From Automated Prompt Engineering to Deepfake Speech: AI Advancements and Ethical Challenges with Dr. Julie Wall

In this episode of ODSC’s Ai X Podcast, we speak with Dr. Julie Wall about recent AI advancements and their ethical implications, particularly automated prompt engineering and deepfake speech. Julie is a Professor of AI and Advanced Computing at the University of West London, where she researches designing intelligent systems to process and model temporal data, focusing on speech and language applications. She is also a member of the British Standards Institution (BSI) and serves as an expert in the field of AI. Show Note Topics: Guest introduction and key moments in their career - Addressing the limitations of manual prompting - Automated prompt engineering - Leveraging advanced techniques for automated prompt engineering including reinforcement learning, genetic algorithms, and gradient-based methods - Practical applications of automated prompt engineering - How automated prompt engineering improves the performance of NLP models - The potential challenges or considerations when implementing automated prompt engineering in real-world applications - How the feedback loop in automated systems help refine prompts over time, and what metrics are used to assess prompt effectiveness - Deepfake speech as misinformation - The rapid evolution of deepfake speech - Deepfake creation tools - The use of deepfake speech for political disinformation - The latest developments in legislation aimed at combating deepfake speech, and how different countries are approaching this issue - Deepfake detection methods and models Show Notes: - Dr. Julie Wall’s Bio: https://www.uwl.ac.uk/staff/julie-wall - Dr. Julie Wall’s Citations: https://scholar.google.co.uk/citations?user=EUlLEm4AAAAJ&hl=en - ODSC Europe: https://odsc.com/europe/ - ODSC West: https://odsc.com/california/ - A Systematic Survey of Prompt Engineering in Large Language Models: Techniques and Applications: https://arxiv.org/html/2402.07927v1 - Unleashing the potential of prompt engineering in Large Language Models: a comprehensive review: https://arxiv.org/abs/2310.14735 - Prompt Engineering a Prompt Engineer: https://arxiv.org/html/2311.05661v3 - Prompt Engineering or Fine Tuning: An Empirical Assessment of Large Language Models in Automated Software Engineering Tasks: https://arxiv.org/pdf/2310.10508 - OpenAI Change Log: https://platform.openai.com/docs/changelog - Reinforcement learning human feedback: https://en.wikipedia.org/wiki/Reinforcement_learning_from_human_feedback - Generic algorithms: https://en.wikipedia.org/wiki/Genetic_algorithm - Gradient-based methods: https://en.wikipedia.org/wiki/Gradient_method - Deep Live Cam: https://github.com/hacksider/deep-live-cam - GANS: https://developers.google.com/machine-learning/gan/gan_structure - Neural Text to Speech: https://www.aane.in/research/neural-text-to-speech - GitHub topic on deepfakes https://github.com/topics/deepfake - GitHub topic on voice cloning: https://github.com/topics/voice-cloning Follow Sheamus McGovern on X (https://x.com/sheamusmcgov) or LinkedIn (https://www.linkedin.com/in/sheamus/) for podcast updates This episode was sponsored by: Ai+ Training ⁠https://aiplus.training/⁠ Home to 600+ hours of on-demand, self-paced AI training, live virtual training, and certifications in in-demand skills like LLMs and prompt engineering. And created in partnership with ODSC ⁠https://odsc.com/⁠ The Leading AI Training Conference, featuring expert-led, hands-on workshops, training sessions, and talks on cutting-edge AI topics and tools, from data science and machine learning to generative AI to LLMOps Join us at our upcoming and highly anticipated conference ODSC West in South San Francisco October 29-31. Never miss an episode, subscribe now!