Author: Tech Jacks Solutions

Recent Advances in Generative AI for Healthcare Applicationscs.AI updates on arXiv.orgon August 18, 2025 at 4:00 am arXiv:2310.00795v2 Announce Type: replace-cross
Abstract: The rapid advancement of Artificial Intelligence (AI) has catalyzed revolutionary changes across various sectors, notably in healthcare. In particular, generative AI-led by diffusion models and transformer architectures-has enabled significant breakthroughs in medical imaging (including image reconstruction, image-to-image translation, generation, and classification), protein structure prediction, clinical documentation, diagnostic assistance, radiology interpretation, clinical decision support, medical coding, and billing, as well as drug design and molecular representation. These innovations have enhanced clinical diagnosis, data reconstruction, and drug synthesis. This review paper aims to offer a comprehensive synthesis of recent advances in healthcare applications of generative AI, with an emphasis on diffusion and transformer models. Moreover, we discuss current capabilities, highlight existing limitations, and outline promising research directions to address emerging challenges. Serving as both a reference for researchers and a guide for practitioners, this work offers an integrated view of the state of the art, its impact on healthcare, and its future potential.

 arXiv:2310.00795v2 Announce Type: replace-cross
Abstract: The rapid advancement of Artificial Intelligence (AI) has catalyzed revolutionary changes across various sectors, notably in healthcare. In particular, generative AI-led by diffusion models and transformer architectures-has enabled significant breakthroughs in medical imaging (including image reconstruction, image-to-image translation, generation, and classification), protein structure prediction, clinical documentation, diagnostic assistance, radiology interpretation, clinical decision support, medical coding, and billing, as well as drug design and molecular representation. These innovations have enhanced clinical diagnosis, data reconstruction, and drug synthesis. This review paper aims to offer a comprehensive synthesis of recent advances in healthcare applications of generative AI, with an emphasis on diffusion and transformer models. Moreover, we discuss current capabilities, highlight existing limitations, and outline promising research directions to address emerging challenges. Serving as both a reference for researchers and a guide for practitioners, this work offers an integrated view of the state of the art, its impact on healthcare, and its future potential. Read More 

The Download: Taiwan’s silicon shield, and ChatGPT’s personality misstepMIT Technology Reviewon August 15, 2025 at 12:10 pm This is today’s edition of The Download, our weekday newsletter that provides a daily dose of what’s going on in the world of technology. Taiwan’s “silicon shield” could be weakening Taiwanese politics increasingly revolves around one crucial question: Will China invade? China’s ruling party has wanted to seize Taiwan for more than half a century. But in…

 This is today’s edition of The Download, our weekday newsletter that provides a daily dose of what’s going on in the world of technology. Taiwan’s “silicon shield” could be weakening Taiwanese politics increasingly revolves around one crucial question: Will China invade? China’s ruling party has wanted to seize Taiwan for more than half a century. But in… Read More 

Losing GPT-4o sent some people into mourning. That was predictable.MIT Technology Reviewon August 15, 2025 at 10:34 am June had no idea that GPT-5 was coming. The Norwegian student was enjoying a late-night writing session last Thursday when her ChatGPT collaborator started acting strange. “It started forgetting everything, and it wrote really badly,” she says. “It was like a robot.” June, who asked that we use only her first name for privacy reasons,…

 June had no idea that GPT-5 was coming. The Norwegian student was enjoying a late-night writing session last Thursday when her ChatGPT collaborator started acting strange. “It started forgetting everything, and it wrote really badly,” she says. “It was like a robot.” June, who asked that we use only her first name for privacy reasons,… Read More 

DeepSeek: The Chinese startup challenging Silicon ValleyAI Newson August 15, 2025 at 9:33 am Market disruption and shockwaves through Silicon Valley marked Chinese startup DeepSeek’s launch, challenging some of the fundamental assumptions of how artificial intelligence companies had operated and scaled. In less than a couple of years, the Beijing-based newcomer has accomplished what many thought impossible: creating AI models that compete with industry giants while spending only a
The post DeepSeek: The Chinese startup challenging Silicon Valley appeared first on AI News.

 Market disruption and shockwaves through Silicon Valley marked Chinese startup DeepSeek’s launch, challenging some of the fundamental assumptions of how artificial intelligence companies had operated and scaled. In less than a couple of years, the Beijing-based newcomer has accomplished what many thought impossible: creating AI models that compete with industry giants while spending only a
The post DeepSeek: The Chinese startup challenging Silicon Valley appeared first on AI News. Read More 

