×
![]()
In this article, I will take you through how to build a basic dashboard using Google Sheets.
AI infrastructure startup E2B secures $21 million funding with 88% Fortune 100 adoption rate, powering secure AI agent deployments at scale.
Get up to speed with the 7 most essential machine learning algorithms. Perfect for beginners and busy devs who want a quick, clear overview.
The price of expensive chatbot subscriptions is driven by vibes—not immediate profitability for AI companies.
Just two years ago, Lorraine He, now a 24-year-old law student, was told to avoid using AI for her assignments. At the time, to get around a national block on ChatGPT, students had to buy a mirror-site version from a secondhand marketplace. Its use was common, but it was at best tolerated and more often…
AI supermodel controversy, Oz in AI, humanity's next chapter, GenAI guides, AI traffic, and more...
What happens if the AI migration accelerates and sizable portions of the workforce are slow to move out of fear, resistance or inability?
Uber’s ability to offer speedy, reliable rides depends on its ability to predict demand. This means predicting when and where people will want rides, often to a city block, and the time at which they could be expecting them. This balancing act relies on complex machine learning (ML) systems that ingest vast amounts of data […]
The post How Uber Uses ML for Demand Prediction? appeared first on Analytics Vidhya.
Meta's new AI genius, GPT-5, ChatGPT agent, build your dream app, AI decodes ancient texts, and more...
The question isn’t, “will you use AI?” The question is, “what kind of AI user do you want to be: driver or passenger?”
The move underscores Meta’s strategy of spending aggressively now to secure a dominant position in what it views as the next foundational technology platform.
Hierarchical Reasoning Models (HRM) tackle complex reasoning tasks while being smaller, faster, and more data-efficient than large AI models.
AI huge mistake, AI honesty, Search playlists, AI record deal, video glow-ups, and more...
Conceptual overview and practical considerations
The post Declarative and Imperative Prompt Engineering for Generative AI appeared first on Towards Data Science.
"Will that break a query folding?” “Does your query fold?”… Maybe someone asked you those questions, but you were like: “Query…Whaaaat?!
In this article, we demistify the query folding and its importance for efficient data refresh in Power BI
The post What Is a Query Folding in Power BI and Why should You Care? appeared first on Towards Data Science.
Researchers at the University of Pennsylvania and the Allen Institute for Artificial Intelligence have developed a groundbreaking tool that allows open-source AI systems to match or surpass the visual understanding capabilities of proprietary models like GPT-4V and Gemini 1.5 Flash, potentially reshaping the competitive landscape between open and closed AI development. The tool, called CoSyn […]
A hands-on journey exploring fine-tuning techniques that unlock the power of small vision models.
The post How I Fine-Tuned Granite-Vision 2B to Beat a 90B Model — Insights and Lessons Learned appeared first on Towards Data Science.
In this post, we demonstrate how to build an intelligent eDiscovery solution using Amazon Bedrock Agents for real-time document analysis. We show how to deploy specialized agents for document classification, contract analysis, email review, and legal document processing, all working together through a multi-agent architecture. We walk through the implementation details, deployment steps, and best practices to create an extensible foundation that organizations can adapt to their specific eDiscovery requirements.
PerformLine operates within the marketing compliance industry, a specialized subset of the broader compliance software market, which includes various compliance solutions like anti-money laundering (AML), know your customer (KYC), and others. In this post, PerformLine and AWS explore how PerformLine used Amazon Bedrock to accelerate compliance processes, generate actionable insights, and provide contextual data—delivering the speed and accuracy essential for large-scale oversight.
The new Qwen3-Thinking-2507, as we'll call it for short, now leads or closely trails top-performing models across several major benchmarks.
The Trump administration says it wants AI models free from ideological bias, as it pressures their developers to reflect the president’s worldview.
In this article, you will learn: • the purpose and benefits of image augmentation techniques in computer vision for improving model generalization and diversity.
Learn the steps for setting up the machine learning pipeline in the top cloud provider.
Researchers at Harvard have created a groundbreaking metasurface that can replace bulky and complex optical components used in quantum computing with a single, ultra-thin, nanostructured layer. This innovation could make quantum networks far more scalable, stable, and compact. By harnessing the power of graph theory, the team simplified the design of these quantum metasurfaces, enabling them to generate entangled photons and perform sophisticated quantum operations — all on a chip thinner than a human hair. It's a radical leap forward for room-temperature quantum technology and photonics.
AI-generated videos are becoming dangerously convincing and UC Riverside researchers have teamed up with Google to fight back. Their new system, UNITE, can detect deepfakes even when faces aren't visible, going beyond traditional methods by scanning backgrounds, motion, and subtle cues. As fake content becomes easier to generate and harder to detect, this universal tool might become essential for newsrooms and social media platforms trying to safeguard the truth.
AI design, app builder, Shorts from selfies, virtual try-ons, intelligent internet, and more...
Anthropic developed its auditing agents while testing Claude Opus 4 for alignment issues.
Rather than chase enterprise contracts with large hospital systems, Freed has focused on small clinics and solo practitioners.
A new theory of class imbalance demonstrates that the optimal training imbalance in a binary problem is not 50%
The post When 50/50 Isn’t Optimal: Debunking Even Rebalancing appeared first on Towards Data Science.
In this post, we demonstrate how to use vLLM for scalable inference and use AWS Deep Learning Containers (DLC) to streamline model packaging and deployment. We’ll generate interest expansions through structured prompts, encode them into embeddings, retrieve candidates with FAISS, apply validation to keep results grounded, and frame the cold-start challenge as a scientific experiment—benchmarking LLM and encoder pairings, iterating rapidly on recommendation metrics, and showing clear ROI for each configuration