The article discusses leveraging generative AI, specifically large language models (LLMs), to create summaries of Pandas DataFrames. It highlights how combining LLMs with data manipulation libraries like Pandas can enhance data analysis and reporting. The author demonstrates techniques for using LLMs to generate concise descriptions of complex datasets, making it easier for users to understand the data’s key insights without diving deep into the raw numbers. The approach not only saves time but also aids in interpreting results by producing human-readable summaries. Various use cases are presented, showcasing the effectiveness of AI in simplifying data tasks and improving productivity for analysts. The integration of generative AI streamlines the analytical process, facilitating better decision-making and enhancing overall data comprehension. Overall, the article emphasizes the potential of AI tools in transforming conventional data analysis methodologies.
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Leveraging Generative AI with Pandas: Crafting DataFrame Summaries Using LLMs – Towards Data Science
Generate AI Videos Using Microsoft’s Search Engine
Microsoft has launched the free Bing Video Creator in its mobile app, allowing users to create short AI-generated videos easily. This release follows the introduction of the Bing Image Creator two years ago and the Copilot Search feature earlier this year. The Bing team aims to empower users to turn their ideas into videos, enhancing creative expression. Currently available on iOS and Android, users can access the tool by tapping “Video Creator” in the app menu or typing “Create a video of…” in the search bar. Videos are limited to 5 seconds and formatted in 9:16, with the option for 16:9 coming soon. Users can queue up to three videos at a time, and while standard generation is free, users also receive 10 Fast creations at launch, with subsequent Fast videos available for 100 Microsoft Rewards points each. Videos are stored for 90 days before deletion.
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FDA Unveils Cutting-Edge AI Tool to Enhance Drug Review Processes
The U.S. Food and Drug Administration (FDA) launched Elsa, a generative AI tool aimed at modernizing its operations, allowing employees to work more efficiently. Marking a significant move into AI, Elsa facilitates quick searches and summaries of internal documents while safeguarding sensitive data, as it doesn’t utilize proprietary information from regulated industries. Developed in a secure GovCloud environment, the tool reduces the time spent on repetitive tasks, enhancing the review process for clinical protocols and scientific evaluations. FDA Commissioner Dr. Marty Makary noted the project was completed ahead of schedule and under budget, thanks to collaborative efforts among internal teams. As Elsa is utilized, its capabilities will be expanded based on employee feedback, focusing on usability and compliance with FDA policies. This launch is seen as the beginning of a broader strategy to fully integrate AI into FDA workflows, led by Chief AI Officer Jeremy Walsh and Senior Officer Sridhar Mantha.
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FDA Unveils AI Tool ‘Elsa’ Ahead of Schedule
The US Food and Drug Administration (FDA) has launched a new AI tool named “Elsa” earlier than anticipated. This agency-wide initiative is designed to enhance regulatory processes related to various sectors, including biologics, vaccines, medical devices, and pharmaceuticals. By leveraging artificial intelligence and machine learning, the tool aims to improve efficiency and effectiveness in regulatory decision-making. The FDA’s leadership anticipates that “Elsa” will streamline workflows and facilitate better oversight in the fast-evolving landscape of medical products. This timely deployment underscores the FDA’s commitment to harnessing technology to ensure safety and innovation in healthcare. The move is part of a broader effort to integrate AI/ML capabilities into regulatory practices, reflecting the agency’s adaptive approach to contemporary challenges in the medical field.
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Uplift Aerospace Unveils EdTech Division to Develop AI and VR Tools
Uplift Aerospace, based in Utah, is launching an EdTech Division to create virtual reality (VR) and artificial intelligence (AI)-driven learning tools. This initiative will focus on developing scalable training modules in two key areas: STEM education for classrooms and workforce development. The goal is to enhance learner engagement and retention while minimizing training costs and time. Building on the success of its STEM platform, Starborn Academy, Uplift has already rolled out programs in schools across Utah, Colorado, and California, with notable results such as increased interest in STEM careers among students. Led by a team of VR and AI specialists, the EdTech Division aims to offer personalized, adaptive learning experiences suitable for both classroom instruction and professional training. The expansion highlights the growing importance of immersive technology in education and workforce upskilling. Educators can explore partnership opportunities via the new Starborn Academy website, which includes testimonials showcasing the program’s impact.
