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This Week in Machine Learning & Artificial Intelligence (AI) Podcast

This Week in Machine Learning & Artificial Intelligence (AI) Podcast

This Week in Machine Learning & AI is the most popular podcast of its kind. TWiML & AI caters to a highly-targeted audience of machine learning & AI enthusiasts. They are data scientists, developers, founders, CTOs, engineers, architects, IT & product leaders, as well as tech-savvy business leaders. These creators, builders, makers and influencers value TWiML as an authentic, trusted and insightful guide to all that?s interesting and important in the world of machine learning and AI. Technologies covered include: machine learning, artificial intelligence, deep learning, natural language processing, neural networks, analytics, deep learning and more.

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Topic Modeling for Customer Insights at USAA with William Fehlman - TWIML Talk #276

Today we?re joined by William Fehlman, director of data science at USAA. We caught up with William a while back to discuss:

His work on topic modeling, which USAA uses in various scenarios, including chat channels with members via mobile and desktop interfaces. How their datasets are generated. Explored methodologies of topic modeling, including latent semantic indexing, latent Dirichlet allocation, and non-negative matrix factorization. We also explore how terms are represented via a document-term matrix, and how they are scored based on coherence.

The complete show notes can be found at twimlai.com/talk/276.

Visit twimlcon.com to learn more about the TWIMLcon: AI Platforms conference! Early-bird registration ends on 6/28!

2019-06-20
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Phronesis of AI in Radiology with Judy Gichoya - TWIML Talk #275

Today we?re joined by Judy Gichoya an interventional radiology fellow at the Dotter Institute at Oregon Health and Science University. In our conversation, we discuss:

? Judy's research in ?Phronesis of AI in Radiology: Superhuman meets Natural Stupidy,? reviewing the claims of ?superhuman? AI performance in radiology.

? We explore potential roles in which AI can have success in radiology, along with some of the different types of biases that can manifest themselves across multiple use cases.

? We look at the CheXNet paper, which details how human and AI performance can complement and improve each other's performance for detecting pneumonia in chest X-rays.

The complete show notes can be found at twimlai.com/talk/275.

Visit twimlcon.com to learn more about the TWIMLcon: AI Platforms conference! 

2019-06-18
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The Ethics of AI-Enabled Surveillance with Karen Levy - TWIML Talk #274

Today we?re joined by Karen Levy, assistant professor in the department of information science at Cornell University. Karen?s research focuses on how rules and technologies interact to regulate behavior, especially the legal, organizational, and social aspects of surveillance and monitoring. In our conversation we discuss:

? Examples of how data tracking and surveillance can be used in ways that can be abusive to various marginalized groups, including detailing her extensive research into truck driver surveillance.

? Her thoughts on how the broader society will react to the increase in surveillance,

? The unintended consequences of surveillant systems, questions surrounding hybridization of jobs and systems, and more!

The complete show notes can be found at twimlai.com/talk/274.

Visit twimlcon.com to learn more about the TWIMLcon: AI Platforms conference! 

2019-06-14
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Supporting Rapid Model Development at Two Sigma with Matt Adereth & Scott Clark - TWIML Talk #273

Today we?re joined by Matt Adereth, managing director of investments at Two Sigma, and return guest Scott Clark, co-founder and CEO of SigOpt, to discuss:

? The end to end modeling platform at Two Sigma, who it serves, and challenges faced in production and modeling.

? How Two Sigma has attacked the experimentation challenge with their platform.

? The relationship between the optimization and infrastructure teams at SigOpt.

? What motivates companies that aren?t already heavily invested in platforms, optimization or automation, to do so.

The complete show notes can be found at twimlai.com/talk/273.

Visit twimlcon.com to learn more about the TWIMLcon: AI Platforms conference! The first 10 listeners who register get their ticket for 75% off using the discount code TWIMLFIRST!

Follow along with the entire AI Platforms Vol 2 series at twimlai.com/aiplatforms2.

Thanks to SigOpt for their continued support of the podcast, and their sponsorship of this episode! Check out their machine learning experimentation and optimization suite, and get a free trial at twimlai.com/sigopt.

2019-06-11
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Scaling Model Training with Kubernetes at Stripe with Kelley Rivoire - TWIML Talk #272

Today we?re joined by Kelley Rivoire, engineering manager working on machine learning infrastructure at Stripe. Kelley and I caught up at a recent Strata Data conference to discuss:

? Her talk "Scaling model training: From flexible training APIs to resource management with Kubernetes."

? Stripe?s machine learning infrastructure journey, including their start from a production focus.

? Internal tools used at Stripe, including Railyard, an API built to manage model training at scale & more!

The complete show notes can be found at twimlai.com/talk/272.

Visit twimlcon.com to learn more about the TWIMLcon: AI Platforms conference! The first 10 listeners who register get their ticket for 75% off using the discount code TWIMLFIRST!

Follow along with the entire AI Platforms Vol 2 series at twimlai.com/aiplatforms2.

Thanks to SigOpt for their continued support of the podcast, and their sponsorship of this episode! Check out their machine learning experimentation and optimization suite, and get a free trial at twimlai.com/sigopt.

2019-06-06
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Productizing ML at Scale at Twitter with Yi Zhuang - TWIML Talk #271

Today we continue our AI Platforms series joined by Yi Zhuang, Senior Staff Engineer at Twitter & Tech Lead for Machine Learning Core Environment at Twitter Cortex. In our conversation, we cover: 

? The machine learning landscape at Twitter, including with the history of the Cortex team

? Deepbird v2, which is used for model training and evaluation solutions, and it's integration with Tensorflow 2.0.

? The newly assembled ?Meta? team, that is tasked with exploring the bias, fairness, and accountability of their machine learning models, and much more!

The complete show notes can be found at twimlai.com/talk/271.

Visit twimlcon.com to learn more about the TWIMLcon: AI Platforms conference! The first 10 listeners who register get their ticket for 75% off using the discount code TWIMLFIRST!

Follow along with the entire AI Platforms Vol 2 series at twimlai.com/aiplatforms2.

Thanks to SigOpt for their continued support of the podcast, and their sponsorship of this episode! Check out their machine learning experimentation and optimization suite, and get a free trial at twimlai.com/sigopt.

Finally, visit twimlai.com/3bday to help us celebrate TWiML's 3rd Birthday!

2019-06-03
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Snorkel: A System for Fast Training Data Creation with Alex Ratner - TWiML Talk #270

Today we?re joined by Alex Ratner, Ph.D. student at Stanford. In our conversation, we discuss:

? Snorkel, the open source framework that is the successor to Stanford's Deep Dive project.

? How Snorkel is used as a framework for creating training data with weak supervised learning techniques.

? Multiple use cases for Snorkel, including how it is used by large companies like Google. 

The complete show notes can be found at twimlai.com/talk/270.

