Erdem Özcan is an esteemed expert with a rich background in computer science, focusing on innovations in AI. With a PhD in computer science and significant industry experience, including work on IBM's Watson and at Elemental Cognition, Dr. Özcan has been at the forefront of blending symbolic AI and deep learning systems. Today, he is actively engaged in developing solutions that enhance the reliability and explainability of AI applications.
Tune in to this enlightening conversation and gain deeper insights into the future trajectories and current challenges within the world of artificial intelligence as explained by one of the leading thinkers in the field.
Key takeaways:
- Symbolic vs. Statistical AI: Erdem discusses the critical differences and applications of symbolic AI versus statistical methods, emphasizing the need for reliably representing concepts for efficient AI outcomes.
- The Role of Cogent English: Insight into how Cogent, a platform developed by Erdem, assists in translating complex business knowledge into APIs and conversational interfaces using a subset of English tailored for formal reasoning.
- Challenges in Generative AI: Exploration of issues that arise with generative AI, particularly around reliability and the operational deployment of reasoning systems.
- Development of Neurosymbolic AI: Erdem predicts a significant shift towards hybrid AI architectures that combine both symbolic and deep learning approaches to handle real-life complex scenarios more efficiently.
- Importance of Explainability in AI: A discussion on why explainability and the ability to audit AI decisions are crucial, especially as AI systems become more integrated into critical decision-making processes.
- Comparison of Formal Reasoning Systems and LLMs: Erdem explains why formal reasoning systems can be more reliable than large language models (LLMs) in complex problem-solving scenarios.
Quote from the show:
"Translating human expertise into AI systems is not just about feeding data into algorithms. It’s about creating structures that allow machines to reason and make decisions transparently and reliably." – Erdem Özcan
Links:
LinkedIn: https://www.linkedin.com/in/aerdemozcan/
Website: https://ec.ai/
Ways to Tune In:
Earley AI Podcast: https://www.earley.com/earley-ai-podcast-home
Apple Podcast: https://podcasts.apple.com/podcast/id1586654770
Spotify: https://open.spotify.com/show/5nkcZvVYjHHj6wtBABqLbE?si=73cd5d5fc89f4781
iHeart Radio: https://www.iheart.com/podcast/269-earley-ai-podcast-87108370/
Stitcher: https://www.stitcher.com/show/earley-ai-podcast
Amazon Music: https://music.amazon.com/podcasts/18524b67-09cf-433f-82db-07b6213ad3ba/earley-ai-podcast
Buzzsprout: https://earleyai.buzzsprout.com/
Thanks to our sponsors:
Erdem Özcan is an esteemed expert with a rich background in computer science, focusing on innovations in AI. With a PhD in computer science and significant industry experience, including work on IBM's Watson and at Elemental Cognition, Dr. Özcan has been at the forefront of blending symbolic AI and deep learning systems. Today, he is actively engaged in developing solutions that enhance the reliability and explainability of AI applications.
Tune in to this enlightening conversation and gain deeper insights into the future trajectories and current challenges within the world of artificial intelligence as explained by one of the leading thinkers in the field.
Key takeaways:
- Symbolic vs. Statistical AI: Erdem discusses the critical differences and applications of symbolic AI versus statistical methods, emphasizing the need for reliably representing concepts for efficient AI outcomes.
- The Role of Cogent English: Insight into how Cogent, a platform developed by Erdem, assists in translating complex business knowledge into APIs and conversational interfaces using a subset of English tailored for formal reasoning.
- Challenges in Generative AI: Exploration of issues that arise with generative AI, particularly around reliability and the operational deployment of reasoning systems.
- Development of Neurosymbolic AI: Erdem predicts a significant shift towards hybrid AI architectures that combine both symbolic and deep learning approaches to handle real-life complex scenarios more efficiently.
- Importance of Explainability in AI: A discussion on why explainability and the ability to audit AI decisions are crucial, especially as AI systems become more integrated into critical decision-making processes.
- Comparison of Formal Reasoning Systems and LLMs: Erdem explains why formal reasoning systems can be more reliable than large language models (LLMs) in complex problem-solving scenarios.
Quote from the show:
"Translating human expertise into AI systems is not just about feeding data into algorithms. It’s about creating structures that allow machines to reason and make decisions transparently and reliably." – Erdem Özcan
Links:
LinkedIn: https://www.linkedin.com/in/aerdemozcan/
Website: https://ec.ai/
Ways to Tune In:
Earley AI Podcast: https://www.earley.com/earley-ai-podcast-home
Apple Podcast: https://podcasts.apple.com/podcast/id1586654770
Spotify: https://open.spotify.com/show/5nkcZvVYjHHj6wtBABqLbE?si=73cd5d5fc89f4781
iHeart Radio: https://www.iheart.com/podcast/269-earley-ai-podcast-87108370/
Stitcher: https://www.stitcher.com/show/earley-ai-podcast
Amazon Music: https://music.amazon.com/podcasts/18524b67-09cf-433f-82db-07b6213ad3ba/earley-ai-podcast
Buzzsprout: https://earleyai.buzzsprout.com/
Thanks to our sponsors: