We talk about current events, the acquisition of data stacks and the closing of the HashiCorp acquisition by IBM. Later, we dive into the productivity of AI and what’s going on – are companies really getting the benefits that they expect from AI chat bot integrations and what the challenges are?
We touch base on a little bit of something more infrastructure focused, where I give a preview of work I’ve been doing on separating Kubernetes virtualization from Kubernetes development use cases, which is something that we will be talking about more in the future.
We discuss Symbolic AI via LLMs for advanced reasoning in manufacturing and real-time analytics. Key points included leveraging symbolic representations and algebraic equations, utilizing knowledge graphs to improve model accuracy, and exploring agentic AI frameworks with specialized agents working together using swarm intelligence principles to tackle complex problems like anomaly detection and process optimization. The group also discussed the challenges of building trust in AI systems and the importance of capturing and storing questions and answers to build a knowledge base.
In this episode, we dive deep into the emerging world of building and training small language models. We’ll discuss the benefits, risks, and challenges companies face as they work to create more targeted and efficient AI models. From managing hardware and power requirements to ensuring data privacy and governance, we’ll cover the key considerations for enterprises looking to leverage the power of small language models. Join us as we unpack this fascinating topic and consider the implications for the future of AI and infrastructure operations.
We dive into AI, manufacturing and how to improve manufacturing outcomes by better analyzing data.
If you are interested in manufacturing or advanced applications of AI and digital twins – which is where we create accurate representations of physical items – this episode will hit all of your favorite topics!
How do we apply the principles of lean to data science and data engineering? We discuss this broadly into using AI and machine learning more generally.
This is a topic that we had discussed over the summer and wanted to come back to six months later because so much has changed and transformed in the industry. What does agile lean process control look like in an infrastructure automation platform? How can we make these very difficult and challenging components of data and data management, more agile, more lean?
I think you will get a lot out of this conversation considering our current hypercharged AI ml and LM environment.
Transcript: otter.ai/u/1ZuALgSXcPw-bIf2GO…?utm_source=copy_url DALL-E Prompt: please create a picture of a very large truck stuck under a low bridge. please label the truck as ai and the bridge as lean
If you haven’t had a chance to join in on our book groups, I strongly recommend you take a look at the upcoming books we are reading! Today we discussed Data Science and Context, which is a relatively academic book by a series of doctors, PhDs, Specter, Norvig, Wiggins and Wing. The book gets into some really fascinating analysis techniques, addressing both the practical and ethical implications of data science applications.
We discuss the biases inherent in the book, the things that are missing and potentially disruptive to the core assumptions of the book. So even if you haven’t read this book, I think you will find the discussion fascinating.
This week I’m keeping our warm up discussion about open AI in the podcast. So you will get about 10 minutes of bonus content before the book group discussion as a warm up and it is very related. Our conversations about what has been going on with open AI, their board and Q* are directly related to the concluding ideas in our discussion about Data Science and Context.
What goes on behind the scenes with AI, and specifically data center infrastructure and hardware?
We discuss broad ranging concerns, opportunities and market blockers around AI. We also address how deeply it can impact innovation companies’ privacy legislation from the frame of hardware and automation.
Today’s discussion leads us to a larger question of what unlocks innovation in general that we will address in future podcasts.
How do you regulate large language models? We look at the challenges of regulating these AI approaches and how governments and companies can approach it. We untangle how these models work, and dive into the mechanics of what information is controllable. We walk through concrete information that is a benefit to you here as our listener, and incentive for you to join us in future conversations as we continue to unravel it.
In addition, John Willis was on the panel today, and he started us off with a story about API’s, Amazon, Jeff Bezos, and O’Reilly from the warmup. So you’ll get a short bonus story by John Willis before we start.
In a Cloud 2030 podcast episode, Rob Hirschfeld, CEO and co-founder of RackN, discussed the complexities of regulating large language models. He highlighted the stark differences in approaches between the US, focusing on model risks, and the EU, emphasizing user rights protection. Hirschfeld expressed concern about reconciling these varying perspectives, especially regarding data rights preservation and understanding the risks associated with using such models, particularly when algorithms cannot be fully validated. He invited listeners to engage in the ongoing conversation at the 2030.cloud.
A coming Data Darkage is on its way, where we’re watching Reddit, Twitter and other companies take what used to be publicly available information and put it behind a paywall or gate.
Because of the way large language models are using this data and the value of the data, we are expecting to see that trend accelerate. This will have profound implications for how we think of, share, and use data in the coming years.
We use ChatGPT to live create DevOps, automation, Ansible, TerraForm, Python, and interact with different clouds to get advice on how to set up clouds.
This discussion includes a screen share session, so if you’re listening to this audio there will be times when we are talking about something you can’t see but I do make a point of working to explain what we’re doing. There’s also a video of the screen share session if you prefer.
