The TWIML AI Podcast (formerly This Week in Machine Learning & Artificial Intelligence)
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799 afleveringen, van nieuw naar oud
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Engineering an ML-Powered Developer-First Search Engine with Richard Socher - #582
11 juli 2022 · 47 min
Today we’re joined by Richard Socher, the CEO of You.com. In our conversation with Richard, we explore the inspiration and motivation behind the You.com search engine, and how it differs from the traditional google search engine experience. We discuss some of the various ways that machine learning is used across the platform including how they surface relevant search results and some of the recent additions like code completion and a text generator that can write complete essays and blog posts. Finally, we talk through some of the projects we covered in our last conversation with Richard, namely his work on Salesforce’s AI Economist project.
The complete show notes for this episode can be found at twimlai.com/go/582 Lees meer -
On The Path Towards Robot Vision with Aljosa Osep - #581
4 juli 2022 · 48 min
Today we wrap up our coverage of the 2022 CVPR conference joined by Aljosa Osep, a postdoc at the Technical University of Munich & Carnegie Mellon University. In our conversation with Aljosa, we explore his broader research interests in achieving robot vision, and his vision for what it will look like when that goal is achieved. The first paper we dig into is Text2Pos: Text-to-Point-Cloud Cross-Modal Localization, which proposes a cross-modal localization module that learns to align textual descriptions with localization cues in a coarse-to-fine manner. Next up, we explore the paper Forecasting from LiDAR via Future Object Detection, which proposes an end-to-end approach for detection and motion forecasting based on raw sensor measurement as opposed to ground truth tracks. Finally, we discuss Aljosa’s third and final paper Opening up Open-World Tracking, which proposes a new benchmark to analyze existing efforts in multi-object tracking and constructs a baseline for these tasks.
The complete show notes for this episode can be found at twimlai.com/go/581 Lees meer -
More Language, Less Labeling with Kate Saenko - #580
27 juni 2022 · 47 min
Today we continue our CVPR series joined by Kate Saenko, an associate professor at Boston University and a consulting professor for the MIT-IBM Watson AI Lab. In our conversation with Kate, we explore her research in multimodal learning, which she spoke about at the Multimodal Learning and Applications Workshop, one of a whopping 6 workshops she spoke at. We discuss the emergence of multimodal learning, the current research frontier, and Kate’s thoughts on the inherent bias in LLMs and how to deal with it. We also talk through some of the challenges that come up when building out applications, including the cost of labeling, and some of the methods she’s had success with. Finally, we discuss Kate’s perspective on the monopolizing of computing resources for “foundational” models, and her paper Unsupervised Domain Generalization by learning a Bridge Across Domains.
The complete show notes for this episode can be found at twimlai.com/go/580 Lees meer -
Optical Flow Estimation, Panoptic Segmentation, and Vision Transformers with Fatih Porikli - #579
20 juni 2022 · 51 min
Today we kick off our annual coverage of the CVPR conference joined by Fatih Porikli, Senior Director of Engineering at Qualcomm AI Research. In our conversation with Fatih, we explore a trio of CVPR-accepted papers, as well as a pair of upcoming workshops at the event. The first paper, Panoptic, Instance and Semantic Relations: A Relational Context Encoder to Enhance Panoptic Segmentation, presents a novel framework to integrate semantic and instance contexts for panoptic segmentation. Next up, we discuss Imposing Consistency for Optical Flow Estimation, a paper that introduces novel and effective consistency strategies for optical flow estimation. The final paper we discuss is IRISformer: Dense Vision Transformers for Single-Image Inverse Rendering in Indoor Scenes, which proposes a transformer architecture to simultaneously estimate depths, normals, spatially-varying albedo, roughness, and lighting from a single image of an indoor scene. For each paper, we explore the motivations and challenges and get concrete examples to demonstrate each problem and solution presented. Lees meer
The complete show notes for this episode can be found at twimlai.com/go/579
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Data Governance for Data Science with Adam Wood - #578
13 juni 2022 · 40 min
Today we’re joined by Adam Wood, Director of Data Governance and Data Quality at Mastercard. In our conversation with Adam, we explore the challenges that come along with data governance at a global scale, including dealing with regional regulations like GDPR and federating records at scale. We discuss the role of feature stores in keeping track of data lineage and how Adam and his team have dealt with the challenges of metadata management, how large organizations like Mastercard are dealing with enabling feature reuse, and the steps they take to alleviate bias, especially in scenarios like acquisitions. Finally, we explore data quality for data science and why Adam sees it as an encouraging area of growth within the company, as well as the investments they’ve made in tooling around data management, catalog, feature management, and more.
The complete show notes for this episode can be found at twimlai.com/go/578 Lees meer -
Feature Platforms for Data-Centric AI with Mike Del Balso - #577
6 juni 2022 · 46 min
In the latest installment of our Data-Centric AI series, we’re joined by a friend of the show Mike Del Balso, Co-founder and CEO of Tecton. If you’ve heard any of our other conversations with Mike, you know we spend a lot of time discussing feature stores, or as he now refers to them, feature platforms. We explore the current complexity of data infrastructure broadly and how that has changed over the last five years, as well as the maturation of streaming data platforms. We discuss the wide vs deep paradox that exists around ML tooling, and the idea around the “ML Flywheel”, a strategy that leverages data to accelerate machine learning. Finally, we spend time discussing internal ML team construction, some of the challenges that organizations face when building their ML platforms teams, and how they can avoid the pitfalls as they arise.
