Category Archives: Artificial Intelligence

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.

Data Center & Hardware Impacts on AI

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.

Links: research.aimultiple.com/wp-content/we…kers.png.webp

Transcript: otter.ai/u/3FUaZ3m8JabYLyJZGH…?utm_source=copy_url
Photo by Tim Samuel: www.pexels.com/photo/woman-handf…hy-chips-6697286/

Bias in LLMs

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.

Transcript: otter.ai/u/v3MaWiCWEe-G1ar2O0…?utm_source=copy_url
Photo by Marta Nogueira: www.pexels.com/photo/pink-and-bl…or-text-17151677/

Rob’s Hot Take:

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.

Can we regulate LLMs? Should we?

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.

References
ised-isde.canada.ca/site/innovation…panion-document
www.europarl.europa.eu/news/en/headl…xt=Parliament‘s%20 priority%20is%20to%20make,automation%2C%20to%20prevent%20harmful%20outcomes
www.trade.gov/market-intelligenc…i-regulations-2023
content.naic.org/cipr-topics/arti…ial-intelligence

Transcript: otter.ai/u/dBRQBFNz8d01taQ-iM…?utm_source=copy_url
Image: www.pexels.com/photo/measuring-g…tar-pick-3988555/

Rob’s Hot Take:

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.

Data Darkages – do LLMs drive paywalls?

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.

Transcript: otter.ai/u/e1XCyhSa9V81bgMpbo…?utm_source=copy_url
Photo by Pollianna Bonnett: www.pexels.com/photo/young-brune…e-chair-17687131/

Generative Coding & DevOps Challenges

What can we expect generative AI to generate and is it going to produce good code? Today we talk about Gluecon and generative DevOps and the different concepts and capabilities around it. What impact is it going to have on developers? How do we control that?

Today’s discussion was in preparation for our session on June 13, where we’re going to group program GPT to see what type of DevOps coding skills we can prompt. We talked about the necessity of prompting in this session and covered some tips to help you think about how to be a better prompt engineer, a skill set that everybody’s going to need to have in the next months if not years.

Transcript: otter.ai/u/lsye_htH0-wksAOrqg…?utm_source=copy_url
Image: www.pexels.com/photo/man-welding…ow-frame-2965260/

Domains And Access For Metadata

What actually is used to describe the provenance and information that comes with our data? Today we discuss metadata and the governance, security, hint, domains, date that accompany data in ways that in some senses are more important than data.

How can we move, change and transform data? We had a really robust conversation about how access to data is so critical in actually understanding how data is used.

This is a topic we struggle with: figuring out the naming, how things work, and the context. More than anything else, the context makes things challenging. As you listen to this, think through how challenging it is to define data, data governance, and using data effectively.

Transcript: otter.ai/u/VE2u_jTwG_h4z9ewfI…?utm_source=copy_url
Image: www.pexels.com/photo/cheerful-bl…hoolkids-5905918/

AI And Technical Debt

We dig into a topic written about by Eric Norlin or SK ventures about technical debt and AI. In this episode, we discuss the consequences of generative AI could be radically transforming the way in which we generate code and deal with code that has been generated in technical debt.

We explore some fascinating concepts about how fast we can iterate, how we change the dynamics of building software, building automation, and the expertise required to architect systems. This leads pretty far down in the path towards disruptive thinking, and how this could reshape the entire industry.

Source: skventures.substack.com/p/societys-te…and-softwares
Transcript: otter.ai/u/MEtVkoNnZeCu0JHa30…?utm_source=copy_url
Image: www.pexels.com/photo/piggy-bank-…a-flower-4886900/

Rob’s Hot Take:

In a discussion on the Cloud 2030 podcast, CEO and co-founder of RackN, Rob Hirschfeld, highlighted the changing landscape of expertise in emerging technologies like AI. With the cost to build and iterate dropping significantly, expertise is no longer primarily applied during the building process, but integrated into design and testing sequences. The advent of generative AI has the potential to revolutionize how we design and build automation, software code, and technical systems, necessitating a redefinition of expertise in this rapidly evolving field.

Generative DevOps

NOTE: This is Rob’s Gluecon topic on 5/24. Save $300 if you register with speaker300 at www.gluecon.com

We dive into the question of whether or not generative AI can be used to productively change DevOps automation and the control of infrastructure.

We’ve discussed the closed loop side of using AI to manage infrastructure in the past, but this episode we really dive into the idea of creating automation and using generative AI.

Transcript: otter.ai/u/VtnznHgydT3_6QSJpk…?utm_source=copy_url
Image: www.pexels.com/photo/tossing-fri…ying-pan-6937457/

AI Time To Decision

We talk about improving the time it takes to make decisions – called time to decision, a topic that we like to address quite a bit. We started with the news of the day around AI, ml Chaffee GP, and learning models.

We asked ourselves if AI/ML and generative AI could change the way expertise is used to make decisions and improve the time to the decision for experts. What type of implications would that have in the market?

If you’ve been tracking this subject, I know you will find this exciting and interesting.

Transcript: otter.ai/u/L3Kb_I4fe0ggAr9nDE…?utm_source=copy_url
Image: www.pexels.com/photo/person-touc…rm-clock-1198264/