Practical AI

Discover the ins and outs of building a deep learning workstation, from hardware choices like GPUs to optimizing performance. The hosts discuss the balance between custom builds and pre-built systems while addressing hardware shortages. Learn about crucial considerations like motherboard design, cooling solutions, and the nuances of network connectivity. They also share insights on development workflows using TensorFlow and PyTorch, plus tips on effectively upgrading your setup. It's a treasure trove of information for AI enthusiasts!

Lukas Biewald, Founder and CEO of Weights & Biases, shares insights from his journey in machine learning and the developer tools his company is creating. He discusses the challenges of tracking experiments and the need for better tools to manage machine learning workflows. The conversation touches on navigating the complexities between DevOps and Data Ops, and the importance of metrics in model performance. Lukas also emphasizes community engagement and his vision for future advancements in machine learning tooling.

Hamish Ogilvy, founder of Sajari and an expert in machine learning and search technology, shares insights on the revolutionary impact of reinforcement learning in search. He discusses algorithmic trade-offs in ranking results and the integration of advanced AI technologies into search functionality. The conversation also touches on the evolution of search from traditional methods to voice queries, the challenges of data privacy, and the ongoing quest for real-time accuracy. Get ready to rethink how we search in the digital age!

Rajiv Shah, a data scientist at DataRobot and professor at the University of Illinois at Chicago, dives into the critical issue of data leakage in machine learning. He explains how this hidden menace can skew model results, emphasizing techniques like activation maps to spot leakage. The conversation also covers the ethical implications of data handling and the importance of robust model development practices. Rajiv encourages aspiring data scientists to prioritize foundational skills over trends for successful machine learning.

Suju Rajan, Senior Director of Enterprise AI at LinkedIn, discusses the practical implementation of cutting-edge AI in recruitment and advertising. She delves into the role of graph-structured data and personalization in enhancing candidate searches. Suju shares insights on overcoming machine learning technical debt and highlights the evolution of AI in decision-making. The conversation also addresses biases in AI systems and the ethical considerations necessary for responsible application in recruitment. Expect a blend of technical depth and real-world application!

Daniel Chen, a data scientist at Lander Analytics and PhD candidate at Virginia Tech, shares his expertise in data science and computational biology. He highlights the importance of robust project organization and effective version control in data science. Daniel also discusses the integration of AI in medicine, particularly in epidemiology and medical imaging. Plus, he emphasizes reducing coding dependencies and maintaining data integrity as key to successful projects. His insights tie into the upcoming R Conference, making for an enlightening conversation.

Will Ramey, Global Head of Developer Programs at NVIDIA, shares his expertise from leading the Deep Learning Institute. He discusses NVIDIA's evolution from a graphics card manufacturer to a leader in AI, emphasizing the role of education in shaping practitioners. Ramey highlights innovative training methods and community engagement, especially at the upcoming GPU Technology Conference. He also addresses the transformation of deep learning education into a global, dynamic platform, reflecting on how rapidly evolving technology impacts various sectors like education and healthcare.

Join Andrew Burt, managing partner at BNH.ai with a rich background in AI law, and Patrick Hall, principal scientist specializing in trustworthy AI, as they dive into the complexities of AI failure. They discuss the urgent need for robust incident response plans and the unique liabilities that AI introduces. Practical insights on debugging models, navigating ethical frameworks, and addressing privacy concerns highlight the importance of collaboration between legal and technical teams. Buckle up for an enlightening discussion on the future of responsible AI!

Join Jenny Zhang from Mozilla, focused on the Common Voice project, Remy Muhire, passionate about VoiceTech, and Josh Meyer, who champions African language tech. They explore the biases in speech data affecting language and accent recognition. Discover Mozilla’s inclusive approach to creating an open-source voice database. The trio also discusses challenges in gathering diverse datasets for marginalized communities, particularly in Sub-Saharan Africa, and emphasizes the need for ethical data practices to support underrepresented languages.

Drago Anguelov, Principal Scientist and Head of Research at Waymo, discusses his journey from machine learning at Stanford to leading advancements in autonomous driving. He explores the integration of diverse sensors like LiDAR and radar for environmental perception. Drago highlights the importance of collaboration with regulators to address community needs and shares insights on how machine learning adapts to various driving conditions. Finally, he envisions a future where autonomous vehicles significantly enhance safety and urban transportation, making travel easier for everyone.