In this episode, we sit down with Michel Bauwens, founder of the P2P Foundation and one of the sharpest thinkers on civilisation, technology, and collective intelligence.

We talk about what AI actually is - and isn't - why the hype creates real risks and damage, and what it looks like to use these tools without giving away your agency or critical thinking.

Article published on Fourth Generation Civilization: https://4thgenerationcivilization.substack.com/p/the-pragmatic-role-of-ai-in-the-civilizational

Michel Bauwens has spent decades mapping the edges of change. From peer-to-peer networks to the commons, from medieval guilds to distributed autonomous organizations, he's been tracking something most people miss: the seeds of a new civilization, already growing underneath the noise.

In this conversation, we explore how the internet didn't just connect computers - it created an entirely new plane of human organization. One where people coordinate across the planet without hierarchies, intermediaries, or anyone giving orders.

They call it cosmolocalism. What is heavy stays local. What can be shared, travels everywhere.

But this isn't just theory. It's a lens. A way of reading history that tells you exactly where you are, and what's worth building right now.

Are you post-seasonal, clinging to a logic whose time has passed? Seasonal, playing the current moment well? Or pre-seasonal, working on the seeds that will matter when the time is ripe?

The caterpillar, Michel reminds us, already carries the DNA of the butterfly.

This is Connected Thinking. Join us.

Article published on Orchestrate all the Things: https://linkeddataorchestration.com/2026/02/25/connected-thinking-on-history-technology-and-the-art-of-seeing-whats-coming/

Greg Foster's journey into software engineering began in an unlikely place: a Nevada high school where he couldn't land a job at Starbucks. Instead of serving coffee, he taught himself software development.

Foster developed what he calls "a lifelong obsession with the craft of software engineering". That obsession evolved from building apps to building for other builders.

From Airbnb cofounding Graphite, where he now serves as CTO running what he calls "a dev tools team for the entire industry". Foster is now building the future of software development with Cursor.

Foster doesn't just have a front seat to watch how AI is changing software engineering - he gets to shape the change. We caught up and talked about the past, present and future of software engineering.

The takeaway? There is a world of difference between vibes and solid foundations for software engineering at scale.

Article published on Orchestrate all the Things: https://linkeddataorchestration.com/2026/01/29/foundations-or-vibes-lessons-learned-from-using-ai-in-software-engineering/

When Elisa Lindinger decided to talk about AI, her intention was to say what she had to say once, and then move on with her life without having anyone ask about AI ever again. The plan backfired heavily, but somehow, that turned into a good thing.

Lindinger is the Co-founder of SUPERRR, an independent non-profit organization. SUPERRR was created to serve the thesis is that digital policy is social policy, and it needs bold visions and feminist values.

Like most other individuals and organizations today, Lindinger's inbox has been flooded with new invitations every day. Invitations to discuss AI, to facilitate workshops on feminist AI, or the inevitable coaching offer to finally learn how to prompt properly.

This made Lindinger feel that other topics that are just as crucial are disappearing from the conversation. "AI and Unlikelihood" was an attempt to situate how the people at SUPERRR view the phenomenon of AI, and why they believe it’s essential to return our attention to other topics as well.

What happened instead was that SUPERRR's post got viral on LinkedIn, reigniting the topic of AI and stealing the limelight. An algorithmic glitch? Perhaps. But SUPERRR's stance of rejecting the narrative of blind adoption of generative AI resonated with many people.

We met with Lindinger to explore the nuance behind what some might superficially call a Luddite approach, and to talk about setting priorities right, imagining futures people want to live in, and how to go From AI literacy to futures literacy.

With cracks in the AI narrative beginning to show, the backdrop could not be more timely.

Article published on Orchestrate all the Things: https://linkeddataorchestration.com/2025/10/02/pragmatic-ai-adoption-from-futurism-to-futuring/

In another twist of abysmal AI politics, OpenAI CEO Sam Altman just admitted that we are in an AI bubble, and AGI is losing relevance. You may find this baffling or hilarious, or you may be wondering where does that leave the AI influencer types.

But despite the absurdity, AI and the associated narrative have gotten way too important to dismiss. Connecting the dots to make sense of it all calls for long-standing experience in AI, engineering, business and beyond.

In other words, for people like Georg Zoeller: a seasoned software and business engineer experienced in frontier technology in the gaming industry and Facebook.

Zoeller has been using AI in his work dating back to the 2010’s, to the point where AI is now at the core of what he does. Zoeller is the VP of Technology of NOVI Health, a Singapore-based healthcare startup, as well as the Co-Founder of the Centre for AI Leadership and the AI Literacy & Transformation Institute.

In a wide-ranging conversation with Zoeller, we addressed everything from AI first principles to its fatal flaws and its place in capitalism.

Today, we discuss regulatory capture, copyright, the limits of the attention economy, the new AI religion, the builder's conundrum, how the AI-powered transformation of software engineering is a glimpse into the future of work, AI literacy and how to navigate the brave new world.

First part of the conversation: https://pod.co/orchestrate-all-the-things-podcast-connecting-the-dots-with-george-anadiotis/poking-holes-in-the-ai-narrative-market-signalling-and-outsourcing-featuring-georg-zoeller-centre-for-ai-leadership-co-founder

Article published on Orchestrate all the Things: https://linkeddataorchestration.com/2025/08/19/breaking-the-ai-bubble-big-tech-plus-ai-equals-economy-takeover/

These are the type of questions you would ask someone with long-standing experience in AI, engineering, business and beyond.

Georg Zoeller is that someone: a seasoned software and business engineer experienced in frontier technology in the gaming industry and Facebook.

Zoeller has been using AI in his work dating back to the 2010's, to the point where AI is now at the core of what he does.

Zoeller is the VP of Technology of NOVI Health, a Singapore-based healthcare startup, as well as the Co-Founder of the Centre for AI Leadership and the AI Literacy & Transformation Institute.

Zoeller has lots of insights to share on AI. And yet, the reason we got to meet and have an extensive, deep and fun conversation was a joke gone wild.

Story published on Orchestrate all the Things: https://linkeddataorchestration.com/2025/07/16/poking-holes-in-the-ai-narrative-market-signalling-and-outsourcing-replace-ceos/

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/

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

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/

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/

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/

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/

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/

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.

