The TWIML AI Podcast (formerly This Week in Machine Learning & Artificial Intelligence)
Sam Charrington
Machine learning and artificial intelligence are dramatically changing the way businesses operate and people live. The TWIML AI Podcast brings the top minds and ideas from the world of ML and AI to a broad and influential community of ML/AI researchers, data scientists, engineers and tech-savvy business and IT leaders. Hosted by Sam Charrington, a sought after industry analyst, speaker, commentator and thought leader. Technologies covered include machine learning, artificial intelligence, deep learning, natural language processing, neural networks, analytics, computer science, data science and more.
Episodes
Mentioned books
Oct 22, 2019 • 31min
Live from TWIMLcon! Operationalizing Responsible AI - #310
Join Rachel Thomas, a leader in AI ethics, Guillaume Saint-Jacques, whose expertise lies in responsible AI design at LinkedIn, and Parinaz Sobahni, a venture capital innovator in machine learning. They discuss the critical need for embedding ethics in AI culture and decision-making. The trio emphasizes diversity in teams and the importance of accountability in product development. They also tackle biases in algorithms that impact job recruitment and highlight innovative solutions to promote fairness and transparency in AI applications.
Oct 18, 2019 • 34min
Live from TWIMLcon! Scaling ML in the Traditional Enterprise - #309
Josh Bloom, a UC Berkeley astrophysics professor, leads a panel featuring Amr Awadallah, Cloudera's Global CTO, and Pallav Agrawal, Data Science Director at Levi Strauss & Co. They explore the challenges traditional enterprises face in adopting machine learning. The discussion delves into digital transformation strategies, the necessity of bridging developer and operations teams, and the critical importance of attracting data science talent. Insights also cover the oil and gas industry's pivot to renewables and the urgency for businesses to innovate in the AI era.
Oct 15, 2019 • 28min
Live from TWIMLcon! Culture & Organization for Effective ML at Scale (Panel) - #308
TWIMLcon brought together so many in the ML/AI community to discuss the unique challenges to building and scaling machine learning platforms. In this episode, hear about changing the way companies think about machine learning from a diverse set of panelists including Pardis Noorzad, Data Science Manager at Twitter, Eric Colson, Chief Algorithms Officer Emeritus at Stitch Fix, and Jennifer Prendki, Founder & CEO at Alectio, moderated by Maribel Lopez, Founder & Principal Analyst at Lopez Research.
Oct 10, 2019 • 32min
Live from TWIMLcon! Use-Case Driven ML Platforms with Franziska Bell - #307
Franziska Bell, Ph.D., the Director of Data Science Platforms at Uber, shares cutting-edge insights on democratizing data science across the company. She discusses how use cases drive platform development, enhancing workflows for all employees. The collaboration with Uber's Michelangelo platform is highlighted, revealing challenges in integrating legacy models. Franziska also emphasizes the importance of open source in machine learning, innovative automation for data analytics, and fostering a collaborative culture within data science teams.
Oct 8, 2019 • 34min
Live from TWIMLcon! Operationalizing ML at Scale with Hussein Mehanna - #306
Join Hussein Mehanna, Head of ML and AI at Cruise, as he shares his journey from Facebook to Cruise, detailing the evolution of machine learning platforms. He dives into the challenges of scaling ML with innovation, showcasing his insights on optimizing workflows and collaboration in tech giants. Hussein also discusses the complexities of autonomous vehicles and predicts the future of machine learning in enterprises, underscoring the role of cloud services and collaborative tools like Kubeflow.
Oct 4, 2019 • 32min
Live from TWIMLcon! Encoding Company Culture in Applied AI Systems - #305
Deepak Agarwal, VP of Engineering at LinkedIn, dives into the synergy between company culture and applied AI systems. He explains how standardizing processes boosts productivity and ML ROI. The conversation highlights the Pro-ML initiative, which focuses on scaling machine learning systems by aligning tools with innovation. Agarwal also emphasizes the importance of a strong business case for tech transitions and the significance of thoughtful experimentation in driving meaningful insights within a centralized AI organization.
Oct 1, 2019 • 34min
Live from TWIMLcon! Overcoming the Barriers to Deep Learning in Production with Andrew Ng - #304
Andrew Ng, the Founder and CEO of Landing AI and a key figure behind Google Brain, delves into the intricate landscape of AI adoption in various industries. He shares insights on overcoming challenges in large enterprises and emphasizes the importance of education in navigating machine learning's complexities. The discussion touches on managing risks during model deployment and the necessity for collaborative tools in data management. Ng's thoughts on integrating AI across manufacturing, agriculture, and healthcare highlight the transformative potential of technology.
Sep 27, 2019 • 44min
The Future of Mixed-Autonomy Traffic with Alexandre Bayen - #303
Join Alexandre Bayen, Director of the Institute for Transportation Studies at UC Berkeley, as he dives into the future of mixed-autonomy traffic. He discusses the two major revolutions expected in the next 10-15 years surrounding AI's transformative role in traffic management. Discover how individual driving behaviors impact congestion, and learn about swarming strategies that self-driving cars can leverage. Bayen emphasizes the balance between innovation and safety, highlighting advancements in reinforcement learning for real-time traffic solutions.
Sep 25, 2019 • 44min
Deep Reinforcement Learning for Logistics at Instadeep with Karim Beguir - #302
Karim Beguir, Co-founder and CEO of InstaDeep, shares his journey from a small Tunisian town to leading innovations in AI for logistics. He discusses how deep reinforcement learning is revolutionizing decision-making in logistics, improving efficiency and cost-effectiveness. The conversation touches on the use of synthetic datasets for model training and the complexities of enhancing passenger experiences in ride-sharing. Karim emphasizes the significance of adaptive reward functions and the balance between learning-based and heuristic approaches to optimize outcomes.
Sep 19, 2019 • 40min
Deep Learning with Structured Data w/ Mark Ryan - #301
Mark Ryan, author of 'Deep Learning with Structured Data' and a member of IBM's Data and AI support team, shares insights on applying deep learning to structured data. He discusses creating predictive models using the Toronto streetcar network dataset, addressing challenges like data preparation and metadata integration. Mark emphasizes that deep learning doesn't always require massive datasets and highlights its potential in various sectors. He also details the interactive feedback process in his book's development and the advantages of collaborative learning in deep learning courses.
