The Daily AI Show Newsletter Issue #10

Did Sam just troll the AI nerds (AKA Us) again?

In this issue, we get into:

• DAS is 1 year old, now what do we do?

• Why the MVP prompt is the only prompt you need

• Is it time to start looking for land in Denmark?

• Thinking about training your own LLM? Read this first.

• The news from the past 7 days that caught our collective eye including Karl’s deep desire to have an AI robot that takes out the recycling.

Plus, we talk about ChatGPT voice mode (AKA Poe light), Heygen’s cheaper avatars, and how ChatGPT’s new voice mode mimicked a user’s voice without being prompted to. Um, Mission Impossible just called and said they have their next movie idea.

Grab some coffee, find a comfy chair, and let’s get into it on this fine Sunday morning.

Your DAS Crew
-Andy, Beth, Brian, Eran, Jyunmi, Karl & Robert

After 262 shows, the Daily AI Show marks its one-year anniversary on Wednesday, reflecting on a journey filled with dynamic discussions, groundbreaking discoveries, and the rapid evolution of AI. This milestone is more than just a celebration of time; it's a testament to the curiosity, dedication, and passion that drives all 7 of us.

Andy highlighted some of the year’s most memorable moments, like the release of ChatGPT Vision in October, which added a new dimension to their conversations, and the rollercoaster saga of Sam Altman's dismissal and reinstatement at OpenAI. Custom GPTs, too, have revolutionized the way we interact with AI, shifting from simple prompts to programmable, shareable assistants that have redefined the AI landscape.

Brian reflected on the show’s organic evolution, comparing it to a live morning radio show where conversations naturally flow from one topic to the next. "We don’t have to hit a home run every day because we’re back tomorrow to continue the conversation," he emphasized, capturing the essence of the show’s unique, continuous format.

Eran and Jyunmi shared their favorite episodes, from early explorations of AI-generated music to deep dives into complex topics like evolutionary model merging. These discussions often go beyond the surface, leading to new insights and shaping their understanding of AI’s future.

Karl offered a broader perspective, appreciating how the show brings together diverse viewpoints to piece together the larger picture of AI’s impact. "It’s like hanging out with friends every morning, discussing something that has such a big impact on everybody—from individuals to society, to business, and beyond," he noted.

As the Daily AI Show moves into its second year, our focus remains on fostering deep, meaningful conversations about AI, embracing both the excitement and challenges of this rapidly evolving field.

The MVP Prompt: Why Starting Imperfect Can Lead to Perfect Results

When it comes to AI prompting, perfection isn’t the goal—getting started is. The concept of the “MVP Prompt” (Minimum Viable Prompt) flips traditional thinking by encouraging users to begin with whatever comes to mind, even if it’s incomplete. As Beth, an expert in prompt engineering, put it, “The definition of a good prompt is one that works.” The idea is to start somewhere, even if it’s rough, and then refine it through interaction with the AI.

Beth shared her technique: “I often ask the AI to interview me about what I’m trying to achieve. This back-and-forth helps flesh out the prompt, making it more accurate and effective.” This approach not only simplifies the process but also makes it easier to generate reusable prompts, which can consistently deliver the desired outcomes.

Jyunmi added, “You can start with just a keyword, like ‘LinkedIn post,’ and let the AI guide you through refining it. By the end, you not only have a good result but also a blueprint for future prompts.” This iterative process transforms even the simplest start into a powerful tool for ongoing use.

#WHY IT MATTERS

• Get Started Quickly: The MVP Prompt approach eliminates the paralysis of perfectionism, allowing you to dive in and start refining your ideas with AI’s help.

• Iterative Improvement: By interacting with the AI, you can refine and optimize prompts in real-time, making the process more dynamic and efficient.

• Reusable Templates: Once you’ve refined a prompt, it becomes a valuable template that can be reused across different tasks, saving time and ensuring consistency.

• Broad Applicability: This approach works across various AI platforms and tasks, from content creation to coding and beyond, making it a versatile tool in your AI toolkit.

• Enhanced Creativity: Starting with a basic prompt and iterating encourages creative exploration, allowing you to discover new possibilities and approaches that might not have been evident at the start.

This method isn’t just about writing prompts—it’s about changing how we interact with AI to achieve better, more consistent results with less effort.

Why is Denmark Winning at AI Adoption?

Denmark is emerging as a leader in AI adoption within the European Union, outpacing many of its counterparts. With nearly double the percentage of AI adoption compared to the EU average, Denmark's approach stands out for its combination of strong government support, robust digital infrastructure, and a collaborative societal culture. As Jyunmi pointed out, "Denmark's success is rooted in their holistic approach, which prioritizes both data privacy and business innovation." The country's strategy is characterized by initiatives like the AI Matters Initiative and the regulatory sandbox, which facilitate the safe and effective deployment of AI across various sectors.

Andy highlighted the importance of Denmark's cultural and educational foundations: "Their focus on lifelong learning and a work-life balance gives citizens the time and energy to adopt new technologies like AI." This cultural emphasis on collaboration and knowledge sharing, coupled with a welfare state model that supports both workers and businesses, creates an environment where AI can thrive. "It's a system that takes care of its people while also driving business forward," Andy added.

Denmark's government also plays a crucial role in fostering AI adoption. With significant investments in digital infrastructure and a business-friendly environment, Denmark is setting itself up as a model for AI integration. As the McKinsey report indicated, 72% of Danish organizations are already using AI, far exceeding the global average.