The complete show notes for this episode can be found at twimlai.com/go/577 Lees meer -
The Fallacy of "Ground Truth" with Shayan Mohanty - #576
30 mei 2022 · 51 min
Today we continue our Data-centric AI series joined by Shayan Mohanty, CEO at Watchful. In our conversation with Shayan, we focus on the data labeling aspect of the machine learning process, and ways that a data-centric approach could add value and reduce cost by multiple orders of magnitude. Shayan helps us define “data-centric”, while discussing the main challenges that organizations face when dealing with labeling, how these problems are currently being solved, and how techniques like active learning and weak supervision could be used to more effectively label. We also explore the idea of machine teaching, which focuses on using techniques that make the model training process more efficient, and what organizations need to be successful when trying to make the aforementioned mindset shift to DCAI. Lees meer
The complete show notes for this episode can be found at twimlai.com/go/576
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Principle-centric AI with Adrien Gaidon - #575
23 mei 2022 · 48 min
This week, we continue our conversations around the topic of Data-Centric AI joined by a friend of the show Adrien Gaidon, the head of ML research at the Toyota Research Institute (TRI). In our chat, Adrien expresses a fourth, somewhat contrarian, viewpoint to the three prominent schools of thought that organizations tend to fall into, as well as a great story about how the breakthrough came via an unlikely source. We explore his principle-centric approach to machine learning as well as the role of self-supervised machine learning and synthetic data in this and other research threads. Make sure you’re following along with the entire DCAI series at twimlai.com/go/dcai.
The complete show notes for this episode can be found at twimlai.com/go/575 Lees meer -
Data Debt in Machine Learning with D. Sculley - #574
19 mei 2022 · 37 min
Today we kick things off with a conversation with D. Sculley, a director on the Google Brain team. Many listeners of today’s show will know D. from his work on the paper, The Hidden Technical Debt in Machine Learning Systems, and of course, the infamous diagram. D. has recently translated the idea of technical debt into data debt, something we spend a bit of time on in the interview.
We discuss his view of the concept of DCAI, where debt fits into the conversation of data quality, and what a shift towards data-centrism looks like in a world of increasingly larger models i.e. GPT-3 and the recent PALM models. We also explore common sources of data debt, what are things that the community can and have done to mitigate these issues, the usefulness of causal inference graphs in this work, and much more! If you enjoyed this interview or want to hear more on this topic, check back on the DCAI series page weekly at https://twimlai.com/podcast/twimlai/series/data-centric-ai.
The complete show notes for this episode can be found at twimlai.com/go/574 Lees meer -
AI for Enterprise Decisioning at Scale with Rob Walker - #573
16 mei 2022 · 39 min
Today we’re joined by Rob Walker, VP of decisioning & analytics and gm of one-to-one customer engagement at Pegasystems. Rob, who you might know from his previous appearances on the podcast, joins us to discuss his work on AI and ML in the context of customer engagement and decisioning, the various problems that need to be solved, including solving the “next best” problem. We explore the distinction between the idea of the next best action and determining it from a recommender system, how the combination of machine learning and heuristics are currently co-existing in engagements, scaling model evaluation, and some of the challenges they’re facing when dealing with problems of responsible AI and how they’re managed. Finally, we spend a few minutes digging into the upcoming PegaWorld conference, and what attendees should anticipate at the event.
The complete show notes for this episode can be found at twimlai.com/go/573 Lees meer -
Data Rights, Quantification and Governance for Ethical AI with Margaret Mitchell - #572
12 mei 2022 · 42 min
Today we close out our coverage of the ICLR series joined by Meg Mitchell, chief ethics scientist and researcher at Hugging Face. In our conversation with Meg, we discuss her participation in the WikiM3L Workshop, as well as her transition into her new role at Hugging Face, which has afforded her the ability to prioritize coding in her work around AI ethics. We explore her thoughts on the work happening in the fields of data curation and data governance, her interest in the inclusive sharing of datasets and creation of models that don't disproportionately underperform or exploit subpopulations, and how data collection practices have changed over the years.
We also touch on changes to data protection laws happening in some pretty uncertain places, the evolution of her work on Model Cards, and how she’s using this and recent Data Cards work to lower the barrier to entry to responsibly informed development of data and sharing of data.
The complete show notes for this episode can be found at twimlai.com/go/572 Lees meer -
Studying Machine Intelligence with Been Kim - #571
9 mei 2022 · 53 min
Today we continue our ICLR coverage joined by Been Kim, a staff research scientist at Google Brain, and an ICLR 2022 Invited Speaker. Been, whose research has historically been focused on interpretability in machine learning, delivered the keynote Beyond interpretability: developing a language to shape our relationships with AI, which explores the need to study AI machines as scientific objects, in isolation and with humans, which will provide principles for tools, but also is necessary to take our working relationship with AI to the next level.
Before we dig into Been’s talk, she characterizes where we are as an industry and community with interpretability, and what the current state of the art is for interpretability techniques. We explore how the Gestalt principles appear in neural networks, Been’s choice to characterize communication with machines as a language as opposed to a set of principles or foundational understanding, and much much more.
