Author: Sana Hassan

Sana Hassan
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Sana Hassan, a consulting intern at Marktechpost and dual-degree student at IIT Madras, is passionate about applying technology and AI to address real-world challenges. With a keen interest in solving practical problems, he brings a fresh perspective to the intersection of AI and real-life solutions.

Transformative Use Cases of Artificial Intelligence AI Across Biotechnology

AI is a pivotal tool for biotechnology, offering transformative solutions to many challenges. From drug discovery and safety to genomics, proteomics, and pharmacology, AI's...

LLMs vs SLMs vs STLMs: A Comprehensive Analysis

The world of language models is getting interesting every day, with new smaller language models adaptable to various purposes, devices, and applications. Large Language...

Advancements and Future Directions in Machine Learning-Assisted Protein Engineering

Protein engineering, a rapidly evolving field in biotechnology, has the potential to revolutionize various sectors, including antibody design, drug discovery, food security, and ecology....

Unveiling the Diagnostic Landscape: Assessing AI and Human Performance in the Long Tail of Rare Diseases

Using extensive labeled data, supervised machine learning algorithms have surpassed human experts in various tasks, leading to concerns about job displacement, particularly in diagnostic...

Advancing Machine Learning with KerasCV and KerasNLP: A Comprehensive Overview

Keras is a widely used machine learning tool known for its high-level abstractions and ease of use, enabling rapid experimentation. Recent advances in CV...

Steerability and Bias in LLMs: Navigating Multifaceted Persona Representation

LLMs need to generate text reflecting the diverse views of multifaceted personas. Prior studies on bias in LLMs have focused on simplistic, one-dimensional personas...

Aligning Large Language Models with Diverse User Preferences Using Multifaceted System Messages: The JANUS Approach

Current methods for aligning LLMs often match the general public's preferences, assuming this is ideal. However, this overlooks the diverse and nuanced nature of...

Matryoshka Multimodal Models With Adaptive Visual Tokenization: Enhancing Efficiency and Flexibility in Multimodal Machine Learning

Multimodal machine learning is a cutting-edge research field combining various data types, such as text, images, and audio, to create more comprehensive and accurate...

MAP-Neo: A Fully Open-Source and Transparent Bilingual LLM Suite that Achieves Superior Performance to Close the Gap with Closed-Source Models

LLMs like GPT, Gemini, and Claude have achieved remarkable performance but remain proprietary, with limited training details disclosed. Open-source models such as LLaMA-3 have...

Enhancing Self-Supervised Learning with Automatic Data Curation: A Hierarchical K-Means Approach

Self-supervised features are central to modern machine learning, typically requiring extensive human effort for data collection and curation, similar to supervised learning. Self-supervised learning...

Google’s Advanced AI Models: Gemini, PaLM, and Bard

With significant advancements through its Gemini, PaLM, and Bard models, Google has been at the forefront of AI development. Each model has distinct capabilities...

AI-Powered Genomic Analysis: Transforming Precision Medicine through Advanced Data Interpretation

The rapid advancements in sequencing technologies have unlocked unprecedented potential in genomic research and precision medicine. However, the challenge of accurately identifying genetic variants...

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