Talking the Future of Tech
We dive into data operations in today’s episode! We cover the idea that with all of the work we’re doing in AI and ML data analytics analysis, you actually have to steward your data.
We also cover processes controls, like what we have with DevOps in infrastructure, but with similar types of concepts (governance controls automation) around how your data is flowing in your system.
Transcript: otter.ai/u/pesotDnHCCD5lyPVx7…?utm_source=copy_url
Image by DALL-E
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
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”
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.
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/
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 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.
Transcript: otter.ai/u/u3bArfIvx40oUXnRLt…?utm_source=copy_url
Photo by Pixabay: www.pexels.com/photo/aeroplane-a…e-aviation-33224/
Rob’s Hot Take:
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.
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.
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/
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.