The complete show notes for this episode can be found at twimlai.com/go/571 Lees meer -
Advances in Neural Compression with Auke Wiggers - #570
2 mei 2022 · 38 min
Today we’re joined by Auke Wiggers, an AI research scientist at Qualcomm. In our conversation with Auke, we discuss his team’s recent research on data compression using generative models. We discuss the relationship between historical compression research and the current trend of neural compression, and the benefit of neural codecs, which learn to compress data from examples. We also explore the performance evaluation process and the recent developments that show that these models can operate in real-time on a mobile device. Finally, we discuss another ICLR paper, “Transformer-based transform coding”, that proposes a vision transformer-based architecture for image and video coding, and some of his team’s other accepted works at the conference.
The complete show notes for this episode can be found at twimlai.com/go/570 Lees meer -
Mixture-of-Experts and Trends in Large-Scale Language Modeling with Irwan Bello - #569
25 april 2022 · 46 min
Today we’re joined by Irwan Bello, formerly a research scientist at Google Brain, and now on the founding team at a stealth AI startup. We begin our conversation with an exploration of Irwan’s recent paper, Designing Effective Sparse Expert Models, which acts as a design guide for building sparse large language model architectures. We discuss mixture of experts as a technique, the scalability of this method, and it's applicability beyond NLP tasks the data sets this experiment was benchmarked against. We also explore Irwan’s interest in the research areas of alignment and retrieval, talking through interesting lines of work for each area including instruction tuning and direct alignment.
The complete show notes for this episode can be found at twimlai.com/go/569 Lees meer -
Daring to DAIR: Distributed AI Research with Timnit Gebru - #568
18 april 2022 · 52 min
Today we’re joined by friend of the show Timnit Gebru, the founder and executive director of DAIR, the Distributed Artificial Intelligence Research Institute. In our conversation with Timnit, we discuss her journey to create DAIR, their goals and some of the challenges shes faced along the way. We start is the obvious place, Timnit being “resignated” from Google after writing and publishing a paper detailing the dangers of large language models, the fallout from that paper and her firing, and the eventual founding of DAIR. We discuss the importance of the “distributed” nature of the institute, how they’re going about figuring out what is in scope and out of scope for the institute’s research charter, and what building an institution means to her. We also explore the importance of independent alternatives to traditional research structures, if we should be pessimistic about the impact of internal ethics and responsible AI teams in industry due to the overwhelming power they wield, examples she looks to of what not to do when building out the institute, and much much more! Lees meer
The complete show notes for this episode can be found at twimlai.com/go/568
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Hierarchical and Continual RL with Doina Precup - #567
11 april 2022 · 50 min
Today we’re joined by Doina Precup, a research team lead at DeepMind Montreal, and a professor at McGill University. In our conversation with Doina, we discuss her recent research interests, including her work in hierarchical reinforcement learning, with the goal being agents learning abstract representations, especially over time. We also explore her work on reward specification for RL agents, where she hypothesizes that a reward signal in a complex environment could lead an agent to develop attributes of intuitive intelligence. We also dig into quite a few of her papers, including On the Expressivity of Markov Reward, which won a NeruIPS 2021 outstanding paper award. Finally, we discuss the analogy between hierarchical RL and CNNs, her work in continual RL, and her thoughts on the evolution of RL in the recent past and present, and the biggest challenges facing the field going forward. Lees meer
The complete show notes for this episode can be found at twimlai.com/go/567
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Open-Source Drug Discovery with DeepChem with Bharath Ramsundar - #566
4 april 2022 · 30 min
Today we’re joined by Bharath Ramsundar, founder and CEO of Deep Forest Sciences. In our conversation with Bharath, we explore his work on the DeepChem, an open-source library for drug discovery, materials science, quantum chemistry, and biology tools. We discuss the challenges that biotech and pharmaceutical companies are facing as they attempt to incorporate AI into the drug discovery process, where the innovation frontier is, and what the promise is for AI in this field in the near term. We also dig into the origins of DeepChem and the problems it's solving for practitioners, the capabilities that are enabled when using this library as opposed to others, and MoleculeNET, a dataset and benchmark focused on molecular design that lives within the DeepChem suite. Lees meer
The complete show notes for this episode can be found at twimlai.com/go/566
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Advancing Hands-On Machine Learning Education with Sebastian Raschka - #565
28 maart 2022 · 41 min
Today we’re joined by Sebastian Raschka, an assistant professor at the University of Wisconsin-Madison and lead AI educator at Grid.ai. In our conversation with Sebastian, we explore his work around AI education, including the “hands-on” philosophy that he takes when building these courses, his recent book Machine Learning with PyTorch and Scikit-Learn, his advise to beginners in the field when they’re trying to choose tools and frameworks, and more.
We also discuss his work on Pytorch Lightning, a platform that allows users to organize their code and integrate it into other technologies, before switching gears and discuss his recent research efforts around ordinal regression, including a ton of great references that we’ll link on the show notes page below!