Follow along with the entire AI Platforms Vol 2 series at twimlai.com/aiplatforms2.

Thanks to SigOpt for their continued support of the podcast, and their sponsorship of this episode! Check out their machine learning experimentation and optimization suite, and get a free trial at twimlai.com/sigopt.

Finally, visit twimlai.com/3bday to help us celebrate TWiML's 3rd Birthday!

2019-05-30
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Advancing Autonomous Vehicle Development Using Distributed Deep Learning with Adrien Gaidon - TWiML Talk #269

In this, the kickoff episode of AI Platforms Vol. 2, we're joined by Adrien Gaidon, Machine Learning Lead at Toyota Research Institute. Adrien and I caught up to discuss his team?s work on deploying distributed deep learning in the cloud, at scale. In our conversation, we discuss: 

? The beginning and gradual scaling up of TRI's platform.

? Their distributed deep learning methods, including their use of stock Pytorch.

? Applying devops to their research infrastructure, and much more!

The complete show notes for this episode can be found at twimlai.com/talk/269.

Thanks to SigOpt for their continued support of the podcast, and their sponsorship of this episode! Check out their machine learning experimentation and optimization suite, and get a free trial at twimlai.com/sigopt.

Finally, visit twimlai.com/3bday to help us celebrate TWiML's 3rd Birthday!

2019-05-28
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Are We Being Honest About How Difficult AI Really Is? w/ David Ferrucci - TWiML Talk #268

Today we?re joined by David Ferrucci, Founder, CEO, and Chief Scientist at Elemental Cognition, a company focused on building natural learning systems that understand the world the way people do. In our conversation, we discuss: 

? His experience leading the team that built the IBM Watson system that won on Jeopardy.


? The role of ?understanding? in the context of AI systems, and the types of commitments and investments needed to achieve even modest levels of understanding in these systems.

? His thoughts on the power of deep learning, what the path to AGI looks like, and the need for hybrid systems to get there.

The complete show notes for this episode can be found at twimlai.com/talk/268.

Visit twimlai.com/3bday to help us celebrate TWiML's 3rd Birthday!

 

2019-05-23
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Gauge Equivariant CNNs, Generative Models, and the Future of AI with Max Welling - TWiML Talk #267

Today we?re joined by Max Welling, research chair in machine learning at the University of Amsterdam, as well as VP of technologies at Qualcomm, and Fellow at the Canadian Institute for Advanced Research, or CIFAR. In our conversation, we discuss: 

? Max?s research at Qualcomm AI Research and the University of Amsterdam, including his work on Bayesian deep learning, Graph CNNs and Gauge Equivariant CNNs, and in power efficiency for AI via compression, quantization, and compilation.

? Max?s thoughts on the future of the AI industry, in particular, the relative importance of models, data and compute.

The complete show notes for this episode can be found at twimlai.com/talk/267.

Thanks to Qualcomm for sponsoring today's episode! Check out what they're up to at twimlai.com/qualcomm.

 

2019-05-20
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Can We Trust Scientific Discoveries Made Using Machine Learning? with Genevera Allen - TWiML Talk #266

Today we?re joined by Genevera Allen, associate professor of statistics in the EECS Department at Rice University, Founder and Director of the Rice Center for Transforming Data to Knowledge and Investigator with the Neurological Research Institute with the Baylor College of Medicine.

Genevera caused quite the stir at the American Association for the Advancement of Science meeting earlier this year with her presentation ?Can We Trust Data-Driven Discoveries?" In our conversation we cover:

? The goal of Genevera's talk, and what was lost in translation.

? Use cases outlining the shortcomings of current machine learning techniques.

? Reproducibility, including inference vs discovery, and the lack of terminology for many of the various reproducibility issues, & much more!

The complete show notes for this episode can be found at twimlai.com/talk/266.

 

2019-05-16
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Creative Adversarial Networks for Art Generation with Ahmed Elgammal - TWiML Talk #265

Today we?re joined by Ahmed Elgammal, a professor in the department of computer science at Rutgers, and director of The Art and Artificial Intelligence Lab. In my conversation with Ahmed, we discuss:

? His work on AICAN, a creative adversarial network that produces original portraits, trained with over 500 years of European canonical art.

? How complex the computational representations of the art actually are, and how he simplifies them.

? Specifics of the training process, including the various types of artwork used, and the constraints applied to the model.

The complete show notes for this episode can be found at twimlai.com/talk/265.

2019-05-13
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Diagnostic Visualization for Machine Learning with YellowBrick w/ Rebecca Bilbro - TWiML Talk #264

Today we close out our PyDataSci series joined by Rebecca Bilbro, head of data science at ICX media and co-creator of the popular open-source visualization library YellowBrick.

In our conversation, Rebecca details:

? Her relationship with toolmaking, which led to the eventual creation of Yellowbrick.

? Popular tools within YellowBrick, including a summary of their unit testing approach.

? Interesting use cases that she?s seen over time.

? The growth she?s seen in the community of contributors and examples of their contributions as they approach the release of YellowBrick 1.0.

The complete show notes for this episode can be found at twimlai.com/talk/264. Check out the rest of the PyDataSci series at twimlai.com/pydatasci.

We want to better understand your views on the importance of open source and the projects and players in this space. To access the survey visit twimlai.com/pythonsurvey.

Thanks to this weeks sponsor, IBM, for their support of the podcast! Visit twimlai.com/ibm to learn more about the IBM Data Science Community.

2019-05-10
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Librosa: Audio and Music Processing in Python with Brian McFee - TWiML Talk #263

Today we continue our PyDataSci series joined by Brian McFee, assistant professor of music technology and data science at NYU, and creator of LibROSA, a python package for music and audio analysis.

Brian walks us through his experience building LibROSA, including:

? Detailing the core functions provided in the library,

? His experience working within Jupyter Notebook,

? We explore a typical LibROSA workflow & more!

The complete show notes for this episode can be found at twimlai.com/talk/263.

Check out the rest of the PyDataSci series at twimlai.com/pydatasci.

We want to better understand your views on the importance of open source and the projects and players in this space. To access the survey visit twimlai.com/pythonsurvey.

Thanks to this weeks sponsor, IBM, for their support of the podcast! Visit twimlai.com/ibm to learn more about the IBM Data Science Community.

2019-05-09
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Practical Natural Language Processing with spaCy and Prodigy w/ Ines Montani - TWiML Talk #262

In this episode of PyDataSci, we?re joined by Ines Montani, Cofounder of Explosion, Co-developer of SpaCy and lead developer of Prodigy.

Ines and I caught up to discuss her various projects, including the aforementioned SpaCy, an open-source NLP library built with a focus on industry and production use cases.

The complete show notes for this episode can be found at twimlai.com/talk/262. Check out the rest of the PyDataSci series at twimlai.com/pydatasci.

