If you follow cloud2030 discussions or any of my podcasting over the last decade, Edge is a very interesting topic to me. Today’s episode is a short update on the state of the edge from a very specific position.
In this discussion, I walk through with Josh why edge has been hard for us to nail down from a technology perspective. This is something of special interest to RackN as we keep honing and refining our IT edge infrastructure technology set.
The TerraForm fork, now known as the OpenTofu project, is our first topic in today’s episode. We discuss what’s going on with that, the challenges, as well as the potential pressures from HashiCorp that created this whole situation.
How do we get experts to recover their authority and how do we look at organizations like that? We have about 20 minutes of really involved conversation about the book, Death of Expertise by Tom Nichols, from the previous podcast. If you haven’t heard our first part of the conversation, I suggest you go back and listen to our full Death of Expertise podcast.
We cover two topics, one of them short term and one of them long term. So it’s a nice, balanced industry discussion around what the fork means, what its impacts are and a little bit of recap. There’s some really spicy opinions around 32 minutes in if you want to jump forward, we resume our discussion about death of expertise.
What are the potentials for biasing LLM models? We dive into biases both in good ways and in bad ways.
Is the expertise that we’re feeding into these models is not sufficient to actually drive the outcomes that we’re looking for? We’re going to be eliminating humans out of the loop in a relatively short period of time. Both outcomes, at the moment, feel equally probable, which is troubling.
We dive into how and why that happens, what’s going on, and some concrete tips for how you can improve your prompting to avoid these same pitfalls.
In the Cloud 2030 Podcast episode from September 7th, Rob Hirschfeld explores the topic of bias in large language models, emphasizing the ease with which the output and tone of these models can be influenced by initiating them with different idiomatic English dialects. By demonstrating that variations in greetings like “bonjour” or “howdy” yield distinct results, Hirschfeld underscores the importance of crafting prompts and setting the right tone to unlock the embedded expertise within the models. The conversation delves into the fascinating and somewhat alarming aspects of bias in large language models, offering insights that encourage listeners to engage with the full discussion. Those interested in participating in ongoing conversations can find more information about Cloud 2030 at the2030.cloud.
We continue our book group series today about the Death of Expertise by Tom Nichols, which is very dense with a lot of provocative and thought provoking comments, topics and ideas. It was so interesting that we decided we needed two sessions to fully unpack this. This is part one, which is about how expertise as a society is handled, how social media changes and the cyclical nature of confidence in our institutions, and how technology is shaped in buying patterns in use by expertise. If you’re interested, please participate in part two of the discussion!
We also talked about the Dunning Kruger effect, the idea that the less you know about something, the more confident you are, and that gaining knowledge makes you more knowledgeable but also less falsely confident in how you present yourself. It’s a more complex topic than that very short summary.
“It is difficult to get a man to understand something, when his salary depends on his not understanding it.” ― Upton Sinclair, I, Candidate for Governor
Can large language models effectively supplant developers and DevOps engineers?
Today we go deeper into how the models can be trained, if they can be trusted, and what is the upside or positive use case in which we really turn LLMs into the type of weighing person experts that they have the potential to be versus simply something that turns up the volume on how fast you generate code.
We also talked about the downsides of that type of model and the potential upsides of how powerful using these tools as assistants could emerge to be as a key aspect here to transform and improve the outcome for work.
In the Cloud 2030 Podcast episode from August 31st, Rob Hirschfeld discusses the potential of using large language models to enhance DevOps and development outcomes. The conversation emphasizes the possibilities of leveraging AI to improve codebases, facilitate refactoring, and encourage code reuse by tapping into the knowledge embedded in existing code bases. Hirschfeld envisions a future where AI assists developers in reducing technical debt, maintaining code more efficiently, and consolidating code intelligently, ultimately leading to improved development practices. The episode explores the challenges and investments required for realizing these outcomes, encouraging listeners to delve into the full podcast for a comprehensive understanding. To engage in further discussions, interested individuals can explore the Cloud 2030 podcasts and join the conversations at the2030.cloud.
What’s going on from VMWare to Broadcom to HashiCorp and their license changes. We discuss current topics, even to the sad news about Kris Nova passing during a mountaineering expedition.
If you’d like to catch up on the tech news, then this topic hopefully has aged well and you will enjoy it!
How do Edge and Compute and SaaS and cloud influence everything that we do? We covered topics from VMware explorer and talked a lot about Edge. That led to AI ml, which led to another topic, which led to another topic.
If you enjoy hearing about how interconnected our technology and choices are, everything from Bitcoin to edge, and cloud and government interaction, this is the podcast for you because we cover pretty much all of it and connect it together.
Remember that on September 14, we are having one of our quarterly book club meetings on the death of expertise.
In the Cloud 2030 podcast episode from August 24th, CEO Rob Hirschfeld discusses the shift from the rental/service economy to owning production assets in the context of cloud and SaaS models. He highlights the financial commitment and decoupling of capital expenses associated with service usage, emphasizing the value of owning assets in certain scenarios. Hirschfeld encourages deliberate decision-making regarding asset ownership, stressing the importance of understanding the long-term consequences and skill-building for businesses. He invites listeners to explore these topics further in the complete August 24th Cloud 2030 conversation.
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.
What is technical debt, and how does it apply to large language models? We dive into a really interesting conversation that goes from technical debt into system and code maintenance, which is probably a much better way to think about the challenges we have in maintaining the infrastructure systems, code, data and data lakes that we have to deal with on an everyday basis.
How do we maintain, store, track and update the LLMs themselves? How do we know and manage which model is being used when we retire a model?
In the Cloud 2030 podcast episode on August 17th, Rob Hirschfeld discusses the distinction between technical debt and system maintenance costs, emphasizing the importance of understanding the ongoing effort needed to maintain and improve systems. He points out that overlooking the maintenance costs while building a system leads to technical debt. Hirschfeld raises the question of whether large language models and AI can change the equation of system maintenance, a topic yet to be explored fully in the podcast.
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.