The complete show notes for this episode can be found at twimlai.com/go/565 Lees meer -
Big Science and Embodied Learning at Hugging Face 🤗 with Thomas Wolf - #564
21 maart 2022 · 47 min
Today we’re joined by Thomas Wolf, co-founder and chief science officer at Hugging Face 🤗. We cover a ton of ground In our conversation, starting with Thomas’ interesting backstory as a quantum physicist and patent lawyer, and how that lead him to a career in machine learning. We explore how Hugging Face began, what the current direction is for the company, and how much of their focus is NLP and language models versus other disciplines. We also discuss the BigScience project, a year-long research workshop where 1000+ researchers of all backgrounds and disciplines have come together to create an 800GB multilingual dataset and model. We talk through their approach to curating the dataset, model evaluation at this scale, and how they differentiate their work from projects like Eluther AI. Finally, we dig into Thomas’ work on multimodality, his thoughts on the metaverse, his new book NLP with Transformers, and much more!
The complete show notes for this episode can be found at twimlai.com/go/564 Lees meer -
Full-Stack AI Systems Development with Murali Akula - #563
14 maart 2022 · 44 min
Today we’re joined by Murali Akula, a Sr. director of Software Engineering at Qualcomm. In our conversation with Murali, we explore his role at Qualcomm, where he leads the corporate research team focused on the development and deployment of AI onto Snapdragon chips, their unique definition of “full stack”, and how that philosophy permeates into every step of the software development process. We explore the complexities that are unique to doing machine learning on resource constrained devices, some of the techniques that are being applied to get complex models working on mobile devices, and the process for taking these models from research into real-world applications. We also discuss a few more tools and recent developments, including DONNA for neural architecture search, X-Distill, a method of improving the self-supervised training of monocular depth, and the AI Model Effeciency Toolkit, a library that provides advanced quantization and compression techniques for trained neural network models.
The complete show notes for this episode can be found at twimlai.com/go/563 Lees meer -
100x Improvements in Deep Learning Performance with Sparsity, w/ Subutai Ahmad - #562
7 maart 2022 · 51 min
Today we’re joined by Subutai Ahmad, VP of research at Numenta. While we’ve had numerous conversations about the biological inspirations of deep learning models with folks working at the intersection of deep learning and neuroscience, we dig into uncharted territory with Subutai. We set the stage by digging into some of fundamental ideas behind Numenta’s research and the present landscape of neuroscience, before exploring our first big topic of the podcast: the cortical column. Cortical columns are a group of neurons in the cortex of the brain which have nearly identical receptive fields; we discuss the behavior of these columns, why they’re a structure worth mimicing computationally, how far along we are in understanding the cortical column, and how these columns relate to neurons. Lees meer
We also discuss what it means for a model to have inherent 3d understanding and for computational models to be inherently sensory motor, and where we are with these lines of research. Finally, we dig into our other big idea, sparsity. We explore the fundamental ideals of sparsity and the differences between sparse and dense networks, and applying sparsity and optimization to drive greater efficiency in current deep learning networks, including transformers and other large language models.
The complete show notes for this episode can be found at twimlai.com/go/562
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Scaling BERT and GPT for Financial Services with Jennifer Glore - #561
28 februari 2022 · 44 min
Today we’re joined by Jennifer Glore, VP of customer engineering at SambaNova Systems. In our conversation with Jennifer, we discuss how, and why, Sambanova, who is primarily focused on building hardware to support machine learning applications, has built a GPT language model for the financial services industry. Jennifer shares her thoughts on the progress of industries like banking and finance, as well as other traditional organizations, in their attempts at using transformers and other models, and where they’ve begun to see success, as well as some of the hidden challenges that orgs run into that impede their progress. Finally, we explore their experience replicating the GPT-3 paper from a R&D perspective, how they’re addressing issues of predictability, controllability, governance, etc, and much more. Lees meer
The complete show notes for this episode can be found at twimlai.com/go/561
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Trends in Deep Reinforcement Learning with Kamyar Azizzadenesheli - #560
21 februari 2022 · 1 uur 18 min
Today we’re joined by Kamyar Azizzadenesheli, an assistant professor at Purdue University, to close out our AI Rewind 2021 series! In this conversation, we focused on all things deep reinforcement learning, starting with a general overview of the direction of the field, and though it might seem to be slowing, thats just a product of the light being shined constantly on the CV and NLP spaces. We dig into themes like the convergence of RL methodology with both robotics and control theory, as well as a few trends that Kamyar sees over the horizon, such as self-supervised learning approaches in RL. We also talk through Kamyar’s predictions for RL in 2022 and beyond. This was a fun conversation, and I encourage you to look through all the great resources that Kamyar shared on the show notes page at twimlai.com/go/560! Lees meer
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Deep Reinforcement Learning at the Edge of the Statistical Precipice with Rishabh Agarwal - #559
14 februari 2022 · 52 min
Today we’re joined by Rishabh Agarwal, a research scientist at Google Brain in Montreal. In our conversation with Rishabh, we discuss his recent paper Deep Reinforcement Learning at the Edge of the Statistical Precipice, which won an outstanding paper award at the most recent NeurIPS conference. In this paper, Rishabh and his coauthors call for a change in how deep RL performance is reported on benchmarks when using only a few runs, acknowledging that typically, DeepRL algorithms are evaluated by the performance on a large suite of tasks. Using the Atari 100k benchmark, they found substantial disparities in the conclusions from point estimates alone versus statistical analysis. We explore the reception of this paper from the research community, some of the more surprising results, what incentives researchers have to implement these types of changes in self-reporting when publishing, and much more. Lees meer
The complete show notes for this episode can be found at twimlai.com/go/559
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Designing New Energy Materials with Machine Learning with Rafael Gomez-Bombarelli - #558
7 februari 2022 · 53 min
Today we’re joined by Rafael Gomez-Bombarelli, an assistant professor in the department of material science and engineering at MIT. In our conversation with Rafa, we explore his goal of fusing machine learning and atomistic simulations for designing materials, a topic he spoke about at the recent SigOpt AI & HPC Summit. We discuss the two ways in which he thinks of material design, virtual screening and inverse design, as well as the unique challenges each technique presents. We also talk through the use of generative models for simulation, the type of training data necessary for these tasks, and if he’s building hand-coded simulations vs existing packages or tools. Finally, we explore the dynamic relationship between simulation and modeling and how the results of one drive the others efforts, and how hyperparameter optimization gets incorporated into the various projects.