As Neo4j just announced its partneship with Microsoft, we met with Chief Product Officer Sudhir Hasbe to talk about:

• What this partnership means for users and how it works
• How graph-powered generative AI aligns with cloud platform AI strategies
• Similarities and differences across them
• How Neo4j's strategy is shaping up, and when Databricks and Snowflake integration are coming.

For additional analysis and a writeup of the conversation, you can read the article published on Orchestrate all the Things: https://linkeddataorchestration.com/2024/03/27/neo4j-partners-with-microsoft-unfolds-strategy-to-power-generative-ai-applications-with-cloud-platforms-and-graph-rag/

This formulation was the premise on which Yonatan Geifman co-founded Deci in 2019. 

Today, with the generative AI explosion in full bloom, demand is growing faster than ever, and Deci is a part of this by contributing a number of open source models.

Join us as we explore:

• How AI models are different than traditional software and what open source means in AI
• Choosing between GPT-4, Claude 3 and open source LLMs
• Customizing LLMs and fine-tuning vs. RAG
• Evaluating LLMs
• Market outlook

Article published on Orchestrate All the Things: https://linkeddataorchestration.com/2024/03/06/evaluating-and-building-applications-on-open-source-large-language-models/

The interest and investment in AI is skyrocketing, and generative AI is fueling it. Over one-third of CxOs have reportedly already embraced GenAI in their operations, with nearly half preparing to invest in it.

What’s powering it all - AI chips - used to receive less attention. Up to the moment OpenAI’s Sam Altman claimed he wants to raise up to $7 trillion for a “wildly-ambitious” tech project to boost the world’s chip capacity. Geopolitics and sensationalism aside, however, keeping an eye on AI chips means being aware of today’s blockers and tomorrow’s opportunities.

According to a recent study by IMARC, the global AI chip market is expected to reach $89.6 Billion by 2029. The demand for AI chips has increased substantially over time. Growth in AI technology, rising demand for AI chips in consumer electronics and AI chip innovation all contribute to this forecast.

Few people have more insights to share on AI hardware than Tony Pialis, CEO and co-founder of Alphawave. In an extensive conversation, Pialis shared his insider’s perspective on the AI chip landscape, the transformative rise of chiplets, specialized hardware for training and inference, emerging directions like analog and optical computing, and much more.

Article published on Orchestrate All the Things: https://linkeddataorchestration.com/2024/02/13/the-future-of-ai-chips-leaders-dark-horses-and-rising-stars/

But analysts David Vellante and George Gilbert believe that the needs of modern data applications coupled with the evolution of open storage management may lead to the emergence of a "sixth data platform".

The sixth data platform hypothesis is that open data formats may enable interoperability, leading the transition away from vertically integrated vendor-controlled platforms towards independent management of data storage and permissions.

It's an interesting scenario, and one that would benefit users by forcing vendors to compete for every workload based on the business value delivered, irrespective of lock-in. But how close are we to realizing this?

To answer this question, we have to examine open data formats and their interoperability potential across clouds and formats, as well as on the semantics and governance layer.

We caught up with Peter Corless and Alex Merced to talk about all of that.

Article published on Orchestrate all the Things: https://linkeddataorchestration.com/2024/01/11/data-management-in-2024-open-data-formats-and-a-common-language-for-a-sixth-data-platform/

As LinkedIn Director of Engineering Sofus Macskássy shares, there's AI, taxonomy, and ontology involved in building the Skills Graph that powers the transition.

We discuss the process of extracting skills from text, building a skills graph, and leveraging it for various product lines within LinkedIn.

We cover aspects related to explicit and implicit skill provenance, credibility, depth and interoperability.

Article published on Orchestrate all the Things: https://linkeddataorchestration.com/2023/12/13/how-linkedin-is-moving-towards-a-skills-based-economy-with-the-skills-graph

It's not every day that you hear product leads questioning the utility of their own products. Brad Beebe, the general manager of Amazon Neptune, was all serious when he said that most customers don't actually want a graph database. However, that statement needs contextualization.

If Bebee had meant that in the literal sense, the team himself and Amazon Neptune Principal Product Manager Denise Gosnell lead would not have bothered developing and releasing a brand new analytics engine for their customers. We caught up with Bebee and Gosnell to discuss Amazon Neptune new features and the broader vision. 

We cover where Amazon Neptune fits in the AWS vision of data management, and how the new analytics engine provides a single service for graph workloads, high performance for graph analytic queries and graph algorithms, and vector store and search capabilities for Generative AI applications. We also share insights on the One Graph vision, the road from serverless to One Graph via HPC, as well as vectors and Graph AI.

Article published on Orchestrate all the Things: https://linkeddataorchestration.com/2023/11/29/amazon-neptune-introduces-a-new-analytics-engine-and-the-one-graph-vision/

00:00:00 Introduction

00:01:44 Amazon Neptune & AWS vision of data management

00:05:35 The Importance of Graph Databases  

00:08:55 Amazon Neptune Use Cases

00:13:13 Introduction to Amazon Neptune Analytics  

00:15:20 Key Features of Neptune Analytics  

00:17:40 Use Cases for Neptune Analytics 

00:21:10 Preparing Data for Generative AI Applications

00:23:37 Neptune Analytics Use Cases and Deployment

00:26:43 Pricing and Roadmap Q&A

00:48:46 Conclusion

We caught up with the company’s Chief Product Officer Lenley Hensarling to discuss this long journey that started about three years ago, as well as Aerospike's differentiation in a very densely populated market.

Article published on Orchestrate all the Things.

LinkedIn's feed has come a long way since the early days of assembling the machine learning infrastructure that powers it.

Recently, a major update to this infrastructure was released. We caught up with the people behind it to discuss how the principle of being people-centric translates to technical terms and implementation.

Article published on Orchestrate all the Things

Although naturally much of what he did was based off things others were already working on, Warden is sometimes credited as having kickstarted the TinyML subdomain of machine learning. Either way TinyML is getting big, and Warden is a big part of it.

