Articles for category: AI News

March 5, 2025

ikayaniaamirshahzad@gmail.com

Weekly Top Picks #100 – by Alberto Romero

No weekly top picks today—this column just hit 100 weeks, which calls for something different. Instead, we’re doing an Ask Me Anything (AMA). It’s been a while since the last one, so I’m sure you have plenty of questions. And as the name suggests, you can ask literally anything. But since infinite possibilities tend to paralyze rather than inspire, here are some ideas to get you started (not that I want to be asked these, of course…): An AI take I’ve changed my mind about in the past year The hardest part of running a tri-weekly newsletter for nearly three

March 5, 2025

ikayaniaamirshahzad@gmail.com

Last Week in AI #301

Anthropic has launched a new AI model, Claude 3.7 Sonnet, which is designed to “think” about questions for as long as users want. This hybrid AI reasoning model can provide both real-time answers and more considered responses, with users able to activate the model’s reasoning abilities. The model is part of Anthropic’s efforts to simplify the user experience around its AI products, and will be available to all users and developers, with premium Claude chatbot plan users having access to the model’s reasoning features. Claude 3.7 Sonnet is more expensive than other models, but it is also a hybrid model,

March 5, 2025

ikayaniaamirshahzad@gmail.com

Best Practices for Building the AI Development Platform in Government 

By John P. Desmond, AI Trends Editor  The AI stack defined by Carnegie Mellon University is fundamental to the approach being taken by the US Army for its AI development platform efforts, according to Isaac Faber, Chief Data Scientist at the US Army AI Integration Center, speaking at the AI World Government event held in-person and virtually from Alexandria, Va., last week.   Isaac Faber, Chief Data Scientist, US Army AI Integration Center “If we want to move the Army from legacy systems through digital modernization, one of the biggest issues I have found is the difficulty in abstracting away the differences in

March 5, 2025

ikayaniaamirshahzad@gmail.com

TSMC diversifies from Taiwan with record US investment in chip production

Summary Taiwan Semiconductor Manufacturing Co (TSMC), the world’s leading contract chipmaker, has announced a massive expansion of its US operations. According to a company press release, TSMC will invest an additional $100 billion in American semiconductor production on top of the $65 billion already committed, bringing the total investment to $165 billion – the largest foreign direct investment in US history. The new investment will fund the construction of three production facilities, two advanced packaging plants, and a research and development center. TSMC expects to create approximately 40,000 construction jobs over the next four years, with tens of thousands of

March 5, 2025

ikayaniaamirshahzad@gmail.com

Automating Artificial Life Discovery: The Power of Foundation Models

The recent Nobel Prize for groundbreaking advancements in protein discovery underscores the transformative potential of foundation models (FMs) in exploring vast combinatorial spaces. These models are poised to revolutionize numerous scientific disciplines, yet the field of Artificial Life (ALife) has been slow to adopt them. This gap presents a unique opportunity to overcome the traditional reliance on manual design and trial-and-error methods for uncovering lifelike simulation configurations. In a new paper Automating the Search for Artificial Life with Foundation Models, a research team from MIT, Sakana AI, OpenAI, The Swiss AI Lab IDSIA and Independent introduces Automated Search for Artificial

March 5, 2025

ikayaniaamirshahzad@gmail.com

Chain of Draft approach allows AI models to carry out tasks using far fewer resources

Comparison of Claude 3.5 Sonnet’s accuracy and token usage across different tasks with three different prompt strategies: direct answer (Standard), Chain of Thought (CoT), and Chain of Draft (CoD). Credit: arXiv (2025). DOI: 10.48550/arxiv.2502.18600 A small team of AI engineers at Zoom Communications has developed a new approach to training AI systems that uses far fewer resources than the standard approach now in use. The team has published their results on the arXiv preprint server. The new approach developed at Zoom is called Chain of Draft (CoD), an update of the traditional approach now in use called Chain of Thought

March 5, 2025

ikayaniaamirshahzad@gmail.com

Visualizing nanoparticle dynamics using AI-based method

Static image taken from video (shown below). Left: a platinum nanoparticle imaged via electron microscopy. Right: using AI-based method to remove the noise. By Patricia Waldron A team of scientists has developed a method to illuminate the dynamic behavior of nanoparticles. The work, reported in Visualizing Nanoparticle Surface Dynamics and Instabilities Enabled by Deep Denoising, in the journal Science, combines artificial intelligence with electron microscopy to render visuals of how these tiny bits of matter respond to stimuli. “The nature of changes in the particle is exceptionally diverse, including fluxional periods, manifesting as rapid changes in atomic structure, particle shape,

March 5, 2025

ikayaniaamirshahzad@gmail.com

What is Apache Arrow? Features, How to Use and More

Data is at the core of everything, from business decisions to machine learning. But processing large-scale data across different systems is often slow. Constant format conversions add processing time and memory overhead. Traditional row-based storage formats struggle to keep up with modern analytics. This leads to slower computations, higher memory usage, and performance bottlenecks. Apache Arrow solves these issues. It is an open source, columnar in-memory data format designed for speed and efficiency. Arrow provides a common way to represent tabular data, eliminating costly conversions and enabling seamless interoperability. Key Benefits of Apache Arrow Zero-Copy Data Sharing – Transfers data

March 5, 2025

ikayaniaamirshahzad@gmail.com

Step by Step Guide to Build an AI Research Assistant with Hugging Face SmolAgents: Automating Web Search and Article Summarization Using LLM-Powered Autonomous Agents

Hugging Face’s SmolAgents framework provides a lightweight and efficient way to build AI agents that leverage tools like web search and code execution. In this tutorial, we demonstrate how to build an AI-powered research assistant that can autonomously search the web and summarize articles using SmolAgents. This implementation runs seamlessly, requiring minimal setup, and showcases the power of AI agents in automating real-world tasks such as research, summarization, and information retrieval. !pip install smolagents beautifulsoup4 First, we install smolagents beautifulsoup4, which enables AI agents to use tools like web search and code execution, and BeautifulSoup4, a Python library for parsing