​[[{“value”:” The widespread adoption of large language models (LLMs) has ushered in significant advancements across fields such as conversational AI, content generation, and on-device applications. However, the heavy reliance on extensive cloud resources to deploy these models raises concerns about latency, cost, and environmental sustainability.… Read More »Meta AI Releases MobileLLM 125M, 350M, 600M and 1B Model Checkpoints Asif Razzaq Artificial Intelligence Category – MarkTechPost

​Python is the most popular data science programming language, as it’s versatile and has a lot of support from the community. Python is the most popular data science programming language, as it’s versatile and has a lot of support from the community.  Read More  

​[[{“value”:” Large language models (LLMs) rely on deep learning architectures that capture complex linguistic relationships within layered structures. Primarily based on Transformer architectures, these models are increasingly deployed across industries for tasks that require nuanced language understanding and generation. However, the demands of large Transformer… Read More »Relaxed Recursive Transformers with Layer-wise Low-Rank Adaptation: Achieving High Performance and Reduced Computational Cost in Large Language Models Nikhil Artificial Intelligence Category – MarkTechPost

​[[{“value”:” Large Language Models (LLMs) have revolutionized software development by enabling code completion, functional code generation from instructions, and complex code modifications for bug fixes and feature implementations. While these models excel at generating code from natural language instructions, significant challenges persist in evaluating the… Read More »CodeFavor: A Machine Learning Framework that Trains Pairwise Preference Models with Synthetic Code Preferences Generated from Code Evolution like Code Commits and Code Critiques Mohammad Asjad Artificial Intelligence Category – MarkTechPost

​[[{“value”:” Task planning in language agents is gaining attention in LLM research, focusing on breaking complex tasks into manageable sub-tasks arranged in a graph format, with nodes as tasks and edges as dependencies. The study explores task planning challenges in LLMs, such as HuggingGPT, which… Read More »Enhancing Task Planning in Language Agents: Leveraging Graph Neural Networks for Improved Task Decomposition and Decision-Making in Large Language Models Sana Hassan Artificial Intelligence Category – MarkTechPost

​[[{“value”:” Deep learning has made advances in various fields, and it has made its way into material sciences as well. From tasks like predicting material properties to optimizing compositions, deep learning has accelerated material design and facilitated exploration in expansive materials spaces. However, explainability is… Read More »XElemNet: A Machine Learning Framework that Applies a Suite of Explainable AI (XAI) for Deep Neural Networks in Materials Science Afeerah Naseem Artificial Intelligence Category – MarkTechPost

​[[{“value”:” Artificial Intelligence (AI) has revolutionized numerous industries, from healthcare to finance. It empowers machines to learn from data, make intelligent decisions, and solve complex problems. Let’s understand a fundamental technique in AI, Artificial Intelligence (AI) clustering. As the term “clustering” suggests, it involves grouping… Read More »What is Artificial Intelligence Clustering? Pragati Jhunjhunwala Artificial Intelligence Category – MarkTechPost

​[[{“value”:” As the world is evolving towards a personal digital experience, recommendation systems, while being a must, from e-commerce to media streaming, fail to simulate users’ preferences to make better recommendations. Conventional models do not capture the subtlety of reasons behind user-item interactions thus generalized… Read More »Knowledge Graph Enhanced Language Agents (KGLA): A Machine Learning Framework that Unifies Language Agents and Knowledge Graph for Recommendation Systems Aswin Ak Artificial Intelligence Category – MarkTechPost

​[[{“value”:” The rise of large language models has been accompanied by significant challenges, particularly around ensuring the factuality of generated responses. One persistent issue is that these models can produce outputs that are factually incorrect or even misleading, a phenomenon often called “hallucination.” These hallucinations… Read More »OpenAI Releases SimpleQA: A New AI Benchmark that Measures the Factuality of Language Models Asif Razzaq Artificial Intelligence Category – MarkTechPost

​[[{“value”:” Transformer-based architectures have revolutionized natural language processing, delivering exceptional performance across diverse language modeling tasks. However, they still face major challenges when handling long-context sequences. The self-attention mechanism in Transformers suffers from quadratic computational complexity, and their memory requirement grows linearly with context length… Read More »Taipan: A Novel Hybrid Architecture that Combines Mamba-2 with Selective Attention Layers (SALs) Sajjad Ansari Artificial Intelligence Category – MarkTechPost