We want to better understand your views on the importance of open source and the projects and players in this space. To access the survey visit twimlai.com/pythonsurvey.

Thanks to this weeks sponsor, IBM, for their support of the podcast! Visit twimlai.com/ibm to learn more about the IBM Data Science Community. 

2019-05-07
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Scaling Jupyter Notebooks with Luciano Resende - TWiML Talk #261

Today we kick off PyDataSci with Luciano Resende, an Open Source AI Platform Architect at IBM and part of the Center for Open Source Data and AI Technology.

Luciano and I caught up to discuss his work on Jupyter Enterprise Gateway, a scalable way to share Jupyter notebooks and other resources in an enterprise environment. In our conversation, we discuss some of the challenges that arise using Jupyter Notebooks at scale, the role of open source projects like Jupyter Hub and Enterprise Gateway, and some potential reasons for investing in and building custom notebooks. We also explore some common requests from the community, such as tighter integration with git repositories, as well as the python-centricity of the vast Jupyter ecosystem.

The complete show notes for this episode can be found at twimlai.com/talk/261. Check out the rest of the PyDataSci series at twimlai.com/pydatasci.

Thanks to this weeks sponsor, IBM, for their support of the podcast! Visit twimlai.com/ibm to learn more about the IBM Data Science Community. 

 

2019-05-06
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Fighting Fake News and Deep Fakes with Machine Learning w/ Delip Rao - TWiML Talk #260

Today we?re joined by Delip Rao, vice president of research at the AI Foundation, co-author of the book Natural Language Processing with PyTorch, and creator of the Fake News Challenge.

Our conversation begins with the origin story of the Fake News Challenge, including Delip?s initial motivations for the project, and what some of his key takeaways were from that experience. We then dive into a discussion about the generation and detection of artificial content, including ?fake news? and ?deep fakes.? We discuss the state of generation and detection for text, video, and audio, the key challenges in each of these modalities, the role of GANs on both sides of the equation, and other potential solutions. Finally, we discuss Delip?s new book, Natural Language Processing with PyTorch and his philosophy behind writing it.

The complete show notes for this episode can be found at https://twimlai.com/talk/260.

For more from the AI Conference NY series, visit twimlai.com/nyai19.

Thanks to our friends at HPE for sponsoring this week's series of shows from the O?Reilly AI Conference in New York City! For more information on HPE InfoSight, visit twimlai.com/hpe.

 

2019-05-03
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Maintaining Human Control of Artificial Intelligence with Joanna Bryson - TWiML Talk #259

Today we?re joined by Joanna Bryson, Reader at the University of Bath.

I was fortunate to catch up with Joanna at the AI Conference where she presented on ?Maintaining Human Control of Artificial Intelligence,? focusing on technological and policy mechanisms that could be used to achieve that goal. In our conversation, we explore our current understanding of ?natural intelligence? and how it can inform the development of AI, the context in which she uses the term ?human control? and its implications, and the meaning of and need to apply ?DevOps? principles when developing AI systems. This was a fun one!

The complete show notes for this episode can be found at https://twimlai.com/talk/259.

For more from the AI Conference NY series, visit twimlai.com/nyai19.

Thanks to our friends at HPE for sponsoring this week's series of shows from the O?Reilly AI Conference in New York City! For more information on HPE InfoSight, visit twimlai.com/hpe.

2019-05-01
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Intelligent Infrastructure Management with Pankaj Goyal & Rochna Dhand - TWiML Talk #258

Today we kick off our AI conference NY series with Pankaj Goyal, VP for AI & HPC product management at HPE, and Rochna Dhand, director of product management for HPE InfoSight.


Today we get things kicked off with Pankaj Goyal, VP for AI & HPC product management at HPE, and Rochna Dhand, director of product management for HPE InfoSight. In our conversation, Pankaj shares some examples of the kind of AI projects HPE is working with customers on And Rochna details hows HPE?s Infosight helps IT organizations better manage and ensure the health of an enterprise?s IT infrastructure using machine learning. We discuss the key use cases addressed by InfoSight, the types of models it uses for its analysis and some of the results seen in real-world deployments.

The complete show notes for this episode can be found at https://twimlai.com/talk/258.

For more from the AI Conference NY series, visit twimlai.com/nyai19.

Thanks to our friends at HPE for sponsoring this week's series of shows from the O?Reilly AI Conference in New York City! For more information on HPE InfoSight, visit twimlai.com/hpe.

2019-04-29
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Organizing for Successful Data Science at Stitch Fix with Eric Colson - TWiML Talk #257

For the final episode of our Strata Data series, we?re joined by Eric Colson, Chief Algorithms Officer at Stitch Fix, whose presentation at the conference explored ?How to make fewer bad decisions.?

Our discussion focuses in on the three key organizational principles for data science teams that he?s developed at Stitch Fix. Along the way, we also talk through the various roles data science plays at the company, explore a few of the 800+ algorithms in use at the company spanning recommendations, inventory management, demand forecasting, and clothing design. We discuss the roles of Stitch Fix?splatforms team in supporting the data science organization, and his unique perspective on how to identify platform features.

The complete show notes for this episode can be found at https://twimlai.com/talk/257.

For more from the Strata Data conference series, visit twimlai.com/stratasf19.

I want to send a quick thanks to our friends at Cloudera for their sponsorship of this series of podcasts from the Strata Data Conference, which they present along with O?Reilly Media. Cloudera?s long been a supporter of the podcast; in fact, they sponsored the very first episode of TWiML Talk, recorded back in 2016. Since that time Cloudera has continued to invest in and build out its platform, which already securely hosts huge volumes of enterprise data, to provide enterprise customers with a modern environment for machine learning and analytics that works both in the cloud as well as the data center. In addition, Cloudera Fast Forward Labs provides research and expert guidance that helps enterprises understand the realities of building with AI technologies without needing to hire an in-house research team. To learn more about what the company is up to and how they can help, visit Cloudera?s Machine Learning resource center at cloudera.com/ml.

2019-04-26
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End-to-End Data Science to Drive Business Decisions at LinkedIn with Burcu Baran - TWiML Talk #256

In this episode of our Strata Data conference series, we?re joined by Burcu Baran, Senior Data Scientist at LinkedIn.

At Strata, Burcu, along with a few members of her team, delivered the presentation ?Using the full spectrum of data science to drive business decisions,? which outlines how LinkedIn manages their entire machine learning production process. In our conversation, Burcu details each phase of the process, including problem formulation, monitoring features, A/B testing and more. We also discuss how her ?horizontal? team works with other more ?vertical? teams within LinkedIn, various challenges that arise when training and modeling such as data leakage and interpretability, best practices when trying to deal with data partitioning at scale, and of course, the need for a platform that reduces the manual pieces of this process, promoting efficiency.

