Distributed Learning (DL) enables the training of machine learning models across multiple devices, yet it faces challenges like non-IID data distributions and device capability disparities, which can impede training efficiency. Communication bottlenecks further complicate traditional Federated Learning (FL) setups. To mitigate these issues, we introduce the Personalized Federated Learning with Decentralized Selection Training (PFedDST) framework. PFedDST enhances model training by allowing devices to strategically evaluate and select peers based on a comprehensive communication score. This score integrates loss, task similarity, and selection frequency, ensuring optimal peer connections. This selection strategy is tailored to increase local personalization and promote beneficial peer collaborations to strengthen the stability and efficiency of the training process. Our experiments demonstrate that PFedDST not only enhances model accuracy but also accelerates convergence. This approach outperforms state-of-the-art methods in handling data heterogeneity, delivering both faster and more effective training in diverse and decentralized systems.

The Parry-Lord oral-formulaic theory was a breakthrough in understanding how oral narrative poetry is learned, composed, and transmitted by illiterate bards. In this paper, we provide an annotated reading of the mechanism underlying this theory from the lens of large language models (LLMs) and generative artificial intelligence (AI). We point out the the similarities and differences between oral composition and LLM generation, and comment on the implications to society and AI policy.

This paper introduces a representative-based approach for distributed learning that transforms multiple raw data points into a virtual representation. Unlike traditional distributed learning methods such as Federated Learning, which do not offer human interpretability, our method makes complex machine learning processes accessible and comprehensible. It achieves this by condensing extensive datasets into digestible formats, thus fostering intuitive human-machine interactions. Additionally, this approach maintains privacy and communication efficiency, and it matches the training performance of models using raw data. Simulation results show that our approach is competitive with or outperforms traditional Federated Learning in accuracy and convergence, especially in scenarios with complex models and a higher number of clients. This framework marks a step forward in integrating human intuition with machine intelligence, which potentially enhances human-machine learning interfaces and collaborative efforts.

Artificial intelligence (AI) has received significant attention in recent years, primarily due to breakthroughs in game playing, computer vision, and natural language processing that captured the imagination of the scientific community and the public at large. Many businesses, industries, and academic disciplines are now contemplating the application of AI to their own challenges. The federal government in the US and other countries have also invested significantly in advancing AI research and created funding initiatives and programs to promote greater collaboration across multiple communities. Some of the investment examples in the US include the establishment of the National AI Initiative Office, the launch of the National AI Research Resource Task Force, and more recently, the establishment of the National AI Advisory Committee. In 2021 INFORMS and ACM SIGAI joined together with the Computing Community Consortium (CCC) to organize a series of three workshops. The objective for this workshop series is to explore ways to exploit the synergies of the AI and Operations Research (OR) communities to transform decision making.

In this work, we consider a binary hypothesis testing problem involving a group of human decision-makers. Due to the nature of human behavior, each human decision-maker observes the phenomenon of interest sequentially up to a random length of time. The humans use a belief model to accumulate the log-likelihood ratios until they cease observing the phenomenon. The belief model is used to characterize the perception of the human decision-maker towards observations at different instants of time, i.e., some decision-makers may assign greater importance to observations that were observed earlier, rather than later and vice-versa. The global decision-maker is a machine that fuses human decisions using the Chair-Varshney rule with different weights for the human decisions, where the weights are determined by the number of observations that were used by the humans to arrive at their respective decisions.

The rapid pace of AI adoption in business could be heading for some major speed bumps. According to Gartner experts presenting at the Gartner CFO & Finance Executive Conference in June 2022, half of all AI deployments are expected to be postponed between now and 2024, as companies face barriers to upscaling AI in-house. AI governance and how enterprises are going to monitor and control the use of data in their AI platforms are emerging as significant snags. AI governance is a relatively new concept, as AI itself is still only in the early stages of development, but there are already complications emerging. For some companies, the governance of AI applications is included in data or model governance structures.

Web3 is the newest version of the internet. That sounds huge, and it is, but the internet isn't being uninstalled and replaced with a new version. Rather, it's about adding to what we already use. And don't worry if you're confused about AR vs. VR, you love VR or you hate them both--there will be something for everyone. Web3 will eventually shape what the metaverse is and how we shop (AI may pick out the best VR headsets for us, for example) and will keep our data more secure.

You are free to share this article under the Attribution 4.0 International license. Using artificial intelligence, researchers have developed a genetic model for the "ultimate" chickpea, with the potential to lift crop yields by up to 12%. Researchers genetically mapped thousands of chickpea varieties, and then used this information to identify the most valuable gene combinations using artificial intelligence (AI). Researchers wanted to to develop a "haplotype" genomic prediction crop breeding strategy, for enhanced performance for seed weight. "Most crop species only have a few varieties sequenced, so it was a massive undertaking by the international team to analyze more than 3,000 cultivated and wild varieties," says Ben Hayes, professor at the University of Queensland.

A massive international research effort has led to development of a genetic model for the'ultimate' chickpea, with the potential to lift crop yields by up to 12 per cent. The research consortium genetically mapped thousands of chickpea varieties, and the UQ team then used this information to identify the most valuable gene combinations using artificial intelligence (AI). Professor Ben Hayes led the UQ component of the project with Professor Kai Voss-Fels and Associate Professor Lee Hickey, to develop a'haplotype' genomic prediction crop breeding strategy, for enhanced performance for seed weight. "Most crop species only have a few varieties sequenced, so it was a massive undertaking by the international team to analyse more than 3000 cultivated and wild varieties," Professor Hayes said. The landmark international study was led by Dr Rajeev Varshney from the International Crops Research Institute for the Semi-Arid Tropics in Hyderabad, India.

As artificial intelligence and machine learning algorithms become increasingly prevalent in society, multiple stakeholders are calling for these algorithms to provide explanations. At the same time, these stakeholders, whether they be affected citizens, government regulators, domain experts, or system developers, have different explanation needs. To address these needs, in 2019, we created AI Explainability 360 (Arya et al. 2020), an open source software toolkit featuring ten diverse and state-of-the-art explainability methods and two evaluation metrics. This paper examines the impact of the toolkit with several case studies, statistics, and community feedback. The different ways in which users have experienced AI Explainability 360 have resulted in multiple types of impact and improvements in multiple metrics, highlighted by the adoption of the toolkit by the independent LF AI & Data Foundation. The paper also describes the flexible design of the toolkit, examples of its use, and the significant educational material and documentation available to its users.