Useful Sensors is Warden's latest venture. They just launched a product called AI in a Box, which they dubs an "offline, private, open source LLM for conversations and more". Even though it's not the first product Useful Sensors has created, it's the first one that's officially launched. That was a good opportunity to catch up with Warden and talk about what they are working on.

Article published on Linked Data Orchestration

We caught up to discuss what the future holds for Neo4j as well as the broader graph database space.

Article published on Orchestrate all the Things.

The company just secured a $100 million Series C funding round to execute on an unconventional strategy.

Redpanda Founder and CEO Alex Gallego explains how things work for the company.

Article published on Orchestrate all the Things

Here's what and how it's likely to effect, its blind spots, what happens next, and how you can prepare for it based on what we know.

Article published on Orchestrate all the Things

If the name Foursquare rings a bell, it means you were around in the 2010s. Your only resort to plausible deniability would be if you are a data professional - although that's not an either/or proposition.

In the 2010s, Foursquare was a consumer-oriented mobile application. The premise was simple: people would check in at different locations and get gamified rewards. Their location data would be shared with Foursquare and used for services such as recommendations.

Facebook and Yelp got the lion's share of that market, but Foursquare is still around. In addition to having 9 billion-plus visits monthly from 500 million unique devices, Foursquare's data is used to power the likes of Apple, Uber and Coca-Cola.

Today the company announced Foursquare Graph, what it dubs the industry’s first application of graph technology to geospatial data.

I caught up with Vikram Gundeti, Distinguished Engineer at Foursquare, to learn more about what kind of data Foursquare deals with, what it does with that data, and how using graph is going to help.

Article published on Orchestrate all the Things

An update from the host on launching the Orchestrate All The Things Newsletter, and some insights on Technology, Data, Media, AI, Writing, and Content.

A new type of podcast episode: AI-generated article narrations.

Article published on Orchestrate All The Things.

The ScyllaDB team is one of those who work with their garage doors up and are not necessarily after making headlines. They believe that incremental change is nothing to shun if it leads to steady progress.

Compared to the release of ScyllaDB 5.0 in ScyllaDB Summit 2022, what ScyllaDB Summit 2023 brought could be labeled “incremental change.” But this is just the tip of the iceberg, as there's more than meets the eye here.

We caught up with ScyllaDB CEO and co-founder Dor Laor to discuss what kept the team busy in 2022, how people are using ScyllaDB, as well as trends and tradeoffs in the world of high performance compute and storage.

Article published on The New Stack

The assumption is that the larger the model the better it will perform. But that may not always be the case.

FAR AI Research Scientist Ian McKenzie is a key member of the team organizing the Inverse Scaling Challenge, an initiative set up to investigate scaling laws. We discuss:

• Large Language Models and how they are trained
• The scaling laws and how they are being revised as research and development progresses
• The Inverse Scaling Challenge and its findings

Article published on Orchestrate all the Things.

Pareek is the Co-founder and EVP of products of Striim, a vendor whose goal and motto is to "help companies make data useful the instant it’s born".

Depending on which angle you look at it, you could say that Pareek is either biased or in the know. Either way, it was not so long ago that real-time data, or streaming data as this market is also called, was estimated to be worth billions.

But then again, as the recent wave of layoffs and market capitalization losses goes to show, many projections around technology are off the mark.

Could real-time data be different? Where does cloud modernization come into play and how does Striim's offering relate to that? As Striim today announced the availability of its fully managed Striim Cloud service on Amazon Web Services (AWS), we connected with Pareek to discuss.

Andrew Ng, among the most prominent figures in AI, is now turning his sights to doing precisely that with his new venture Netail

Founded in 2022 as part of Landing AI, Netail, a technology that enables retailers to auto-identify competitors across the internet and track their assortments, availability and optimize prices in real-time, today announced the closing of $5M in seed funding.

We connected with retail veteran Mark Chrystal who is Netail's CEO to discuss the changing landscape in retail and Netail's offering.

D-Wave is the company that pioneered quantum computing. In this exclusive interview, D-Wave CEO Alan Baratz talks about about quantum computing fundamentals and how this is related to the market’s current state, real-world clients and use cases, and what the future holds for this space.

Article published on VentureBeat

We caught up with Jim Webber, Chief Scientist at Neo4j, to discuss Neo4j 5 as well as the bigger picture in the graph market.

Article published on VentureBeat

Based on this definition, as well as coverage of vendors that have products they label with the AIOps moniker, you'd be inclined to think that AIOps is mostly about anomaly detection and remediation. But what about provisioning, configuration, deployment and orchestration?

These are all essential parts of IT operations which have not received as much AIOps attention. They also happen to be at the core of Ansible, Red Hat's open source IT automation tool. 

Now Red Hat is embarking on a new direction for Ansible with Project Wisdom, aiming to take automation to the next level in collaboration with IBM Research.

Red Hat refers to Project Wisdom as the first community project to create an intelligent, natural language processing capability for Ansible and the IT automation industry.

We connected with Red Hat Vice President & General Manager for the Ansible Business Unit Tom Anderson to discuss Project Wisdom's premises, status and trajectory. 

Article published on VentureBeat

If you think AI is moving at a breakneck speed and it's almost impossible to keep up, you are not alone. Even if being on top of all things AI is part of your job, it's getting increasingly hard to do that. Nathan Benaich and Ian Hogarth know this all too well, yet somehow they manage.

Benaich and Hogarth have solid backgrounds in AI as well as tons of experience and involvement in research, community- and market-driven initiatives. AI is their both their job and their passion and being on top of all things AI comes with the territory.

Nathan Benaich is the General Partner of Air Street Capital, a venture capital firm investing in Al-first technology and life science companies. lan Hogarth is a co-founder at Plural, an investment platform for experienced founders to help the most ambitious European startups.

Since 2018 Benaich and Hogarth have been publishing their yearly State of AI report, aiming to summarize and share their knowledge with the world. This ever-growing and evolving work covers all the latest and greatest across industry, research and politics. Over time, new sections are added, with this year featuring AI Safety for the first time.

Traditionally, Benaich and Hogarth have also been venturing on predictions, with remarkable success. Equally traditionally, we have been connecting with them to discuss their findings every year upon release of the report. This year was no exception, so buckle up and let the ride begin.