Prompt Attacks Reveal Superficial Knowledge Removal in Unlearning Methodscs.AI updates on arXiv.orgon August 15, 2025 at 4:00 am arXiv:2506.10236v2 Announce Type: replace-cross
Abstract: In this work, we demonstrate that certain machine unlearning methods may fail under straightforward prompt attacks. We systematically evaluate eight unlearning techniques across three model families using output-based, logit-based, and probe analysis to assess the extent to which supposedly unlearned knowledge can be retrieved. While methods like RMU and TAR exhibit robust unlearning, ELM remains vulnerable to specific prompt attacks (e.g., prepending Hindi filler text to the original prompt recovers 57.3% accuracy). Our logit analysis further indicates that unlearned models are unlikely to hide knowledge through changes in answer formatting, given the strong correlation between output and logit accuracy. These findings challenge prevailing assumptions about unlearning effectiveness and highlight the need for evaluation frameworks that can reliably distinguish between genuine knowledge removal and superficial output suppression. To facilitate further research, we publicly release our evaluation framework to easily evaluate prompting techniques to retrieve unlearned knowledge.

 arXiv:2506.10236v2 Announce Type: replace-cross
Abstract: In this work, we demonstrate that certain machine unlearning methods may fail under straightforward prompt attacks. We systematically evaluate eight unlearning techniques across three model families using output-based, logit-based, and probe analysis to assess the extent to which supposedly unlearned knowledge can be retrieved. While methods like RMU and TAR exhibit robust unlearning, ELM remains vulnerable to specific prompt attacks (e.g., prepending Hindi filler text to the original prompt recovers 57.3% accuracy). Our logit analysis further indicates that unlearned models are unlikely to hide knowledge through changes in answer formatting, given the strong correlation between output and logit accuracy. These findings challenge prevailing assumptions about unlearning effectiveness and highlight the need for evaluation frameworks that can reliably distinguish between genuine knowledge removal and superficial output suppression. To facilitate further research, we publicly release our evaluation framework to easily evaluate prompting techniques to retrieve unlearned knowledge. Read More 

What Does “Following Best Practices” Mean in the Age of AI?Towards Data Scienceon August 14, 2025 at 7:34 pm How data and ML practitioners should navigate a rapidly changing landscape
The post What Does “Following Best Practices” Mean in the Age of AI? appeared first on Towards Data Science.

 How data and ML practitioners should navigate a rapidly changing landscape
The post What Does “Following Best Practices” Mean in the Age of AI? appeared first on Towards Data Science. Read More 

“My biggest lesson was realizing that domain expertise matters more than algorithmic complexity.“Towards Data Scienceon August 14, 2025 at 1:59 pm Claudia Ng reflects on real-world ML lessons, mentoring newcomers, and her journey from corporate ML to freelance AI.
The post “My biggest lesson was realizing that domain expertise matters more than algorithmic complexity.“ appeared first on Towards Data Science.

 Claudia Ng reflects on real-world ML lessons, mentoring newcomers, and her journey from corporate ML to freelance AI.
The post “My biggest lesson was realizing that domain expertise matters more than algorithmic complexity.“ appeared first on Towards Data Science. Read More 

A Lightweight Learned Cardinality Estimation Modelcs.AI updates on arXiv.orgon August 14, 2025 at 4:00 am arXiv:2508.09602v1 Announce Type: cross
Abstract: Cardinality estimation is a fundamental task in database management systems, aiming to predict query results accurately without executing the queries. However, existing techniques either achieve low estimation accuracy or incur high inference latency. Simultaneously achieving high speed and accuracy becomes critical for the cardinality estimation problem. In this paper, we propose a novel data-driven approach called CoDe (Covering with Decompositions) to address this problem. CoDe employs the concept of covering design, which divides the table into multiple smaller, overlapping segments. For each segment, CoDe utilizes tensor decomposition to accurately model its data distribution. Moreover, CoDe introduces innovative algorithms to select the best-fitting distributions for each query, combining them to estimate the final result. By employing multiple models to approximate distributions, CoDe excels in effectively modeling discrete distributions and ensuring computational efficiency. Notably, experimental results show that our method represents a significant advancement in cardinality estimation, achieving state-of-the-art levels of both estimation accuracy and inference efficiency. Across various datasets, CoDe achieves absolute accuracy in estimating more than half of the queries.

 arXiv:2508.09602v1 Announce Type: cross
Abstract: Cardinality estimation is a fundamental task in database management systems, aiming to predict query results accurately without executing the queries. However, existing techniques either achieve low estimation accuracy or incur high inference latency. Simultaneously achieving high speed and accuracy becomes critical for the cardinality estimation problem. In this paper, we propose a novel data-driven approach called CoDe (Covering with Decompositions) to address this problem. CoDe employs the concept of covering design, which divides the table into multiple smaller, overlapping segments. For each segment, CoDe utilizes tensor decomposition to accurately model its data distribution. Moreover, CoDe introduces innovative algorithms to select the best-fitting distributions for each query, combining them to estimate the final result. By employing multiple models to approximate distributions, CoDe excels in effectively modeling discrete distributions and ensuring computational efficiency. Notably, experimental results show that our method represents a significant advancement in cardinality estimation, achieving state-of-the-art levels of both estimation accuracy and inference efficiency. Across various datasets, CoDe achieves absolute accuracy in estimating more than half of the queries. Read More