The complete show notes for this episode can be found at https://twimlai.com/talk/256.

For more from the Strata Data conference series, visit twimlai.com/stratasf19.

I want to send a quick thanks to our friends at Cloudera for their sponsorship of this series of podcasts from the Strata Data Conference, which they present along with O?Reilly Media. Cloudera?s long been a supporter of the podcast; in fact, they sponsored the very first episode of TWiML Talk, recorded back in 2016. Since that time Cloudera has continued to invest in and build out its platform, which already securely hosts huge volumes of enterprise data, to provide enterprise customers with a modern environment for machine learning and analytics that works both in the cloud as well as the data center. In addition, Cloudera Fast Forward Labs provides research and expert guidance that helps enterprises understand the realities of building with AI technologies without needing to hire an in-house research team. To learn more about what the company is up to and how they can help, visit Cloudera?s Machine Learning resource center at cloudera.com/ml.

I?d also like to send a huge thanks to LinkedIn for their continued support and sponsorship of the show! Now that I?ve had a chance to interview several of the folks on LinkedIn?s Data Science and Engineering teams, it?s really put into context the complexity and scale of the problems that they get to work on in their efforts to create enhanced economic opportunities for every member of the global workforce. AI and ML are integral aspects of almost every product LinkedIn builds for its members and customers and their massive, highly structured dataset gives their data scientists and researchers the ability to conduct applied research to improve member experiences. To learn more about the work of LinkedIn Engineering, please visit engineering.linkedin.com/blog.

2019-04-24
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Learning with Limited Labeled Data with Shioulin Sam - TWiML Talk #255

Today, in the first episode of our Strata Data conference series, we?re joined by Shioulin Sam, Research Engineer with Cloudera Fast Forward Labs.

Shioulin and I caught up to discuss the newest report to come out of CFFL, ?Learning with Limited Label Data,? which explores active learning as a means to build applications requiring only a relatively small set of labeled data. We start our conversation with a review of active learning and some of the reasons why it?s recently become an interesting technology for folks building systems based on deep learning. We then discuss some of the differences between active learning approaches or implementations, and some of the common requirements of an active learning system. Finally, we touch on some packaged offerings in the marketplace that include active learning, including Amazon?s SageMaker Ground Truth, and review Shoulin?s tips for getting started with the technology.

The complete show notes for this episode can be found at https://twimlai.com/talk/255.

For more from the Strata Data conference series, visit twimlai.com/stratasf19.

I want to send a quick thanks to our friends at Cloudera for their sponsorship of this series of podcasts from the Strata Data Conference, which they present along with O?Reilly Media. Cloudera?s long been a supporter of the podcast; in fact, they sponsored the very first episode of TWiML Talk, recorded back in 2016. Since that time Cloudera has continued to invest in and build out its platform, which already securely hosts huge volumes of enterprise data, to provide enterprise customers with a modern environment for machine learning and analytics that works both in the cloud as well as the data center. In addition, Cloudera Fast Forward Labs provides research and expert guidance that helps enterprises understand the realities of building with AI technologies without needing to hire an in-house research team. To learn more about what the company is up to and how they can help, visit Cloudera?s Machine Learning resource center at cloudera.com/ml.

2019-04-22
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cuDF, cuML & RAPIDS: GPU Accelerated Data Science with Paul Mahler - TWiML Talk #254

Today we're joined by Paul Mahler, senior data scientist and technical product manager for machine learning at NVIDIA.

In our conversation, Paul and I discuss NVIDIA's RAPIDS open source project, which aims to bring GPU acceleration to traditional data science workflows and machine learning tasks. We dig into the various subprojects like cuDF and cuML that make up the RAPIDS ecosystem, as well as the role of lower-level libraries like mlprims and the relationship to other open-source projects like Scikit-learn, XGBoost and Dask.

The complete show notes for this episode can be found at https://twimlai.com/talk/254.

Visit twimlai.com/gtc19 for more from our GTC 2019 series.

To learn more about Dell Precision workstations, and some of the ways they?re being used by customers in industries like Media and Entertainment, Engineering and Manufacturing, Healthcare and Life Sciences, Oil and Gas, and Financial services, visit Dellemc.com/Precision.

2019-04-19
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Edge AI for Smart Manufacturing with Trista Chen - TWiML Talk #253

Today we?re joined by Trista Chen, chief scientist of machine learning at Inventec.

At GTC, Trista spoke on ?Edge AI in Smart Manufacturing: Defect Detection and Beyond.? In our conversation, we discuss a few of the challenges that Industry 4.0 initiatives aim to address and dig into a few of the various use cases she?s worked on, such as the deployment of machine learning in an industrial setting to perform defect detection, safety improvement, demand forecasting, and more. We also dig into the role of edge, cloud, and what she calls hybrid AI, which is inference happening both in the cloud and on the edge concurrently. Finally, we discuss the challenges associated with estimating the ROI of industrial AI projects and the need that often arises to redefine the problem to understand the ultimate impact of the solution.

The complete show notes for this episode can be found at https://twimlai.com/talk/253.

Visit twimlai.com/gtc19 for more from our GTC 2019 series.

To learn more about Dell Precision workstations, and some of the ways they?re being used by customers in industries like Media and Entertainment, Engineering and Manufacturing, Healthcare and Life Sciences, Oil and Gas, and Financial services, visit Dellemc.com/Precision.

2019-04-18
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Machine Learning for Security and Security for Machine Learning with Nicole Nichols - TWiML Talk #252

Today we?re joined by Nicole Nichols, a senior research scientist at the Pacific Northwest National Lab.

Nicole joined me to discuss her recent presentation at GTC, which was titled ?Machine Learning for Security, and Security for Machine Learning.? Our conversation explores the two use cases she presented, insider threat detection, and software fuzz testing. We discuss the effectiveness of standard and bidirectional RNN language models for detecting malicious activity within the Los Alamos National Laboratory cybersecurity dataset, the augmentation of software fuzzing techniques using deep learning, and light-based adversarial attacks on image classification systems. I?d love to hear your thoughts on these use cases!

The complete show notes for this episode can be found at https://twimlai.com/talk/252.

Visit twimlai.com/gtc19 for more from our GTC 2019 series.

To learn more about Dell Precision workstations, and some of the ways they?re being used by customers in industries like Media and Entertainment, Engineering and Manufacturing, Healthcare and Life Sciences, Oil and Gas, and Financial services, visit Dellemc.com/Precision.

2019-04-16
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Domain Adaptation and Generative Models for Single Cell Genomics with Gerald Quon - TWiML Talk #251

Today we?re joined by Gerald Quon, assistant professor in the Molecular and Cellular Biology department at UC Davis.

