Category Archives: All

Lean In Data Science

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

Data Science in Context [Book Discussion]

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.

Transcript: otter.ai/u/qYBKNhDBKqaghEaxE-…?utm_source=copy_url
Image: Data Science In Context cover

Identity vs Privacy? Trade-offs required?

How can digital identity be used to build better trust and systems in our daily transactions? There are really significant challenges and consequences to having a national guaranteed identity – a single identity provider.

Knowing who you’re interacting with, in every form, in every situation is not as simple as you might think. There’s a lot of analogues to physical identity that are worth considering.

What would it mean for us to not have privacy? Does identity mean we don’t have privacy in our interactions? Who can we trust and what authority do they have?

Transcript: otter.ai/u/o_43fyGjxu24Ur5rpz…?utm_source=copy_url
Image by Dall-e prompte: a cartoon like image of a humanoid robot looking into a mirror and seeing a masked pirate version of itself

Time to Panic at Incidental Surveillance?

What incidental, or accidental, surveillance state is being created by all of the video and listening devices that are now embedded in our world?

Today we talk through the ramifications of those networks being in private hands in which companies can actually review, analyze and monetize data from these systems. For example – autonomous vehicle cameras and delivery van cameras. This episode discusses the ramifications of this example and more.

References:
arstechnica.com/tech-policy/2023/…s-invade-privacy/
www.vzbv.de/en/court-prohibits-…ivacy-infringements
last-chance-for-eidas.org/

Transcript: otter.ai/u/NszGAX95R70ydlW_fJ…?utm_source=copy_url
Image by Dall-e prompt: “1950s era cartoon of an autonomous car with a lot of cameras spying on people”

Predicting Innovation: Three Horizons Model

What is innovation? Today we continue this discussion, specifically drilling into the three horizons model for creating growth and value.

We spend a lot of time talking about how companies innovate using that model, what it means and what are examples of it? How does that spark take place? We bridge you further down the innovation learning process in this meeting.

Transcript: otter.ai/u/He9h2NVxazKMDN9a13…?utm_source=copy_url
Image by Dall-E prompt “please create a close up picture of a flock of birds navigating between three different horizons. the birds are smart and know which why they need to go”

Compliance is Fun! (and why you care)

We dive deep into the technical subject of governance and policy enforcement, including the tools, techniques and processes that you need to be aware of to do a good job with policy and governance enforcement.

We cover how to get started, what to think about, what to be aware of, and chip away at your governance and policy challenges including developer development portals, infrastructure pipelines and DevSecOps.

Transcript: otter.ai/u/ND90jKHwbklUBOAwT1…?utm_source=copy_url
Image by Dall-E prompt “please make a carton that shows a regulator who is managing cloud and IT assets using impractical tools”

Rob’s Hot Take:

Rob Hirschfeld, CEO and co-founder of RackN and host of the Cloud 2030 Podcast, discusses the October 19th conversation about limiting large language models (LLMs) and AI. The discussion focused on creating legal limitations for artificial intelligence and technology, highlighting the potential impact of regulations such as Section 230, which governs internet service providers’ moderation of content. Hirschfeld suggests that changes to Section 230 could be a critical component in controlling emerging technologies, inviting listeners to explore the insightful conversation at the2030.cloud.

Building Open Ecosystems [Tofu vs Terraform]

We dive into the dynamics of open source projects and monetization today, specifically starting around the TerraForm and open tofu split. That topic is one that we love to chew over and potentially over analyze, but today’s discussion is different.

We go into how ecosystems are built both in open and proprietary and cloud systems, and look at sort of a historical perspective on what makes a project successful from an ecosystem perspective. We also dive into why some projects work like that, and why some projects don’t.

Today’s episode gives a new take on some of the dynamics going on in the open source communities through the lens of what happened with Open Tofu and TerraForm.

Transcript: otter.ai/u/ONDvgS9yGMrSN-bXMT…?utm_source=copy_url
Photo by James Wheeler: www.pexels.com/photo/lake-pebble…of-water-1574181/

Innovators vs Techno Optimists

We discuss innovation, a favorite topic of ours, today. Instead of diving in for a structured conversation, we dove at the bait that was offered by Marc Andreessen in his techno optimist manifesto. If you haven’t read it, I would suggest taking a moment to read it before you listen to the rest of the podcast, but you do not have to!

It is definitely an interesting opinion piece about the power of innovation, which is why it was a good input for our discussion. We have our own unique perspective and a robust discussion about how innovation should work that tees up further conversations about the three horizons model for innovation.

References:
a16z.com/the-techno-optimist-manifesto/

Transcript: otter.ai/u/6qOpnFW0LMvh-rvZfw…?utm_source=copy_url
Photo by RDNE Stock project: www.pexels.com/photo/woman-in-bl…-sweater-7413891/

Compliance Comes to Kubernetes

What does it take to implement governance and compliance, because they are process controls much more than individual technologies. Today we discuss that a lot of the talks seem to be about governance and compliance, and we have a fascinating discussion about governance compliance and Kubernetes.

The idea that Kubernetes is maturing, losing the drama that is a hallmark of its first decade now and moving into a focus on managing how to control and have security, compliance and normality. Yet all of those things have a degree of tension with the vendors and users, which puts single choice compliance and governance
in direct conflict with open source competitive ecosystems.

This makes for a fascinating conversation where we touch on some really important issues for the industry.

Transcript: otter.ai/u/mAkvsYgMYMp_W8Bizk…?utm_source=copy_url
Image: Generated by Dall-E

Is Limiting LLMs possible?

How do we limit and regulate LLMs and AI? We approach this at multiple angles and look through what it’s like to regulate this type of technology.

If you’re interested in the limits of any technology, and specifically how AI gets regulated, and where we’re likely to impose legislative barriers or restrictions on this, then this will be a fascinating podcast for you.

Transcript: otter.ai/u/8IsFB-H-U3XzpQ751l…?utm_source=copy_url
Photo by Pixabay: www.pexels.com/photo/black-andro…white-book-39584/

Rob’s Hot Take:

In the Cloud 2030 Podcast episode from October 19th, Rob Hirschfeld delves into the topic of limiting large language models (LLMs) in AI and explores the potential legal frameworks for regulating artificial intelligence and technology. The conversation highlights the intriguing idea that Section 230, a core governing principle of the internet that exempts internet service companies from extensive content moderation, could play a pivotal role in shaping technology use. Hirschfeld suggests that changes to Section 230 might serve as a critical component in influencing the control and regulation of emerging technologies like AI. Listeners are encouraged to check out the full October 19th episode for a detailed exploration of these regulatory considerations and can join ongoing discussions at the2030.cloud.