Article published on VentureBeat.

Article published on ZDNet

Having worked in academia teaching applied mathematics and statistics for years, at some point Lorica realized that he wanted his work to have more practical implications. At that point the term "data science" was not yet coined, and Lorica's exit strategy was to become a quant. Fast forwarding to today, Lorica still has friends in the venture capital world.

That includes Intel Capital's Assaf Araki, with whom Lorica co-authored two recent posts on data management and AI trends. We caught up with Lorica to discuss those, as well as new areas for growth and the trouble with unicorns and what to do about it.

Lars Ewe is, and his answer may be different than most people's. With 180 meteorologists on staff providing weather predictions worldwide, DTN​ is the largest weather company you've probably never heard of.

Weather forecast, too, is all about data and models these days. But balancing accuracy and viability is a fine act, especially at global scale and when the stakes are high.

Article published on ZDNet

It's complicated, but that does not mean that people are not trying. That's what Abhishek Gupta and the Green Software Foundation (GSF) are relentlessly working on.

Article published on VentureBeat

Phare shares his experience and assessment of sustainability and its relationship with the ESG space, its current state and trajectory, and how data and analytics can help.

Here is why and how he aims to do that.

Article published on ZDNet

In October 2021, Gartner identified DI as a 2022 Top Trend. A number of vendors have identified with that category, and Aera Technology is among them, claiming to have been doing DI before it was called DI.

Today, Aera is announcing new capabilities for its Aera Decision Cloud at the Gartner Supply Chain Symposium/Xpo™ 2022. Aera founder and CTO Shariq Mansoor weighed in on DI, Aera's offering, and how it is relevant for supply chains and beyond.

Article published on VentureBeat

Previously, we have explored various aspects of the ways data science and machine learning intertwine with natural events - from weather prediction, to the impact of climate change on extreme phenomena and measuring the impact of disaster relief. AiDash, however, is aiming at something different: helping utility and energy companies as well as governments and cities manage the impact of natural disasters, including storms and wildfires.

We connected with AiDash co-founder and CEO Abhishek Singh to learn more about its mission and approach, as well its newly released Disaster and Disruption Management System (DDMS)

Article published on ZDNet

Machine learning and its deep learning variety are practically household names by now. There is lots of hype around deep learning, as well as a growing number of applications. However, as applications of deep learning are proliferating, its limitations are also becoming better understood. Presumably that's the reason why Bengio turned his attention to operations research.

In 2020, Bengio and his collaborators surveyed recent attempts, both from the machine learning and operations research communities, at leveraging machine learning to solve combinatorial optimization problems. They advocate for pushing further the integration of machine learning and combinatorial optimization and detail a methodology to do so.

To this day, however, there is no publicly visible operations research renaissance to speak of, and commercial applications remain few compared to machine learning. Nikolaj van Omme and Funartech want to change that.

Article published on VentureBeat

Plus, if you are someone like Alation, a vendor whose core business is not data quality -- if you can't beat them, join them. As Alation CEO and co-founder Satyen Sangani shared, that was the thinking behind today's announcement of the Alation Open Data Quality Initiative (ODQI) for the modern data stack.

As Alation notes, the program provides customers with the freedom of choice and flexibility when choosing the best data quality and data observability vendors to fit the needs of their modern, data-driven organizations. We caught up with Sangani to discuss the ODQI and where it fits in the broader data intelligence landscape, as well as Alation's strategy and evolution.

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Kendall is the co-founder and CEO of Wayve, a company founded in 2017 to tackle the challenge of autonomous driving based on a deep learning approach. Today, Wayve announced a partnership with Microsoft to leverage the supercomputing infrastructure needed to support the development of AI-based models for autonomous vehicles on a global scale.

We caught up with Kendall to discuss Wayve's philosophy and approach, its current status, as well as where the partnership with Microsoft fits in. Spoiler alert: this is not your typical "commercial application provider partners with cloud vendor" story.

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But when it comes to customer feedback, perhaps the most important data is qualitative: text contained in sources such as feedback forms and surveys, tickets, chat and email messages. The problem with that data is that, while valuable, they require domain experts and a lot of time to read through and classify. Or at least, that was the case up until now.

This is the problem Viable is looking to address.

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Here's what it is, what it means for the world at large, when it's expected to take effect, how it will work in practice, as well as Mozilla's recommendations for improving it, and ways for everyone be involved in the process.

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The CrowdStrike Falcon Fund is an investment vehicle managed by CrowdStrike, in partnership with Accel, that makes cross-stage private investments within cybersecurity and adjacent markets.

DARPA is also known to have an interest in cybersecurity, and this is what motivated the decision to fund the development of a framework recently released by thatDot as an open source project dubbed Quine.

Many solutions exist on the market both for streaming data processing as well as for graph analytics, oftentimes working in tandem. However, thatDot Founder and CEO Ryan Wright claims that Quine's technology is unique, enabling it scale to orders of magnitude beyond what any other system today is capable of.

We caught up with Wright to discuss the key premises behind Quine and thatDot, as well as the practical aspects of using Quine and the next steps in its evolution.

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We connected with Bratin Saha, AWS VP of Machine Learning, to discuss where Amazon SageMaker Serverless fits into the big picture of Amazon’s machine learning offering and how it affects ease of use and TCO, as well as Amazon’s philosophy and process in developing its machine learning portfolio.

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What could a multi-national corporation like Unilever and an environmental leader and activist like Vandana Shiva possibly have in common?

Shiva has a history of actively opposing the commodification and appropriation of natural resources for the benefit of corporate interests. Unilever is at the heart of the international corporate web.

Shiva, a prolific author, just published her latest book: "Agroecology and Regenerative Agriculture: Sustainable Solutions for Hunger, Poverty, and Climate Change". 

Unilever, whose products need around 4 million hectares of land to grow the raw materials for, recently published a new set of regenerative agriculture principles.

There has to be something about regenerative agriculture. Let's take a look at what it is and why it's important, what the data tells us about it, and how analytics and AI may help going forward.

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As we've elaborated previously, graphs are a universal data structure with manifestations that span a wide spectrum: from analytics to databases, and from knowledge management to data science, machine learning and even hardware.

