AI/ML News & Innovations Hub

Are there existing regulations or technical standards requiring AI systems to clearly signal they are not human?

Modern AI systems can now generate speech that is difficult to distinguish from a real human voice. This raises concerns about AI being used in phone calls, customer service, or social interactions without clear disclosure. Some have suggested that AI outputs should be required to use explicit “machine-signaling” language (e.g., more analytical phrasing) so users cannot subconsciously interpret it as a human speaker. Question: Are there any existing laws, regulations, or technical standards (in the US, EU, or elsewhere) that require AI systems—particularly voice-based assistants or automated callers—to clearly disclose that they are AI or prevent human impersonation? If so, what frameworks or enforcement mechanisms currently exist?

Inside an AI-Run Company

AI agents are moving from demos to real workplaces, but what actually happens when they run a company? In this episode, journalist Evan Ratliff, host of Shell Game , joins Chris to discuss his immersive journalism experiment building a real startup staffed almost entirely by AI agents. They explore how AI agents behave as coworkers, how humans react when interacting with them, and where ethical and workplace boundaries begin to break down. Featuring: Evan Ratliff – LinkedIn , X Chris Benson – Website , LinkedIn , Bluesky , GitHub , X Links: Shell Game Upcoming Events: Register for upcoming webinars here !

Import AI 443: Into the mist: Moltbook, agent ecologies, and the internet in transition

Plus, a story about agents corrupting other agents

State of AI 2026 with Sebastian Raschka, Nathan Lambert, and Lex Fridman

I recently sat down with Lex Fridman and Nathan Lambert for a comprehensive 4.5 h interview to discuss the current state of progress of AI, and what the...

#490 – State of AI in 2026: LLMs, Coding, Scaling Laws, China, Agents, GPUs, AGI

Nathan Lambert and Sebastian Raschka are machine learning researchers, engineers, and educators. Nathan is the post-training lead at the Allen Institute for AI (Ai2) and the author of The RLHF Book. Sebastian Raschka is the author of Build a Large Language Model (From Scratch) and Build a Reasoning Model (From Scratch). Thank you for listening ❤ Check out our sponsors: https://lexfridman.com/sponsors/ep490-sc See below for timestamps, transcript, and to give feedback, submit questions, contact Lex, etc. Transcript: https://lexfridman.com/ai-sota-2026-transcript CONTACT LEX: Feedback – give feedback to Lex: https://lexfridman.com/survey AMA – submit questions, videos or call-in: https://lexfridman.com/ama Hiring – join our team: https://lexfridman.com/hiring

Transcript for State of AI in 2026: LLMs, Coding, Scaling Laws, China, Agents, GPUs, AGI | Lex Fridman Podcast #490

This is a transcript of Lex Fridman Podcast #490 with Nathan Lambert & Sebastian Raschka. The timestamps in the transcript are clickable links that take you directly to that point in the main video. Please note that the transcript is human generated, and may have errors. Here are some useful links: Go back to this episode’s main page Watch the full YouTube version of the podcast Table of Contents Here are the loose “chapters” in the conversation. Click link to jump approximately to that part in the transcript: 0:00 – Introduction 1:57 – China vs US: Who wins the AI

Fact checking Moravec's paradox

This famous aphorism is neither true nor useful

The Evolution of Reasoning in Small Language Models with Yejin Choi - #761

Today, we're joined by Yejin Choi, professor and senior fellow at Stanford University in the Computer Science Department and the Institute for Human-Centered AI (HAI). In this conversation, we explore Yejin’s recent work on making small language models reason more effectively. We discuss how high-quality, diverse data plays a central role in closing the intelligence gap between small and large models, and how combining synthetic data generation, imitation learning, and reinforcement learning can unlock stronger reasoning capabilities in smaller models. Yejin explains the risks of homogeneity in model outputs and mode collapse highlighted in her “Artificial Hivemind” paper, and its impacts on human creativity and knowledge. We also discuss her team's novel approaches, including reinforcement learning as a pre-training objective, where models are incentivized to “think” before predicting the next token, and "Prismatic Synthesis," a gradient-based method for generating diverse synthetic math data while filtering overrepresented examples. Additionally, we cover the societal implications of AI and the concept of pluralistic alignment—ensuring AI reflects the diverse norms and values of humanity. Finally, Yejin shares her mission to democratize AI beyond large organizations and offers her predictions for the coming year. The complete show notes for this episode can be found at https://twimlai.com/go/761.