The complete show notes for this episode can be found at twimlai.com/go/558 Lees meer -
Differentiable Programming for Oceanography with Patrick Heimbach - #557
31 januari 2022 · 34 min
Today we’re joined by Patrick Heimbach, a professor at the University of Texas working at the intersection of ML and oceanography. In our conversation with Patrick, we explore some of the challenges of computational oceanography, the potential use cases for machine learning in this field, as well as how it can be used to support scientists in solving simulation problems, and the role of differential programming and how it is expressed in his work.
The complete show notes for this episode can be found at twimlai.com/go/557 Lees meer -
Trends in Machine Learning & Deep Learning with Zachary Lipton - #556
27 januari 2022 · 1 uur 9 min
Today we continue our AI Rewind 2021 series joined by a friend of the show, assistant professor at Carnegie Mellon University, and AI Rewind veteran, Zack Lipton! In our conversation with Zack, we touch on recurring themes like “NLP Eating AI” and the recent slowdown in innovation in the field, the redistribution of resources across research problems, and where the opportunities for real breakthroughs lie. We also discuss problems facing the current peer-review system, notable research from last year like the introduction of the WILDS library, and the evolution of problems (and potential solutions) in fairness, bias, and equity. Of course, we explore some of the use cases and application areas that made notable progress in 2021, what Zack is looking forward to in 2022 and beyond, and much more! Lees meer
The complete show notes for this episode can be found at twimlai.com/go/556
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Solving the Cocktail Party Problem with Machine Learning, w/ Jonathan Le Roux - #555
24 januari 2022 · 36 min
Today we’re joined by Jonathan Le Roux, a senior principal research scientist at Mitsubishi Electric Research Laboratories (MERL). At MERL, Jonathan and his team are focused on using machine learning to solve the “cocktail party problem”, focusing on not only the separation of speech from noise, but also the separation of speech from speech. In our conversation with Jonathan, we focus on his paper The Cocktail Fork Problem: Three-Stem Audio Separation For Real-World Soundtracks, which looks to separate and enhance a complex acoustic scene into three distinct categories, speech, music, and sound effects. We explore the challenges of working with such noisy data, the model architecture used to solve this problem, how ML/DL fits into solving the larger cocktail party problem, future directions for this line of research, and much more! Lees meer
The complete show notes for this episode can be found at twimlai.com/go/555
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Machine Learning for Earthquake Seismology with Karianne Bergen - #554
20 januari 2022 · 36 min
Today we’re joined by Karianne Bergen, an assistant professor at Brown University. In our conversation with Karianne, we explore her work at the intersection of earthquake seismology and machine learning, where she’s working on interpretable data classification for seismology. We discuss some of the challenges that present themselves when trying to solve this problem, and the state of applying machine learning to seismological events and earth sciences. Karianne also shares her thoughts on the different relationships that computer scientists and natural scientists have with machine learning, and how to bridge that gap to create tools that work broadly for all scientists. Lees meer
The complete show notes for this episode can be found at twimlai.com/go/554
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The New DBfication of ML/AI with Arun Kumar - #553
17 januari 2022 · 46 min
Today we’re joined by Arun Kumarm, an associate professor at UC San Diego. We had the pleasure of catching up with Arun prior to the Workshop on Databases and AI at NeurIPS 2021, where he delivered the talk “The New DBfication of ML/AI.” In our conversation, we explore this “database-ification” of machine learning, a concept analogous to the transformation of relational SQL computation. We discuss the relationship between the ML and database fields and how the merging of the two could have positive outcomes for the end-to-end ML workflow, and a few tools that his team has developed, Cerebro, a tool for reproducible model selection, and SortingHat, a tool for automating data prep, and how tools like these and others affect Arun’s outlook on the future of machine learning platforms and MLOps. Lees meer
The complete show notes for this episode can be found at twimlai.com/go/553
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Building Public Interest Technology with Meredith Broussard - #552
13 januari 2022 · 30 min
Today we’re joined by Meredith Broussard, an associate professor at NYU & research director at the NYU Alliance for Public Interest Technology. Meredith was a keynote speaker at the recent NeurIPS conference, and we had the pleasure of speaking with her to discuss her talk from the event, and her upcoming book, tentatively titled More Than A Glitch: What Everyone Needs To Know About Making Technology Anti-Racist, Accessible, And Otherwise Useful To All.
