How a rocket scientist turned entrepreneur created the "ChatGPT for Earth data" using transformers and satellite imagery.

Bruno Sánchez is a rocket scientist with a somewhat deviant trajectory. An astrophysicist by training, he used the tools of his trade - mathematics and science - at the broadest possible scale: the universe. At some point, however, his focus switched to using those same tools for more down to earth goals.

Sánchez had a stint at the World Bank, where as a member of interdisciplinary teams he helped make sense of geospatial data. Then he realized the core of what he was doing was mapping, which prompted him to launch a company called Mapbox, providing online maps on the web.

This experience brought another realization for Sánchez - that we have so much data about Earth that we don't really know how to use it: "We know what are the trees in the world. We know what are the forests in the world. It's just a matter of processing [data] properly", as he put it.

So when he got the opportunity to attempt to put all of that together in the same data center and in one workbench, he went for it. That was the Planetary Computer project at Microsoft, and Sánchez loved it. Then, ChatGPT happened.

Sánchez noted that the T in ChatGPT - the transformer - was an architecture that seemed to work great for modalities such as text, images, and audio, but no one seemed to be using it for earth data. So he decided to give it a try. He built a team, raised funds, created a non-profit, and built an open source model using open data. And this is how Clay was born.

Read the article published on Orchestrate all the Things here: https://linkeddataorchestration.com/2025/06/11/building-ai-for-earth-with-clay-the-intelligence-platform-transforming-geospatial-data-analysis/

In business, they say it takes ten years to become an overnight success. In technology, they say it takes ten years to build a file system. ScyllaDB is in the technology business, offering a distributed NoSQL database that is monstrously fast and scalable. It turns out that it also takes ten years or more to build a successful database.

This is something that Felipe Mendes and Guilherme Nogueira know well. Mendes and Nogueira are Technical Directors at ScyllaDB, working directly on the product as well as consulting clients. Recently, they presented some of the things they've been working on at ScyllaDB's Monster Scale Summit, and they shared their insights in an exclusive fireside chat.

This episode is sponsored by ScyllaDB. Read the article published on ScyllaDB's blog here: https://www.scylladb.com/2025/05/05/from-raw-performance-to-price-performance/

#NoSQL #Database #DatabaseEvolution #RaftProtocol #Cloud #DataConsistency #DatabaseScaling #TechInnovation #Opensource

As enterprise systems shift from deterministic software to probabilistic AI, forward-thinking organizations leverage proactive quality assessment to maximize value, minimize risk and ensure regulatory compliance

In today's rapidly evolving technological landscape, ensuring the quality of both traditional software and AI systems has become more critical than ever. Organizations are increasingly relying on complex digital systems to drive innovation and maintain competitive advantage, yet many struggle to effectively evaluate these systems before, during, and after deployment.

Yiannis Kanellopoulos is on the forefront of software and AI system quality assessment. He is the founder and CEO of code4thought, a startup specializing in assessing large-scale software systems and AI applications.

We connected to explore the challenges and opportunities of quality assessment and share insights both for developers and for people responsible for technology decisions within their organizations.

Read the article published on Orchestrate all the Things here: https://linkeddataorchestration.com/2025/04/09/the-quality-imperative-why-leading-organizations-proactively-evaluate-software-and-ai-systems-and-how-you-can-too/

Organizations are facing a critical challenge to AI adoption: how to leverage their domain-specific knowledge to use AI in a way that delivers trustworthy results. Knowledge graphs provide the missing "truth layer" that transforms probabilistic AI outputs into real world business acceleration.

Knowledge graphs are powering products for the likes of Amazon and Samsung. The Knowledge graph market is expected to grow to $6.93 Billion by 2030, at a CAGR of 36.6%. Gartner has been advocating for the role of knowledge graphs in AI and the downstream effects in organizations going forward for the last few years. Neither the technology nor the vision are new. Knowledge graph technology has been around for decades, and people like Tony Seale were early to identify its potential for AI.

Seale, also known as "The Knowledge Graph Guy", is the founder of the eponymous consulting firm. In this extensive conversation, we covered everything from knowledge graph first principles to application patterns for safe, verifiable AI, real-world experience, trends, predictions, and the way forward.