Gerald presented his work on Deep Domain Adaptation and Generative Models for Single Cell Genomics at GTC this year, which explores single cell genomics as a means of disease identification for treatment. In our conversation, we discuss how Gerald and his team use deep learning to generate novel insights across diseases, the different types of data that was used, and the development of ?nested? Generative Models for single cell measurement.

The complete show notes for this episode can be found at https://twimlai.com/talk/251.

Visit twimlai.com/gtc19 for more from our GTC 2019 series.

To learn more about Dell Precision workstations, and some of the ways they?re being used by customers in industries like Media and Entertainment, Engineering and Manufacturing, Healthcare and Life Sciences, Oil and Gas, and Financial services, visit Dellemc.com/Precision.

2019-04-15
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Mapping Dark Matter with Bayesian Neural Networks w/ Yashar Hezaveh - TWiML Talk #250

You might have seen the news yesterday that MIT researcher Katie Bouman produced the first image of a black hole. What?s been less reported is that the algorithm she developed to accomplish this is based on machine learning. Machine learning is having a huge impact in the fields of astronomy and astrophysics, and I?m excited to bring you interviews with some of the people innovating in this area.

Today we?re joined by Yashar Hezaveh, Assistant Professor at the University of Montreal, and Research Fellow at the Center for Computational Astrophysics at Flatiron Institute.

Yashar and I caught up to discuss his work on gravitational lensing, which is the bending of light from distant sources due to the effects of gravity. In our conversation, Yashar and I discuss how machine learning can be applied to undistort images, including some of the various techniques used and how the data is prepared to get the best results. We also discuss the intertwined roles of simulation and machine learning in generating images, incorporating other techniques such as domain transfer or GANs, and how he assesses the results of this project.

For even more on this topic, I?d also suggest checking out the following interviews, TWiML Talk #117 with Chris Shallue, where we discuss the discovery of exoplanets, TWiML Talk #184, with Viviana Acquaviva, where we explore dark energy and star formation, and if you want to go way back, TWiML Talk #5 with Joshua Bloom which provides a great overview of the application of ML in astronomy.

Thanks to Pegasystems for sponsoring today's show! I'd like to invite you to join me at PegaWorld, the company?s annual digital transformation conference, which takes place this June in Las Vegas. To learn more about the conference or to register, visit pegaworld.com and use TWIML19 in the promo code field when you get there for $200 off.

The complete show notes for this episode can be found at https://twimlai.com/talk/250.

2019-04-11
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Deep Learning for Population Genetic Inference with Dan Schrider - TWiML Talk #249

Today we?re joined by Dan Schrider, assistant professor in the department of genetics at The University of North Carolina at Chapel Hill.

My discussion with Dan starts with an overview of population genomics and from there digs into his application of machine learning in the field, allowing us to, for example, better understand population size changes and gene flow from DNA sequences. We then dig into Dan?s paper ?The Unreasonable Effectiveness of Convolutional Neural Networks in Population Genetic Inference,? which was published in the Molecular Biology and Evolution journal, which examines the idea that CNNs are capable of outperforming expert-derived statistical methods for some key problems in the field.

Thanks to Pegasystems for sponsoring today's show! I'd like to invite you to join me at PegaWorld, the company?s annual digital transformation conference, which takes place this June in Las Vegas. To learn more about the conference or to register, visit pegaworld.com and use TWIML19 in the promo code field when you get there for $200 off.

The complete show notes for this episode can be found at https://twimlai.com/talk/249.

2019-04-09
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Empathy in AI with Rob Walker - TWiML Talk #248

Today we?re joined by Rob Walker, Vice President of Decision Management at Pegasystems.

Rob joined us back in episode 127 to discuss ?Hyperpersonalizing the customer experience.? Today, he?s back for a discussion about the role of empathy in AI systems. In our conversation, we dig into the role empathy plays in consumer-facing human-AI interactions, the differences between empathy and ethics, and a few examples of ways empathy should be considered when building enterprise AI systems.

What do you think? Should empathy be a consideration in AI systems? If so, do any examples jump out for you of where and how it should be applied? I?d love to hear your thoughts on the topic! Feel free to shoot me a tweet at @samcharrington or leave a comment on the show notes page with your thoughts.

Thanks to Pegasystems for sponsoring today's show! I'd like to invite you to join me at PegaWorld, the company?s annual digital transformation conference, which takes place this June in Las Vegas. To learn more about the conference or to register, visit pegaworld.com and use TWIML19 in the promo code field when you get there for $200 off.

The complete show notes for this episode can be found at https://twimlai.com/talk/248.

2019-04-05
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Benchmarking Custom Computer Vision Services at Urban Outfitters with Tom Szumowski - TWiML Talk #247

Today we?re joined by Tom Szumowski, Data Scientist at URBN, the parent company of Urban Outfitters, Anthropologie, and other consumer fashion brands.

Tom and I caught up recently to discuss his project ?Exploring Custom Vision Services for Automated Fashion Product Attribution.? We start our discussion with a definition of the product attribution problem in retail and fashion, and a discussion of the challenges it offers to data scientists. We then look at the process Tom and his team took to building custom attribution models, and the results of their evaluation of various custom vision APIs for this purpose, with a focus on the various roadblocks and lessons he and his team encountered along the way.

Thanks to Pegasystems for sponsoring today's show! I'd like to invite you to join me at PegaWorld, the company?s annual digital transformation conference, which takes place this June in Las Vegas. To learn more about the conference or to register, visit pegaworld.com and use TWIML19 in the promo code field when you get there for $200 off.

The complete show notes for this episode can be found at https://twimlai.com/talk/247.

2019-04-03
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Pragmatic Quantum Machine Learning with Peter Wittek - TWiML Talk #245

Today we?re joined by Peter Wittek, Assistant Professor at the University of Toronto working on quantum-enhanced machine learning and the application of high-performance learning algorithms in quantum physics.

Peter and I caught up back in November to discuss a presentation he gave at re:Invent, ?Pragmatic Quantum Machine Learning Today.? In our conversation, we start with a bit of background including the current state of quantum computing, a look ahead to what the next 20 years of quantum computing might hold, and how current quantum computers are flawed. We then dive into our discussion on quantum machine learning, and Peter?s new course on the topic, which debuted in February. I?ll link to that in the show notes. Finally, we briefly discuss the work of Ewin Tang, a PhD student from the University of Washington, who?s undergrad thesis ?A quantum-inspired classical algorithm for recommendation systems,? made quite a stir last summer. As a special treat for those interested, I?m also sharing my interview with Ewin as a bonus episode alongside this one. I?d love to hear your thoughts on how you think quantum computing will impact machine learning in the next 20 years! Send me a tweet or leave a comment on the show notes page.

Thanks to Pegasystems for sponsoring today's show! I'd like to invite you to join me at PegaWorld, the company?s annual digital transformation conference, which takes place this June in Las Vegas. To learn more about the conference or to register, visit pegaworld.com and use TWIML19 in the promo code field when you get there for $200 off.

