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AI Today Podcast: Artificial Intelligence Insights, Experts, and Opinion

Latest episodes

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8 snips
Apr 17, 2024 • 13min

Applying CPMAI Methodology in the real world: Interview with George Fountain, Booz Allen Hamilton (BAH) [AI Today Podcast]

George Fountain, AI expert at Booz Allen Hamilton, discusses CPMAI methodology in AI projects. Topics include navigating data challenges, key considerations in project implementation, guidance on AI implementation, and exploring AI's potential in various sectors.
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24 snips
Apr 12, 2024 • 27min

Prompt Engineering Best Practices: What is Prompt Chaining? [AI Today Podcast]

Explore the concept of prompt chaining in language model systems to enhance understanding and execution. Discover how breaking down prompts into smaller steps can optimize AI performance for complex tasks. Learn about the benefits of reflex muscle training in prompt engineering and how to optimize AI prompt chaining for improved performance.
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23 snips
Apr 10, 2024 • 33min

Prompt Engineering Best Practices: Using a Prompt Pattern [AI Today Podcast]

Exploring the significance of prompt engineering in generative AI, understanding prompt patterns, comparing RTF and CREATE prompt patterns, exploring extensive capabilities of prompt engineering with LLMs in AI systems, discussing transformative potential of AI in education
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Apr 5, 2024 • 26min

How AI will Re-imagine Air Travel: Interview with Bernadette Berger, Alaska Airlines [AI Today Podcast]

Interview with Bernadette Berger, Director of Innovation at Alaska Airlines, discussing AI's impact on air travel, including personalized experiences, challenges in AI adoption, and future modalities of AI interaction in aviation technology.
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Apr 3, 2024 • 29min

AI’s impact in the practice of medicine: Interview with Dr. Jag Singh, Mass General Brigham [AI Today Podcast]

Dr. Jag Singh, a professor at Harvard Med School, discusses AI's impact on medicine. Topics include personalized medicine, virtual care, sensor integration, disease prediction, and hyper-personalized healthcare. The podcast also explores AI in education and resources for AI project management.
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Mar 29, 2024 • 34min

AI Everywhere with Software and Hardware: Interview with Wei Li, Intel [AI Today Podcast]

In this podcast, Wei Li, VP/GM of AI Software Engineering at Intel, discusses AI being integrated everywhere in hardware and software. Topics include AI evolution at Intel, collaboration with Microsoft for AI in collaboration tools, the role of open source in AI development, AI implementation strategies, and future trends in AI applications.
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Mar 27, 2024 • 28min

Lowering Barriers to Entry for AI: Interview with Nuri Cankaya, Intel [AI Today Podcast]

Nuri Cankaya, VP of AI Marketing at Intel, discusses lowering barriers to entry for AI adoption. Topics include challenges in integrating AI with real-world data, Intel's efforts to make AI more accessible, collaborations for sustainable AI solutions, importance of energy-efficient products, utilizing appropriate tools for AI projects, and AI's future impact on society.
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Mar 22, 2024 • 24min

AI’s Impact on Healthcare: Interview with Dr. Jesse Ehrenfeld, AMA [AI Today Podcast]

The podcast discusses AI's impact on healthcare with Dr. Jesse Ehrenfeld, President of the AMA. Topics include ethical AI in healthcare, trust and transparency in AI-driven platforms, augmented intelligence in healthcare workflows, and the future of AI in healthcare.
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Mar 20, 2024 • 19min

The People Side of AI: Interview with Ian Beacraft, Signal and Cipher [AI Today podcast]

Discover how AI is reshaping teamwork and creativity with generative AI, the importance of responsible AI, and the transformative impact on industries. Dive into discussions about societal perceptions, cultural influences, and the future of work in the age of AI.
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Mar 15, 2024 • 7min

Explainable AI Concepts [AI Today Podcast]

The podcast delves into the Explainable AI Layer of the Cognilytica Trustworthy AI Framework, discussing the importance of AI algorithms being able to explain decisions. It explores the significance of transparent AI systems in building trust, highlighting pitfalls of black box technology. The contrast between explainable algorithms and less transparent machine learning approaches is also emphasized.

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