Given the high power density low discharge rate and decreasing cost rechargeable lithium-ion batteries LiBs have found a wide range of applications such as power grid level storage systems electric vehicles and mobile devices. Developing a framework to accurately model the nonlinear degradation process of LiBs which is indeed a supervised learning problem becomes an important research topic. This paper presents a classical-quantum hybrid machine learning approach to capture the LiB degradation model that assesses battery cell life loss from operating profiles. Our work is motivated by recent advances in quantum computers as well as the similarity between neural networks and quantum circuits. Similar to adjusting weight parameters in conventional neural networks the parameters of the quantum circuit namely the qubits degree of freedom can be tuned to learn a nonlinear function in a supervised learning fashion. As a proof of concept paper our obtained numerical results with the battery dataset provided by NASA demonstrate the ability of the quantum neural networks in modeling the nonlinear relationship between the degraded capacity and the operating cycles. We also discuss the potential advantage of the quantum approach compared to conventional neural networks in classical computers in dealing with massive data especially in the context of future penetration of EVs and energy storage.

Recent years have witnessed a swelling rise of hateful and abusive content over online social networks. While detection and moderation of hate speech have been the early go-to countermeasures, the solution requires a deeper exploration of the dynamics of hate generation and propagation. We analyze more than 32 million posts from over 6.8 million users across three popular online social networks to investigate the interrelations between hateful behavior, information dissemination, and polarised organization mediated by echo chambers. We find that hatemongers play a more crucial role in governing the spread of information compared to singled-out hateful content. This observation holds for both the growth of information cascades as well as the conglomeration of hateful actors. Dissection of the core-wise distribution of these networks points towards the fact that hateful users acquire a more well-connected position in the social network and often flock together to build up information cascades. We observe that this cohesion is far from mere organized behavior; instead, in these networks, hatemongers dominate the echo chambers -- groups of users actively align themselves to specific ideological positions. The observed dominance of hateful users to inflate information cascades is primarily via user interactions amplified within these echo chambers. We conclude our study with a cautionary note that popularity-based recommendation of content is susceptible to be exploited by hatemongers given their potential to escalate content popularity via echo-chambered interactions.

Large language models such as GPT-3 (Brown et al., 2020) can perform arbitrary tasks without undergoing fine-tuning after being prompted with only a few labeled examples. An arbitrary task can be reformulated as a natural language prompt, and a language model can be asked to generate the completion, indirectly performing the task in a paradigm known as prompt-based learning. To date, emergent prompt-based learning capabilities have mainly been demonstrated for unidirectional language models. However, bidirectional language models pre-trained on denoising objectives such as masked language modeling produce stronger learned representations for transfer learning. This motivates the possibility of prompting bidirectional models, but their pre-training objectives have made them largely incompatible with the existing prompting paradigm. We present SAP (Sequential Autoregressive Prompting), a technique that enables the prompting of bidirectional models. Utilizing the machine translation task as a case study, we prompt the bidirectional mT5 model (Xue et al., 2021) with SAP and demonstrate its few-shot and zero-shot translations outperform the few-shot translations of unidirectional models like GPT-3 and XGLM (Lin et al., 2021), despite mT5's approximately 50% fewer parameters. We further show SAP is effective on question answering and summarization. For the first time, our results demonstrate prompt-based learning is an emergent property of a broader class of language models, rather than only unidirectional models.

Artificial intelligence (AI) has been making a lot of headlines lately, and with good reason. AI is quickly becoming more and more sophisticated, allowing it to be used in a variety of ways that can benefit both businesses and individuals alike. But while the potential benefits are clear, there's also some concern about how these powerful technologies may be misused or abused by those who don't have our best interests at heart. As such, many governments around the world are looking into various forms of regulation for AI technology as they seek to protect their citizens from any potentially negative consequences that could arise from its misuse. However, despite this increased focus on regulating AI technology for safety purposes – one thing remains unclear: How effective will this form of regulation really end up being?

The Department of Mechanical Engineering at the University of Maryland, College Park invites applications for exceptionally qualified candidates to apply for tenure-track faculty positions, with a target start date of August 2023 or later. Priority will be given to candidates with expertise in the Design and Industrial AI area. Exceptional candidates with expertise outside these areas are also welcome to apply. Qualifications: Candidates for the rank of Assistant Professor should have received or expect to receive their PhD in Mechanical Engineering or a related discipline prior to employment. Additionally, candidates should be creative and adaptable, and have a high potential for both research and teaching.