Leadership in AI depends on energy, not on chips

Leadership in the world of artificial intelligence (AI) will in the coming years increasingly depend on energy, instead of the current focus on chips, writes Alfonso Velazquez, Head of Data and AI at Kyndryl.

Mauricio Torres Echenagucia (IBM) shares AI trends to watch in 2026

Over the past year, artificial intelligence (AI) has dominated headlines and rapidly scaled across the business landscape. This year, the technology is set to move beyond hype to deliver measurable business impact, writes Mauricio Torres Echenagucia, General Manager of Mexico at IBM.

Mexico’s supply chains and trade logistics: Navigating tariffs and USMCA

As Mexico enters 2026, supply chain and logistics leaders are navigating the most complex trade environment the region has faced in over a decade. Rising tariff uncertainty, evolving US trade policy, and the upcoming USMCA review are no longer abstract geopolitical discussions; they are operational realities with direct impact on cost, service levels, network design, and strategic decision-making.

Get started with Angular: Introducing the modern reactive workflow

Angular is a cohesive, all-in-one reactive framework for web development. It is one of the larger reactive frameworks, focused on being a single architectural system that handles all your web development needs under one idiom. While Angular was long criticized for being heavyweight as compared to React , many of those issues were addressed in Angular 19 . Modern Angular is built around the Signals API and minimal formality, while still delivering a one-stop-shop that includes dependency injection and integrated routing. Angular is popular with the enterprise because of its stable, curated nature, but it is becoming more attractive to the wider developer community thanks to its more community engaged development philosophy . That, along with its recent technical evolution, make Angular one of the most interesting projects to watch right now. Why choose Angular? Choosing a JavaScript development framework sometimes feels like a philosophical debate, but it should be a practical decision. Angular is unique because it is strongly opinionated. It doesn’t just give you a view layer; it provides a complete toolkit for building web applications. Like other reactive frameworks, Angular is built around its reactive engine, which lets you bind state (variables) to the view. But if that’s all you needed, one of the smaller, more focused frameworks would be more than enough. What Angular has that some of these other frameworks don’t is its ability to use data binding to automatically syn…

From pixels to characters: The engineering behind GitHub Copilot CLI’s animated ASCII banner

Learn how GitHub built an accessible, multi-terminal-safe ASCII animation for the Copilot CLI using custom tooling, ANSI color roles, and advanced terminal engineering. The post From pixels to characters: The engineering behind GitHub Copilot CLI’s animated ASCII banner appeared first on The GitHub Blog .

LWiAI Podcast #232 - ChatGPT Ads, Thinking Machines Drama, STEM

OpenAI to test ads in ChatGPT as it burns through billions, The Drama at Thinking Machines, STEM: Scaling Transformers with Embedding Modules

Scalable Distributed Training: From Single-GPU Limits to Reliable Multi-Node Runs with Ray on Anyscale

Management as AI superpower

Thriving in a world of agents

Terminally online Mistral Vibe.

Integration Consulting appoints Carolina Flores as partner in Brazil office

Integration Consulting, a Brazil-headquartered management consultancy with offices worldwide, has announced the appointment of Carolina Flores as partner. She becomes the firm’s 13th partner. Now in her 15th year at Integration Consulting, Carolina Flores leads complex projects for major Brazilian companies across retail, consumer goods, healthcare and financial services.

