The artificial intelligence (AI) industry has seen many advancements, particularly in the area of game-playing agents such as AlphaGo, which are capable of superhuman performance via self-play techniques. Now, researchers from the University of California, Berkeley, have turned to these techniques to tackle a persistent challenge in AI—improving performance in cooperative or partially cooperative language…
The advancement of generative AI technologies in recent years has facilitated an evolution in user interfaces, shaping how users interact with digital tools and platforms. Seven emerging generative AI user interfaces, namely; the Chatbot, the Augmented Browser, the AI Workspace, the AI Workbook, the Universal Interface, the AI Form, and the Faceless Workflow, have made…
Managing multiple AI agents in a system can often be a daunting task due to the need for effective communication, reliable task execution, and optimal scalability. Many of the available frameworks for managing multi-agent systems often lack in features such as flexibility, usability, and scalability. They also often require extensive manual setup, making it challenging…
Advancements in Large Language Models (LLMs) have significantly improved the field of information extraction (IE), a task in Natural Language Processing (NLP) that involves identifying and extracting specific information from text. LLMs demonstrate impressive results in IE, particularly when combined with instruction tuning, training the models to annotate text according to predefined standards, enhancing their…
Introducing Claude 3.5 Sonnet by Anthropic AI–an advanced large language model (LLM) that impresses with remarkable capabilities, far exceeding its predecessors. This development in artificial intelligence transcends previously identified boundaries, paving the way for numerous new applications.
Claude 3.5 Sonnet is exceptional in multiple ways. Firstly, it can efficiently generate complex n-body particle animations quickly and…
A study conducted by researchers from Carnegie Mellon University, Google DeepMind, and MultiOn focuses on the role of synthetic data in enhancing the mathematical reasoning capabilities of large language models (LLMs). Predictions indicate that high-quality internet data necessary for training models could be depleted by 2026. As a result, model-generated or synthetic data is considered…
Researchers from Stony Brook University, the US Naval Academy, and the University of Texas at Austin have developed CAT-BENCH, a benchmark to assess language models' ability to predict the sequence of steps in cooking recipes. The research's main focus was on how language models comprehend plans by examining their understanding of the temporal sequencing of…
Artificial Intelligence (AI) innovations continue to pose particular challenges to existing legal frameworks, particularly in the realm of assigning liability due to the lack of discernible intentions, which is traditionally important in establishing liability. This problem is addressed in a new paper from Yale Law School, which suggests employing objective standards in regulating AI.
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Two AI, a new startup in the artificial intelligence (AI) space, has launched SUTRA, an innovative language model capable of proficiency in over 30 languages. It includes many South Asian languages such as Gujarati, Marathi, Tamil, and Telugu, aiming to address the unique linguistic challenges and opportunities in South Asia.
Constructed by using two mixture-of-experts transformers…
Large language models (LLMs), instrumental in natural language processing tasks like translation, summarization, and text generation, face challenges in consistently adhering to logical constraints during text generation. This adherence is crucial in sensitive applications where precision and instruction compliance are crucial. Traditional methods for imposing constraints on LLMs, such as the GeLaTo framework, have limitations…
Large language models (LLMs) are central to the field of natural language processing, being utilized in tasks like translation, summarization, and creative text generation. They utilize extensive data to learn patterns and relationships in languages, enabling them to undertake tasks necessitating an understanding of context, syntax, and semantics. However, there's a persistent challenge in ensuring…
Researchers from various international institutions have developed a computational method called RAIN to rapidly identify broadly neutralizing antibodies (bNAbs) against HIV-1. bNAbs can target the virus's envelope proteins to reduce viral loads and stop infection, but the process of discovering them is an arduous one due to the need for B-cell isolation and next-generation sequencing,…