​ Transformer-based Language Models have uplifted the domain of Natural Language Processing (NLP) in recent years. Their capacity to comprehend and produce text that is human-like has resulted in ground-breaking improvements across a range of NLP tasks. However, these models have a serious flaw: when… Read More »How Can Transformers Handle Longer Inputs? CMU and Google Researchers Unveil a Novel Approach (FIRE): A Functional Interpolation for Relative Position Encoding Tanya Malhotra Artificial Intelligence Category – MarkTechPost

​ The pace of innovation in Artificial Intelligence (AI) is astonishing. AI is now the driving force behind technologies like Robotics, IoT, and Big Data, and Generative AI tools like ChatGPT are gaining widespread attention. With AI, computers can make smart decisions and discoveries from… Read More »Why are Humans Dreading Artificial Intelligence AI? Asif Razzaq Artificial Intelligence Category – MarkTechPost

​ LLMs have proven valuable for reasoning and decision-making tasks. They excel in breaking down complex problems into sequential steps, but their performance can be improved through methods like self-consistency and multi-step decomposition. LLMs are also effective for decision-making in various domains, though they often… Read More »Can Large Language Models Truly Act and Reason? Researchers from the University of Illinois at Urbana-Champaign Introduce LATS for Enhanced Decision-Making Sana Hassan Artificial Intelligence Category – MarkTechPost

​ In Large Language Models (LLMs), Partially-Binarized LLMs (PB-LLM) is a cutting-edge technique for achieving extreme low-bit quantization in LLMs without sacrificing language reasoning capabilities. PB-LLM strategically filters salient weights during binarization, reserving them for higher-bit storage. Moreover, it introduces post-training quantization (PTQ) and quantization-aware… Read More »How Can We Effectively Compress Large Language Models with One-Bit Weights? This Artificial Intelligence Research Proposes PB-LLM: Exploring the Potential of Partially-Binarized LLMs Adnan Hassan Artificial Intelligence Category – MarkTechPost

​ Adopting the Transformer architecture with self-attention and increases in model size and pre-training data has led to significant progress in large language models (LLMs). Users want to use longer input sequences during inference more frequently as LLMs improve capacity. As a result, there is… Read More »Researchers from Princeton and Meta AI Introduce MemWalker: A New Method that First Processes the Long Context into a Tree of Summary Nodes Dhanshree Shripad Shenwai Artificial Intelligence Category – MarkTechPost

​ Speech-driven expression animation, a complex problem at the intersection of computer graphics and artificial intelligence, involves the generation of realistic facial animations and head poses based on spoken language input. The challenge in this domain arises from the intricate, many-to-many mapping between speech and… Read More »Meet DiffPoseTalk: A New Speech-to-3D Animation Artificial Intelligence Framework Madhur Garg Artificial Intelligence Category – MarkTechPost

​Posted by Han Zhou, Student Researcher, and Subhrajit Roy, Senior Research Scientist, Google Research Prompting large language models (LLMs) has become an efficient learning paradigm for adapting LLMs to a new task by conditioning on human-designed instructions. The remarkable in-context learning (ICL) ability of LLMs… Read More »Batch calibration: Rethinking calibration for in-context learning and prompt engineering Google AI Google AI Blog

​ Recent developments in text-to-image generation have made the creation of detailed graphics from straightforward natural language descriptions possible. Results using models like Stable Diffusion and DALL-E frequently resemble actual images or works of art created by humans. These models do not produce the best… Read More »Can We Transform Text into Scientific Vector Graphics? This AI Paper Introduces AutomaTikZ and Explains the Power of TikZ Aneesh Tickoo Artificial Intelligence Category – MarkTechPost

​ A key aspect of generative AI is audio generation. In recent years, the popularity of generative AI has led to increasingly diverse and emerging needs in audio production. For example, text-to-sound and text-to-music technologies are projected to produce audio based on human requests for… Read More »Can One AI Model Master All Audio Tasks? Meet UniAudio: A New Universal Audio Generation System Aneesh Tickoo Artificial Intelligence Category – MarkTechPost

​ Alibaba DAMO Academy’s GTE-tiny is a lightweight and speedy text embedding model. It uses the BERT framework and has been trained on a massive corpus of relevant text pairs that span numerous areas and use cases. Removes half the layers from gte-small, resulting in… Read More »Meet GTE-tiny: A Powerful Text Embedding Artificial Intelligence Model for Downstream Tasks Dhanshree Shripad Shenwai Artificial Intelligence Category – MarkTechPost