Orchestrate all the Things podcast: Connecting the Dots with George Anadiotis

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 useHow data is breaking boundariesPlatforms and choiceThe illusion of objectivityAlgorithms, agency and surveillanceFrom 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.

From better together to full native integration, Neo4j is creating an ecosystem around all major cloud platforms to provide graph-powered features for Generative AI and beyond.  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 worksHow graph-powered generative AI aligns with cloud platform AI strategiesSimilarities and differences across themHow 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/

If we look at the current status quo in AI as a case of demand and supply, what can we do to close the gap between the exponentially growing demand on the side of AI models and the linearly growing supply on the side of AI hardware? 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 AIChoosing between GPT-4, Claude 3 and open source LLMsCustomizing LLMs and fine-tuning vs. RAGEvaluating LLMsMarket outlook Article published on Orchestrate All the Things: https://linkeddataorchestration.com/2024/03/06/evaluating-and-building-applications-on-open-source-large-language-models/

There's more to AI chips than NVIDIA: AMD, Intel, chiplets, upstarts, analog AI, optical computing, and AI chips designed by AI. 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/

For many organizations today, data management comes down to handing over their data to one of the "Big 5" data vendors: Amazon, Microsoft Azure and Google, plus Snowflake and Databricks.  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/

What is a skills-based economy and how is LinkedIn moving from vision to implementation? 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

Amazon Neptune, the managed graph database service by AWS, makes analytics faster and more agile while introducing a vision aiming to simplify graph databases. 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/

“Graph database growth is going strong through the Trough of Disillusionment.” And “Graph Analytics go big and real-time.” These were two of the headlines of the Spring 2023 update of the Year of the Graph newsletter. In combination, they seem like an appropriate summary of the reasoning behind a new entry in the graph database market: Aerospike Graph, which Aerospike officially unveiled in June 2023. 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 is a case study in terms of how its newsfeed has evolved over the years. 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

Would you leave a Google Staff Research Engineer role just because you want your TV to automatically pause when you get up to get a cup of tea? Actually, how is that even relevant, you might ask. Let's see what Pete Warden, former Google Staff Research Engineer and now CEO and Founder of Useful Sensors, has to say about that. 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

Neo4j recently announced new product features in collaboration with Google, as well as a new Chief Product Officer coming from Google: Sudhir Hasbe. 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.

In an era of dried-up funding and Data Lakehouse vendor supremacy, Redpanda is going against the grain. 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

The EU Parliament just voted to bring the EU AI Act regulation into effect. If GDPR is anything to go by, that's a big deal. 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

From a consumer-oriented application, Foursquare has evolved to a data and product provider for enterprises. The next steps in its evolution will be powered by the Foursquare Graph 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

On AI-generated content, writing, new, old, and broken media, platforms, models, audiences, and body parts. 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.

Is incremental change a bad thing? The answer, as with most things in life, is "it depends". In the world of technology specifically, the balance between innovation and tried and true concepts and solutions seems to have tipped in favor of the former. Or at least, that's the impression reading the headlines gives. Good thing there's more to life than headlines. 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 last couple of years have been an AI model arms race. 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 trainedThe scaling laws and how they are being revised as research and development progressesThe Inverse Scaling Challenge and its findings Article published on Linked Data Orchestration.

Can technology, and real-time technology in particular, help companies achieve savings during economic hardship? Alok Pareek thinks it can. 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.

Retail is big business. But like many other sectors it's undergoing a transformation, largely affected by the shift of consumer behavior from physical to digital. Many retailers are looking to analytics and AI to help them cope with the challenges. 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.

Quantum computing could be a disruptive technology. It's founded on exotic-sounding physics and it bears the promise of solving certain classes of problems with unprecedented speed and efficiency. The problem, however, is that to this day there's been too much promise and not enough delivery. 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.

Graph database Neo4j has just released version 5, which promises better ease of use and performance through improvements in its query language and engine, as well as automated scale-out and convergence across deployments. 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

AIOps is what you get when you combine big data and machine learning to automate IT operations processes, including event correlation, anomaly detection and causality determination. At least, that's how Gartner defines AIOps. 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. 

Shining light on AI research and applications in a conversation with the curators of the most comprehensive report on all things AI. 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 Linked Data Orchestration.

Economic theory is known to be constrained by a number of inefficiencies in its modeling. Salesforce researchers claim AI can help address that, leading to more robust economic policies. Article published on ZDNet

As Ben Lorica will readily admit, at the risk of dating himself, he belongs to the first generation of data scientists. In addition to having served as Chief Data Scientist for the likes of Databricks and O'Reilly, Lorica advises and works with a number of venture capitals, startups and enterprises, conducts surveys, and chairs some of the top data and AI events in the world. That gives him a unique vantage point to identify developments in this space. 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.