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Major Upgrade Unveiled for Indonesian Language Model
GoTo, Indonesia’s largest digital ecosystem, and Indosat Ooredoo Hutchison (IOH) have launched an upgraded version of Sahabat-AI, an Indonesian large language model (LLM). Initially featuring eight to nine billion parameters, the new version boasts 70 billion parameters, enhancing accuracy and enabling a chat service with strong reasoning capabilities. Sahabat-AI now operates in five local languages—Bahasa Indonesia, Javanese, Sundanese, Balinese, and Bataknese—alongside several international languages, highlighting efforts to preserve local dialects amid global AI advancements predominantly driven by the US and China. This LLM connects with rural Indonesians who may not speak English or standard Bahasa. Additionally, it supports Indonesia’s digital sovereignty by storing data within the country, in compliance with national regulations. The model can be downloaded freely, promoting collaboration and innovation within Indonesia’s AI ecosystem, allowing diverse users—from startups to public institutions—to incorporate AI into their operations.
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Expert Sounds Alarm on ‘AI Wrappers’ Disguised as SaaS Solutions
The emergence of HTML prompt wrappers, which are basic web interfaces over public large language models (LLMs), raises concerns among tech-savvy users and developers. These wrappers often mimic polished SaaS platforms, misleading teams into believing they are acquiring secure and scalable tools. In reality, they are merely simple interfaces that connect to LLM APIs, lacking proprietary models or substantial added functionality. Many are marketed as sophisticated AI solutions with high subscription fees, yet offer only features replicable through free LLM accounts. The lack of transparency regarding data processing and compliance raises further red flags. Genuine products must solve real consumer problems and provide superior value compared to basic AI prompts. Key considerations include ensuring clear documentation and security practices. While some wrappers may evolve into full-fledged platforms, the focus should remain on true transparency and delivering tangible utility to users.
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Microsoft Bing Introduces Free AI Video Generator Powered by Sora
Microsoft Bing has launched the Bing Video Creator, utilizing OpenAI’s Sora model to allow users to generate videos from text prompts. This marks the first time Sora’s video generation capabilities are accessible for free, despite being restricted to paying customers before. Currently, the feature is not available on desktop, and even “fast” video generation can take hours. Users can create up to 10 video clips without charge by logging into their Microsoft account, after which they must pay 100 Microsoft Rewards points per video. Points can be earned through Bing searches or Microsoft Store purchases, capped at 150 points daily. Users can queue three 5-second videos at a time, but can’t adjust video length for now. Videos are generated in a vertical 9:16 aspect ratio, suitable for platforms like TikTok and Instagram, with plans for horizontal uploads in the future.
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IBM and Roche Collaborate to Develop an AI-Driven Glucose Prediction System to Enhance Daily Life for People with Diabetes
IBM and Roche have collaborated to develop the Accu-Chek® SmartGuide Predict app, an AI-enabled tool designed to assist individuals with diabetes in managing their glycemic control. Launched recently in Switzerland, the app integrates predictive algorithms with Roche’s continuous glucose monitoring (CGM) sensor to help users anticipate potential hypoglycemic and hyperglycemic events, enhancing their daily diabetes management. Key features include real-time glucose forecasting and alerts for low glucose levels. Additionally, IBM’s watsonx platform will streamline the analysis of clinical study data, improving efficiency and insight generation. This innovative project will be showcased at the Swiss Economic Forum on June 5-6, highlighting the significance of cross-industry partnerships in advancing healthcare solutions. The collaboration exemplifies how AI technology can empower patients and improve therapeutic outcomes, demonstrating the potential of synergy between healthcare and technological expertise.
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Evaluating AI Agents in Research: Insights from the Deep Research Bench Report
As large language models (LLMs) advance, they are increasingly marketed as powerful research assistants capable of undertaking complex tasks involving multi-step reasoning and data synthesis. Major players like OpenAI, Anthropic, Google, and Perplexity are developing features branded as “Deep Research” and other variations. A report from FutureSearch, titled Deep Research Bench (DRB), evaluates these AI agents’ performance on 89 challenging, web-based research tasks. OpenAI’s model, o3, emerged as the top performer, scoring 0.51, highlighting that even high scorers fall short of human researchers. Common issues include forgetfulness, repetitive searches, and incomplete conclusions, particularly detrimental in complex tasks. Interestingly, “toolless” models performed comparably in simpler tasks, revealing strong internal capabilities, though they struggled with more demanding queries. Overall, while advanced LLMs can surpass average humans on specific tasks, they still lag behind skilled researchers, particularly in adapting and reasoning throughout complex research processes.
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