Author: Pragati Jhunjhunwala

Pragati Jhunjhunwala
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Pragati Jhunjhunwala is a consulting intern at MarktechPost. She is currently pursuing her B.Tech from the Indian Institute of Technology(IIT), Kharagpur. She is a tech enthusiast and has a keen interest in the scope of software and data science applications. She is always reading about the developments in different field of AI and ML.

MedVersa: A Generalist Learner that Enables Flexible Learning and Tasking for Medical Image Interpretation

Despite the advancement of artificial intelligence in the field of medical science, these systems have limited application. This limitation creates a gap in developing...

Large Language Models-Guided Dynamic Adaptation (LLM-DA): A Machine Learning Method for Reasoning on Temporal Knowledge Graphs TKGs

Researchers from Beijing University of Technology, China, Monash University, Australia, the University of Hong Kong, China, and Griffith University, Australia, introduced LLM-DA to interpret...

Google AI Introduce AGREE: A Machine Learning Framework that Enables LLMs to Self-Ground the Claims in their Responses and to Provide Precise Citations

Maintaining the accuracy of Large Language Models (LLMs), such as GPT, is crucial, particularly in cases requiring factual accuracy, like news reporting or educational...

Google AI Propose LANISTR: An Attention-based Machine Learning Framework to Learn from Language, Image, and Structured Data

Google Cloud AI Researchers have introduced LANISTR to address the challenges of effectively and efficiently handling unstructured and structured data within a framework.  In...

Theory of Mind: How GPT-4 and LLaMA-2 Stack Up Against Human Intelligence

A team of psychologists and researchers from the University Medical Center Hamburg-Eppendorf, Italian Institute of Technology, Genoa, University of Trento, and others have researched...

Google DeepMind Introduces the Frontier Safety Framework: A Set of Protocols Designed to Identify & Mitigate Potential Harms Related to Future AI Systems

As AI technology progresses, models may acquire powerful capabilities that could be misused, resulting in significant risks in high-stakes domains such as autonomy, cybersecurity,...

Google AI Described New Machine Learning Methods for Generating Differentially Private Synthetic Data

Google AI researchers describe their novel approach to addressing the challenge of generating high-quality synthetic datasets that preserve user privacy, which are essential for...

Model Explorer: A Powerful Graph Visualization Tool that Helps One Understand, Debug, and Optimize Machine Learning Models

Machine Learning (ML) is everywhere these days, playing a crucial role in countless fields worldwide. Its applications are endless, and we rely on it...

Microsoft Researchers Introduce MatterSim: A Deep-Learning Model for Materials Under Real-World Conditions

Methods like Molecular Dynamics simulations, Quantitative Structure-Property Relationships (QSPR), and First-Principles calculations are based on scientific principles and complex mathematical models. They require expensive...

NVIDIA AI Releases the TensorRT Model Optimizer: A Library to Quantize and Compress Deep Learning Models for Optimized Inference on GPUs

Generative AI, despite its impressive capabilities, needs to improve with slow inference speed in its real-world applications. The inference speed is how long it...

Redundancy in AI: A Hybrid Convolutional Neural Networks CNN Approach to Minimize Computational Overhead in Reliable Execution

The inherent risks associated with AI systems, especially in applications like autonomous driving and medical diagnosis, where errors can have severe consequences, should be...

AI21 Labs Introduces Jamba-Instruct Model: An Instruction-Tuned Version of Their Hybrid SSM-Transformer Jamba Model

AI21 Labs has introduced the Jamba-Instruct model, which addresses the challenge of leveraging large context windows in natural language processing tasks for enterprise use....

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