Check out all the on-demand sessions from the Intelligent Security Summit here. U.S. space agency NASA isn't just concerned about exploring outer space, it's also concerned about helping humanity to learn more about the planet Earth and the impacts of climate change. Today, NASA and IBM announced a partnership that will see the development of new artificial intelligence (AI) foundation models to help analyze geospatial satellite data, in a bid to help better understand and take action on climate change. To date, NASA has largely relied on the development of its own set of bespoke AI models to serve specific use cases. The promise of the foundation model approach is a large language model (LLM) that has been trained on lots of data that can serve as a more general purpose system that can be customized as needed.

Check out all the on-demand sessions from the Intelligent Security Summit here. Artificial intelligence (AI) is an ever-growing technology. More than nine out of 10 of the nation's leading companies have ongoing investments in AI-enabled products and services. As the popularity of this advanced technology grows and more businesses adopt it, the responsible use of AI -- often referred to as "ethical AI" -- is becoming an important factor for businesses and their customers. AI poses a number of risks to individuals and businesses.

NASA's Marshall Space Flight Center and computing giant IBM plan to use artificial intelligence (AI) tech to improve climate change research, according to an announcement IBM posted on Feb. 1. Under the new partnership, NASA and IBM will create AI foundation models to analyze petabytes of text and remote-sensing data to make it easier to build AI applications tailored to specific climate change questions and tasks. "We hope these models will make information and knowledge more accessible to everyone and encourage people to build applications that make it easier to use our datasets to make discoveries and decisions based on the latest science," said Rahul Ramachandran, a senior research scientist at NASA's Marshall Space Flight Center. Foundational AI models can ingest massive amounts of raw data and find their underlying structure without explicit instruction. NASA is currently sitting on 70 petabytes of earth science data – a number expected to quadruple this year and into 2024 with future mission launches.

Programmable logic controller (PLC) based industrial control systems (ICS) are used to monitor and control critical infrastructure. Integration of communication networks and an Internet of Things approach in ICS has increased ICS vulnerability to cyber-attacks. This work proposes novel unsupervised machine learning ensemble methods for anomaly detection in PLC-based ICS. The work presents two broad approaches to anomaly detection: a weighted voting ensemble approach with a learning algorithm based on coefficient of determination and a stacking-based ensemble approach using isolation forest meta-detector. The two ensemble methods were analyzed via an open-source PLC-based ICS subjected to multiple attack scenarios as a case study. The work considers four different learning models for the weighted voting ensemble method. Comparative performance analyses of five ensemble methods driven diverse base detectors are presented. Results show that stacking-based ensemble method using isolation forest meta-detector achieves superior performance to previous work on all performance metrics. Results also suggest that effective unsupervised ensemble methods, such as stacking-based ensemble having isolation forest meta-detector, can robustly detect anomalies in arbitrary ICS datasets. Finally, the presented results were validated by using statistical hypothesis tests.

In recent days, the proliferation of several existing and new cyber-attacks pose an axiomatic threat to the stability of financial services. It is hard to predict the nature of attacks that can trigger a serious financial crisis. The unprecedented digital transformation to financial services has been accelerated during the COVID-19 pandemic and it is still ongoing. Attackers are taking advantage of this transformation and pose a new global threat to financial stability and integrity. Many large organizations are switching from centralized finance (CeFi) to decentralized finance (DeFi) because decentralized finance has many advantages. Blockchain can bring big and far-reaching effects on the trustworthiness, safety, accessibility, cost-effectiveness, and openness of the financial sector. The present paper gives an in-depth look at how blockchain and federated learning (FL) are used in financial services. It starts with an overview of recent developments in both use cases. This paper explores and discusses existing financial service vulnerabilities, potential threats, and consequent risks. So, we explain the problems that can be fixed in financial services and how blockchain and FL could help solve them. These problems include data protection, storage optimization, and making more money in financial services. We looked at many blockchain-enabled FL methods and came up with some possible solutions that could be used in financial services to solve several challenges like cost-effectiveness, automation, and security control. Finally, we point out some future directions at the end of this study.