Graph data science is when you want to answer questions, not just with your data, but with the connections between your data points -- that's the the 30 second explanation according to Alicia Frame.

Frame is the Senior Director of Product Management for Data Science at Neo4j, a leading graph database vendor. She has a PhD in computational biology, and has spent ten years as a practicing data scientist working with connected data.

When she joined Neo4j about 3 years ago, she set out to build a best in class solution for dealing with connected data for data scientists.

Today, the product Frame is leading at Neo4j, aptly called Graph Data Science, is celebrating its two-year anniversary with version 2.0 which brings some important advancements: new features, a native Python client, and availability as a managed service under the name AuraDS on Google Cloud.

We caught up with Frame to discuss graph data science the concept, and Graph Data Science the product.

A multitude of streaming data alternatives, each with its own focus and approach, has emerged in the last few years. One of those alternatives is Apache Pulsar. In 2021, Pulsar ranked as a Top 5 Apache Software Foundation project and surpassed Apache Kafka in monthly active contributors.

In another episode in the data streaming saga, StreamNative just released a report comparing Apache Pulsar to Apache Kafka in terms of performance benchmarks.

We caught up with StreamNative Chief Architect & Head of Cloud Engineering Addison Higham to discuss the report's findings, as well as the bigger picture in data streaming.

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The global accounting services market is expected to reach $735.94 billion in 2025, and digital transformation has targeted it for impact. The size of the accounting software market is projected to reach $22,9 billion by 2027.

This includes names from Microsoft, Oracle and SAP to Intuit and Xero, plus many others. Yet, there's something none of these names are applying, says Isaac Heller: AI in accounting. That's why Heller and Amir Boldo co-founded Trullion in 2019.

Trullion seems to be on a mission to disrupt the accounting software market by "unifying the unstructured and structured worlds of accounting by reading PDF or Excel-based contracts and translating them into financial workflows, connected to the data source".

In February 2022, Trullion announced that it has closed $15 million in Series A funding. Trullion has more than 100 clients, a mixture of large enterprise clients and audit firms which procure directly for their clients, and has crossed the seven figure mark in recurring revenue.

We caught up with Heller to discuss Trullion's focus and its use of AI to innovate in the accounting software market.

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Ammanath is the executive director of the Global Deloitte AI Institute and is well-qualified to write a book on trustworthy AI. She has had stints at GE, HPE and Bank of America in roles such as vice president of data science and innovation,  and CTO of artificial intelligence as well as lead of data and analytics.

 Ammanath said part of her motivation to write the book was her desire to help organizations start off with ethics and trust in AI on the right foot and another part was her frustration with existing approaches to AI Ethics.

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Now, Lexmark, is taking note of Kodak’s failure and Amazon’s success and piloting into its own. Moving beyond printers, supplies and accessories — which is how Lexmark first made a name for itself — the company’s current motto seems to be “Print, secure and manage your information.”

In 2022, Lexmark lists Print and Capture as just two of its solutions, which also include Cloud, Security and IoT. The Lexmark Optra IoT Platform, unveiled September, is featured prominently on the company’s site.

Today, the company is unveiling Optra Edge, the latest addition to its Optra IoT solutions portfolio. Vishal Gupta, Lexmark’s senior VP of connected technology, CTO and CIO, says the company’s new tools and move into IoT are just the beginning.

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Besides the Great Resignation, however, there are also less radical approaches, like the one Andrew Ng is taking.

Ng is among the most prominent figures in AI. Founder of deeplearning.ai, Co-Chairman and Co-Founder of Coursera, and Adjunct Professor at Stanford University. He was also Chief Scientist at Baidu Inc., and Founder & Lead for the Google Brain Project.

Yet, his current priority has shifted -- from bits to things, as he puts it.

Andrew Ng is also the Founder & CEO of Landing AI, a startup working on facilitating the adoption of AI in manufacturing since 2017. This effort has apparently contributed in shaping Ng's perception of what it takes to get AI to work beyond Big Tech, in what he calls the data-centric approach.

We connected with Ng to discuss the data-centric approach to AI, and how it relates to his work with Landing AI and the big picture of AI today.

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Here's how the evolution of the AI landscape has shaped its growth.

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From algorithmic and dataset bias, to the use of AI in asymmetrical / unlawful ways, to privacy and environmental impact.

All of that, and more, could potentially fall under the AI Ethics umbrella.

We try to navigate this domain, and lay out some concrete definitions and actions that could help move the domain forward

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API3 is a bold effort to achieve decentralization in connecting Web3 applications to the outside world.

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As the company continues to execute on its vision, we caught up with CEO Rodrigo Liang to look both at the big picture and under the hood.

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We discuss this release, as well as progress made since moving to the new network and other new features in the works with IOTA Foundation co-founder and CEO Dominik Schiener.

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In what is becoming a valued yearly tradition, we caught up with AI investors and authors of the State of AI report, Nathan Benaich and Ian Hogarth, to discuss the 2021 release.

Some of the topics we covered are lessons learned from operationalizing AI and MLOps, new concepts and datasets, language models, AI ethics, and AI-powered biotech and pharma.

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For that to happen, hardware and software must work in tandem. Arm's partner ecosystem exemplifies this, with hardware and software vendors like Alif and Neuton working together.

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But if it's going to, it will have to assimilate how classical computer science algorithms work. This is what DeepMind is working on, and its success is important to the eventual uptake of neural networks in wider commercial applications.

This work goes by the name of Neural Algorithmic Reasoning. Join us as we discuss roots and first principles, the defining characteristics, similarities and differences of algorithms and Deep Learning models with the people who came up with this. 

We also cover the details of how Neural Algorithmic Reasoning works, as well as future directions and applications in areas such as path finding for Google Maps

Could this be the one algorithm to rule them all?

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Image: Getty

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Philip Vollet is somewhat of a celebrity, all things considered. Miley Cyrus or Lebron James he is not, at least not yet, but if data science lives up to the hype, who knows.

