Forecasting transformative technologies remains a critical but challenging task, particularly in fast-evolving domains such as Information and Communication Technologies (ICTs). Traditional expert-based methods struggle to keep pace with short innovation cycles and ambiguous early-stage terminology. In this work, we propose a novel, data-driven pipeline to monitor the emergence of transformative technologies by identifying patterns of technological convergence. Our approach leverages advances in Large Language Models (LLMs) to extract semantic triples from unstructured text and construct a large-scale graph of technology-related entities and relations. We introduce a new method for grouping semantically similar technology terms (noun stapling) and develop graph-based metrics to detect convergence signals. The pipeline includes multi-stage filtering, domain-specific keyword clustering, and a temporal trend analysis of topic co-occurence. We validate our methodology on two complementary datasets: 278,625 arXiv preprints (2017--2024) to capture early scientific signals, and 9,793 USPTO patent applications (2018-2024) to track downstream commercial developments. Our results demonstrate that the proposed pipeline can identify both established and emerging convergence patterns, offering a scalable and generalizable framework for technology forecasting grounded in full-text analysis.

In large-scale maintenance organizations, identifying subject matter experts and managing communications across complex entities relationships poses significant challenges -- including information overload and longer response times -- that traditional communication approaches fail to address effectively. We propose a novel framework that combines RDF graph databases with LLMs to process natural language queries for precise audience targeting, while providing transparent reasoning through a planning-orchestration architecture. Our solution enables communication owners to formulate intuitive queries combining concepts such as equipment, manufacturers, maintenance engineers, and facilities, delivering explainable results that maintain trust in the system while improving communication efficiency across the organization.

Conversational recommender systems proactively query users with relevant "key terms" and leverage the feedback to elicit users' preferences for personalized recommendations. Conversational contextual bandits, a prevalent approach in this domain, aim to optimize preference learning by balancing exploitation and exploration. However, several limitations hinder their effectiveness in real-world scenarios. First, existing algorithms employ key term selection strategies with insufficient exploration, often failing to thoroughly probe users' preferences and resulting in suboptimal preference estimation. Second, current algorithms typically rely on deterministic rules to initiate conversations, causing unnecessary interactions when preferences are well-understood and missed opportunities when preferences are uncertain. To address these limitations, we propose three novel algorithms: CLiSK, CLiME, and CLiSK-ME. CLiSK introduces smoothed key term contexts to enhance exploration in preference learning, CLiME adaptively initiates conversations based on preference uncertainty, and CLiSK-ME integrates both techniques. We theoretically prove that all three algorithms achieve a tighter regret upper bound of $O(\sqrt{dT\log{T}})$ with respect to the time horizon $T$, improving upon existing methods. Additionally, we provide a matching lower bound $Ω(\sqrt{dT})$ for conversational bandits, demonstrating that our algorithms are nearly minimax optimal. Extensive evaluations on both synthetic and real-world datasets show that our approaches achieve at least a 14.6% improvement in cumulative regret.

The remarkable generative capability of large language models (LLMs) has sparked a growing interest in automatically generating responses for different applications. Given the dynamic nature of user preferences and the uncertainty of LLM response performance, it is crucial to design efficient online learning algorithms to identify optimal LLM responses (i.e., high-quality responses that also meet user preferences). Most existing online algorithms adopt a centralized approach and fail to leverage explicit user preferences for more efficient and personalized LLM response identification. In contrast, this paper introduces \textit{MACO} (\underline{M}ulti-\underline{A}gent \underline{C}onversational \underline{O}nline Learning for Adaptive LLM Response Identification): 1) The online LLM response identification process is accelerated by multiple local agents (such as smartphones), while enhancing data privacy; 2) A novel conversational mechanism is proposed to adaptively conduct conversations for soliciting user preferences (e.g., a preference for a humorous tone over a serious one in generated responses), so to minimize uncertainty in preference estimation. Our theoretical analysis demonstrates that \cadi\ is near-optimal regarding cumulative regret. Additionally, \cadi\ offers reduced communication costs and computational complexity by eliminating the traditional, computing-intensive ``G-optimal design" found in previous works. Extensive experiments with the open LLM \textit{Llama}, coupled with two different embedding models from Google and OpenAI for text vector representation, demonstrate that \cadi\ significantly outperforms the current state-of-the-art in online LLM response identification.

Conversational recommender systems have emerged as a potent solution for efficiently eliciting user preferences. These systems interactively present queries associated with "key terms" to users and leverage user feedback to estimate user preferences more efficiently. Nonetheless, most existing algorithms adopt a centralized approach. In this paper, we introduce FedConPE, a phase elimination-based federated conversational bandit algorithm, where $M$ agents collaboratively solve a global contextual linear bandit problem with the help of a central server while ensuring secure data management. To effectively coordinate all the clients and aggregate their collected data, FedConPE uses an adaptive approach to construct key terms that minimize uncertainty across all dimensions in the feature space. Furthermore, compared with existing federated linear bandit algorithms, FedConPE offers improved computational and communication efficiency as well as enhanced privacy protections. Our theoretical analysis shows that FedConPE is minimax near-optimal in terms of cumulative regret. We also establish upper bounds for communication costs and conversation frequency. Comprehensive evaluations demonstrate that FedConPE outperforms existing conversational bandit algorithms while using fewer conversations.

In developed nations assassinations are rare and thus the impact of such acts on the electoral and political landscape is understudied. In this paper, we focus on Twitter data to examine the effects of Japan's former Primer Minister Abe's assassination on the Japanese House of Councillors elections in 2022. We utilize sentiment analysis and emotion detection together with topic modeling on over 2 million tweets and compare them against tweets during previous election cycles. Our findings indicate that Twitter sentiments were negatively impacted by the event in the short term and that social media attention span has shortened. We also discuss how "necropolitics" affected the outcome of the elections in favor of the deceased's party meaning that there seems to have been an effect of Abe's death on the election outcome though the findings warrant further investigation for conclusive results.. Keywords Japanese House of Councillors Elections; Abe assassination; sentiment analysis ...

Contributions on capabilities and projected capabilities of Artificial Intelligence (AI) by researchers and other experts, many of them from non-engineers, trained outside the field of AI research or computer science abound. My book "The quest for a universal theory of intelligence: The mind, the machine, and singularity hypotheses" to be published with De Gruyter on May 23, 2022 is one of them. Yet, at the same time misperceptions and associated fears of AI abound too and are being nurtured by the absence of refutable, rigorous and bold perspectives on what just happened in the scientific and technical development of AI, therefore, leaving much to imagination. My book is not one of them. Unlike some red herrings where the respective authors lack pertinent (book) knowledge or promote ill-guided, spurious scenarios, my work attempts to avoid that trap by adhering mainly to a strictly philosophical endeavor (a field where its author has been trained). There is an art of which every human being should be a master, the art of reflection.

When it comes to machine learning, there are some broad concepts and terms that everyone in search should know. We should all know where machine learning is used, and the different types of machine learning that exist. Read on to gain a better grasp of how machine learning impacts search, what the search engines are doing and how to recognize machine learning at work. Let's start with a few definitions. Then we'll get into machine learning algorithms and models.

Machine Learning: A field of artificial intelligence, focused on the creation of algorithms, models and systems to perform tasks and generally to …

In Part 1, we left off talking about the custom training loop that you need to write in order to tap into the power of the extended framework. If you have a simple network, it's likely TrainNetwork will do the trick. For everything else, we can write the training loop ourselves.