By studying how machine learning powers artificial intelligence, students can further develop their own thought processes and tackle societal impacts. When thinking of classes on artificial intelligence, you probably imagine students on a computer writing code. But that's not where Joseph South, chief learning officer for the International Society for Technology in Education, says educators should start. Instead, teachers should help students learn how to approach decisions the way digital programming might -- by working through information, finding patterns and making a choice. "At ISTE, we feel strongly students need to learn how the digital world works," South said.

Over the last week, millions of people have tried the new AIchat release from OpenAI, built on an upgrade to GPT3 (Generative Pre-trained Transformer). The tool uses a neural network to generate responses from data sources from the internet. OpenAI, supported by Microsoft, also built and released the currently free DALL-E – AI-generated art. By creating an easy user interface, the ChatGPT likely has many educators wondering about the future of learning. This platform, based on GPT3 models, will be rapidly improved when next-generation GPT4 models emerge in the next 1-2 years – meaning, it's only going to get better AI already does and will continue to impact education – along with every other sector.

MobiSpaces delivers an end-to-end mobility-aware and mobility optimised data governance platform, that concerns the offerings of data acquisition, in-situ processing and all the security- and privacy-related operations. MobiSpaces envisions a set of toolboxes, suites, and tools that implement the MobiSpaces concept. MobiSpaces identifies the AI-based Data Operations Toolbox, including an additional list of tools, namely the Declarative querying, Decentralized Data Management, and Online Data Aggregator. In addition MobiSpaces utilises the Edge Analytics Suite, including a further list of tools, namely the XAI Prediction Modelling, Edge-driven Federated Learning, and Visual Analytics. MobiSpaces currently has five use cases utilising these tools including; iRoute, SmartSense, MarineTrafficTracker, Vessel Edge, and CrowdSeaMapping. Join us in our first introductory webinar " Meet the MobiSpaces Use Cases: Innovations for Urban and Maritime Domains " on 31 January 2023, 11:00-12:15 CEST where you can hear directly from the project.

In the second season of BBC mystery thriller The Capture, deepfakes threaten the future of democracy and UK national security. In a dystopia set in present day London, hackers use AI to insert these highly realistic false images and videos of people into live news broadcasts to destroy the careers of politicians. But my team's research has shown how difficult it is to create convincing deepfakes in reality. In fact, technology and creative professionals have started collaborating on solutions to help people spot bogus videos of politicians and celebrities. We stand a decent chance of staying one step ahead of fraudsters.

Minister Inder Singh Parmar said that the subject of'Artificial Intelligence' has been started in a total of 53 schools in the state run by the Madhya Pradesh State Open School Education Board. It has been started for the students of class 8th and 9that present. Internet-enabled laboratories of 40 modern computers have also been set up in these schools by the State Open School Education Board. In order to provide quality education in Madhya Pradesh, the Education Department is emphasizing on the inclusion and application of modern technical subjects. Minister Inder Singh Parmar said that Madhya Pradesh is the first state where education is being imparted through a total of 240 hours of'Artificial Intelligence' classes in classes VIII and IX in EFA (Education for All) schools.

Machine learning (ML) models are nowadays used in complex applications in various domains, such as medicine, bioinformatics, and other sciences. Due to their black box nature, however, it may sometimes be hard to understand and trust the results they provide. This has increased the demand for reliable visualization tools related to enhancing trust in ML models, which has become a prominent topic of research in the visualization community over the past decades. To provide an overview and present the frontiers of current research on the topic, we present a State-of-the-Art Report (STAR) on enhancing trust in ML models with the use of interactive visualization. We define and describe the background of the topic, introduce a categorization for visualization techniques that aim to accomplish this goal, and discuss insights and opportunities for future research directions. Among our contributions is a categorization of trust against different facets of interactive ML, expanded and improved from previous research. Our results are investigated from different analytical perspectives: (a) providing a statistical overview, (b) summarizing key findings, (c) performing topic analyses, and (d) exploring the data sets used in the individual papers, all with the support of an interactive web-based survey browser. We intend this survey to be beneficial for visualization researchers whose interests involve making ML models more trustworthy, as well as researchers and practitioners from other disciplines in their search for effective visualization techniques suitable for solving their tasks with confidence and conveying meaning to their data.

