Building Blocks of Machine Learning at LEGO with Francesc Joan Riera - #533
Today we’re joined by Francesc Joan Riera, an applied machine learning engineer at The LEGO Group. In our conversation, we explore the ML infrastructure at LEGO, specifically around two use cases, content moderation and user engagement. While content moderation is not a new or novel task, but because their apps and products are marketed towards children, their need for heightened levels of moderation makes it very interesting. We discuss if the moderation system is built specifically to weed out bad actors or passive behaviors if their system has a human-in-the-loop component, why they built a feature store as opposed to a traditional database, and challenges they faced along that journey. We also talk through the range of skill sets on their team, the use of MLflow for experimentation, the adoption of AWS for serverless, and so much more! The complete show notes for this episode can be found at twimlai.com/go/534.
4 Nov 202143min
Exploring the FastAI Tooling Ecosystem with Hamel Husain - #532
Today we’re joined by Hamel Husain, Staff Machine Learning Engineer at GitHub. Over the last few years, Hamel has had the opportunity to work on some of the most popular open source projects in the ML world, including fast.ai, nbdev, fastpages, and fastcore, just to name a few. In our conversation with Hamel, we discuss his journey into Silicon Valley, and how he discovered that the ML tooling and infrastructure weren’t quite as advanced as he’d assumed, and how that led him to help build some of the foundational pieces of Airbnb’s Bighead Platform. We also spend time exploring Hamel’s time working with Jeremy Howard and the team creating fast.ai, how nbdev came about, and how it plans to change the way practitioners interact with traditional jupyter notebooks. Finally, talk through a few more tools in the fast.ai ecosystem, fastpages, fastcore, how these tools interact with Github Actions, and the up and coming ML tools that Hamel is excited about. The complete show notes for this episode can be found at twimlai.com/go/532.
1 Nov 202139min
Multi-task Learning for Melanoma Detection with Julianna Ianni - #531
In today’s episode, we are joined by Julianna Ianni, vice president of AI research & development at Proscia. In our conversation, Julianna shares her and her team’s research focused on developing applications that would help make the life of pathologists easier by enabling tasks to quickly and accurately be diagnosed using deep learning and AI. We also explore their paper “A Pathology Deep Learning System Capable of Triage of Melanoma Specimens Utilizing Dermatopathologist Consensus as Ground Truth”, while talking through how ML aids pathologists in diagnosing Melanoma by building a multitask classifier to distinguish between low-risk and high-risk cases. Finally, we discussed the challenges involved in designing a model that would help in identifying and classifying Melanoma, the results they’ve achieved, and what the future of this work could look like. The complete show notes for this episode can be found at twimlai.com/go/531.
28 Okt 202137min
House Hunters: Machine Learning at Redfin with Akshat Kaul - #530
Today we’re joined by Akshat Kaul, the head of data science and machine learning at Redfin. We’re all familiar with Redfin, but did you know that redfin.com is the largest real estate brokerage site in the US? In our conversation with Akshat, we discuss the history of ML at Redfin and a few of the key use cases that ML is currently being applied to, including recommendations, price estimates, and their “hot homes” feature. We explore their recent foray into building their own internal platform, which they’ve coined “Redeye”, how they’ve built Redeye to support modeling across the business, and how Akshat thinks about the role of the cloud when building and delivering their platform. Finally, we discuss the impact the pandemic has had on ML at the company, and Akshat’s vision for the future of their platform and machine learning at the company more broadly. The complete show notes for this episode can be found at twimlai.com/go/530.
26 Okt 202144min
Attacking Malware with Adversarial Machine Learning, w/ Edward Raff - #529
Today we’re joined by Edward Raff, chief scientist and head of the machine learning research group at Booz Allen Hamilton. Edward’s work sits at the intersection of machine learning and cybersecurity, with a particular interest in malware analysis and detection. In our conversation, we look at the evolution of adversarial ML over the last few years before digging into Edward’s recently released paper, Adversarial Transfer Attacks With Unknown Data and Class Overlap. In this paper, Edward and his team explore the use of adversarial transfer attacks and how they’re able to lower their success rate by simulating class disparity. Finally, we talk through quite a few future directions for adversarial attacks, including his interest in graph neural networks. The complete show notes for this episode can be found at twimlai.com/go/529.
21 Okt 202146min
Learning to Ponder: Memory in Deep Neural Networks with Andrea Banino - #528
Today we’re joined by Andrea Banino, a research scientist at DeepMind. In our conversation with Andrea, we explore his interest in artificial general intelligence by way of episodic memory, the relationship between memory and intelligence, the challenges of applying memory in the context of neural networks, and how to overcome problems of generalization. We also discuss his work on the PonderNet, a neural network that “budgets” its computational investment in solving a problem, according to the inherent complexity of the problem, the impetus and goals of this research, and how PonderNet connects to his memory research. The complete show notes for this episode can be found at twimlai.com/go/528.
18 Okt 202137min
Advancing Deep Reinforcement Learning with NetHack, w/ Tim Rocktäschel - #527
Take our survey at twimlai.com/survey21! Today we’re joined by Tim Rocktäschel, a research scientist at Facebook AI Research and an associate professor at University College London (UCL). Tim’s work focuses on training RL agents in simulated environments, with the goal of these agents being able to generalize to novel situations. Typically, this is done in environments like OpenAI Gym, MuJuCo, or even using Atari games, but these all come with constraints. In Tim’s approach, he utilizes a game called NetHack, which is much more rich and complex than the aforementioned environments. In our conversation with Tim, we explore the ins and outs of using NetHack as a training environment, including how much control a user has when generating each individual game and the challenges he's faced when deploying the agents. We also discuss his work on MiniHack, an environment creation framework and suite of tasks that are based on NetHack, and future directions for this research. The complete show notes for this episode can be found at twimlai.com/go/527.
14 Okt 202142min
Building Technical Communities at Stack Overflow with Prashanth Chandrasekar - #526
In this special episode of the show, we’re excited to bring you our conversation with Prashanth Chandrasekar, CEO of Stack Overflow. This interview was recorded as a part of the annual Prosus AI Marketplace event. In our discussion with Prashanth, we explore the impact the pandemic has had on Stack Overflow, how they think about community and enable collaboration in over 100 million monthly users from around the world, and some of the challenges they’ve dealt with when managing a community of this scale. We also examine where Stack Overflow is in their AI journey, use cases illustrating how they’re currently utilizing ML, what their role is in the future of AI-based code generation, what other trends they’ve picked up on over the last few years, and how they’re using those insights to forge the path forward. The complete show notes for this episode can be found at twimlai.com/go/526.
11 Okt 202140min