The complete show notes for this episode can be found at https://twimlai.com/talk/245.

2019-04-01
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*Bonus Episode* A Quantum Machine Learning Algorithm Takedown with Ewin Tang - TWiML Talk #246

In this special bonus episode of the podcast, I?m joined by Ewin Tang, a PhD student in the Theoretical Computer Science group at the University of Washington.

In our conversation, Ewin and I dig into her paper ?A quantum-inspired classical algorithm for recommendation systems,? which took the quantum computing community by storm last summer. We haven?t called out a Nerd-Alert interview in a long time, but this interview inspired us to dust off that designation, so get your notepad ready!

The complete show notes for this episode can be found at https://twimlai.com/talk/246.

2019-04-01
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Supporting TensorFlow at Airbnb with Alfredo Luque - TWiML Talk #244

This interview features my conversation with Alfredo Luque, a software engineer on the machine infrastructure team at Airbnb.

If you?re among the many TWiML fans interested in AI Platforms and ML infrastructure, you probably remember my interview with Airbnb?s Atul Kale, in which we discussed their Bighead platform. In my conversation with Alfredo, we dig a bit deeper into Bighead?s support for TensorFlow, discuss a recent image categorization challenge they solved with the framework, and explore what the new 2.0 release means for their users. The complete show notes for this episode can be found at https://twimlai.com/talk/244

I?d like to send a huge thanks to the TensorFlow team for helping us bring you this podcast series and giveaway. With all the great announcements coming out of the TensorFlow Dev Summit, including the 2.0 alpha, you should definitely check out the latest and greatest at https://tensorflow.org where you can also download and start building with the framework.

In conjunction with the TensorFlow 2.0 alpha release, and our TensorFlow Dev Summit series, we invite you to enter our TensorFlow Edge Kit Giveaway. Winners will receive a gift box from Google that includes some fun toys including the new Coral Edge TPU device and the SparkFun Edge development board powered by TensorFlow. Find out more at https://twimlai.com/tfgiveaway.

2019-03-28
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Mining the Vatican Secret Archives with TensorFlow w/ Elena Nieddu - TWiML Talk #243

Today we?re joined by Elena Nieddu, PhD Student at Roma Tre University, who presented on her project ?In Codice Ratio? at the TF Dev Summit.

In our conversation, Elena provides an overview of the project, which aims to annotate and transcribe Vatican secret archive documents via machine learning. We discuss the many challenges associated with transcribing this vast archive of handwritten documents, including overcoming the high cost of data annotation. I think you?ll agree that her team?s approach to that challenge was particularly creative. The complete show notes for this episode can be found at https://twimlai.com/talk/243

I?d like to send a huge thanks to the TensorFlow team for helping us bring you this podcast series and giveaway. With all the great announcements coming out of the TensorFlow Dev Summit, including the 2.0 alpha, you should definitely check out the latest and greatest at https://tensorflow.org where you can also download and start building with the framework.

In conjunction with the TensorFlow 2.0 alpha release, and our TensorFlow Dev Summit series, we invite you to enter our TensorFlow Edge Kit Giveaway. Winners will receive a gift box from Google that includes some fun toys including the new Coral Edge TPU device and the SparkFun Edge development board powered by TensorFlow. Find out more at https://twimlai.com/tfgiveaway.

2019-03-27
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Exploring TensorFlow 2.0 with Paige Bailey - TWiML Talk #242

Today we're joined by Paige Bailey, a TensorFlow developer advocate at Google to discuss the TensorFlow 2.0 alpha release.

Paige and I sat down to talk through the latest TensorFlow updates, and we cover a lot of ground, including the evolution of the TensorFlow APIs and the role of eager mode, tf.keras and tf.function, the evolution of TensorFlow for Swift and its inclusion in the new fast.ai course, new updates to TFX (or TensorFlow Extended), Google?s end-to-end machine learning platform, the emphasis on community collaboration with TF 2.0, and a bunch more. The complete show notes for this episode can be found at https://twimlai.com/talk/242

I?d like to send a huge thanks to the TensorFlow team for helping us bring you this podcast series and giveaway. With all the great announcements coming out of the TensorFlow Dev Summit, including the 2.0 alpha, you should definitely check out the latest and greatest at https://tensorflow.org where you can also download and start building with the framework.

In conjunction with the TensorFlow 2.0 alpha release, and our TensorFlow Dev Summit series, we invite you to enter our TensorFlow Edge Kit Giveaway. Winners will receive a gift box from Google that includes some fun toys including the new Coral Edge TPU device and the SparkFun Edge development board powered by TensorFlow. Find out more at https://twimlai.com/tfgiveaway.

 

 

2019-03-25
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Privacy-Preserving Decentralized Data Science with Andrew Trask - TWiML Talk #241

Today we?re joined by Andrew Trask, PhD student at the University of Oxford and Leader of the OpenMined Project.

OpenMined is an open-source community focused on researching, developing, and promoting tools for secure, privacy-preserving, value-aligned artificial intelligence. Andrew and I caught up back at NeurIPS to dig into why OpenMined is important and explore some of the basic research and technologies supporting Private, Decentralized Data Science. We touch on ideas such as Differential Privacy, and Secure Multi-Party Computation, and how these ideas come into play in, for example, federated learning.

Thanks to Pegasystems for sponsoring today's show! I'd like to invite you to join me at PegaWorld, the company?s annual digital transformation conference, which takes place this June in Las Vegas. To learn more about the conference or to register, visit pegaworld.com and use TWIML19 in the promo code field when you get there for $200 off.

The complete show notes for this episode can be found at https://twimlai.com/talk/241.

2019-03-21
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The Unreasonable Effectiveness of the Forget Gate with Jos Van Der Westhuizen - TWiML Talk #240

Today we?re joined by Jos Van Der Westhuizen, PhD student in Engineering at Cambridge University.

Jos? research focuses on applying LSTMs, or Long Short-Term Memory neural networks, to biological data for various tasks. In our conversation, we discuss his paper The unreasonable effectiveness of the forget gate, in which he explores the various ?gates? that make up an LSTM module and the general impact of getting rid of gates on the computational intensity of training the networks. Jos eventually determines that leaving only the forget-gate results in an unreasonably effective network, and we discuss why. Jos also gives us some great LSTM related resources, including references to Jurgen Schmidhuber, whose research group invented the LSTM, and who I spoke to back in Talk #44.

Thanks to Pegasystems for sponsoring today's show! I'd like to invite you to join me at PegaWorld, the company?s annual digital transformation conference, which takes place this June in Las Vegas. To learn more about the conference or to register, visit pegaworld.com and use TWIML19 in the promo code field when you get there for $200 off.

