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
Apr 15, 2021 • 36min
AutoML for Natural Language Processing with Abhishek Thakur - #475
Abhishek Thakur, a machine learning engineer at Hugging Face and the world’s first quadruple Kaggle Grandmaster, shares insights from his fascinating journey. He discusses his evolution in Kaggle competitions, emphasizing practical skills gained along the way. Abhishek dives into his work on AutoNLP, revealing its goals and how it stacks up against handcrafted models. He also highlights key lessons in NLP techniques and the importance of blending theory with practice, alongside his experiences writing his book, Approaching (Almost) Any Machine Learning Problem.
Apr 12, 2021 • 36min
Inclusive Design for Seeing AI with Saqib Shaikh - #474
Saqib Shaikh, a Software Engineer at Microsoft and the lead for the Seeing AI Project, shares insights on his groundbreaking app that narrates the world for the visually impaired. He discusses its evolution from a hackathon project to a powerful tool, the technical challenges behind real-time machine learning, and the significance of user intent in enhancing interaction. Saqib also explores future innovations like smart glasses and the role of AI in promoting accessibility, emphasizing the balance between automation and user trust.
8 snips
Apr 8, 2021 • 34min
Theory of Computation with Jelani Nelson - #473
Jelani Nelson, a professor in the Theory Group at UC Berkeley, dives into the fascinating realms of computational theory, streaming algorithms, and dimensionality reduction. He explores the delicate balance between innovating new algorithms and optimizing existing ones. Listeners will discover practical applications of random projections in machine learning and essential theoretical tools for practitioners. Additionally, Jelani discusses his nonprofit, AddisCoder, which empowers Ethiopian high school students through programming and algorithm education.
Apr 5, 2021 • 41min
Human-Centered ML for High-Risk Behaviors with Stevie Chancellor - #472
Stevie Chancellor, an Assistant Professor at the University of Minnesota, tackles the intersection of human-centered machine learning and high-risk behaviors. She shares insights on using machine learning to assess mental illness severity and discusses how convolutional graph neural networks can reveal new behaviors in opioid use disorder. Chancellor also delves into the ethical challenges of mining social media data for mental health research, underscores the importance of clear communication in mental health, and emphasizes ethical considerations in AI-driven crisis detection.
Apr 1, 2021 • 24min
Operationalizing AI at Dataiku with Conor Jensen - #471
Conor Jensen, Director of Data Science at Dataiku and an expert in AI operationalization, shares his unique journey from a military background to leading data science teams. He discusses the hurdles of managing real-world data inputs and the importance of a strong evaluation program. Jensen highlights the need for a cultural shift in organizations to embrace data-driven decision-making. He also explores strategies for effectively implementing AI across product lines and the significance of collaboration in transforming data science practices.
Apr 1, 2021 • 26min
ML Lifecycle Management at Algorithmia with Diego Oppenheimer - #470
Diego Oppenheimer, Founder and CEO of Algorithmia, shares insights on overcoming challenges in transitioning AI from theory to practice. He discusses the findings from a recent survey on AI market trends and the importance of translating analytics into actionable strategies. Diego contrasts the machine learning approaches of small versus large firms, noting how smaller businesses capitalize on rapid tech adoption. Also covered are the obstacles to deploying machine learning models, including IT and security concerns, especially in a post-pandemic landscape.
Mar 29, 2021 • 22min
End to End ML at Cloudera with Santiago Giraldo - #469 [TWIMLcon Sponsor Series]
Santiago Giraldo, Director of Product Marketing for Data Engineering & Machine Learning at Cloudera, dives into the dynamic world of AI and data engineering. He shares insights from Cloudera's impactful presence at TWIMLcon, emphasizing practical machine learning applications. The conversation highlights innovations in data engineering and the launch of the Cloudera Data Platform. Santiago also explores enhancing model explainability and introduces applied machine learning prototypes to tackle real-world challenges effectively.
Mar 29, 2021 • 22min
ML Platforms for Global Scale at Prosus with Paul van der Boor - #468 [TWIMLcon Sponsor Series]
Join Paul van der Boor, Senior Director of Data Science at Prosus, as he shares his journey from aerospace engineering to leading data science at a major tech firm. He discusses the hurdles AI builders face transitioning from demos to real-world applications and the importance of thorough evaluations. Paul explores building ML capabilities across diverse teams in a global organization, emphasizing collaboration and standardization. He also reflects on insights gained from a recent industry conference, highlighting networking as a key to overcoming challenges.
Mar 24, 2021 • 54min
Can Language Models Be Too Big? 🦜 with Emily Bender and Margaret Mitchell - #467
Join linguist Emily M. Bender and AI researcher Margaret Mitchell as they unravel the complex implications of large language models. They discuss the environmental cost of training these models and the biases they perpetuate, highlighting the need for ethical AI practices. The duo emphasizes the importance of addressing language's impact on identity and the risks of misconceptions in AI interactions. With a focus on transparency and the importance of documentation, Bender and Mitchell advocate for a thoughtful approach to building responsible AI systems.
Mar 22, 2021 • 36min
Applying RL to Real-World Robotics with Abhishek Gupta - #466
In this discussion, Abhishek Gupta, a PhD student at UC Berkeley specializing in reinforcement learning for robotics, shares his exciting journey from Lego competitions to groundbreaking research. He dives into how robots learn reward functions from video data and the importance of supervised experts. Gupta also tackles real-world challenges of robotic learning, including multitask learning and the innovative concept of 'gradient surgery' to boost efficiency. The conversation highlights the fascinating relationship between humans and robots in everyday settings.