Breaking the Spell of Vibe Coding

Sinister variations on the positive state of flow

How is AI shaping democracy?

As AI increasingly shapes geopolitics, elections, and civic life, its impact on democracy is becoming impossible to ignore. In this episode, Daniel and Chris are joined by security expert Bruce Schneier to explore how AI and technology are transforming democracy, governance, and citizenship. Drawing from his book Rewiring Democracy , they explore real examples of AI in elections, legislation, courts, and public AI models, the risks of concentrated power, and how these tools can both strengthen and strain democratic systems worldwide. Featuring: Bruce Schneier – X Chris Benson – Website , LinkedIn , Bluesky , GitHub , X Daniel Whitenack – Website , GitHub , X Links: Schneier on Security Sponsors: Framer - The website builder that turns your dot com from a formality into a tool for growth. Check it out at framer.com/PRACTICALAI Zapier - The AI orchestration platform that puts AI to work across your company. Check it out at zapier.com/practical Upcoming Events: Register for upcoming webinars here !

How to evaluate the fit of a COM-poisson Model?

My response variable showed significant underdispersion, which was due to uneven sampling. I searched for the right models and was advised to fit a COM Poisson model, as quasi-Poisson and other generalisations might be too optimistic. I am now wondering whether the evaluation of my model in terms of explanatory power and fit needs to be done differently than with standard procedures for GLMs (measured using likelihood ratio tests, chi-square tests, and deviations). I ask this because I cannot find any literature that is not too specific or doesn't require too much prior knowledge.

Inurgural ITU AI for Good Impact Africa Event in Partnership with AI Expo Africa Delivers Success

AI for Good Impact Africa, our second regional event brought together innovators, policymakers, startups, and youth from across the continent for the transformative week of dialogue and discovery. Held alongside AI Expo Africa in Johannesburg, the event marked a milestone in fostering local innovation, building capacity, and strengthening partnerships to advance responsible artificial intelligence across […]

16 open source projects transforming AI and machine learning

For several decades now, the most innovative software has always emerged from the world of open source software. It’s no different with machine learning and large language models . If anything, the open source ecosystem has grown richer and more complex, because now there are open source models to complement the open source code. For this article, we’ve pulled together some of the most intriguing and useful projects for AI and machine learning . Many of these are foundation projects, nurturing their own niche ecology of open source plugins and extensions. Once you’ve started with the basic project, you can keep adding more parts. Most of these projects offer demonstration code, so you can start up a running version that already tackles a basic task. Additionally, the companies that build and maintain these projects often sell a service alongside them. In some cases, they’ll deploy the code for you and save you the hassle of keeping it running. In others, they’ll sell custom add-ons and modifications. The code itself is still open, so there’s no vendor lock in. The services simply make it easier to adopt the code by paying someone to help. Here are 16 open source projects that developers can use to unlock the potential in machine learning and large language models of any size—from small to large, and even extra large. Agent Skills AI coding agents are often used to tackle standard tasks like writing React components or reviewing parts of the user interface . If you are writin…

The Brain Is Just Specialized Agents Talking To Each Other — Dr. Jeff Beck

What makes something truly *intelligent?* Is a rock an agent? Could a perfect simulation of your brain actually *be* you? In this fascinating conversation, Dr. Jeff Beck takes us on a journey through the philosophical and technical foundations of agency, intelligence, and the future of AI. Jeff doesn't hold back on the big questions. He argues that from a purely mathematical perspective, there's no structural difference between an agent and a rock – both execute policies that map inputs to outputs. The real distinction lies in *sophistication* – how complex are the internal computations? Does the system engage in planning and counterfactual reasoning, or is it just a lookup table that happens to give the right answers? *Key topics explored in this conversation:* *The Black Box Problem of Agency* – How can we tell if something is truly planning versus just executing a pre-computed response? Jeff explains why this question is nearly impossible to answer from the outside, and why the best we can do is ask which model gives us the simplest explanation. *Energy-Based Models Explained* – A masterclass on how EBMs differ from standard neural networks. The key insight: traditional networks only optimize weights, while energy-based models optimize *both* weights and internal states – a subtle but profound distinction that connects to Bayesian inference. *Why Your Brain Might Have Evolved from Your Nose* – One of the most surprising moments in the conversation. Jeff proposes that the…

