Author: George Anadiotis

The computational complexity of AI models is growing exponentially, while the compute capability provided by hardware is growing linearly. Therefore, there is a growing gap between those two numbers, which can be seen as a supply and demand problem.

On the demand side, we have everyone wanting to train or deploy an AI model. On the supply side, we have Nvidia and a number of competitors. Currently, the supply side is seeing earnings skyrocket, and the demand side is stockpiling and vying for access to compute.

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.

What data management in 2024 and beyond will look like hangs on one question. Can open data formats lead to a best-of-breed data management platform? It will take Interoperability across clouds and formats, as well as on the semantics and governance layer.

Sixth Platform. Atlas. Debezium. DCAT. Egeria. Nessie. Mesh. Paimon. Transmogrification.

Is a generative AI preamble necessary for a newsletter focused on Knowledge Graphs, Graph Databases, Graph Analytics, and Graph AI? Normally, it should not be. However, the influence of generative AI on the items included in this issue was overwhelming. There is a simple explanation for that.

It's been a year since Generative AI burst into the mainstream with the release of ChatGPT. Notwithstanding a rather spotty record both in terms of technical performance and accuracy as well as in terms of business reliability, there's no denying that Generative AI has captured the attention of executives worldwide.

It's not every day that you hear product leads questioning the utility of their own products. Brad Bebee, 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.

Suppose 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's new features and the broader vision.

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. 

Introduction

How do data and machine learning-powered algorithms work to control newsfeeds and spread stories? How much of that is automated, how much should you be able to understand and control, and where is it all headed?