In a world where technology keeps getting smaller and smarter, researchers are constantly looking for new ways to integrate electronics into everyday items like clothing. A recent study addresses the challenge of creating touch-sensitive fabric that can control electronic devices. This is important because as wearable electronics become more popular, having a way to control them with just a touch could make them even more convenient. Currently, there are some wearable devices that can be controlled through touch, like smartwatches or fitness trackers. However, these devices are often limited ..read more

Natural language processing (NLP) has rapidly evolved with the development of large language models (LLMs), which are increasingly central to various tech applications. These models process and generate text in ways that mimic human understanding, but inefficiencies often hamper their potential in resource use and limitations imposed by proprietary data. The overarching problem in LLMs is their resource intensity and restricted access due to reliance on private datasets. This increases the computational cost and limits the scope for academic research and development in open communities. In re ..read more

Large Language Models (LLMs) have made it economically possible to perform tasks involving structured outputs, such as converting natural language into code or SQL. LLMs are also being used to convert natural language into workflows, which are collections of actions with logical connections between them. These workflows increase worker productivity by encapsulating actions that can run automatically under certain circumstances.  Particularly in tasks like producing natural language from prompts, Generative Artificial Intelligence, or GenAI, has demonstrated impressive capabilities. Howev ..read more

As countries shift towards renewable energy like wind and solar power, understanding how climate change will affect these energy sources becomes crucial. Climate change brings extreme weather events, making it hard for energy planners to predict future energy needs. While some data exists, it’s limited in detail and doesn’t show how climate change will specifically impact renewable energy. Researchers at the National Renewable Energy Laboratory (NREL) recognized this problem and developed a new tool called Sup3rCC to address it. Sup3rCC (Super-Resolution for Renewable Energy Resource Data wit ..read more

Are you curious about the intricate world of large language models (LLMs) and the technical jargon that surrounds them? Understanding the terminology, from the foundational aspects of training and fine-tuning to the cutting-edge concepts of transformers and reinforcement learning, is the first step towards demystifying the powerful algorithms that drive modern AI language systems. In this article, we delve into 25 essential terms to enhance your technical vocabulary and provide insights into the mechanisms that make LLMs so transformative. Heatmap representing the relative importance of terms ..read more

MIT CSAIL researchers introduced MAIA (Multimodal Automated Interpretability Agent) to address the challenge of understanding neural models, especially in computer vision, where interpreting the behavior of complex models is essential for improving accuracy and robustness and identifying biases. Current methods rely on manual effort, like exploratory data analysis, hypothesis formulation, and controlled experimentation, making the process slow and expensive. MAIA (Multimodal Automated Interpretability Agent) uses neural models to automate interpretability tasks, such as feature interpretation ..read more

The increasing complexity of AI systems, particularly with the rise of opaque models like Deep Neural Networks (DNNs), has highlighted the need for transparency in decision-making processes. As black-box models become more prevalent, stakeholders in AI demand explanations to justify decisions, especially in critical contexts like medicine and autonomous vehicles. Transparency is essential for ethical AI and improving system performance, as it helps detect biases, enhance robustness against adversarial attacks, and ensure meaningful variables influence the output. To ensure practicality, inter ..read more

Automation in modern industries often involves repetitive tasks, but the challenge arises when tasks require flexibility and spontaneous decision-making. Traditional robotic process automation (RPA) systems are designed for static, routine activities, falling short when unpredictability is introduced. These systems are typically confined to predefined workflows, limiting their ability to handle tasks that deviate from standard procedures or require immediate adaptation. In many sectors, particularly financial services, dynamic workflow automation is critical. Traditional approaches cannot eff ..read more

Gene editing is a rapidly evolving field with profound implications for agriculture, biotechnology, and medicine. One of the most promising tools in this arena is the CRISPR-Cas system, originally derived from bacterial immune defence mechanisms. This technology offers a precise means of altering genetic sequences, but its adaptation from microbial environments to more complex eukaryotic cells often compromises efficiency and specificity. Researchers have sought to enhance the functionality of CRISPR systems to address this challenge. Traditional methods like directed evolution and structure ..read more

LLMs have grown remarkably over the past few years, largely driven by global initiatives to scale up both model sizes and datasets. From just one billion parameters five years ago, exemplified by GPT-2 with 1.5 billion parameters, LLMs now boast trillion-parameter architectures. This push stems from the perceived benefits of training larger models, as indicated by scaling laws. However, these laws traditionally presuppose a static data source, a notion challenged by the emergence of cutting-edge LLMs, enabling novel interactions with data. Previous research on the phi models demonstrated that ..read more