Are We Still Repeating the Same Mistakes with AI? [AI Today Podcast]

Are We Still Repeating the Same Mistakes with AI? [AI Today Podcast]

Author: AI & Data Today July 17, 2024 Duration: 22:06
Artificial intelligence has been on the horizon for over seventy years. In fact, the term AI was officially coined in 1956. So, why does it seem so close but also so unattainable? In this episode of AI Today hosts Kathleen Walch and Ron Schmelzer discuss the question: Are we still repeating the same mistakes with AI? Continue reading Are We Still Repeating the Same Mistakes with AI? [AI Today Podcast] at Cognilytica.

Cut through the noise and get straight to what matters in artificial intelligence. The AI Today Podcast, from AI & Data Today, moves beyond theoretical discussions and speculative hype to focus on tangible applications happening right now. Each episode grounds itself in reality, offering practical insights drawn directly from the field. You'll hear how enterprises are actually deploying machine learning, the challenges public sector agencies are navigating, and the grounded perspectives from technology leaders who are building these systems. This isn't about far-off futures; it's a clear-eyed look at the strategies, use cases, and lessons learned from organizations implementing AI today. The conversations are built for professionals who need real-world understanding, whether you're a technical practitioner, a business leader, or simply someone curious about the practical shift AI is creating. Tune in for a straightforward, no-fluff exploration of how intelligence is being integrated into our world, one concrete example at a time. This podcast serves as an essential resource for anyone looking to separate signal from noise in the rapidly evolving technology landscape.
Author: Language: English Episodes: 100

AI Today Podcast
Podcast Episodes
Why Critical Thinking is Crucial for AI [AI Today Podcast] [not-audio_url] [/not-audio_url]

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The widespread adoption and use of generative AI means that folks no longer need to be an expert in “hard” skills such as statistics & probability, calculus, or linear algebra to get value from using Generative AI. Inste…
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