Buy AND Build for Production Machine Learning with Nir Bar-Lev - #488
Today we’re joined by Nir Bar-Lev, co-founder and CEO of ClearML. In our conversation with Nir, we explore how his view of the wide vs deep machine learning platforms paradox has changed and evolved over time, how companies should think about building vs buying and integration, and his thoughts on why experiment management has become an automatic buy, be it open source or otherwise. We also discuss the disadvantages of using a cloud vendor as opposed to a software-based approach, the balance between mlops and data science when addressing issues of overfitting, and how ClearML is applying techniques like federated machine learning and transfer learning to their solutions. The complete show notes for this episode can be found at https://twimlai.com/go/488.
31 Maj 202143min
Applied AI Research at AWS with Alex Smola - #487
Today we’re joined by Alex Smola, Vice President and Distinguished Scientist at AWS AI. We had the pleasure to catch up with Alex prior to the upcoming AWS Machine Learning Summit, and we covered a TON of ground in the conversation. We start by focusing on his research in the domain of deep learning on graphs, including a few examples showcasing its function, and an interesting discussion around the relationship between large language models and graphs. Next up, we discuss their focus on AutoML research and how it's the key to lowering the barrier of entry for machine learning research. Alex also shares a bit about his work on causality and causal modeling, introducing us to the concept of Granger causality. Finally, we talk about the aforementioned ML Summit, its exponential growth since its inception a few years ago, and what speakers he's most excited about hearing from. The complete show notes for this episode can be found at https://twimlai.com/go/487.
27 Maj 202155min
Causal Models in Practice at Lyft with Sean Taylor - #486
Today we’re joined by Sean Taylor, Staff Data Scientist at Lyft Rideshare Labs. We cover a lot of ground with Sean, starting with his recent decision to step away from his previous role as the lab director to take a more hands-on role, and what inspired that change. We also discuss his research at Rideshare Labs, where they take a more “moonshot” approach to solving the typical problems like forecasting and planning, marketplace experimentation, and decision making, and how his statistical approach manifests itself in his work. Finally, we spend quite a bit of time exploring the role of causality in the work at rideshare labs, including how systems like the aforementioned forecasting system are designed around causal models, if driving model development is more effective using business metrics, challenges associated with hierarchical modeling, and much much more. The complete show notes for this episode can be found at twimlai.com/go/486.
24 Maj 202140min
Using AI to Map the Human Immune System w/ Jabran Zahid - #485
Today we’re joined by Jabran Zahid, a Senior Researcher at Microsoft Research. In our conversation with Jabran, we explore their recent endeavor into the complete mapping of which T-cells bind to which antigens through the Antigen Map Project. We discuss how Jabran’s background in astrophysics and cosmology has translated to his current work in immunology and biology, the origins of the antigen map, the biological and how the focus was changed by the emergence of the coronavirus pandemic. We talk through the biological advancements, and the challenges of using machine learning in this setting, some of the more advanced ML techniques that they’ve tried that have not panned out (as of yet), the path forward for the antigen map to make a broader impact, and much more. The complete show notes for this episode can be found at twimlai.com/go/485.
20 Maj 202141min
Learning Long-Time Dependencies with RNNs w/ Konstantin Rusch - #484
Today we conclude our 2021 ICLR coverage joined by Konstantin Rusch, a PhD Student at ETH Zurich. In our conversation with Konstantin, we explore his recent papers, titled coRNN and uniCORNN respectively, which focus on a novel architecture of recurrent neural networks for learning long-time dependencies. We explore the inspiration he drew from neuroscience when tackling this problem, how the performance results compared to networks like LSTMs and others that have been proven to work on this problem and Konstantin’s future research goals. The complete show notes for this episode can be found at twimlai.com/go/484.
17 Maj 202137min
What the Human Brain Can Tell Us About NLP Models with Allyson Ettinger - #483
Today we continue our ICLR ‘21 series joined by Allyson Ettinger, an Assistant Professor at the University of Chicago. One of our favorite recurring conversations on the podcast is the two-way street that lies between machine learning and neuroscience, which Allyson explores through the modeling of cognitive processes that pertain to language. In our conversation, we discuss how she approaches assessing the competencies of AI, the value of control of confounding variables in AI research, and how the pattern matching traits of Ml/DL models are not necessarily exclusive to these systems. Allyson also participated in a recent panel discussion at the ICLR workshop How Can Findings About The Brain Improve AI Systems?, centered around the utility of brain inspiration for developing AI models. We discuss ways in which we can try to more closely simulate the functioning of a brain, where her work fits into the analysis and interpretability area of NLP, and much more! The complete show notes for this episode can be found at twimlai.com/go/483.
13 Maj 202138min
Probabilistic Numeric CNNs with Roberto Bondesan - #482
Today we kick off our ICLR 2021 coverage joined by Roberto Bondesan, an AI Researcher at Qualcomm. In our conversation with Roberto, we explore his paper Probabilistic Numeric Convolutional Neural Networks, which represents features as Gaussian processes, providing a probabilistic description of discretization error. We discuss some of the other work the team at Qualcomm presented at the conference, including a paper called Adaptive Neural Compression, as well as work on Guage Equvariant Mesh CNNs. Finally, we briefly discuss quantum deep learning, and what excites Roberto and his team about the future of their research in combinatorial optimization. The complete show notes for this episode can be found at https://twimlai.com/go/482
10 Maj 202141min
Building a Unified NLP Framework at LinkedIn with Huiji Gao - #481
Today we’re joined by Huiji Gao, a Senior Engineering Manager of Machine Learning and AI at LinkedIn. In our conversation with Huiji, we dig into his interest in building NLP tools and systems, including a recent open-source project called DeText, a framework for generating models for ranking classification and language generation. We explore the motivation behind DeText, the landscape at LinkedIn before and after it was put into use broadly, and the various contexts it’s being used in at the company. We also discuss the relationship between BERT and DeText via LiBERT, a version of BERT that is trained and calibrated on LinkedIn data, the practical use of these tools from an engineering perspective, the approach they’ve taken to optimization, and much more! The complete show notes for this episode can be found at https://twimlai.com/go/481.
6 Maj 202134min