In our conversation, we explore Meredith’s work in the field of public interest technology, and her view of the relationship between technology and artificial intelligence. Meredith and Sam talk through real-world scenarios where an emphasis on monitoring bias and responsibility would positively impact outcomes, and how this type of monitoring parallels the infrastructure that many organizations are already building out. Finally, we talk through the main takeaways from Meredith’s NeurIPS talk, and how practitioners can get involved in the work of building and deploying public interest technology.
The complete show notes for this episode can be found at twimlai.com/go/552 Lees meer -
A Universal Law of Robustness via Isoperimetry with Sebastien Bubeck - #551
10 januari 2022 · 39 min
Today we’re joined by Sebastian Bubeck a sr principal research manager at Microsoft, and author of the paper A Universal Law of Robustness via Isoperimetry, a NeurIPS 2021 Outstanding Paper Award recipient. We begin our conversation with Sebastian with a bit of a primer on convex optimization, a topic that hasn’t come up much in previous interviews. We explore the problem that convex optimization is trying to solve, the application of convex optimization to multi-armed bandit problems, metrical task systems and solving the K-server problem. We then dig into Sebastian’s paper, which looks to prove that for a broad class of data distributions and model classes, overparameterization is necessary if one wants to interpolate the data. Finally, we discussed the relationship between the paper and the work being done in the adversarial robustness community. Lees meer
The complete show notes for this episode can be found at twimlai.com/go/551
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Trends in NLP with John Bohannon - #550
6 januari 2022 · 1 uur 18 min
Today we’re joined by friend of the show John Bohannon, the director of science at Primer AI, to help us showcase all of the great achievements and accomplishments in NLP in 2021! In our conversation, John shares his two major takeaways from last year, 1) NLP as we know it has changed, and we’re back into the incremental phase of the science, and 2) NLP is “eating” the rest of machine learning. We explore the implications of these two major themes across the discipline, as well as best papers, up and coming startups, great things that did happen, and even a few bad things that didn’t. Finally, we explore what 2022 and beyond will look like for NLP, from multilingual NLP to use cases for the influx of large auto-regressive language models like GPT-3 and others, as well as ethical implications that are reverberating across domains and the changes that have been ushered in in that vein. Lees meer
The complete show notes for this episode can be found at twimlai.com/go/550
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Trends in Computer Vision with Georgia Gkioxari - #549
3 januari 2022 · 58 min
Happy New Year! We’re excited to kick off 2022 joined by Georgia Gkioxari, a research scientist at Meta AI, to showcase the best advances in the field of computer vision over the past 12 months, and what the future holds for this domain.
Welcome back to AI Rewind!
In our conversation Georgia highlights the emergence of the transformer model in CV research, what kind of performance results we’re seeing vs CNNs, and the immediate impact of NeRF, amongst a host of other great research. We also explore what is ImageNet’s place in the current landscape, and if it's time to make big changes to push the boundaries of what is possible with image, video and even 3D data, with challenges like the Metaverse, amongst others, on the horizon. Finally, we touch on the startups to keep an eye on, the collaborative efforts of software and hardware researchers, and the vibe of the “ImageNet moment” being upon us once again.
The complete show notes for this episode can be found at twimlai.com/go/549 Lees meer -
Kids Run the Darndest Experiments: Causal Learning in Children with Alison Gopnik - #548
27 december 2021 · 37 min
Today we close out the 2021 NeurIPS series joined by Alison Gopnik, a professor at UC Berkeley and an invited speaker at the Causal Inference & Machine Learning: Why now? Workshop. In our conversation with Alison, we explore the question, “how is it that we can know so much about the world around us from so little information?,” and how her background in psychology, philosophy, and epistemology has guided her along the path to finding this answer through the actions of children. We discuss the role of causality as a means to extract representations of the world and how the “theory theory” came about, and how it was demonstrated to have merit. We also explore the complexity of causal relationships that children are able to deal with and what that can tell us about our current ML models, how the training and inference stages of the ML lifecycle are akin to childhood and adulthood, and much more!
The complete show notes for this episode can be found at twimlai.com/go/548 Lees meer -
Hypergraphs, Simplicial Complexes and Graph Representations of Complex Systems with Tina Eliassi-Rad - #547
23 december 2021 · 36 min
Today we continue our NeurIPS coverage joined by Tina Eliassi-Rad, a professor at Northeastern University, and an invited speaker at the I Still Can't Believe It's Not Better! Workshop. In our conversation with Tina, we explore her research at the intersection of network science, complex networks, and machine learning, how graphs are used in her work and how it differs from typical graph machine learning use cases. We also discuss her talk from the workshop, “The Why, How, and When of Representations for Complex Systems”, in which Tina argues that one of the reasons practitioners have struggled to model complex systems is because of the lack of connection to the data sourcing and generation process. This is definitely a NERD ALERT approved interview! Lees meer
The complete show notes for this episode can be found at twimlai.com/go/547
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Deep Learning, Transformers, and the Consequences of Scale with Oriol Vinyals - #546
20 december 2021 · 53 min
Today we’re excited to kick off our annual NeurIPS, joined by Oriol Vinyals, the lead of the deep learning team at Deepmind. We cover a lot of ground in our conversation with Oriol, beginning with a look at his research agenda and why the scope has remained wide even through the maturity of the field, his thoughts on transformer models and if they will get us beyond the current state of DL, or if some other model architecture would be more advantageous. We also touch on his thoughts on the large language models craze, before jumping into his recent paper StarCraft II Unplugged: Large Scale Offline Reinforcement Learning, a follow up to their popular AlphaStar work from a few years ago. Finally, we discuss the degree to which the work that Deepmind and others are doing around games actually translates into real-world, non-game scenarios, recent work on multimodal few-shot learning, and we close with a discussion of the consequences of the level of scale that we’ve achieved thus far. Lees meer
The complete show notes for this episode can be found at twimlai.com/go/546
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Optimization, Machine Learning and Intelligent Experimentation with Michael McCourt - #545
16 december 2021 · 46 min
Today we’re joined by Michael McCourt the head of engineering at SigOpt. In our conversation with Michael, we explore the vast space around the topic of optimization, including the technical differences between ML and optimization and where they’re applied, what the path to increasing complexity looks like for a practitioner and the relationship between optimization and active learning. We also discuss the research frontier for optimization and how folks think about the interesting challenges and open questions for this field, how optimization approaches appeared at the latest NeurIPS conference, and Mike’s excitement for the emergence of interdisciplinary work between the machine learning community and other fields like the natural sciences.