Categories of Inference-Time Scaling for Improved LLM Reasoning

And an Overview of Recent Inference-Scaling Papers

Why AI Has a Plato Problem — Mazviita Chirimuuta

Professor Mazviita Chirimuuta joins us for a fascinating deep dive into the philosophy of neuroscience and what it really means to understand the mind. *What can neuroscience actually tell us about how the mind works?* In this thought-provoking conversation, we explore the hidden assumptions behind computational theories of the brain, the limits of scientific abstraction, and why the question of machine consciousness might be more complicated than AI researchers assume. Mazviita, author of *The Brain Abstracted,* brings a unique perspective shaped by her background in both neuroscience research and philosophy. She challenges us to think critically about the metaphors we use to understand cognition — from the reflex theory of the late 19th century to today's dominant view of the brain as a computer. *Key topics explored:* *The problem of oversimplification* — Why scientific models necessarily leave things out, and how this can sometimes lead entire fields astray. The cautionary tale of reflex theory shows how elegant explanations can blind us to biological complexity. *Is the brain really a computer?* — Mazviita unpacks the philosophical assumptions behind computational neuroscience and asks: if we can model anything computationally, what makes brains special? The answer might challenge everything you thought you knew about AI. *Haptic realism* — A fresh way of thinking about scientific knowledge that emphasizes interaction over passive observation. Knowledge isn't about read…

Google’s study shows how public perceptions on AI have evolved

Ipsos’ and Google’s study shows how public perceptions on AI have evolved as people have come to see AI as a practical tool for learning, work, and daily life. In 2025, the world decisively crossed the AI adoption threshold. People moved past casual experimentation – the highest use case in 2024 – and embraced AI as an […]

Last Week in AI #333 - ChatGPT Ads, Zhipu+Huawei, Drama at Thinking Machines

OpenAI to test ads in ChatGPT as it burns through billions, Sequoia to invest in Anthropic, Zhipu AI breaks US chip reliance, The Drama at Thinking Machines Is Riveting Silicon Valley

The New Cartography of the Invisible

By John Knechtel From the telescope to the balance sheet – a Foundation Models for Science Workshop recap relates how scientists can help businesses solve their most stubborn data problems and […] The post The New Cartography of the Invisible appeared first on Vector Institute for Artificial Intelligence .

Heaps do lie: debugging a memory leak in vLLM.

Dutch sustainability consultancy Earthwise launches in Colombia

Earthwise, a boutique advisory firm from the Netherlands specialising in sustainability, has opened a new office in Colombia as part of its international expansion. Founded in 2024 by a team of “sustainability enthusiasts”, Earthwise advises mid-sized and large organisations on ESG and sustainability-related challenges.

How to use Pandas for data analysis in Python

When it comes to working with data in a tabular form, most people reach for a spreadsheet. That’s not a bad choice: Microsoft Excel and similar programs are familiar and loaded with functionality for massaging tables of data. But what if you want more control, precision, and power than Excel alone delivers? In that case, the open source Pandas library for Python might be what you are looking for. Pandas augments Python with new data types for loading data fast from tabular sources, and for manipulating, aligning, merging, and doing other processing at scale. Your first Pandas data set Pandas is not part of the Python standard library. It’s a third-party project, so you’ll need to install it in your Python runtime with pip install pandas . Once installed, you can import it into Python with import pandas . Pandas gives you two new data types: Series and DataFrame . The DataFrame represents your entire spreadsheet or rectangular data, whereas the Series is a single column of the DataFrame . In Python terms, you can think of the Pandas DataFrame as a dictionary or collection of Series objects. You’ll also find later that you can use dictionary- and list-like methods for finding elements in a DataFrame . You typically work with Pandas by importing data from some other format. A common external tabular data format is CSV, a text file with values separated by commas. If you have a CSV handy, you can use it. For this article, we’ll be using an excerpt from the Gapminder data set pre…

GPU (In)efficiency in AI Workloads

Cross-repository code navigation

Cross-repo search provides semantic code understanding across repositories.