What's a good enough weather prediction? That's a question most people probably don't give much thought to, as the answer seems obvious -- an accurate one. But then again, most people are not CTOs at DTN. 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

Everything counts in large amounts. You don't have to be Google, or to build large AI models, to benefit from writing efficient code. But how do you measure that? 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

What does sustainability actually mean for organizations? Can it be measured, and if yes, how? Obvious questions with less than obvious answers, even for sustainability and ESG professionals like James Phare. 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.

Award-winning AI research - check. Startup and enterprise experience - check. Venture capital and Mark Benioff backing - check. Is that enough for Richard Socher's you.com to take on Google? Here is why and how he aims to do that. Article published on ZDNet

Decision intelligence is one of those terms that sound vaguely familiar, even if you've never come across it before. Like many category-defining terms, it can mean different things to different people. This is a feature category-defining terms either have by design, or acquire through extensive use. 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

It sounds like a contradiction in terms, but disaster and disruption management is a thing. Disaster and disruption is precisely what ensues when catastrophic natural events occur, and unfortunately, the trajectory the world is on seems to be exacerbating the issue. In 2021 alone, the US experienced 15+ weather/climate disaster events with damages exceeding $1 billion. 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

Is deep learning really going to be able to do everything? Opinions on the potential of this opinion to prove true vary. Geoffrey Hinton, awarded for pioneering deep learning, is not entirely unbiased in so opining. However others, including Hinton's deep learning collaborator Yoshua Bengio, are looking to infuse deep learning with elements of a domain still under the radar: operations research. 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

Data quality is part of data intelligence. It's a topic that lot of people are concerned about, and it makes engagement and adoption around data intelligence solutions better. With many data quality solutions with different approaches available in the market, customers need to be able to choose the one that works best for them. 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. Article published on VentureBeat

Circa 2017, there was a lot of hype around autonomous driving. If one were to take that at face value, it would mean that by now autonomous driving would have been a reality already. Apparently that's not the case, and Alex Kendall claims to have known that all along. Still, that did not stop him from setting out then, and he's still working on it today. 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. Article published on VentureBeat

There is an implicit assumption in most analytics solutions: the data being analyzed, and the insights derived, are almost exclusively quantitative. That is, they refer to numerical data, such as number of customers, sales, and the like.  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. Article published on VentureBeat

After data privacy and GDPR, the EU wants to leave its mark on AI by regulating it with the EU AI Act. 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. Article published on ZDNet

What do you get when you combine two of the most up-and-coming paradigms in data processing - streaming and graphs? A potential game-changer, and this is the bet first DARPA and now CrowdStrike Falcon Fund have taken on thatDot and its open source framework Quine. 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. Article published on VentureBeat

Amazon just unveiled Serverless Inference, a new option for SageMaker, its fully managed machine learning (ML) service. The goal for Amazon SageMaker Serverless Inference is to serve use cases with intermittent or infrequent traffic patterns, lowering total cost of ownership (TCO) and making the service easier to use. 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. Article published on VentureBeat

Modern agriculture was broken long before pandemics, wars, supply chain disruptions and fertilizer shortages. Regenerative agriculture can fix that, and data can help. 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. Article published on ZDNet

Whether you're genuinely interested in getting insights and solving problems using data, or just attracted by what has been called “the most promising career” by LinkedIn and the “best job in America” by Glassdoor, chances are you're familiar with data science. But what about graph data science? 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.

Processing data in real-time is on the rise. The streaming analytics market (which depending on definitions, may just be one segment of the streaming data market) is projected to grow from $15.4 billion in 2021 to $50.1 billion in 2026, at a Compound Annual Growth Rate (CAGR) of 26.5% during the forecast period as per Markets and Markets. 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. Article published on VentureBeat

There's no business like powering business. That may seem like a paradox, and most of us don't really think of accounting as a separate line of business. In that sense, it's a secret hiding in plain sight: accounting is big business, in and of itself. 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. Article published on VentureBeat

A pragmatic and direct approach to ethics and trust in artificial intelligence (AI) — who would not want that? This is how Beena Ammanath describes her new book "Trustworthy AI.”  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. Article published on VentureBeat

Arguably, two of the most iconic examples of digital transformation are Kodak and Amazon. Kodak failed to keep with the times, leading to the demise of a once dominant commercial empire. Amazon, however, had the foresight to not just “stay in its lane,” but step out and shape the world's digital infrastructure with AWS. 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. Article published on VentureBeat

Did you ever feel you've had enough of your current line of work, and wanted to shift gears? If you have, you're definitely not alone. 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.

Run:AI offers a virtualization layer for AI, aiming to facilitate AI infrastructure. It's seeing good traction, and just raised a $75M Series C funding round. Here's how the evolution of the AI landscape has shaped its growth. Article published on ZDNet