The complete show notes for this episode can be found at twimlai.com/go/545 Lees meer -
Jupyter and the Evolution of ML Tooling with Brian Granger - #544
13 december 2021 · 57 min
Today we conclude our AWS re:Invent coverage joined by Brian Granger, a senior principal technologist at Amazon Web Services, and a co-creator of Project Jupyter. In our conversion with Brian, we discuss the inception and early vision of Project Jupyter, including how the explosion of machine learning and deep learning shifted the landscape for the notebook, and how they balanced the needs of these new user bases vs their existing community of scientific computing users. We also explore AWS’s role with Jupyter and why they’ve decided to invest resources in the project, Brian's thoughts on the broader ML tooling space, and how they’ve applied (and the impact of) HCI principles to the building of these tools. Finally, we dig into the recent Sagemaker Canvas and Studio Lab releases and Brian’s perspective on the future of notebooks and the Jupyter community at large.
The complete show notes for this episode can be found at twimlai.com/go/544 Lees meer -
Creating a Data-Driven Culture at ADP with Jack Berkowitz - #543
9 december 2021 · 35 min
Today we continue our 2021 re:Invent series joined by Jack Berkowitz, chief data officer at ADP. In our conversation with Jack, we explore the ever evolving role and growth of machine learning at the company, from the evolution of their ML platform, to the unique team structure. We discuss Jack’s perspective on data governance, the broad use cases for ML, how they approached the decision to move to the cloud, and the impact of scale in the way they deal with data. Finally, we touch on where innovation comes from at ADP, and the challenge of getting the talent it needs to innovate as a large “legacy” company. Lees meer
The complete show notes for this episode can be found at twimlai.com/go/543
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re:Invent Roundup 2021 with Bratin Saha - #542
6 december 2021 · 42 min
Today we’re joined by Bratin Saha, vice president and general manager at Amazon.
In our conversation with Bratin, we discuss quite a few of the recent ML-focused announcements coming out of last weeks re:Invent conference, including new products like Canvas and Studio Lab, as well as upgrades to existing services like Ground Truth Plus. We explore what no-code environments like the aforementioned Canvas mean for the democratization of ML tooling, and some of the key challenges to delivering it as a consumable product. We also discuss industrialization as a subset of MLOps, and how customer patterns inform the creation of these tools, and much more! Lees meerThe complete show notes for this episode can be found at twimlai.com/go/542.
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Multi-modal Deep Learning for Complex Document Understanding with Doug Burdick - #541
2 december 2021 · 46 min
Today we’re joined by Doug Burdick, a principal research staff member at IBM Research. In a recent interview, Doug’s colleague Yunyao Li joined us to talk through some of the broader enterprise NLP problems she’s working on. One of those problems is making documents machine consumable, especially with the traditionally archival file type, the PDF. That’s where Doug and his team come in.
In our conversation, we discuss the multimodal approach they’ve taken to identify, interpret, contextualize and extract things like tables from a document, the challenges they’ve faced when dealing with the tables and how they evaluate the performance of models on tables. We also explore how he’s handled generalizing across different formats, how fine-tuning has to be in order to be effective, the problems that appear on the NLP side of things, and how deep learning models are being leveraged within the group.
The complete show notes for this episode can be found at twimlai.com/go/541 Lees meer -
Predictive Maintenance Using Deep Learning and Reliability Engineering with Shayan Mortazavi - #540
29 november 2021 · 49 min
Today we’re joined by Shayan Mortazavi, a data science manager at Accenture.
In our conversation with Shayan, we discuss his talk from the recent SigOpt HPC & AI Summit, titled A Novel Framework Predictive Maintenance Using Dl and Reliability Engineering. In the talk, Shayan proposes a novel deep learning-based approach for prognosis prediction of oil and gas plant equipment in an effort to prevent critical damage or failure. We explore the evolution of reliability engineering, the decision to use a residual-based approach rather than traditional anomaly detection to determine when an anomaly was happening, the challenges of using LSTMs when building these models, the amount of human labeling required to build the models, and much more!
The complete show notes for this episode can be found at twimlai.com/go/540 Lees meer -
Building a Deep Tech Startup in NLP with Nasrin Mostafazadeh - #539
24 november 2021 · 51 min
Today we’re joined by friend-of-the-show Nasrin Mostafazadeh, co-founder of Verneek.
