Practical AI: Machine Learning, Data Science

Practical AI: Machine Learning, Data Science

Making artificial intelligence practical, productive & accessible to everyone. Practical AI is a show in which technology professionals, business people, students, enthusiasts, and expert guests engage in lively discussions about Artificial Intelligence and related topics (Machine Learning, Deep Learning, Neural Networks, GANs, MLOps, AIOps, LLMs & more). The focus is on productive implementations and real-world scenarios that are accessible to everyone. If you want to keep up with the latest advances in AI, while keeping one foot in the real world, then this is the show for you!

Practical AI: Machine Learning, Data Science

Wed Apr 10 2024

RAG continues to rise

AIGenerative ModelsMulti-Model FutureChallenges in AI/ML CommunityTransformers

The episode discusses the popularity shift from fine-tuning to RAG (Retrieval-Augmented Generation) in AI workflows. Generative models are being recognized for their role as assistants and automators rather than predictors or analytics tools. Enterprises are moving towards a multi-model future, driven partially by open models for generative AI. Challenges in the AI/ML community include data set creation, labeling accuracy, lack of best practices, and fragmented communities. The limitations of using transformers in AI models and alternative approaches are explored. An upcoming conference about AI quality is announced with various exciting activities.

Practical AI: Machine Learning, Data Science

Tue Apr 02 2024

Should kids still learn to code?

AICoding EducationData ScienceAI CommunityGenerative AI Tools

This episode covers a range of topics related to AI, including the impact on coding education, evolving roles in data science and AI, navigating and connecting in the AI community, driving adoption of generative AI tools, utilizing generative AI for productivity, and the evolving landscape of information retrieval. Key insights include the need for human involvement in AI systems, the importance of community engagement in the AI field, strategies for driving adoption of generative AI tools, and the evolving ways people find information using tools like Google and language models.

Practical AI: Machine Learning, Data Science

Tue Mar 26 2024

AI vs software devs

AISoftware EngineeringProductivity ToolsCommunity EngagementClosed-Source Software

The episode is a comprehensive exploration of AI-related topics, covering the challenges and limitations of AI software engineers, the use of scenarios and prompts to improve AI, advanced tooling and AI in software development, the future possibilities of AI technology, specialization and productivity boosts with AI, AI coding tools and community engagement, the role of open source tools and community in software development, and the impact of closed-source software on user control. The episode emphasizes the importance of human oversight and validation in AI development and highlights the potential disruptions and opportunities that AI presents in various industries.

Practical AI: Machine Learning, Data Science

Wed Mar 20 2024

Prompting the future

prompt engineeringgenerative AIlanguage modelsprobabilistic technologyblack box models

This episode explores the field of prompt engineering in generative AI, focusing on the insights, challenges, approaches, and optimization strategies. It highlights the importance of adapting to different language models, the mix of skills required for prompt engineering, and the evolving landscape of AI products. The episode also delves into the unique challenges of working with probabilistic technology and black box models, emphasizing the need for systematic approaches and frequent prompt updates. Additionally, it discusses the role of user feedback, logging, monitoring, and analytics in optimizing prompt engineering processes. The episode concludes by emphasizing the ongoing need for human guidance in AI tasks.

Practical AI: Machine Learning, Data Science

Tue Mar 12 2024

Generating the future of art & entertainment

AIGenerative ModelsArtificial IntelligenceArtTechnology

Anastasis Draminitis, co-founder and CTO of Runway, discusses how the company started as a passion project at art school. Runway focuses on making generative models and AI more accessible and user-friendly for artists. The intersection of art and technology was a key focus for Anastasis, leading to the unique approach taken by Runway in blending creativity with AI. The episode covers topics such as the initial vision for using AI in the industry, the decision to become an entrepreneur, early adoption of generative technology, Gen 2 model development, and the focus on building useful tools for artists.

Practical AI: Machine Learning, Data Science

Wed Mar 06 2024

YOLOv9: Computer vision is alive and well

Facial RecognitionYOLO V9Parameter EfficiencyEdge ComputingMLOps

The episode covers topics such as facial recognition technology at airports, efficiency and parameter efficiency in YOLO V9, parameter efficiency and performance in computer vision models, efficiency and optimization for edge devices, deployment strategies and MLOps, and MLOps and AI model performance.

Practical AI: Machine Learning, Data Science

Wed Feb 28 2024

Representation Engineering (Activation Hacking)

AI NewsTechnical AdvancementsControl EngineeringLanguage ModelsVideo Generation

The episode covers recent AI news and technical advancements, control and prompt engineering in AI models, mechanisms of control in language models, applications and implications of control mechanisms in AI models, advancements in video generation models and open-source language models, as well as critiques and practicality of open-source language models.

Practical AI: Machine Learning, Data Science

Tue Feb 20 2024

Leading the charge on AI in National Security

AI initiativesDepartment of DefenseProject Mavenscaling AI applicationsadoption of AI

The episode features an interview with Jack Shanahan, a senior military officer responsible for leading AI initiatives in the US Department of Defense. The conversation covers topics such as the inception of Project Maven, scaling AI applications in national security, navigating the adoption of AI in a large organization, AI models in combat operations, modernizing the Department of Defense with AI, human-machine teaming in national security, technology and decision-making in warfare, and assessing technological capabilities for national security.

Practical AI: Machine Learning, Data Science

Wed Feb 14 2024

Gemini vs OpenAI

AI voicesRobocallsGovernment regulationGoogle GeminiAI models

The episode covers the FCC ruling on AI voices in robocalls, the introduction of Google's Gemini AI models, challenges in AI model usage, combining generative AI models with traditional data science, utilizing local language models, and future innovations. It also discusses the concerns and ethical issues surrounding AI voice technology, government regulation on AI, and the potential of AI models for data analysis and automation. The episode emphasizes the importance of considering the ecosystem around a model and highlights areas of interest such as multimodality and image editing capabilities. It concludes with insights on utilizing local language models for automation and experimentation purposes and the need to integrate AI tools into the learning process.

Practical AI: Machine Learning, Data Science

Tue Feb 06 2024

Data synthesis for SOTA LLMs

NUS ResearchOpen-Source Language ModelsCollaborationSynthetic DataCompressing Information

NUS Research, an open-source research organization, discusses their projects, collaboration, training techniques, and focus on empowering users. They emphasize compressing information, licensing concerns, and the importance of ethical practices in the AI space.

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