Articles for author: ikayaniaamirshahzad@gmail.com

March 3, 2025

ikayaniaamirshahzad@gmail.com

Understanding Reasoning LLMs – by Sebastian Raschka, PhD

This article describes the four main approaches to building reasoning models, or how we can enhance LLMs with reasoning capabilities. I hope this provides valuable insights and helps you navigate the rapidly evolving literature and hype surrounding this topic. In 2024, the LLM field saw increasing specialization. Beyond pre-training and fine-tuning, we witnessed the rise of specialized applications, from RAGs to code assistants. I expect this trend to accelerate in 2025, with an even greater emphasis on domain- and application-specific optimizations (i.e., “specializations”). Stages 1-3 are the common steps to developing LLMs. Stage 4 specializes LLMs for specific use cases.

March 3, 2025

ikayaniaamirshahzad@gmail.com

Customize DeepSeek-R1 distilled models using Amazon SageMaker HyperPod recipes – Part 1

Increasingly, organizations across industries are turning to generative AI foundation models (FMs) to enhance their applications. To achieve optimal performance for specific use cases, customers are adopting and adapting these FMs to their unique domain requirements. This need for customization has become even more pronounced with the emergence of new models, such as those released by DeepSeek. However, customizing DeepSeek models effectively while managing computational resources remains a significant challenge. Tuning model architecture requires technical expertise, training and fine-tuning parameters, and managing distributed training infrastructure, among others. This often forces companies to choose between model performance and practical implementation constraints,

March 3, 2025

ikayaniaamirshahzad@gmail.com

Designing courses with an AI assistant

AI is “expanding and augmenting the range of possibilities I consider as a teacher,” shares , Director of Teaching and Learning at CUNY’s Newmark Graduate School of Journalism. Whether he is teaching entrepreneurial journalism or training professors on pedagogy, Jeremy finds “AI helps me generate many more possible approaches to the teaching I am doing.” By using AI as his assistant, he not only comes up with new ideas for how and what to teach, he’s also reclaiming his time. “New workflows allow me to reallocate time I would have spent on menial aspects of planning to actually speaking with

March 3, 2025

ikayaniaamirshahzad@gmail.com

“Translation is the tip of the iceberg”: A deep dive into specialty models

February 28, 2025 Olga Beregovaya, VP of AI at Smartling, joins Ryan and Ben to explore the evolution and specialization of language models in AI. They discuss the shift from rule-based systems to transformer models, the importance of fine-tuning for translation tasks, and the role of human translators in ensuring reliable, high-quality output. They also touch on the implications of AI in language education and the challenges faced in implementing LLMs in enterprise workflows. Credit: Alexandra Francis Source link

March 3, 2025

ikayaniaamirshahzad@gmail.com

TSMC announces $100 billion investment in US chipmaking

Taiwan Semiconductor Manufacturing Co. – the world’s biggest chipmaker – will invest at least $100 billion to expand chip manufacturing in the US. During a press conference on Monday, President Donald Trump said the funding would go toward building two additional chip manufacturing facilities in Phoenix, Arizona. The $100 billion investment builds upon the $65 billion TSMC has already committed to building three Arizona factories, as well as the $6.6 billion the Biden administration awarded to TSMC under the CHIPS Act. TSMC began producing 4-nanometer chips at its Arizona plant in January, but its future factories are expected to make

March 3, 2025

ikayaniaamirshahzad@gmail.com

[2410.18210] Towards Understanding the Fragility of Multilingual LLMs against Fine-Tuning Attacks

[Submitted on 23 Oct 2024 (v1), last revised 27 Feb 2025 (this version, v2)] View a PDF of the paper titled Towards Understanding the Fragility of Multilingual LLMs against Fine-Tuning Attacks, by Samuele Poppi and 6 other authors View PDF HTML (experimental) Abstract:Recent advancements in Large Language Models (LLMs) have sparked widespread concerns about their safety. Recent work demonstrates that safety alignment of LLMs can be easily removed by fine-tuning with a few adversarially chosen instruction-following examples, i.e., fine-tuning attacks. We take a further step to understand fine-tuning attacks in multilingual LLMs. We first discover cross-lingual generalization of fine-tuning attacks:

March 3, 2025

ikayaniaamirshahzad@gmail.com

Judges Are Fed up With Lawyers Using AI That Hallucinate Court Cases

This article was produced in collaboration with Court Watch, an independent outlet that unearths overlooked court records. Subscribe to them here. After a group of attorneys were caught using AI to cite cases that didn’t actually exist in court documents last month, another lawyer was told to pay $15,000 for his own AI hallucinations that showed up in several briefs.  Attorney Rafael Ramirez, who represented a company called HoosierVac in an ongoing case where the Mid Central Operating Engineers Health and Welfare Fund claims the company is failing to allow the union a full audit of its books and records,

March 3, 2025

ikayaniaamirshahzad@gmail.com

Generative AI to quantify uncertainty in weather forecasting

In December 1972, at the American Association for the Advancement of Science meeting in Washington, D.C., MIT meteorology professor Ed Lorenz gave a talk entitled, “Does the Flap of a Butterfly’s Wings in Brazil Set Off a Tornado in Texas?”, which contributed to the term “butterfly effect”. He was building on his earlier, landmark 1963 paper where he examined the feasibility of “very-long-range weather prediction” and described how errors in initial conditions grow exponentially when integrated in time with numerical weather prediction models. This exponential error growth, known as chaos, results in a deterministic predictability limit that restricts the use

March 3, 2025

ikayaniaamirshahzad@gmail.com

Grok 3 vs Other AI Tools: Key Differences Explained

Artificial intelligence tools have become essential for tasks like answering questions, coding, and conducting research. Among the latest contenders in this space is Grok 3, developed by Elon Musk’s xAI. Grok 3 is designed to excel in reasoning and problem-solving, but how does it compare to other popular AI tools like OpenAI’s GPT-4, Google’s Gemini, and DeepSeek V3? This article explores Grok 3’s performance, accessibility, and features while comparing it to its competitors. Key Takeaways Performance: Grok 3 leads in reasoning and coding tasks but falls short in real-time data integration compared to Google Gemini. Accessibility: While Grok 3 offers