This paper presents the development of an AI-based language learning platform Revita. It is a freely available intelligent online tutor, developed to support learners of multiple languages, from low-intermediate to advanced levels. It has been in pilot use by hundreds of students at several universities, whose feedback and needs are shaping the development. One of the main emerging features of Revita is the introduction of a system of linguistic constructs as the representation of domain knowledge. The system of constructs is developed in close collaboration with experts in language teaching. Constructs define the types of exercises, the content of the feedback, and enable the detailed modeling and evaluation of learning progress.

While witnessing the noisy intermediate-scale quantum (NISQ) era and beyond, quantum federated learning (QFL) has recently become an emerging field of study. In QFL, each quantum computer or device locally trains its quantum neural network (QNN) with trainable gates, and communicates only these gate parameters over classical channels, without costly quantum communications. Towards enabling QFL under various channel conditions, in this article we develop a depth-controllable architecture of entangled slimmable quantum neural networks (eSQNNs), and propose an entangled slimmable QFL (eSQFL) that communicates the superposition-coded parameters of eS-QNNs. Compared to the existing depth-fixed QNNs, training the depth-controllable eSQNN architecture is more challenging due to high entanglement entropy and inter-depth interference, which are mitigated by introducing entanglement controlled universal (CU) gates and an inplace fidelity distillation (IPFD) regularizer penalizing inter-depth quantum state differences, respectively. Furthermore, we optimize the superposition coding power allocation by deriving and minimizing the convergence bound of eSQFL. In an image classification task, extensive simulations corroborate the effectiveness of eSQFL in terms of prediction accuracy, fidelity, and entropy compared to Vanilla QFL as well as under different channel conditions and various data distributions.

Machine learning models often encounter samples that are diverged from the training distribution. Failure to recognize an out-of-distribution (OOD) sample, and consequently assign that sample to an in-class label significantly compromises the reliability of a model. The problem has gained significant attention due to its importance for safety deploying models in open-world settings. Detecting OOD samples is challenging due to the intractability of modeling all possible unknown distributions. To date, several research domains tackle the problem of detecting unfamiliar samples, including anomaly detection, novelty detection, one-class learning, open set recognition, and out-of-distribution detection. Despite having similar and shared concepts, out-of-distribution, open-set, and anomaly detection have been investigated independently. Accordingly, these research avenues have not cross-pollinated, creating research barriers. While some surveys intend to provide an overview of these approaches, they seem to only focus on a specific domain without examining the relationship between different domains. This survey aims to provide a cross-domain and comprehensive review of numerous eminent works in respective areas while identifying their commonalities. Researchers can benefit from the overview of research advances in different fields and develop future methodology synergistically. Furthermore, to the best of our knowledge, while there are surveys in anomaly detection or one-class learning, there is no comprehensive or up-to-date survey on out-of-distribution detection, which our survey covers extensively. Finally, having a unified cross-domain perspective, we discuss and shed light on future lines of research, intending to bring these fields closer together.

Artificial intelligence is starting to play a huge role in education. It has transformed how one performs their educational job and also how students receive and retain information. While this is not a new technology, it is one that is being introduced into classrooms around the globe and has had a major impact on how one learns and develops academically. Here, we look at the 5 ways in which AI is being used in classroom settings. Personalization plays a key role in an academic setting.

From phones to watches to TVs, everything around us is becoming'smart'. Education is not so far behind. The'smart' approach to education is typically the incorporation of Machine Learning (ML) in learning and development. Machine Learning leverages Artificially Intelligent methods to teach systems how to make informed decisions without any human intervention. This is done by feeding data to a machine learning algorithm which is then able to process the data and make inferences for future events.

HERE Technologies today announced that HERE Workspace is expanding to give enterprises more ways to integrate spatial intelligence into their business operations, supply chains and fleets. Launched two years ago as a platform tool for building and scaling customized maps, services and experiences, HERE Workspace is offering new and improved capabilities, including a low-code environment for developing spatial intelligence and a new intuitive and predictable value-based pricing model. HERE is also pleased to announce that HERE Workspace now integrates seamlessly with Amazon SageMaker, enabling users to leverage familiar value-added machine learning tools to enhance their spatial intelligence development. "We believe that every smart enterprise will want its own private map, leveraging its own spatial data at scale," says Giovanni Lanfranchi, Chief Product & Technology Officer at HERE Technologies. "Building on our progress of the last years, we're expanding the possibilities of HERE Workspace by connecting it to Amazon SageMaker, an end-to-end machine learning solution, to deliver even greater value for customers."

Incorporating machine learning into data pipelines increases the ability of businesses to extract insights from their data. This course covers several ways machine learning can be included in data pipelines on Google Cloud depending on the level of customization required. For little to no customization, this course covers AutoML. For more tailored machine learning capabilities, this course introduces Notebooks and BigQuery machine learning (BigQuery ML). Also, this course covers how to productionalize machine learning solutions using Vertex AI.

The graph represents a network of 2,068 Twitter users whose tweets in the requested range contained "iot bigdata", or who were replied to or mentioned in those tweets. The network was obtained from the NodeXL Graph Server on Wednesday, 30 November 2022 at 12:48 UTC. The requested start date was Wednesday, 30 November 2022 at 01:01 UTC and the maximum number of tweets (going backward in time) was 7,500. The tweets in the network were tweeted over the 4-day, 8-hour, 45-minute period from Friday, 25 November 2022 at 16:13 UTC to Wednesday, 30 November 2022 at 00:59 UTC. Additional tweets that were mentioned in this data set were also collected from prior time periods.

The graph represents a network of 2,721 Twitter users whose tweets in the requested range contained "iot ai", or who were replied to or mentioned in those tweets. The network was obtained from the NodeXL Graph Server on Wednesday, 30 November 2022 at 12:31 UTC. The requested start date was Wednesday, 30 November 2022 at 01:01 UTC and the maximum number of tweets (going backward in time) was 7,500. The tweets in the network were tweeted over the 4-day, 2-hour, 0-minute period from Friday, 25 November 2022 at 23:00 UTC to Wednesday, 30 November 2022 at 01:00 UTC. Additional tweets that were mentioned in this data set were also collected from prior time periods.

The graph represents a network of 5,915 Twitter users whose tweets in the requested range contained "(Artificial Intelligence) OR #AI", or who were replied to or mentioned in those tweets. The network was obtained from the NodeXL Graph Server on Thursday, 01 December 2022 at 05:40 UTC. The requested start date was Thursday, 01 December 2022 at 01:01 UTC and the maximum number of days (going backward) was 14. The maximum number of tweets collected was 7,500. The tweets in the network were tweeted over the 2-day, 1-hour, 12-minute period from Monday, 28 November 2022 at 23:10 UTC to Thursday, 01 December 2022 at 00:23 UTC.