Artificial intelligence

  Accurately transcribing spoken language into written text is becoming increasingly essential in speech recognition. This technology is crucial for accessibility services, language processing, and clinical assessments. However, the challenge lies in capturing the words and the intricate details of human speech, including pauses, filler words, and other disfluencies. These nuances provide valuable insights into…

  Integrating No-Code AI in Non-Technical Higher Education: Recent developments in ML underscore its ability to drive value across diverse sectors. Nevertheless, incorporating ML into non-technical academic programs, such as those in social sciences, presents challenges due to its usual ties with technical fields like computer science. To overcome this barrier, a case-based approach utilizing…

  Digital Twin (DT) technology is becoming more and more popular as a method that gives Internet of Things (IoT) devices dynamic topology mapping and real-time status updates. However, there are difficulties in deploying DT in industrial IoT networks, especially when significant and dispersed data support is required. This frequently results in the creation of…

  Enhancing Agricultural Resilience through Remote Sensing and AI: Modern agriculture faces significant challenges from climate change, limited water resources, rising production costs, and disruptions like the COVID-19 pandemic. These issues jeopardize the sustainability of food production systems, necessitating innovative solutions to meet the demands of a growing global population. Recent advancements in remote sensing…

  Future reward estimation is crucial in RL as it predicts the cumulative rewards an agent might receive, typically through Q-value or state-value functions. However, these scalar outputs lack detail about when or what specific rewards the agent anticipates. This limitation is significant in applications where human collaboration and explainability are essential. For instance, in…

  AnswerAI has unveiled a robust model called answerai-colbert-small-v1, showcasing the potential of multi-vector models when combined with advanced training techniques. This proof-of-concept model, developed using the innovative JaColBERTv2.5 training recipe and additional optimizations, demonstrates remarkable performance despite its compact size of just 33 million parameters. The model’s efficiency is particularly noteworthy, as it achieves…

  To understand housing prices better, simplicity and clarity in our models are key. Our aim with this post is to demonstrate how straightforward yet powerful techniques in feature selection and engineering can lead to creating an effective, simple linear regression model. Working with the Ames dataset, we use a Sequential Feature Selector (SFS) to…

  In the realm of human-computer interaction (HCI), dialogue stands out as the most natural form of communication. The advent of speech language models (SLMs) has significantly enhanced speech-based conversational AI, yet these models remain constrained to turn-based interactions, limiting their applicability in real-time scenarios. This gap in real-time interaction presents a significant challenge, particularly…

  Zyphra’s release of Zamba2-2.7B marks a pivotal moment in developing small language models, demonstrating a significant advancement in efficiency and performance. The model is trained on a substantial enough dataset of approximately 3 trillion tokens derived from Zyphra’s proprietary datasets, which allows it to match the performance of larger models like Zamba1-7B and other…

  In the field of Artificial Intelligence, open, generative models stand out as a cornerstone for progress. These models are vital for advancing research and fostering creativity by allowing fine-tuning and serving as benchmarks for new innovations. However, a significant challenge persists as many state-of-the-art text-to-audio models remain proprietary, limiting their accessibility for researchers. Recently,…