Read the article published on Orchestrate all the Things here: https://linkeddataorchestration.com/2025/03/11/knowledge-graphs-as-the-essential-truth-layer-for-pragmatic-ai/

It’s early 2025, and we may already be witnessing a redefining moment for AI as we’ve come to know it in the last couple of years. Is the canon of “more GPUs is all you need” about to change?

Truth is, when we arranged a conversation on AI chips with Chris Kachris, neither the Stargate Project nor DeepSeek R1 had burst onto the AI scene. Even though we did not consciously anticipate these developments, we knew AI chips is a topic that deserves attention, and Kachris is an insider.

Join us as we explore how the AI chip market is shaped today and tomorrow.

AI chips and open source AI models are all part of the comprehensive curriculum on Pragmatic AI Training that is being developed by Orchestrate all the Things:

https://linkeddataorchestration.com/services/training/pragmatic-ai-training/

Subscribe to the newsletter to be the first to know and enjoy discounted rates: https://linkeddataorchestration.com/orchestrate-all-the-things/newsletter/

Check out the article published here for additional background and references: : https://linkeddataorchestration.com/2025/01/29/ai-chips-in-2025-the-end-of-more-gpus-is-all-you-need/

You.com showcases the state of AI today

The story of you.com is multi-faceted and telling in many ways. You.com was founded in 2020 by Richard Socher, one of the leading NLP (Natural Language Processing) researchers in the world, to offer a better search experience to users and compete with Google.

With a startup exit and a Chief Data Scientist stint at Salesforce, Socher got the experience, network and backing he needed to pursue his long-time ambition of taking on Google. That's something few people have tried, with moderate success.

Socher diagnosed early enough that the way to success is by carving a niche for you.com. You.com focuses on serving knowledge workers in "complex informational / action searches": elaborate queries, and queries that are really about accomplishing a task, respectively.

In 2022, in the pre-ChatGPT era, Socher set out a course for you.com based on AI, apps, privacy, and personalization. In 2024, you.com is staying the course, but a few things have changed. In the GenAI era the competition is growing, and borrowing pages from you.com’s book.

Language model providers such as OpenAI and Anthropic now offer services similar to you.com. Upstarts such as perplexity.ai have sprung up, and Google itself is embracing the AI approach to search.

You.com is making progress too. Since launching in November 2021, you.com has served 1 billion queries and has millions of active users, including from Fortune 500. The company's ARR has grown by 500% since January 2024.

Today, you.com announced a $50 million Series B funding round, as well as a new team plan called Multiplayer AI. We caught up with Socher, talked about the news, and took you.com for a spin.

Article published on Orchestrate all the Things: https://linkeddataorchestration.com/2024/09/04/you-com-raises-50m-to-lead-ai-for-knowledge-workers/

"Data Rules" is a book about data, but not just about big data crunching. A book about the relationship of data with economic institutions and society, but also about the interplay with data technologies by which data are being generated and processed. A book that is critical, but not ideological.

This is how Jannis Kallinikos describes "Data Rules: Reinventing the Market Economy", a book co-authored by himself and Cristina Alaimo and recently published by The MIT Press.

Jannis Kallinikos is Full Professor of Organization Studies and the CISCO Chair in Digital Transformation and Data Driven Innovation at LUISS University, Rome.

This is where we met to talk about the key concepts in "Data Rules":

• Understanding data generation and use
• How data is breaking boundaries
• Platforms and choice
• The illusion of objectivity
• Algorithms, agency and surveillance
• From market and design rules to data rules

Article published on Orchestrate all the Things: https://linkeddataorchestration.com/2024/07/01/data-rules-from-interoperability-to-commensurability/

What is a universal semantic layer, and how is it different from a semantic layer? Are there actual semantics involved? Who uses that, how, and what for?

When Cube Co-founder Artyom Keydunov started hacking away a Slack chatbot back in 2017, he probably didn't have answers to those questions. All he wanted to do was find a way to access data using a text interface, and Slack seemed like a good place to do that.

Keydunov had plenty of time to experiment, validate, and develop Cube, as well as get insights along the way. We caught up and talked about all of the above, as well as Cube's latest features and open source core.

Article published on Orchestrate all the Things.