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How can we reason with uncertainty and make smarter decisions from data? This article explains the key probability ideas in data science.
Understanding the process behind agentic planning and task management in LangChain
The post How Agents Plan Tasks with To-Do Lists appeared first on Towards Data Science.
A data scientist's guide to population stability index (PSI)
The post Stop Retraining Blindly: Use PSI to Build a Smarter Monitoring Pipeline appeared first on Towards Data Science.
Looking ahead to 2026, the most impactful trends are not flashy frameworks but structural changes in how data pipelines are designed, owned, and operated.
A brief overview of the math behind the Harsanyi Dividend and a real-world application in Streamlit
The post Synergy in Clicks: Harsanyi Dividends for E-Commerce appeared first on Towards Data Science.
Users of AI image generators are offering each other instructions on how to use the tech to alter pictures of women into realistic, revealing deepfakes.
Demis Hassabis, CEO of Google DeepMind, summed it up in three words: “This is embarrassing.” Hassabis was replying on X to an overexcited post by Sébastien Bubeck, a research scientist at the rival firm OpenAI, announcing that two mathematicians had used OpenAI’s latest large language model, GPT-5, to find solutions to 10 unsolved problems in…
OpenAI browser warning, ChatGPT wrapped, 3D head avatars, AI fighter jets, and more...
Understanding text embeddings through simple models and Excel
The post The Machine Learning “Advent Calendar” Day 22: Embeddings in Excel appeared first on Towards Data Science.
Gradient descent in function space with decision trees
The post The Machine Learning “Advent Calendar” Day 21: Gradient Boosted Decision Tree Regressor in Excel appeared first on Towards Data Science.
From Random Ensembles to Optimization: Gradient Boosting Explained
The post The Machine Learning “Advent Calendar” Day 20: Gradient Boosted Linear Regression in Excel appeared first on Towards Data Science.
For the last couple of years, a lot of the conversation around AI has revolved around a single, deceptively simple question: Which model is the best? But the next question was always, the best for what? The best for reasoning? Writing? Coding? Or maybe it’s the best for images, audio, or video? That framing made […]
The post ChatLLM Presents a Streamlined Solution to Addressing the Real Bottleneck in AI appeared first on Towards Data Science.
This post explores Chain-of-Draft (CoD), an innovative prompting technique introduced in a Zoom AI Research paper Chain of Draft: Thinking Faster by Writing Less, that revolutionizes how models approach reasoning tasks. While Chain-of-Thought (CoT) prompting has been the go-to method for enhancing model reasoning, CoD offers a more efficient alternative that mirrors human problem-solving patterns—using concise, high-signal thinking steps rather than verbose explanations.
In this post, we demonstrate hosting Voxtral models on Amazon SageMaker AI endpoints using vLLM and the Bring Your Own Container (BYOC) approach. vLLM is a high-performance library for serving large language models (LLMs) that features paged attention for improved memory management and tensor parallelism for distributing models across multiple GPUs.
To address the need for businesses to quickly analyze information and unlock actionable insights, we are announcing Analytics Agent, a new feature that is seamlessly integrated into the GenAI IDP Accelerator. With this feature, users can perform advanced searches and complex analyses using natural language queries without SQL or data analysis expertise. In this post, we discuss how non-technical users can use this tool to analyze and understand the documents they have processed at scale with natural language.
In this post, we demonstrate how to implement a predictive maintenance solution using Foundation Models (FMs) on Amazon Bedrock, with a case study of Amazon's manufacturing equipment within their fulfillment centers. The solution is highly adaptable and can be customized for other industries, including oil and gas, logistics, manufacturing, and healthcare.
MIT community members made headlines with key research advances and their efforts to tackle pressing challenges.
Look back on Google AI news in 2025 across Gemini, Search, Pixel and more products.
The company made 80 times as many reports to the National Center for Missing & Exploited Children during the first six months of 2025 as it did in the same period a year prior.
This article explains how Gistr transforms the way data professionals interact with their most valuable asset: their accumulated knowledge.
A story about failing forward, spheres you can’t visualize, and why sometimes the math knows things before we do
The post The Geometry of Laziness: What Angles Reveal About AI Hallucinations appeared first on Towards Data Science.
This is a list of top LLM and VLMs that are fast, smart, and small enough to run locally on devices as small as a Raspberry Pi or even a smart fridge.
How to make LLMs reason with verifiable, step-by-step logic (Part 1)
The post Understanding Vibe Proving appeared first on Towards Data Science.
An LLM that's 41× more efficient and 9× faster than today's standard models
The post What Happens When You Build an LLM Using Only 1s and 0s appeared first on Towards Data Science.
Videos such as fake ads featuring AI children playing with vibrators or Jeffrey Epstein and Diddy-themed playsets are being made with Sora 2 and posted to TikTok.
Two decades ago social media promised to connect people with pals far and wide. Twenty years online has left us turning to AI for kinship. IRL companionship is the future.
