Dask + Data Science Careers with Jacqueline Nolis - #480

Dask + Data Science Careers with Jacqueline Nolis - #480

Today we’re joined by Jacqueline Nolis, Head of Data Science at Saturn Cloud, and co-host of the Build a Career in Data Science Podcast. You might remember Jacqueline from our Advancing Your Data Science Career During the Pandemic panel, where she shared her experience trying to navigate the suddenly hectic data science job market. Now, a year removed from that panel, we explore her book on data science careers, top insights for folks just getting into the field, ways that job seekers should be signaling that they have the required background, and how to approach and navigate failure as a data scientist. We also spend quite a bit of time discussing Dask, an open-source library for parallel computing in Python, as well as use cases for the tool, the relationship between dask and Kubernetes and docker containers, where data scientists are in regards to the software development toolchain and much more! The complete show notes for this episode can be found at https://twimlai.com/go/480.

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Learning to Ponder: Memory in Deep Neural Networks with Andrea Banino - #528

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

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

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

Deep Learning is Eating 5G. Here’s How, w/ Joseph Soriaga - #525

Deep Learning is Eating 5G. Here’s How, w/ Joseph Soriaga - #525

Today we’re joined by Joseph Soriaga, a senior director of technology at Qualcomm.  In our conversation with Joseph, we focus on a pair of papers that he and his team will be presenting at Globecom later this year. The first, Neural Augmentation of Kalman Filter with Hypernetwork for Channel Tracking, details the use of deep learning to augment an algorithm to address mismatches in models, allowing for more efficient training and making models more interpretable and predictable. The second paper, WiCluster: Passive Indoor 2D/3D Positioning using WiFi without Precise Labels, explores the use of rf signals to infer what the environment looks like, allowing for estimation of a person’s movement.  We also discuss the ability for machine learning and AI to help enable 5G and make it more efficient for these applications, as well as the scenarios that ML would allow for more effective delivery of connected services, and look towards what might be possible in the near future.  The complete show notes for this episode can be found at twimlai.com/go/525.

7 Okt 202139min

Modeling Human Cognition with RNNs and Curriculum Learning, w/ Kanaka Rajan - #524

Modeling Human Cognition with RNNs and Curriculum Learning, w/ Kanaka Rajan - #524

Today we’re joined by Kanaka Rajan, an assistant professor at the Icahn School of Medicine at Mt Sinai. Kanaka, who is a recent recipient of the NSF Career Award, bridges the gap between the worlds of biology and artificial intelligence with her work in computer science. In our conversation, we explore how she builds “lego models” of the brain that mimic biological brain functions, then reverse engineers those models to answer the question “do these follow the same operating principles that the biological brain uses?” We also discuss the relationship between memory and dynamically evolving system states, how close we are to understanding how memory actually works, how she uses RNNs for modeling these processes, and what training and data collection looks like. Finally, we touch on her use of curriculum learning (where the task you want a system to learn increases in complexity slowly), and of course, we look ahead at future directions for Kanaka’s research.  The complete show notes for this episode can be found at twimlai.com/go/524.

4 Okt 202147min

Do You Dare Run Your ML Experiments in Production? with Ville Tuulos - #523

Do You Dare Run Your ML Experiments in Production? with Ville Tuulos - #523

Today we’re joined by a friend of the show and return guest Ville Tuulos, CEO and co-founder of Outerbounds. In our previous conversations with Ville, we explored his experience building and deploying the open-source framework, Metaflow, while working at Netflix. Since our last chat, Ville has embarked on a few new journeys, including writing the upcoming book Effective Data Science Infrastructure, and commercializing Metaflow, both of which we dig into quite a bit in this conversation.  We reintroduce the problem that Metaflow was built to solve and discuss some of the unique use cases that Ville has seen since it's release, the relationship between Metaflow and Kubernetes, and the maturity of services like batch and lambdas allowing a complete production ML system to be delivered. Finally, we discuss the degree to which Ville is catering is Outerbounds’ efforts to building tools for the MLOps community, and what the future looks like for him and Metaflow.  The complete show notes for this episode can be found at twimlai.com/go/523.

30 Sep 202140min

Delivering Neural Speech Services at Scale with Li Jiang - #522

Delivering Neural Speech Services at Scale with Li Jiang - #522

Today we’re joined by Li Jiang, a distinguished engineer at Microsoft working on Azure Speech.  In our conversation with Li, we discuss his journey across 27 years at Microsoft, where he’s worked on, among other things, audio and speech recognition technologies. We explore his thoughts on the advancements in speech recognition over the past few years, the challenges, and advantages, of using either end-to-end or hybrid models.  We also discuss the trade-offs between delivering accuracy or quality and the kind of runtime characteristics that you require as a service provider, in the context of engineering and delivering a service at the scale of Azure Speech. Finally, we walk through the data collection process for customizing a voice for TTS, what languages are currently supported, managing the responsibilities of threats like deep fakes, the future for services like these, and much more! The complete show notes for this episode can be found at twimlai.com/go/522.

27 Sep 202149min

AI’s Legal and Ethical Implications with Sandra Wachter - #521

AI’s Legal and Ethical Implications with Sandra Wachter - #521

Today we’re joined by Sandra Wacther, an associate professor and senior research fellow at the University of Oxford.  Sandra’s work lies at the intersection of law and AI, focused on what she likes to call “algorithmic accountability”. In our conversation, we explore algorithmic accountability in three segments, explainability/transparency, data protection, and bias, fairness and discrimination. We discuss how the thinking around black boxes changes when discussing applying regulation and law, as well as a breakdown of counterfactual explanations and how they’re created. We also explore why factors like the lack of oversight lead to poor self-regulation, and the conditional demographic disparity test that she helped develop to test bias in models, which was recently adopted by Amazon. The complete show notes for this episode can be found at twimlai.com/go/521.

23 Sep 202149min

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