As the senior data engineer with KPMG Germany, Vollet leads a small team of machine learning and data engineers building the integration layer for internal company data, with access standardization for internal and external stakeholders. Outside of KPMG, Vollet has built a tool chain to find, process, and share content on data science, machine learning, natural language processing, and open source using exactly those technologies, which makes for a case of meta, if nothing else.

There is a flood of social media influencers sharing perspectives on data science and machine learning. While most influencers direct their attention solely toward issues of model building and infrastructure scaling, Vollet also looks at the user view, or frameworks for building user interfaces for applications utilizing machine learning. We were intrigued to discuss with him how building these user interfaces is necessary to unlock AI's true potential.

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Photo by Kelly Sikkema on Unsplash

Open source software is a lot of things to a lot of people. For some people -- engineers, mostly -- it's a way to work on their passion, be involved in a community, and give something back to the world. For others -- business people, mostly -- it's a way to grow projects organically, and sell software without actually investing too much in sales.

It's a nuanced topic, and we've tried to explore it from many angles. From the business angle, exploring open source vendors relationships with hyperscalers, mostly Google and Amazon. From the contributor and license engineering angle, exploring different models for commercial open source projects. And from the community angle, exploring metrics for community health and value generation evaluation.

Today, we explore open source software (OSS) and its commercialization from yet another angle - the investment angle. There are a couple of venture capitals out there that seem to be ahead of the curve in terms of their understanding of, and investment in, commercial open source companies. Runa Capital is one of them, and we caught up with Konstantin Vinogradov, Runa Capital Principal, who shared views, findings, and outlook for commercial OSS.

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Image: Shutterstock

Hopefully technology investment decisions in your organization are made based on more than hype. But as technology is evolving faster than ever, it's hard to keep up with all the terminology that describes it. Some people see terminology as an obfuscation layer meant to glorify the ones who come up with it, hype products, and make people who throw terms around appear smart.

There may be some truth in this, but that does not mean terminology is useless. Terminology is there to address a real need, which is to describe emerging concepts in a fast moving domain. Ideally, a shared vocabulary should facilitate understanding of different concepts, market segments, and products.

Case in point - data and metadata management. Have you heard the terms data management, data observability, data fabric, data mesh, DataOps, MLOps and AIOps before? Do you know what each of them means, exactly, and how they are all related? Here's your chance to find out, getting definitions right from the source - seasoned experts working in the field.

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If you're into the market and investment side of things, how does a Series F funding round as part of a $325 million investment led by Eurazeo and GV (formerly Google Ventures), bringing Neo4j's valuation to over $2 billion sound? Pretty impressive, probably.

If you're into the technology and applications side of things, how does a Neo4j demo of a social network application with 3 billion people, running queries designed to test the limits of graph query languages and databases across a 1000 node cluster sound? Equally impressive, probably.

Graph database vendor Neo4j​ CEO and co-founder Emil Eifrem is announcing the former and showcasing the latter today, at the company's annual virtual conference NODES. We caught up with Eifrem to get a taste of things to come.

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Edge computing is booming. Although the definition of what constitutes edge computing is a bit fuzzy, the idea is simple. It's about taking compute out of the data center, and bringing it as close to where the action is as possible.

Whether it's stand-alone IoT sensors, devices of all kinds, drones, or autonomous vehicles, there's one thing in common. Increasingly, data generated on the edge are used to feed applications powered by machine learning models.

There's just one problem: machine learning models were never designed to be deployed on the edge. Not until now, at least. Enter TinyML.

Tiny machine learning (TinyML) is broadly defined as a fast growing field of machine learning technologies and applications including hardware, algorithms and software capable of performing on-device sensor data analytics at extremely low power, typically in the mW range and below, and hence enabling a variety of always-on use-cases and targeting battery operated devices.

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How would you feel if you saw demand for your favorite topic, which also happens to be your line of business, grow 1000% in two-years time? Vindicated, overjoyed, and a bit overstretched in trying to keep up with demand, probably.

Although Emil Eifrem never used those exact words when we discussed the past, present and future of graphs, that's a reasonable projection to make. Eifrem is the CEO and co-founder of Neo4j, a graph database company which lays claims to having popularized the term "graph database", and to leading the graph database category.

Eifrem and Neo4j's story and insights are interesting because through them we can trace what is shaping up as a foundational technology stack for the 2020s and beyond: graphs.

"Graph Relates Everything" is how Gartner put it, when including graphs in its top 10 data and analytics technology trends for 2021. Interest is expanding as graph data takes on a role in master data management, tracking laundered money, connecting Facebook friends and powering Google, in search and beyond. 

Think Panama Papers researchers, NASA engineers, and Fortune 500 leaders: they all use graphs. Here's why, and how.

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Technical debt is a well-known concept in software development. It's what happens when unclear or forgotten assumptions are buried inside a complex, interconnected codebase, and it leads to poor software quality. The same thing also applies to data pipelines, it's called pipeline debt, and it's time we did something about it.

That's the gist of what motivated Abe Gong and James Campbell to start Great Expectations in 2018. Great Expectations is an open-source tool that aims to make it easier to test data pipelines, and therefore increase data quality.

Superconductive, the force behind Great Expectations, has announced it has received $21 million in Series A funding led by Index Ventures with CRV and Root Ventures participating. We caught up with Gong to learn more about Great Expectations.

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Databases are the substrate on which most applications run. Although different applications have different needs served by different databases, they all have one thing in common: they are complex systems that need continuous fine tuning to work optimally.

Databases come with a plethora of parameters that can be tuned by "turning knobs". Traditionally, this has been the job of Database Administrators (DBAs). Their job is a hard one, as they need to know the specifics of the database, the hardware it's running on, and the workloads it serves.

Some database vendors like IBM, Microsoft and Oracle have taken steps to automate this work. OtterTune is a startup that wants to democratize this capability. Today OtterTune is announcing the private beta of its new automatic database tuning service, as well as an initial $2.5 million seed funding round led by Accel.

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The AI chip space is booming, with innovation coming from a slew of startups in addition to the usual suspects. You may never have heard of NeuReality before, but it seems likely you'll be hearing more about it after today.

NeuReality is a startup founded in Israel in 2019. Today it has announced NR1-P, which it dubs a novel AI-centric inference platform. That's a bold claim for a previously unknown, and a very short time to arrive there -- even if it is the first of more implementations to follow.

