The Daily AI Show on Apple Podcasts
The Daily AI Show is a panel discussion broadcasted LIVE each weekday at 10am Eastern time. We delve into a wide range of AI topics and use cases that are of great significance to today's busy professionals.
No fluff. Just a concise 30-minute session to cover the latest AI news, stories, and knowledge essential for business professionals.
About the Crew:
Our panel consists of experienced professionals from various industries who have hands-on experience with AI deployment or are actively involved in coaching, consulting, and teaching AI best practices. Meet your hosts:
- Brian Maucere
- Beth Lyons
- Andy Halliday
- Eran Malloch
- Robert Mitchell
- Jyunmi Hatcher
- Karl Yeh
Crazy AI News: August 14, 2024
In the latest episode of The Daily AI Show, Brian, Beth, Andy, Jyunmi, and Karl came together to discuss the most captivating AI news from the past week. The conversation covered various topics, from AI companionship and personalized chocolate to advancements in AI science and the evolving capabilities of AI models such as Grok and Flux.
Key Points Discussed:
- AI Companionship: Delving into the trend of forming emotional connections with AI, the team explored the societal implications and the future of AI relationships.
- Personalized Chocolate: Highlighting the use of AI in the chocolate industry to create customized experiences, the team discussed how AI is reshaping industries like agriculture and manufacturing.
- Sakana AI Scientist: Introducing Sakana AI's revolutionary "AI Scientist" capable of handling the research lifecycle, sparking discussions on AI's role in education and research.
- Google's AI Advancements: Updates on Google's latest AI releases, including new features in their Pixel 9 phones, were shared, signaling Google's advancement in AI technology.
- Grok's Rapid Progress: The discussion revolved around Grok's swift advancements in AI, posing a challenge to established AI systems like GPT-4.
- Flux and LoRa in Image Generation: Exploring Flux, an open-source image generator, and the use of LoRa filters for realistic images, highlighted the evolution of open-source AI tools.
- Data Privacy and Legal Challenges: Notable developments in data privacy and legal challenges in the AI industry, including Samsung's investment in Sahara AI, were discussed.
Is The Cost of Using LLMs Racing to Zero?
Today's episode focused on the decreasing costs of utilizing Large Language Models (LLMs) and its implications for businesses. The conversation touched upon factors driving down costs, the impact on businesses, open source contributions, workforce implications, and AI's role as a knowledge preserver.
Key Points Discussed:
- Factors Driving Down Costs: Discussing optimizations in model training and the emergence of more efficient models making AI more accessible to businesses.
- Impact on Businesses: Exploring how reduced costs enable businesses to innovate and streamline operations with minimal financial risk.
- The Role of Open Source and Market Competition: Leveraging open-source models to build cost-effective AI solutions and lower barriers to entry.
- Long-term Implications for Workforce and ROI: Speculating on reduced labor needs and AI's role as a business co-pilot.
- AI as a Knowledge Preserver: Highlighting AI's potential to preserve institutional knowledge and expertise for businesses.
Open AI Strawberry: Is It Coming This Week?
In the latest episode, Brian, Beth, Andy, and Jyunmi discussed the anticipated release of OpenAI's "Strawberry" update, speculating on its features and implications. The conversation centered on LLMs, mathematical reasoning, and the future of AI reasoning and testing.
Key Points Discussed:
- Understanding Large Language Models (LLMs) and Reasoning: Examining LLM functionalities and predicting improvements with the new "Strawberry" update.
- Q-Star and Self-Taught Reasoning: Comparing the features of Q-Star with potential advancements in the Strawberry update.
- Mathematical Reasoning and LLM Testing: Using math as a benchmark to evaluate LLMs' reasoning capabilities.
- Speculation and Hype Around Strawberry: Addressing the buzz surrounding OpenAI's new release and its potential impact on AI development.
- The Future of AI Reasoning and the ARC Test: Discussing the ARC test and its implications for AI reasoning capabilities.
Is Training Your Own LLM Worth The Risk?
In this episode, Andy, Jyunmi, and Karl explored the complexities and risks of training custom Large Language Models (LLMs) versus fine-tuning existing models. The discussion covered examples, cost considerations, security factors, emerging AI agents, data quality, and the future of AI models.
Key Points Discussed:
- The Bloomberg GPT Example: Analyzing Bloomberg's AI model development and the challenges of staying competitive.
- Cost and Complexity of Building LLMs: Highlighting the expenses involved in training LLMs and the need for continuous updates.
- Security and Control Considerations: Debating the trade-offs between using third-party models and proprietary developments.
- Emergence of AI Agents: Discussing the role of AI agents in reducing the necessity for custom LLMs.
- Data Curation and Quality: Emphasizing the importance of high-quality data in training AI models.
Big News, Little News, Good News, and More
In the most recent episode, Brian, Beth, Karl, Andy, and Jyunmi covered a range of AI news, from social media hints to advancements in disease prediction models. The discussion addressed various topics related to AI developments and future trends in the industry.