The complete show notes for this episode can be found at https://twimlai.com/talk/240.

2019-03-18
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Building a Recommendation Agent for The North Face with Andrew Guldman - TWiML Talk #239

Today we?re joined by Andrew Guldman, VP of Product Engineering and Research and Development at Fluid.

Andrew and I caught up a while back to discuss Fluid XPS, a user experience built to help the casual shopper decide on the best product choices during online retail interactions. While XPS has expanded since we recorded this interview, we specifically discuss its origins as a product to assist outerwear retailer The North Face. In our conversation, we discuss their use of heat-sink algorithms and graph databases, and their use of chat and other interfaces, and the challenges associated with staying on top of a constantly changing technology landscape. This was a fun interview!

Thanks to Pegasystems for sponsoring today's show! I'd like to invite you to join me at PegaWorld, the company?s annual digital transformation conference, which takes place this June in Las Vegas. To learn more about the conference or to register, visit pegaworld.com and use TWIML19 in the promo code field when you get there.

The complete show notes for this episode can be found at https://twimlai.com/talk/239.

2019-03-14
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Active Learning for Materials Design with Kevin Tran - TWiML Talk #238

Today we?re joined by Kevin Tran, PhD student in the department of chemical engineering at Carnegie Mellon University.

Kevin?s research focuses on creating and using automated, active learning workflows to perform density functional theory, or DFT, simulations, which are used to screen for new catalysts for a myriad of materials applications. In our conversation, we explore the challenges surrounding one such application?the creation of renewable energy fuel cells, which is discussed in his recent Nature paper ?Active learning across intermetallics to guide discovery of electrocatalysts for CO2 reduction and H2 evolution.? We dig into the role and need for good catalysts in this application, the role that quantum mechanics plays in finding them, and how Kevin uses machine learning and optimization to predict electrocatalyst performance.

The complete show notes for this episode can be found at twimlai.com/talk/238.

The Artificial Intelligence Conference is returning to New York in April and we have one FREE conference pass for a lucky listener! Visit twimlai.com/ainygiveaway to enter!

2019-03-11
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Deep Learning in Optics with Aydogan Ozcan - TWiML Talk #237

Today, we?re joined by Aydogan Ozcan, Professor of Electrical and Computer Engineering at UCLA, where his research group focuses on photonics and its applications to nano- and biotechnology.

In our conversation, we explore his group's research into the intersection of deep learning and optics, holography and computational imaging. We specifically look at a really interesting project to create all-optical neural networks which work based on diffraction, where the printed pixels of the network are analogous to neurons. We also explore some of the practical applications for their research and other areas of interest for their group.

The complete show notes for this episode can be found at twimlai.com/talk/237

Be sure to subscribe to our weekly newsletter at twimlai.com/newsletter!

2019-03-07
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Scaling Machine Learning on Graphs at LinkedIn with Hema Raghavan and Scott Meyer - TWiML Talk #236

Today we?re joined by Hema Raghavan and Scott Meyer of LinkedIn.

Hema is an Engineering Director Responsible for AI for Growth and Notifications, while Scott serves as a Principal Software Engineer. In this conversation, Hema, Scott and I dig into the graph database and machine learning systems that power LinkedIn features such as ?People You May Know? and second-degree connections. Hema shares her insight into the motivations for LinkedIn?s use of graph-based models and some of the challenges surrounding using graphical models at LinkedIn?s scale, while Scott details his work on the software used at the company to support its biggest graph databases.

We'd like to send a huge thanks to LinkedIn for sponsoring today?s show! LinkedIn Engineering solves complex problems at scale to create economic opportunity for every member of the global workforce. AI and ML are integral aspects of almost every product the company builds for its members and customers. LinkedIn?s highly structured dataset gives their data scientists and researchers the ability to conduct applied research to improve member experiences. To learn more about the work of LinkedIn Engineering, please visit engineering.linkedin.com/blog.

For the complete show notes, visit https:/twimlai.com/talk/236. 

2019-03-04
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Safer Exploration in Deep Reinforcement Learning using Action Priors with Sicelukwanda Zwane - TWiML Talk #235

Today we conclude our Black in AI series with Sicelukwanda Zwane, a masters student at the University of Witwatersrand and graduate research assistant at the CSIR.

At the workshop, he presented on ?Safer Exploration in Deep Reinforcement Learning using Action Priors,? which explores transferring action priors between robotic tasks to reduce the exploration space in reinforcement learning, which in turn reduces sample complexity. In our conversation, we discuss what ?safer exploration? means in this sense, the difference between this work and other techniques like imitation learning, and how this fits in with the goal of ?lifelong learning.?

The complete show notes for this episode can be found at https://twimlai.com/talk/235. To follow along with the Black in AI series, visit https://twimlai.com/blackinai19.

2019-03-01
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Dissecting the Controversy around OpenAI's New Language Model - TWiML Talk #234

If you?re listening to this podcast, you?ve likely seen some of the press coverage and discussion surrounding the release, or lack thereof, of OpenAI?s new GPT-2 Language Model. The announcement caused quite a stir, with reactions spanning confusion, frustration, concern, and many points in between. Several days later, many open questions remained about the model and the way the release was handled.

Seeing the continued robust discourse, and wanting to offer the community a forum for exploring this topic with more nuance than Twitter?s 280 characters allow, we convened the inaugural ?TWiML Live? panel. I was joined on the panel by Amanda Askell and Miles Brundage of OpenAI, Anima Anandkumar of NVIDIA and CalTech, Robert Munro of Lilt, and Stephen Merity, the latter being some of the most outspoken voices in the online discussion of this issue.

Our discussion thoroughly explored the many issues surrounding the GPT-2 release controversy. We cover the basics like what language models are and why they?re important, and why this announcement caused such a stir, and dig deep into why the lack of a full release of the model raised concerns for so many.

The discussion initially aired via Youtube Live and we?re happy to share it with you via the podcast as well. To be clear, both the panel discussion and live stream format were a bit of an experiment for us and we?d love to hear your thoughts on it. Would you like to see, or hear, more of these TWiML Live conversations? If so, what issues would you like us to take on?

If you have feedback for us on the format or if you?d like to join the discussion around OpenAI?s GPT-2 model, head to the show notes page for this show at twimlai.com/talk/234 and leave us a comment.

2019-02-25
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Human-Centered Design with Mira Lane - TWiML Talk #233

Today we present the final episode in our AI for the Benefit of Society series, in which we?re joined by Mira Lane, Partner Director for Ethics and Society at Microsoft.

Mira and I focus our conversation on the role of culture and human-centered design in AI. We discuss how Mira defines human-centered design, its connections to culture and responsible innovation, and how these ideas can be scalably implemented across large engineering organizations.