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By now, the tension between commercial interests and open source projects is well known. 

Trying to balance building a sustainable business and a community around open source software, in a cloud-first world is not the easiest thing in the world.

In the latest episodes of a long-winding saga, two more commercial open source vendors, Elastic and Grafana, changed their licenses.

Their rationale was clearly communicated as trying to protect the business they have built around the respective open source projects from cloud vendors that they feel compete unfairly with them, without contributing as much as they do.

Interestingly, all parties involved refer to "the community" as being front and center in what they do. While obviously important, however, what constitutes an open source community, how it's faring, and what value it generates all seem rather vaguely defined.

The people working on the CHAOSS project under the auspices of the Linux Foundation want to change that. We caught up with Georg J.P. Link, CHAOSS project co-founder, to find out more.

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Dan Jeffries was there when the LAMP stack enabled software to eat the world. Perhaps you don’t know, or remember, what the LAMP stack is, but it's actually pretty important.

LAMP is an acronym made out of the initials of key open-source technologies used in software development - Linux, Apache, MySQL, and PHP. These technologies were hotly debated back in the day. Today, they are so successful that the LAMP stack has become ubiquitous, invisible, and boring.

AI, on the other hand, is a hot topic today. Just like the LAMP stack turned software development into a commodity and made it a bit boring (especially if you're not a professional software engineer), an AI stack should turn AI into a commodity - and make it a bit boring, except maybe for data engineers. This is what Dan Jeffries is out to do with the AI Infrastructure Alliance (AIIA).

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Chainlink provides an oracle service, enabling smart contracts to interoperate with the world. Today, Chainlink released a whitepaper outlining what they dub Chainlink 2.0. We connected with Chainlink co-founder Sergey Nazarov to discuss what this means.

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Bob van Luijt's career in technology started at age 15, building web sites to help people sell toothbrushes online. Not many 15 year-olds do that today, and fewer still did it then. Apparently that gave van Luijt enough of a head start to arrive at the confluence of technology trends today.

Van Luijt went on to study arts, but ended up working full time in technology anyway. In 2015, when Google introduced its RankBrain Algorithm, the quality of search results jumped up. It was a watershed moment, as it introduced machine learning in search.

A few people noticed, including van Luijt, who saw a business opportunity, and decided to bring this to the masses.

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Manish Jain and Josh McKenzie are both engineer rock stars who wear many hats. They also have something else in common: they are both avid GraphQL users and builders, despite getting there from different start points.

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In 2020, few things went well and saw growth. Artificial intelligence was one of them, and healthcare was another one. Artificial intelligence remained on a steady course of growth and further exploration -- perhaps because of the Covid-19 crisis. Healthcare was a big area for AI investment.

Today, the results of a new survey focusing precisely on the adoption of AI in healthcare are being unveiled. We caught up with 2 of its architects: Gradient Flow Principal Ben Lorica, and John Snow Labs CTO David Talby, to discuss findings and the state of AI in healthcare.

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IOTA's main premise, namely solving the issues around blockchain by introducing a different data structure, remains. IOTA, like blockchains such as Bitcoin or Ethereum, is a distributed ledger. Unlike those, however, the data structure it uses is a directed acyclic graph, called the Tangle.

We discuss with IOTA Foundation Co-founder and CEO, Dominik Schiener, on what IOTA got wrong, what it got right, and what is being done to build on what it got right and fix what it got wrong. 

Starting with the release of a new wallet, IOTA has been reinvented and rewritten from the ground up over the last one and a half years. This new phase of the project is called Chrysalis, introducing a network upgrade. It promises a truly decentralized solution with high throughput and no transaction fees, oracles, and smart contracts.

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It's been a hot topic for the last couple of years, and for good reason. Going from innovation to scalability and repeatability are the hallmarks of generating business value, and MLOps represents precisely that for machine learning.

Apache TVM has become a de facto standard in MLOps, and OctoML is the company gearing its commercialization and scale up. 

As OctoML secured a $28 million Series B funding round, we caught up with its CEO and co-founder Luis Ceze to discuss TVM, OctoML, and MLOps.

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Katana Graph, a high-performance scale-out graph processing, AI and analytics company, announced a $28.5 million Series A financing round led by Intel Capital.

We discuss with Keshav Pingali, Katana Graph CEO and co-founder, on the company's background, technology, and prospects

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We review TigerGraph's progress and the market landscape with TigerGraph CEO Yu Xu and COO Todd Blaschka

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Today's episode features Determined AI CEO and Founder, Evan Sparks. Sparks is a PhD veteran of Berkeley's AmpLab with a long track record of accurate predictions in the chip market.

We talk about an interoperability layer for disparate hardware stacks, ONNX and TVM -- two ways to solve similar problems, AI constraints and energy efficiency, and Infusing knowledge in models

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In this in-depth conversation, we cover everything from his background and early work in cognitive psychology as a way to understand the human mind, to his critique on Deep Learning, a holistic proposal for robust AI, the role of background knowledge, and knowledge graphs in particular, and the future of AI

Saturn Cloud, a startup offering Dask as service, is now a Snowflake partner, making Dask available to the masses.

We discuss all about Dask and Saturn Cloud with co-founder Sebastian Metti.

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We discuss with Blaize CEO Dinakar Munagala and VP R&D Dmitry Zakharchenko to explore AI Studio, its potential and philosophy, and where it fits in Blaize's strategy.

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A discussion with Ed Anuff, formerly of Apogee and Google Cloud, and currently DataStax Chief Product Officer, on the rationale behind Stargate, its architecture and operation, how it compares to GraphQL, and a roadmap for the future.

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Learn all about Fluree, its origins and milestones, use cases, data integration and integrity, open source, and more

Featuring Brian Platz, Fluree co-founder and co-CEO. 

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A discussion on all things AI, with authors of the State of AI 2020 Report, Nathan Benaich and Ian Hogarth.

Benaich and Hogarth work on the intersection of industry, research, investment and policy, with extensive background and various currently held positions such as venture capital investor and researcher. This gives them a unique vantage point on all things AI.