Controlling AI Models from the Inside

As generative AI moves into production, traditional guardrails and input/output filters can prove too slow, too expensive, and/or too limited. In this episode, Alizishaan Khatri of Wrynx joins Daniel and Chris to explore a fundamentally different approach to AI safety and interpretability. They unpack the limits of today’s black-box defenses, the role of interpretability, and how model-native, runtime signals can enable safer AI systems. Featuring: Alizishaan Khatri – LinkedIn Chris Benson – Website , LinkedIn , Bluesky , GitHub , X Daniel Whitenack – Website , GitHub , X Upcoming Events: Register for upcoming webinars here !

How To Use AI for the Ancient Art of Close Reading

Experiments in reading with LLMs

Study: Colombians supportive of ESG and renewable energy transition

Colombians are largely supportive of ESG initiatives, with a new study indicating that over 80% of the population believes it is essential for both the government and large corporations to transition toward renewable energy and make a real effort for a more sustainable system.

Why Every Brain Metaphor in History Has Been Wrong [SPECIAL EDITION]

What if everything we think we know about the brain is just a really good metaphor that we forgot was a metaphor? This episode takes you on a journey through the history of scientific simplification, from a young Karl Friston watching wood lice in his garden to the bold claims that your mind is literally software running on biological hardware. We bring together some of the most brilliant minds we've interviewed — Professor Mazviita Chirimuuta, Francois Chollet, Joscha Bach, Professor Luciano Floridi, Professor Noam Chomsky, Nobel laureate John Jumper, and more — to wrestle with a deceptively simple question: *When scientists simplify reality to study it, what gets captured and what gets lost?* *Key ideas explored:* *The Spherical Cow Problem* — Science requires simplification. We're limited creatures trying to understand systems far more complex than our working memory can hold. But when does a useful model become a dangerous illusion? *The Kaleidoscope Hypothesis* — Francois Chollet's beautiful idea that beneath all the apparent chaos of reality lies simple, repeating patterns — like bits of colored glass in a kaleidoscope creating infinite complexity. Is this profound truth or Platonic wishful thinking? *Is Software Really Spirit?* — Joscha Bach makes the provocative claim that software is literally spirit, not metaphorically. We push back hard on this, asking whether the "sameness" we see across different computers running the same program exists in nature or only in our…

OpenMined Joins Open Forum for AI to Advance Responsible Data Governance

We’re excited to announce that OpenMined has joined the Open Forum for AI (OFAI), an international initiative led by Carnegie Mellon University that’s bringing together academic institutions and nonprofit organizations to advance human-centered and ethical approaches to artificial intelligence. Launched at Carnegie Mellon University in 2024, OFAI was created to foster collaboration, transparency, and inclusion […] The post OpenMined Joins Open Forum for AI to Advance Responsible Data Governance appeared first on OpenMined .

When protections outlive their purpose: A lesson on managing defense systems at scale

User feedback led us to clean up outdated mitigations. See why observability and lifecycle management are critical for defense systems. The post When protections outlive their purpose: A lesson on managing defense systems at scale appeared first on The GitHub Blog .

Reframing Impact: AI Summit 2026

The 2026 AI Impact Summit in India is the latest iteration of an event that has become a bellwether for global discourse around the AI industry, especially the question of whether, and how, it can be governed. But it also demonstrates how important ideas can be invoked in ways that dilute their meaning or co-opt their force. In this series—produced by AI Now Institute, Aapti Institute, and The Maybe—we bring together leading advocates, builders, and thinkers from around the world who live and breathe substance, analysis, and meaningful action into these ideas. The post Reframing Impact: AI Summit 2026 appeared first on AI Now Institute .