We?d like to thank Microsoft for their support and their sponsorship of this series.?Microsoft is committed to ensuring the responsible development and use of AI and is empowering people around the world with intelligent technology to help solve previously intractable societal challenges spanning sustainability, accessibility and humanitarian action. Learn more at Microsoft.ai.

The complete show notes for this episode can be found at twimlai.com/talk/233. For more information on the AI for the Benefit of Society series, visit twimlai.com/ai4society.

2019-02-22
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Fairness in Machine Learning with Hanna Wallach - TWiML Talk #232

Today we?re joined by Hanna Wallach, a Principal Researcher at Microsoft Research.

Hanna and I really dig into how bias and a lack of interpretability and transparency show up across machine learning. We discuss the role that human biases, even those that are inadvertent, play in tainting data, and whether deployment of ?fair? ML models can actually be achieved in practice, and much more. Along the way, Hanna points us to a TON of papers and resources to further explore the topic of fairness in ML. You?ll definitely want to check out the notes page for this episode, which you?ll find at twimlai.com/talk/232.

We?d like to thank Microsoft for their support and their sponsorship of this series.?Microsoft is committed to ensuring the responsible development and use of AI and is empowering people around the world with intelligent technology to help solve previously intractable societal challenges spanning sustainability, accessibility and humanitarian action. Learn more at Microsoft.ai.

2019-02-18
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AI for Healthcare with Peter Lee - TWiML Talk #231

In this episode, we?re joined by Peter Lee, Corporate Vice President at Microsoft Research responsible for the company?s healthcare initiatives.

Peter and I met a few months ago at the Microsoft Ignite conference, where he gave me some really interesting takes on AI development in China. You can find more on that topic in the show notes. This conversation centers the three impact areas Peter sees for AI in healthcare, namely diagnostics and therapeutics, tools, and the future of precision medicine. We dig into some examples in each area, and Peter details the realities of applying machine learning and some of the impediments to rapid scale.

We?d like to thank Microsoft for their support and their sponsorship of this series.?Microsoft is committed to ensuring the responsible development and use of AI and is empowering people around the world with intelligent technology to help solve previously intractable societal challenges spanning sustainability, accessibility and humanitarian action. Learn more at Microsoft.ai.

The complete show notes for this episode can be found at twimlai.com/talk/231.

2019-02-18
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An Optimized Recurrent Unit for Ultra-Low Power Acoustic Event Detection with Justice Amoh Jr. - TWiML Talk #230

Today, we're joined by Justice Amoh Jr., a Ph.D. student at Dartmouth?s Thayer School of Engineering.

Justice presented his work on ?An Optimized Recurrent Unit for Ultra-Low Power Acoustic Event Detection.? In our conversation, we discuss his goal of bringing low cost, high-efficiency wearables to market for monitoring asthma. We explore the many challenges of using classical machine learning models on microcontrollers, and how he went about developing models optimized for constrained hardware environments. We?d also like to wish Justice the best of luck as he should be defending his Ph.D. any day now!

The complete show notes for this episode can be found at https://twimlai.com/talk/230. To follow along with the Black in AI series, visit https://twimlai.com/blackinai19.

 

2019-02-11
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Pathologies of Neural Models and Interpretability with Alvin Grissom II - TWiML Talk #229

Today, we're excited to continue our Black in AI series with Alvin Grissom II, Assistant Professor of Computer Science at Ursinus College.

Alvin?s research is focused on computational linguistics, and we begin with a brief chat about some of his prior work on verb prediction using reinforcement learning. We then dive into the paper he presented at the workshop, ?Pathologies of Neural Models Make Interpretations Difficult.? We talk through some of the ?pathological behaviors? he identified in the paper, how we can better understand the overconfidence of trained deep learning models in certain settings, and how we can improve model training with entropy regularization. We also touch on the parallel between his work and the work being done on adversarial examples by Ian Goodfellow and others.

For the complete show notes, visit https://twimlai.com/talk/229. To follow along with our Black in AI series, visit https://twimlai.com/blackinai19.

 

2019-02-11
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AI for Earth with Lucas Joppa - TWiML Talk #228

In this episode of our AI For the Benefit of Society with Microsoft series, we?re joined by Lucas Joppa and Zach Parisa.

Lucas is the Chief Environmental Officer at Microsoft, spearheading their 5 year, $50 million AI for Earth commitment, which seeks to apply machine learning and AI across four key environmental areas: agriculture, water, biodiversity, and climate change. Zack is Co-founder and president of SilviaTerra, a Microsoft AI for Earth grantee whose mission is to help people use modern data sources to better manage forest habitats and ecosystems.

In our conversation we discuss the ways that machine learning and AI can be used to advance our understanding of forests and other ecosystems and support conservation efforts. We discuss how SilviaTerra uses computer vision and data from a wide array of sensors like LIDAR, combined with AI, to yield more detailed small-area estimates of the various species in our forests. We also briefly discuss another AI for Earth project, WildMe, a computer vision based wildlife conservation project we discussed with Jason Holmberg back on episode 166.

The complete show notes for this episode can be found at https://twimlai.com/talk/288. To follow along with the entire AI for the Benefit of Society series, visit https://twimlai.com/ai4society.

We?d like to thank Microsoft for their support and their sponsorship of this series.?Microsoft is committed to ensuring the responsible development and use of AI and is empowering people around the world with intelligent technology to help solve previously intractable societal challenges spanning sustainability, accessibility and humanitarian action. Learn more at https://Microsoft.ai.

 

 

2019-02-08
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AI for Accessibility with Wendy Chisholm - TWiML Talk #227

Today we?re joined by Wendy Chisholm, Lois Brady, and Matthew Guggemos. Wendy is a principal accessibility architect at Microsoft, and one of the chief proponents of the AI for Accessibility program, which extends grants to AI-powered accessibility projects the areas of Employment, Daily Life, and Communication & Connection. Lois and Matthew are Co-Founders and CEO and CTO, respectively, of iTherapy, an AI for Accessibility grantee and creator of the Inner Voice app, which utilizes visual language to strengthen communication in children on the autism scale.

In our conversation, we discuss the intersection of AI and accessibility, the lasting impact that innovation in AI can have for people with disabilities and society as a whole, and the importance of programs like AI for Accessibility in bringing projects in this area to fruition. 

For the complete show notes, visit https://twimlai.com/talk/226.

The transcript for this interview can be found at https://twimlai.com/talk/206/tx.

To follow along with the AI for the Benefit of Society series, visit https://twimlai.com/ai4society.

Thanks to Microsoft for their support and their sponsorship of this series.?Microsoft is committed to ensuring the responsible development and use of AI and is empowering people around the world with intelligent technology to help solve previously intractable societal challenges spanning sustainability, accessibility and humanitarian action. Learn more at https://microsoft.ai.

 

2019-02-06
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En liten tjänst av I'm With Friends. Finns även på engelska.
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