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.

Jaksot(777)

Responsible AI in Practice with Sarah Bird - #322

Responsible AI in Practice with Sarah Bird - #322

Today we continue our Azure ML at Microsoft Ignite series joined by Sarah Bird, Principal Program Manager at Microsoft. At Ignite, Microsoft released new tools focused on responsible machine learning, which fall under the umbrella of the Azure ML 'Machine Learning Interpretability Toolkit.’ In our conversation, Sarah walks us this toolkit, detailing use cases and the user experience. We also discuss her work in differential privacy, and in the broader ML community, in particular, the MLSys conference.

4 Joulu 201938min

Enterprise Readiness, MLOps and Lifecycle Management with Jordan Edwards - #321

Enterprise Readiness, MLOps and Lifecycle Management with Jordan Edwards - #321

Today we’re joined by Jordan Edwards, Principal Program Manager for MLOps on Azure ML at Microsoft. In our conversation, Jordan details how Azure ML accelerates model lifecycle management with MLOps, which enables data scientists to collaborate with IT teams to increase the pace of model development and deployment. We discuss various problems associated with generalizing ML at scale at Microsoft, what exactly MLOps is, the “four phases” along the journey of customer implementation of MLOps, and much m

2 Joulu 201939min

DevOps for ML with Dotscience - #320

DevOps for ML with Dotscience - #320

Today we’re joined by Luke Marsden, Founder and CEO of Dotscience. Luke walks us through the Dotscience platform and their manifesto on DevOps for ML. Thanks to Luke and Dotscience for their sponsorship of this Democast and their continued support of TWIML.   Head to https://twimlai.com/democast/dotscience to watch the full democast!

26 Marras 201946min

Building an Autonomous Knowledge Graph with Mike Tung - #319

Building an Autonomous Knowledge Graph with Mike Tung - #319

Today we’re joined by Mike Tung, Founder, and CEO of Diffbot. In our conversation, we discuss Diffbot’s Knowledge Graph, including how it differs from more mainstream use cases like Google Search and MSFT Bing. We also discuss the developer experience with the knowledge graph and other tools, like Extraction API and Crawlbot, challenges like knowledge fusion, balancing being a research company that is also commercially viable, and how they approach their role in the research community.

21 Marras 201944min

The Next Generation of Self-Driving Engineers with Aaron Ma - Talk #318

The Next Generation of Self-Driving Engineers with Aaron Ma - Talk #318

Today we’re joined by our youngest guest ever (by far), Aaron Ma, an 11-year-old middle school student and machine learning engineer in training. Aaron has completed over 80(!) Coursera courses and is the recipient of 3 Udacity nano-degrees. In our conversation, we discuss Aaron’s research interests in reinforcement learning and self-driving cars, his journey from programmer to ML engineer, his experiences participating in kaggle competitions, and how he balances his passion for ML with day-to-day life.

18 Marras 201947min

Spiking Neural Networks: A Primer with Terrence Sejnowski - #317

Spiking Neural Networks: A Primer with Terrence Sejnowski - #317

On today’s episode, we’re joined by Terrence Sejnowski, to discuss the ins and outs of spiking neural networks, including brain architecture, the relationship between neuroscience and machine learning, and ways to make NN’s more efficient through spiking. Terry also gives us some insight into hardware used in this field, characterizes the major research problems currently being undertaken, and the future of spiking networks.

14 Marras 201949min

Bridging the Patient-Physician Gap with ML and Expert Systems w/ Xavier Amatriain - #316

Bridging the Patient-Physician Gap with ML and Expert Systems w/ Xavier Amatriain - #316

Today we’re joined by return guest Xavier Amatriain, Co-founder and CTO of Curai, whose goal is to make healthcare accessible and scaleable while bringing down costs. In our conversation, we touch on the shortcomings of traditional primary care, and how Curai fills that role, and some of the unique challenges his team faces in applying ML in the healthcare space. We also discuss the use of expert systems, how they train them, and how NLP projects like BERT and GPT-2 fit into what they’re building.

11 Marras 201939min

What Does it Mean for a Machine to "Understand"? with Thomas Dietterich - #315

What Does it Mean for a Machine to "Understand"? with Thomas Dietterich - #315

Today we have the pleasure of being joined by Tom Dietterich, Distinguished Professor Emeritus at Oregon State University. Tom recently wrote a blog post titled "What does it mean for a machine to “understand”, and in our conversation, he goes into great detail on his thoughts. We cover a lot of ground, including Tom’s position in the debate, his thoughts on the role of systems like deep learning in potentially getting us to AGI, the “hype engine” around AI advancements, and so much more.

7 Marras 201938min

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