Orchestrate all the Things George Anadiotis

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