#WHY IT MATTERS

• High Adoption Rates: Denmark's approach has led to significantly higher AI adoption rates compared to other EU countries, making it a leader in the field.

• Holistic Approach: By integrating data privacy, business innovation, and societal welfare, Denmark creates a sustainable and secure environment for AI development.

• Cultural Integration: Denmark's focus on lifelong learning and collaboration ensures that its citizens are well-equipped to embrace AI, making the technology more accessible and widely accepted.

• Government Support: With strong government backing and investments in digital infrastructure, Denmark is providing the necessary tools and resources for businesses to successfully integrate AI into their operations.

• Scalability Potential: While Denmark is a smaller country, its approach offers a scalable model that other nations could adopt, particularly those looking to balance technological advancement with social welfare.

Denmark’s success in AI adoption offers valuable lessons for other countries, particularly in how a balanced approach can lead to both economic growth and societal well-being.

Is Training Your Own LLM Worth the Risk?

The rise of custom Large Language Models (LLMs) has prompted many companies to consider whether they should invest in training their own AI from scratch or simply fine-tune an existing model. Bloomberg's ambitious project to create Bloomberg GPT, trained on 350 billion parameters of financial data, is a prime example of this dilemma. While the model was specifically designed for financial services, the project raised questions about whether such a massive investment is justifiable, especially when competing with powerhouse models like GPT-4.

Karl highlighted the immense resources required for this endeavor: "Training Bloomberg’s GPT cost around $10 million and involved 1.3 million hours of GPU time over 49 days. Yet, GPT-4 still outshines it in many respects." This comparison underscores the challenges companies face when trying to match the capabilities of well-established, high-performing models.

Jyunmi added a crucial perspective on the importance of control: "For large enterprises, control over their data and processes is paramount. Creating a proprietary model allows for customization and tighter security, but it comes with significant ongoing costs for maintenance and upgrades." The discussion also explored the evolving landscape of AI agents, with Karl noting, "The future is heading towards agents that can autonomously manage tasks and interact with other systems, which could make custom models even more complex to manage."

The consensus among the co-hosts was that while custom models offer certain benefits, the majority of companies would be better served by fine-tuning an existing model, balancing cost, control, and performance.

#WHY IT MATTERS

• Resource Intensive: Training a model from scratch requires significant investment in time, money, and computational resources, making it a feasible option only for the largest enterprises with very specific needs.

• Control vs. Cost: While a proprietary model offers more control and customization, it also comes with the ongoing burden of maintenance and upgrades, which may not be sustainable for all companies.

• Agentic Capabilities: The rise of AI agents capable of autonomous action adds another layer of complexity to the decision, as these agents may require even more sophisticated models and management.

• Fine-Tuning as a Solution: For most companies, fine-tuning an existing model offers a more balanced approach, providing the benefits of customization without the overwhelming costs associated with training from scratch.

• Future-Proofing: As AI technologies rapidly evolve, starting with a strong foundational model and building upon it may provide better long-term value and flexibility, allowing companies to adapt to new advancements without starting from square one.

This discussion highlights the critical considerations companies must weigh when deciding how to integrate AI into their operations, emphasizing the need for strategic thinking in a rapidly changing technological landscape.

JUST JOKES

The Mystery of Sam Altman’s ‘Strawberry’ Post

A cryptic post from Sam Altman this week has sparked discussions across the AI community. As Brian pointed out, “There was some trolling for sure” which is nothing new from Sam. This post, potentially linked to OpenAI's rumored "Q-Star" project, hints at deeper developments in AGI that could shift how AI is integrated into business strategies.

Mechanical Orchard Secures $50M from Google Ventures

Google Ventures has invested $50 million in Mechanical Orchard, focusing on AI-driven system modernization. Andy explained, “They have an AI system that looks at an enterprise's complex legacy systems… and it figures out a way to rebuild that into a modern cloud-hosted application suite.” This investment underscores a broader trend where AI is being leveraged to overcome the challenges of outdated technology, enabling more agile and efficient operations.

AI Health Predictions: A 95% Success Rate

A new AI model achieving 95% accuracy in disease prediction was a standout topic. Jyunmi highlighted the potential impact: “95%? That blows out every other, you know, prediction, right? Every other doctor.” The real value here lies in the transformative potential of AI for early disease detection, which could lead to more personalized healthcare and better outcomes, particularly in managing chronic conditions.

Mistral’s Customization Leap

Mistral AI announced new customization capabilities, a significant move in the AI landscape. Jyunmi said, “I think this is really important and this is something we'll continue to see… there's other options here like Mistral which continues to impress people.” This development signals a shift toward more flexible and accessible AI tools, allowing businesses to customize AI solutions to their specific needs, potentially democratizing AI usage across industries.

Humanoid Robots in the Workforce

Figure AI’s humanoid robots are now being tested in BMW’s manufacturing plants, a major leap in robotics. Beth emphasized the significance, saying, “In the capability, it is kind of like a GPT-3 to GPT-4 kind of leap.” This advancement suggests that we are on the cusp of a new era in manufacturing, where AI-driven robots could handle tasks previously thought to require human dexterity and decision-making.

Smarter Water Heaters: AI in Everyday Products

Kayla Systems introduced an AI-powered water heater this week, marking the integration of AI into consumer products. “They have now created a heat pump type water heater that has applied AI built into it,” Karl noted. This development points to the growing trend of AI integration into everyday products, setting new standards for energy efficiency and convenience in the consumer market.

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