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
Mar 7, 2019 • 42min
Deep Learning in Optics with Aydogan Ozcan - TWiML Talk #237
Aydogan Ozcan, a UCLA professor specializing in electrical and computer engineering, discusses pioneering research at the intersection of deep learning and optics. He explains the concept of all-optical neural networks that mimic neuron behavior through diffraction. The conversation dives into practical applications such as enhanced biomedical imaging and environmental monitoring. Ozcan also highlights the role of 3D printing in reducing costs and dimensions of optical networks, showcasing the possibilities for future innovations in defense and security.
Mar 4, 2019 • 46min
Scaling Machine Learning on Graphs at LinkedIn with Hema Raghavan and Scott Meyer - TWiML Talk #236
Today we’re joined by Hema Raghavan and Scott Meyer of LinkedIn to discuss the graph database and machine learning systems that power LinkedIn features such as “People You May Know” and second-degree connections. Hema shares her insight into the motivations for LinkedIn’s use of graph-based models and some of the challenges surrounding using graphical models at LinkedIn’s scale, while Scott details his work on the software used at the company to support its biggest graph databases.
Mar 1, 2019 • 54min
Safer Exploration in Deep Reinforcement Learning using Action Priors with Sicelukwanda Zwane - TWiML Talk #235
Today we conclude our Black in AI series with Sicelukwanda Zwane, a masters student at the University of Witwatersrand and graduate research assistant at the CSIR, who presented on “Safer Exploration in Deep Reinforcement Learning using Action Priors” at the workshop. In our conversation, we discuss what “safer exploration” means in this sense, the difference between this work and other techniques like imitation learning, and how this fits in with the goal of “lifelong learning.”
Feb 25, 2019 • 1h 5min
Dissecting the Controversy around OpenAI's New Language Model - TWiML Talk #234
In the inaugural TWiML Live, Sam Charrington is joined by Amanda Askell (OpenAI), Anima Anandkumar (NVIDIA/CalTech), Miles Brundage (OpenAI), Robert Munro (Lilt), and Stephen Merity to discuss the controversial recent release of the OpenAI GPT-2 Language Model.
We cover the basics like what language models are and why they’re important, and why this announcement caused such a stir, and dig deep into why the lack of a full release of the model raised concerns for so many.
Feb 22, 2019 • 47min
Human-Centered Design with Mira Lane - TWiML Talk #233
Today we present the final episode in our AI for the Benefit of Society series, in which we’re joined by Mira Lane, Partner Director for Ethics and Society at Microsoft.
Mira and I focus our conversation on the role of culture and human-centered design in AI. We discuss how Mira defines human-centered design, its connections to culture and responsible innovation, and how these ideas can be scalably implemented across large engineering organizations.
Feb 18, 2019 • 49min
Fairness in Machine Learning with Hanna Wallach - TWiML Talk #232
Hanna Wallach, Principal Researcher at Microsoft Research, discusses the impact of bias in machine learning, the challenges of deploying fair ML models, and the importance of transparency and interpretability. She highlights the need for diverse stakeholders and fair metrics in model training to address fairness challenges.
Feb 18, 2019 • 57min
AI for Healthcare with Peter Lee - TWiML Talk #231
In this episode, we’re joined by Peter Lee, Corporate Vice President at Microsoft Research responsible for the company’s healthcare initiatives. Peter and I met back at Microsoft Ignite, where he gave me some really interesting takes on AI development in China, which is linked in the show notes. This conversation centers around impact areas Peter sees for AI in healthcare, namely diagnostics and therapeutics, tools, and the future of precision medicine.
Feb 11, 2019 • 46min
An Optimized Recurrent Unit for Ultra-Low Power Acoustic Event Detection with Justice Amoh Jr. - TWiML Talk #230
Today, we're joined by Justice Amoh Jr., a Ph.D. student at Dartmouth’s Thayer School of Engineering.
Justice presented his work on “An Optimized Recurrent Unit for Ultra-Low Power Acoustic Event Detection.” In our conversation, we discuss his goal of bringing low cost, high-efficiency wearables to market for monitoring asthma. We explore the challenges of using classical machine learning models on microcontrollers, and how he went about developing models optimized for constrained hardware environm
Feb 11, 2019 • 33min
Pathologies of Neural Models and Interpretability with Alvin Grissom II - TWiML Talk #229
Today, we continue our Black in AI series with Alvin Grissom II, Assistant Professor of Computer Science at Ursinus College. In our conversation, we dive into the paper he presented at the workshop, “Pathologies of Neural Models Make Interpretations Difficult.” We talk through some of the “pathological behaviors” he identified in the paper, how we can better understand the overconfidence of trained deep learning models in certain settings, and how we can improve model training with entropy regulariz
Feb 8, 2019 • 56min
AI for Earth with Lucas Joppa - TWiML Talk #228
Today we’re joined by Lucas Joppa, Chief Environmental Officer at Microsoft and Zach Parisa, Co-founder and president of Silvia Terra, a Microsoft AI for Earth grantee.
In our conversation, we explore the ways that ML & AI can be used to advance our understanding of forests and other ecosystems, supporting conservation efforts. We discuss how Silvia Terra uses computer vision and data from a wide array of sensors, combined with AI, to yield more detailed estimates of the various species in our forests.