Recent advances in distributed artificial intelligence (AI) have led to tremendous breakthroughs in various communication services, from fault-tolerant factory automation to smart cities. When distributed learning is run over a set of wirelessly connected devices, random channel fluctuations and the incumbent services running on the same network impact the performance of both distributed learning and the coexisting service. In this paper, we investigate a mixed service scenario where distributed AI workflow and ultra-reliable low latency communication (URLLC) services run concurrently over a network. Consequently, we propose a risk sensitivity-based formulation for device selection to minimize the AI training delays during its convergence period while ensuring that the operational requirements of the URLLC service are met. To address this challenging coexistence problem, we transform it into a deep reinforcement learning problem and address it via a framework based on soft actor-critic algorithm. We evaluate our solution with a realistic and 3GPP-compliant simulator for factory automation use cases. Our simulation results confirm that our solution can significantly decrease the training delay of the distributed AI service while keeping the URLLC availability above its required threshold and close to the scenario where URLLC solely consumes all network resources.

We developed a simulator to quantify the effect of exercise ordering on both student engagement and retention. Our approach combines the construction of neural network representations for users and exercises using a dynamic matrix factorization method. We further created a machine learning models of success and dropout prediction. As a result, our system is able to predict student engagement and retention based on a given sequence of exercises selected. This opens the door to the development of versatile reinforcement learning agents which can substitute the role of private tutoring in exam preparation.

Speech emotion recognition (SER) processes speech signals to detect and characterize expressed perceived emotions. Many SER application systems often acquire and transmit speech data collected at the client-side to remote cloud platforms for inference and decision making. However, speech data carry rich information not only about emotions conveyed in vocal expressions, but also other sensitive demographic traits such as gender, age and language background. Consequently, it is desirable for SER systems to have the ability to classify emotion constructs while preventing unintended/improper inferences of sensitive and demographic information. Federated learning (FL) is a distributed machine learning paradigm that coordinates clients to train a model collaboratively without sharing their local data. This training approach appears secure and can improve privacy for SER. However, recent works have demonstrated that FL approaches are still vulnerable to various privacy attacks like reconstruction attacks and membership inference attacks. Although most of these have focused on computer vision applications, such information leakages exist in the SER systems trained using the FL technique. To assess the information leakage of SER systems trained using FL, we propose an attribute inference attack framework that infers sensitive attribute information of the clients from shared gradients or model parameters, corresponding to the FedSGD and the FedAvg training algorithms, respectively. As a use case, we empirically evaluate our approach for predicting the client's gender information using three SER benchmark datasets: IEMOCAP, CREMA-D, and MSP-Improv. We show that the attribute inference attack is achievable for SER systems trained using FL. We further identify that most information leakage possibly comes from the first layer in the SER model.

Attribute-controlled text rewriting, also known as text style-transfer, has a crucial role in regulating attributes and biases of textual training data and a machine generated text. In this work we present SimpleStyle, a minimalist yet effective approach for style-transfer composed of two simple ingredients: controlled denoising and output filtering. Despite the simplicity of our approach, which can be succinctly described with a few lines of code, it is competitive with previous state-of-the-art methods both in automatic and in human evaluation. To demonstrate the adaptability and practical value of our system beyond academic data, we apply SimpleStyle to transfer a wide range of text attributes appearing in real-world textual data from social networks. Additionally, we introduce a novel "soft noising" technique that further improves the performance of our system. We also show that teaching a student model to generate the output of SimpleStyle can result in a system that performs style transfer of equivalent quality with only a single greedy-decoded sample. Finally, we suggest our method as a remedy for the fundamental incompatible baseline issue that holds progress in the field. We offer our protocol as a simple yet strong baseline for works that wish to make incremental advancements in the field of attribute controlled text rewriting.