OpenMined Featured in Communications of the ACM on the Future of Synthetic Data and AI Training

In a recent article published by the Communications of the ACM — the flagship publication of the Association for Computing Machinery — OpenMined’s Executive Director, Andrew Trask, was featured as a key voice in the growing conversation around synthetic data, AI training, and the critical importance of controlling how data shapes model behavior. The Growing […] The post OpenMined Featured in Communications of the ACM on the Future of Synthetic Data and AI Training appeared first on OpenMined .

What is GitOps? Extending devops to Kubernetes and beyond

Over the past decade, software development has been shaped by two closely related transformations. One is the rise of devops and continuous integration and continuous delivery (CI/CD), which brought development and operations teams together around automated, incremental software delivery. The other is the shift from monolithic applications to distributed, cloud-native systems built from microservices and containers, typically managed by orchestration platforms such as Kubernetes . While Kubernetes and similar platforms simplify many aspects of running distributed applications, operating these systems at scale is still complicated. Configuration sprawl, environment drift, and the need for rapid, reliable change all introduce operational challenges. GitOps emerged as a way to address those challenges by extending familiar devops and CI/CD techniques beyond application code and into infrastructure and system configuration. At the heart of GitOps is the concept of infrastructure as code (IaC). In a GitOps model, not only application code but also infrastructure definitions, deployment configurations, and operational settings are described in files stored in a version control system. Automated processes continuously compare the running system with those declarations and work to bring the live environment back into alignment when differences appear. In this approach, the version control repository serves as the system of record for how applications and their supporting infrastruct…

React tutorial: Get started with the React JavaScript library

Despite many worthy contenders , React remains the most popular front-end framework, and a key player in the JavaScript development landscape. React is the quintessential reactive engine , continually innovating alongside the rest of the industry. A flagship open source project at Facebook, React is now part of Meta Open Source. For developers new to JavaScript and web development, this tutorial will get you started with this vital technology. React is not only a front-end framework, but is a component in full-stack frameworks like Next.js . Newer additions like React server-side rendering (SSR) and React server components (RSC) further blur the line between server and client. Also see: Is the React compiler ready for primetime? Why React? React’s prominence makes it an obvious choice for developers just starting out with web development. It is often chosen for its ability to offer a smooth and encompassing developer experience (DX), which distinguishes it from frameworks like Vue, Angular, and Svelte . It could be said that React’s true “killer feature” is the perks that come with longstanding popularity: learning resources, community support, libraries, and developers are all plentiful in the React ecosystem. Installing React Real-world React requires running on the server with a build tool, which we will explore in the next section. But to get your feet wet, we can start out with an online playground. There are several high-quality playgrounds for React, including full-bl…

As artificial intelligence reshapes news, media double down on investigations and field reporting

“One newsroom response to the disruption of artificial intelligence in content generation is not technological but editorial. The Journalism and Technology Trends and Predictions 2026 report says that, in a world where generative systems can create and repackage information at scale, news outlets are redefining which content is worth producing. According to the report, prepared […] The post As artificial intelligence reshapes news, media double down on investigations and field reporting appeared first on LatAm Journalism Review by the Knight Center .

As artificial intelligence reshapes news, media double down on investigations and field reporting

“One newsroom response to the disruption of artificial intelligence in content generation is not technological but editorial. The Journalism and Technology Trends and Predictions 2026 report says that, in a world where generative systems can create and repackage information at scale, news outlets are redefining which content is worth producing. According to the report, prepared […] The post As artificial intelligence reshapes news, media double down on investigations and field reporting appeared first on LatAm Journalism Review by the Knight Center .

