from simple rule-based systems, research has progressed towards increasingly complex architectures trained on a massive corpus of datasets, like deep learning systems. Motivated with the intuition of resembling human dialogues, progress has been made towards incorporating emotions into the natural language generator, using reinforcement learning. While we see a trend of highly marginal improvement on some metrics, we find that limited justification exists for the metrics, and evaluation practices are not uniform. To conclude, we flag these concerns and highlight possible research directions.

In the past few years, AI has crossed the threshold from hype to reality. Today, with unstructured data growing by 23% annually in an average organization, the combination of knowledge graphs and high performance computing (HPC) is enabling organizations to exploit AI on massive datasets. Full disclosure: Before I talk about how critical graph computing HPC is going to be, I should tell you that I'm CEO of a graph computing, AI and analytics company, so I certainly have a vested interest and perspective here. But I'll also tell you that our company is one of many in this space -- DGraph, MemGraph, TigerGraph, Neo4j, Amazon Neptune, and Microsoft's CosmosDB, for example, all use some form of HPC graph computing. And there are many other graph companies and open-source graph options, including OrientDB, Titan, ArangoDB, Nebula Graph, and JanusGraph.

In the hospital setting, a small percentage of recurrent frequent patients contribute to a disproportional amount of healthcare resource usage. Moreover, in many of these cases, patient outcomes can be greatly improved by reducing reoccurring visits, especially when they are associated with substance abuse, mental health, and medical factors that could be improved by social-behavioral interventions, outpatient or preventative care. To address this, we developed a computationally efficient and interpretable framework that both identifies recurrent patients with high utilization and determines which comorbidities contribute most to their recurrent visits. Specifically, we present a novel algorithm, called the minimum similarity association rules (MSAR), balancing confidence-support trade-off, to determine the conditions most associated with reoccurring Emergency department (ED) and inpatient visits. We validate MSAR on a large Electric Health Record (EHR) dataset. Part of the solution is deployed in Philips product Patient Flow Capacity Suite (PFCS).

ML Model Explainability (sometimes referred to as Model Interpretability or ML Model Transparency) is a fundamental pillar of AI Quality. It is impossible to trust a machine learning model without understanding how and why it makes its decisions, and whether these decisions are justified. Peering into ML models is absolutely necessary before deploying them in the wild, where a poorly understood model can not only fail to achieve its objective, but also cause negative business or social impacts, or encounter regulatory trouble. Explainability is also an important backbone to other trustworthy ML pillars like fairness and stability. Yet "explainability" is often a broad and sometimes confusing concept.