Though Verneek is still in stealth, Nasrin was gracious enough to share a bit about the company, including their goal of enabling anyone to make data-informed decisions without the need for a technical background, through the use of innovative human-machine interfaces. In our conversation, we explore the state of AI research in the domains relevant to the problem they’re trying to solve and how they use those insights to inform and prioritize their research agenda. We also discuss what advice Nasrin would give to someone thinking about starting a deep tech startup or going from research to product development.
The complete show notes for today’s show can be found at twimlai.com/go/539. Lees meer -
Models for Human-Robot Collaboration with Julie Shah - #538
22 november 2021 · 42 min
Today we’re joined by Julie Shah, a professor at the Massachusetts Institute of Technology (MIT). Julie’s work lies at the intersection of aeronautics, astronautics, and robotics, with a specific focus on collaborative and interactive robotics. In our conversation, we explore how robots would achieve the ability to predict what their human collaborators are thinking, what the process of building knowledge into these systems looks like, and her big picture idea of developing a field robot that doesn’t “require a human to be a robot” to work with it. We also discuss work Julie has done on cross-training between humans and robots with the focus on getting them to co-learn how to work together, as well as future projects that she’s excited about. Lees meer
The complete show notes for this episode can be found at twimlai.com/go/538.
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Four Key Tools for Robust Enterprise NLP with Yunyao Li - #537
18 november 2021 · 58 min
Today we’re joined by Yunyao Li, a senior research manager at IBM Research.
Yunyao is in a somewhat unique position at IBM, addressing the challenges of enterprise NLP in a traditional research environment, while also having customer engagement responsibilities. In our conversation with Yunyao, we explore the challenges associated with productizing NLP in the enterprise, and if she focuses on solving these problems independent of one another, or through a more unified approach.
We then ground the conversation with real-world examples of these enterprise challenges, including enabling level document discovery at scale using combinations of techniques like deep neural networks and supervised and/or unsupervised learning, and entity extraction and semantic parsing to identify text. Finally, we talk through data augmentation in the context of NLP, and how we enable the humans in-the-loop to generate high-quality data.
The complete show notes for this episode can be found at twimlai.com/go/537 Lees meer -
Machine Learning at GSK with Kim Branson - #536
15 november 2021 · 1 uur 1 min
Today we’re joined by Kim Branson, the SVP and global head of artificial intelligence and machine learning at GSK.
We cover a lot of ground in our conversation, starting with a breakdown of GSK’s core pharmaceutical business, and how ML/AI fits into that equation, use cases that appear using genetics data as a data source, including sequential learning for drug discovery. We also explore the 500 billion node knowledge graph Kim’s team built to mine scientific literature, and their “AI Hub”, the ML/AI infrastructure team that handles all tooling and engineering problems within their organization. Finally, we explore their recent cancer research collaboration with King’s College, which is tasked with understanding the individualized needs of high- and low-risk cancer patients using ML/AI amongst other technologies.
The complete show notes for this episode can be found at twimlai.com/go/536. Lees meer -
The Benefit of Bottlenecks in Evolving Artificial Intelligence with David Ha - #535
11 november 2021 · 59 min
Today we’re joined by David Ha, a research scientist at Google.
In nature, there are many examples of “bottlenecks”, or constraints, that have shaped our development as a species. Building upon this idea, David posits that these same evolutionary bottlenecks could work when training neural network models as well. In our conversation with David, we cover a TON of ground, including the aforementioned biological inspiration for his work, then digging deeper into the different types of constraints he’s applied to ML systems. We explore abstract generative models and how advanced training agents inside of generative models has become, and quite a few papers including Neuroevolution of self-interpretable agents, World Models and Attention for Reinforcement Learning, and The Sensory Neuron as a Transformer: Permutation-Invariant Neural Networks for Reinforcement Learning.
This interview is Nerd Alert certified, so get your notes ready!
PS. David is one of our favorite follows on Twitter (@hardmaru), so check him out and share your thoughts on this interview and his work!
The complete show notes for this episode can be found at twimlai.com/go/535 Lees meer -
Facebook Abandons Facial Recognition. Should Everyone Else Follow Suit? With Luke Stark - #534
8 november 2021 · 42 min
Today we’re joined by Luke Stark, an assistant professor at Western University in London, Ontario.
In our conversation with Luke, we explore the existence and use of facial recognition technology, something Luke has been critical of in his work over the past few years, comparing it to plutonium. We discuss Luke’s recent paper, “Physiognomic Artificial Intelligence”, in which he critiques studies that will attempt to use faces and facial expressions and features to make determinations about people, a practice fundamental to facial recognition, also one that Luke believes is inherently racist at its core.
Finally, briefly discuss the recent wave of hires at the FTC, and the news that broke (mid-recording) announcing that Facebook will be shutting down their facial recognition system and why it's not necessarily the game-changing announcement it seemed on its… face.
The complete show notes for this episode can be found at twimlai.com/go/534. Lees meer -
Building Blocks of Machine Learning at LEGO with Francesc Joan Riera - #533
4 november 2021 · 43 min
Today we’re joined by Francesc Joan Riera, an applied machine learning engineer at The LEGO Group. Lees meer
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.