60% higher death risk, AI tops music charts, a cool AI gadget, 1K agents society, and more...
A new AI developed at Duke University can uncover simple, readable rules behind extremely complex systems. It studies how systems evolve over time and reduces thousands of variables into compact equations that still capture real behavior. The method works across physics, engineering, climate science, and biology. Researchers say it could help scientists understand systems where traditional equations are missing or too complicated to write down.
João Freitas is GM and VP of engineering for AI and automation at PagerDutyAs AI use continues to evolve in large organizations, leaders are increasingly seeking the next development that will yield major ROI. The latest wave of this ongoing trend is the adoption of AI agents. However, as with any new technology, organizations must ensure they adopt AI agents in a responsible way that allows them to facilitate both speed and security. More than half of organizations have already deployed AI agents to some extent, with more expecting to follow suit in the next two years. But many early adopters are now reevaluating their approach. Four-in-10 tech leaders regret not establishing a stronger governance foundation from the start, which suggests they adopted AI rapidly, but with margin to improve on policies, rules and best practices designed to ensure the responsible, ethical and legal development and use of AI.As AI adoption accelerates, organizations must find the right balance between their exposure risk and the implementation of guardrails to ensure AI use is secure.Where do AI agents create potential risks?There are three principal areas of consideration for safer AI adoption.The first is shadow AI, when employees use unauthorized AI tools without express permission, bypassing approved tools and processes. IT should create necessary processes for experimentation and innovation to introduce more efficient ways of working with AI. While shadow AI has existed as long as AI tools themselves, AI agent autonomy makes it easier for unsanctioned tools to operate outside the purview of IT, which can introduce fresh security risks.Secondly, organizations must close gaps in AI ownership and accountability to prepare for incidents or processes gone wrong. The strength of AI agents lies in their autonomy. However, if agents act in unexpected ways, teams must be able to determine who is responsible for addressing any issues.The third risk arises when there is a lack of explainability for actions AI agents have taken. AI agents are goal-oriented, but how they accomplish their goals can be unclear. AI agents must have explainable logic underlying their actions so that engineers can trace and, if needed, roll back actions that may cause issues with existing systems.While none of these risks should delay adoption, they will help organizations better ensure their security.The three guidelines for responsible AI agent adoptionOnce organizations have identified the risks AI agents can pose, they must implement guidelines and guardrails to ensure safe usage. By following these three steps, organizations can minimize these risks.1: Make human oversight the default AI agency continues to evolve at a fast pace. However, we still need human oversight when AI agents are given the capacity to act, make decisions and pursue a goal that may impact key systems. A human should be in the loop by default, especially for business-critical use cases and systems. The teams that use AI must understand the actions it may take and where they may need to intervene. Start conservatively and, over time, increase the level of agency given to AI agents.In conjunction, operations teams, engineers and security professionals must understand the role they play in supervising AI agents’ workflows. Each agent should be assigned a specific human owner for clearly defined oversight and accountability. Organizations must also allow any human to flag or override an AI agent’s behavior when an action has a negative outcome.When considering tasks for AI agents, organizations should understand that, while traditional automation is good at handling repetitive, rule-based processes with structured data inputs, AI agents can handle much more complex tasks and adapt to new information in a more autonomous way. This makes them an appealing solution for all sorts of tasks. But as AI agents are deployed, organizations should control what actions the agents can take, particularly in the early stages of a project. Thus, teams working with AI agents should have approval paths in place for high-impact actions to ensure agent scope does not extend beyond expected use cases, minimizing risk to the wider system.2: Bake in security The introduction of new tools should not expose a system to fresh security risks. Organizations should consider agentic platforms that comply with high security standards and are validated by enterprise-grade certifications such as SOC2, FedRAMP or equivalent. Further, AI agents should not be allowed free rein across an organization’s systems. At a minimum, the permissions and security scope of an AI agent must be aligned with the scope of the owner, and any tools added to the agent should not allow for extended permissions. Limiting AI agent access to a system based on their role will also ensure deployment runs smoothly. Keeping complete logs of every action taken by an AI agent can also help engineers understand what happened in the event of an incident and trace back the problem.3: Make outputs explainable AI use in an organization must never be a black box. The reasoning behind any action must be illustrated so that any engineer who tries to access it can understand the context the agent used for decision-making and access the traces that led to those actions.Inputs and outputs for every action should be logged and accessible. This will help organizations establish a firm overview of the logic underlying an AI agent’s actions, providing significant value in the event anything goes wrong.Security underscores AI agents’ successAI agents offer a huge opportunity for organizations to accelerate and improve their existing processes. However, if they do not prioritize security and strong governance, they could expose themselves to new risks. As AI agents become more common, organizations must ensure they have systems in place to measure how they perform and the ability to take action when they create problems.Read more from our guest writers. Or, consider submitting a post of your own! See our guidelines here.
Comparing metrics across datasets and models
The post Running Evals on a Bloated RAG Pipeline appeared first on Towards Data Science.