Their report, which is published for the 3rd year in a row, is their way of sharing their insights with the AI ecosystem at large.

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The company's approach to applications of AI in industrial settings originates from NASA Jet Propulsion Labs. It's a hybrid approach, combining data-driven machine learning, with knowledge-based symbolic AI, plus the elements of spatial and temporal cognition.

We discuss business, technology, applications, and the future of AI with CEO and Founder AJ Abdallat

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We take the opportunity to dive in the Netdata story with CEO and Founder Costas Tsaousis: what they do and why, their open source approach and distributed architecture, and their position in the overall observability ecosystem.

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A sui generis, multi-model open source database, designed from the ground up to be distributed. ArangoDB keeps up with the times and uses graph, and machine learning, as the entry points for its offering.

ArangoDB CEO and co-founder Claudius Weinberger, and ArangoDB Head of Engineering and Machine Learning Jörg Schad discuss multi-model, machine learning, hype, and the database market in general.

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Open source is eating the world, and observability is no exception. New Relic and Grafana Labs just announced a partnership, and we discuss the specifics as well as the broader open source and observability landscape with Grafana Labs CEO Raj Dutt and New Relic Chief Product Officer Bill Staples.

We cover everything from the rationale of the partnership, what it brings for users and how the integration was done, to open source, de facto and de jure standards for telemetry, and the use of AI and machine learning.

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In the early 90s,the evidence-based medicine movement tried to make medicine more data-driven. Three decades later, we have more data, but not enough context, or transparency.

Today's episode features David Scales, Chief Medical Officer at Critica, and an Assistant Professor of Medicine at Weill Cornell Medical College. Scales specialized in internal medicine, and also has a PhD in sociology, with a particular interest in the sociology of science.

The conversation with David covers the fundamentals of evidence-based medicine, how data is generated through Randomized controlled trials and accessed via Cochrane Reviews.

We draw parallels with best practices in data science and data governance, touching upon provenance, context, and metadata.

We also explore the dark side of data in the medical industry: pharmaceutical company involvement, bias, the controversy around Cochrane, and the tyranny of the Randomized controlled trial. We also explore predictive models and data parasites in COVID-19 times, and the role of the World Health Organization.

Articles published on ZDNet: https://linkeddataorchestration.com/2020/07/20/garbage-in-garbage-out-data-science-meet-evidence-based-medicine/

https://linkeddataorchestration.com/2020/07/27/data-governance-and-context-for-evidence-based-medicine-transparency-and-bias-in-covid-19-times/

That, however, has not stopped him and the jSonar team from carving out a spot for themselves. And it has not stopped Goldman Sachs from investing 50 million dollars in jSonar either.

Let's hear why, and what makes jSonar special.

Article published on ZDNet in June 2020

A new query engine and a visualization layer are the next pieces in Databricks' puzzle. We connected with Ali Ghodsi, co-founder and CEO of Databricks, to discuss their latest news: the announcement of a new query engine called Delta Engine, and the acquisition of Redash, an open source visualization product.

Our discussion started with the background on data lakehouses, which is the term Databricks is advocating to signify the coalescing of data warehouses and data lakes. 

We talked about trends such as multi cloud and machine learning that lead to a new reality, how data warehouses and data lakes work, and what does the data lakehouse bring to the table.

We also talked about Delta Engine and Redash of course, and we wrapped up with an outlook on Databricks business growth.

ZDNet article published in June 2020

The Ocean Protocol Foundation wants to help resolve that conflict, by introducing a way of letting AI work with data without giving up control.

The goal is to decentralize data science and AI using blockchain, and the latest milestone called Compute-to-Data brings this one step closer

We connected with Trent McConaghy, Ocean Protocol Foundation Founder, to discuss how it all works.

Article published on ZDNet in June 2020

Or at least, this is what the Yugabyte crew thinks. The company, founded by Facebook data infrastructure veterans, announced that it has raised $30 million in an oversubscribed Series B round to double down on community and team growth. This is a crowded market, but big enough to be a non-zero-sum game.

We connected with Yugabyte founders Kannan Muthukkaruppan and Karthik Ranganathan, and newly recruited CEO Bill Cook, previously of Sun Microsystems and Pivotal, for a deep dive in the company, the funding, and the market.

Article published on ZDNet in June 2020

Streamlit wants to be for data science what business intelligence tools have been for databases: a quick way to get results, without bothering much with the details.

In today's episode, we welcome Streamlit CEO and Founder, Adrien Treuille.

We discuss what makes Streamlit special, how it works, and where data-driven applications at large are going next.

Article published on ZDNet in June 2020.

If you think fact-checking is hard, which it is, then what would you say about verifying scientific claims, on COVID-19 no less? Hint: it's also hard -- different in some ways, similar in some others.

Fact or Fiction: Verifying Scientific Claims is the title of a research paper published on pre-print server Arxiv by a team of researchers from the Allen Institute for Artificial Intelligence (AI2), with data and code available on GitHub. 

In this backstage chat, David Wadden, lead author of the paper and a visiting researcher at AI2 and George Anadiotis connected to discuss the rationale, details, and directions for this work.

Article published on ZDNet in May 2020

Few people, Nvidia's competitors included, would dispute the fact that Nvidia is calling the shots in the AI chip game today. The announcement of the new Ampere AI chip in Nvidia's main event, GTC, stole the spotlight.

Let's put the new architecture into perspective by comparing against the competition in terms of performance, economics, and software.

Article published on ZDNet in May 2020

GoodData is evolving more than its relationship with select users.

In this backstage chat, GoodData CEO and Founder Roman Stanek and George Anadiotis discuss the ins and outs of the deal, the data landscape, the way data are used to shape directions for organizations big and small, and what's next for GoodData.

Article published on ZDNet, May 2020

This comes only a few months after Grafana Labs scored $24 million in Series A funding to double down on open-source strategy and build what it dubs the world's first open and composable observability platform.

In this backstage chat, CEO and Founder Raj Dutt and George Anadiotis connected to discuss Grafana 7.0, and the road forward.

Article published on ZDNet, May 2020.

Here is how they are evolving, with vendors and standard bodies listening, and platforms becoming fluent in many query languages.

Article published on ZDNet, January 2020.