#489 – Paul Rosolie: Uncontacted Tribes in the Amazon Jungle

Paul Rosolie is a naturalist, explorer, author of a new book titled Junglekeeper, and is someone who has dedicated his life to protecting the Amazon rainforest. Thank you for listening ❤ Check out our sponsors: https://lexfridman.com/sponsors/ep489-sc See below for timestamps, transcript, and to give feedback, submit questions, contact Lex, etc. Transcript: https://lexfridman.com/paul-rosolie-3-transcript CONTACT LEX: Feedback – give feedback to Lex: https://lexfridman.com/survey AMA – submit questions, videos or call-in: https://lexfridman.com/ama Hiring – join our team: https://lexfridman.com/hiring Other – other ways to get in touch: https://lexfridman.com/contact EPISODE LINKS: Junglekeeper (new book): https://amzn.to/4q7vpAp Paul’s Instagram: https://instagram.com/paulrosolie Junglekeepers Website: https://junglekeepers.org Paul’s Website: https://paulrosolie.com Mother

Transcript for Paul Rosolie: Uncontacted Tribes in the Amazon Jungle | Lex Fridman Podcast #489

This is a transcript of Lex Fridman Podcast #489 with Paul Rosolie. The timestamps in the transcript are clickable links that take you directly to that point in the main video. Please note that the transcript is human generated, and may have errors. Here are some useful links: Go back to this episode’s main page Watch the full YouTube version of the podcast Table of Contents Here are the loose “chapters” in the conversation. Click link to jump approximately to that part in the transcript: 0:00 – Episode highlight 1:08 – Introduction 3:59 – Uncontacted tribes in the Amazon Jungle

Vision RAG: Enabling Search on Any Documents

Information comes in many shapes and forms. While retrieval-augmented generation (RAG) primarily focuses on plain text, it overlooks vast amounts of data along the way. Most enterprise knowledge resides in complex documents, slides, graphics, and other multimodal sources. Yet, extracting useful information from these formats using optical character recognition (OCR) or other parsing techniques is often low-fidelity, brittle, and expensive. Vision RAG makes complex documents—including their figures and tables—searchable by using multimodal embeddings, eliminating the need for complex and costly text extraction. This guide explores how Voyage AI’s latest model powers this capability and provides a step-by-step implementation walkthrough. Vision RAG: Building upon text RAG Vision RAG is an evolution of traditional RAG built on the same two components: retrieval and generation. In traditional RAG, unstructured text data is indexed for semantic search. At query time, the system retrieves relevant documents or chunks and appends them to the user’s prompt so the large language model (LLM) can produce more grounded, context-aware answers. Figure 1. Text RAG with Voyage AI and MongoDB. Text RAG with Voyage AI and MongoDB Enterprise data, however, is rarely just clean plain text. Critical information often lives in PDFs, slides, diagrams, dashboards, and other visual formats. Today, this is typically handled by parsing tools and OCR services. Those approaches create several problems:…

Information-Driven Design of Imaging Systems

An encoder (optical system) maps objects to noiseless images, which noise corrupts into measurements. Our information estimator uses only these noisy measurements and a noise model to quantify how well measurements distinguish objects. Many imaging systems produce measurements that humans never see or cannot interpret directly. Your smartphone processes raw sensor data through algorithms before producing the final photo. MRI scanners collect frequency-space measurements that require reconstruction before doctors can view them. Self-driving cars process camera and LiDAR data directly with neural networks. What matters in these systems is not how measurements look, but how much useful information they contain. AI can extract this information even when it is encoded in ways that humans cannot interpret. And yet we rarely evaluate information content directly. Traditional metrics like resolution and signal-to-noise ratio assess individual aspects of quality separately, making it difficult to compare systems that trade off between these factors. The common alternative, training neural networks to reconstruct or classify images, conflates the quality of the imaging hardware with the quality of the algorithm. We developed a framework that enables direct evaluation and optimization of imaging systems based on their information content. In our NeurIPS 2025 paper , we show that this information metric predicts system performance across four imaging domains, and that optimizing it prod…