Author: Mohammad Asjad

Mohammad Asjad
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Asjad is an intern consultant at Marktechpost. He is persuing B.Tech in mechanical engineering at the Indian Institute of Technology, Kharagpur. Asjad is a Machine learning and deep learning enthusiast who is always researching the applications of machine learning in healthcare.

Optimizing Agent Planning: A Parametric AI Approach to World Knowledge

Large Language Models (LLMs) have advanced natural language processing tasks significantly. Recently, using LLMs for physical world planning tasks has shown promise. However, LLMs,...

Unlocking the Potential of SirLLM: Advancements in Memory Retention and Attention Mechanisms

The rapid growth of large language models (LLMs) has catalyzed the development of numerous NLP applications, such as chatbots, writing assistants, and programming aids....

Achieving Balance in Lifelong Learning: The WISE Memory Approach

LLMs demonstrate emergent intelligence with increased parameters, computes, and data, hinting at artificial general intelligence. Despite advancements, deployed LLMs still exhibit errors like hallucinations,...

A Paradigm Shift: MoRA’s Role in Advancing Parameter-Efficient Fine-Tuning Techniques

Parameter-efficient fine-tuning (PEFT) techniques adapt large language models (LLMs) to specific tasks by modifying a small subset of parameters, unlike Full Fine-Tuning (FFT), which...

Transparency in Foundation Models: The Next Step in Foundation Model Transparency Index FMTI

Foundation models are central to AI's influence on the economy and society. Transparency is crucial for accountability, competition, and understanding, particularly regarding the data...

An Efficient AI Approach to Memory Reduction and Throughput Enhancement in LLMs

The efficient deployment of large language models (LLMs) necessitates high throughput and low latency. However, LLMs' substantial memory consumption, particularly by the key-value (KV)...

Apple Researchers Propose KV-Runahead: An Efficient Parallel LLM Inference Technique to Minimize the Time-to-First-Token

Large language models (LLMs), particularly Generative Pre-trained Transformer (GPT) models, have demonstrated strong performance across various language tasks. However, challenges persist in their decoder...

Toward Responsible Innovation: Evaluating Risks and Opportunities in Open Generative AI

Generative AI (Gen AI), capable of producing robust content based on input, is poised to impact various sectors like science, economy, education, and the...

TII Releases Falcon 2-11B: The First AI Model of the Falcon 2 Family Trained on 5.5T Tokens with a Vision Language Model

The Technology Innovation Institute (TII) in Abu Dhabi has introduced Falcon, a cutting-edge family of language models available under the Apache 2.0 license. Falcon-40B...

This AI Paper from Stanford University Evaluates the Performance of Multimodal Foundation Models Scaling from Few-Shot to Many-Shot-In-Context Learning ICL

Incorporating demonstrating examples, known as in-context learning (ICL), significantly enhances large language models (LLMs) and large multimodal models (LMMs) without requiring parameter updates. Recent...

Meta AI Introduces Chameleon: A New Family of Early-Fusion Token-based Foundation Models that Set a New Bar for Multimodal Machine Learning

Although recent multimodal foundation models are extensively utilized, they tend to segregate various modalities, typically employing specific encoders or decoders for each. This approach...

SpeechVerse: A Multimodal AI Framework that Enables LLMs to Follow Natural Language Instructions for Performing Diverse Speech-Processing Tasks

Large language models (LLMs) have excelled in natural language tasks and instruction following, yet they struggle with non-textual data like images and audio. Incorporating...

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