Bra podcast

Sveriges 100 mest populära podcasts

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

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

Machine learning and artificial intelligence are dramatically changing the way businesses operate and people live. The TWIML AI Podcast brings the top minds and ideas from the world of ML and AI to a broad and influential community of ML/AI researchers, data scientists, engineers and tech-savvy business and IT leaders. Hosted by Sam Charrington, a sought after industry analyst, speaker, commentator and thought leader. Technologies covered include machine learning, artificial intelligence, deep learning, natural language processing, neural networks, analytics, deep learning, computer science, data science and more.

Prenumerera

iTunes / Overcast / RSS

Webbplats

twimlai.com

Avsnitt

Deep Learning with Structured Data w/ Mark Ryan - #301

Today we are joined by Mark Ryan, author of Deep Learning with Structured Data, currently in the Manning Early Access Program (MEAP), due for publication in Spring 2020. While working on the Support team at IBM Data and AI, he saw that there was a lack of general structured data sets that people could apply their models to. Using the streetcar network in his hometown of Toronto, Mark created a deep learning model to predict delays, but more importantly, gathered an open data set that was the perfect size and variety, and jump started the research for his latest book. In this episode, Mark shares the benefits of applying deep learning to structured data (and recent reduced barriers to entry), details of his experience with a range of data sets, the everlasting appreciation he and Sam shares for the Fast.ai course by Jeremy Howard, and the contents of his new book, aimed to help set up and maintain deep learning models with structured data.

With just two weeks left, time is running out for you to register for TWIMLcon: AI Platforms. Don't be left out! Register NOW at twimlcon.com/register

2019-09-19
Länk till avsnitt

Time Series Clustering for Monitoring Fueling Infrastructure Performance with Kalai Ramea - #300

Today we are joined by Kalai Ramea, Data Scientist at PARC, a Xerox Company. With a background in transportation, energy efficiency, art, and machine learning, Kalai has been fortunate enough to follow her passions through her work. In this episode we discuss:

Her environmentally efficient pursuit that lead to the purchase of a hydrogen car, and the subsequent journey and paper that followed assessing fueling stations  Kalai?s next paper, looking at fuel consumption at hydrogen stations using temporal clustering to identify signatures of usage over time, grouping the stations into categories  With the construction of fueling stations is planned to increase dramatically in the next 5 years, building reliability on their performance is crucial A sneak peek into how Kalai incorporates her love of art into her work!

Check out the show notes, and the refresh, at twimlai.com

2019-09-18
Länk till avsnitt

Swarm AI for Event Outcome Prediction with Gregg Willcox - TWIML Talk #299

Today we are joined by Gregg Willcox, Director of Research and Development at Unanimous AI. Inspired by the natural phenomenon called 'swarming', which uses the collective intelligence of a group to produce more accurate results than an individual alone, ?Swarm AI? was born. A game-like platform that channels the convictions of individuals to come to a consensus and using a behavioral neural network trained on people?s behavior called ?Conviction?, to further amplify the results. 

The complete show notes for this episode can be found at twimlai.com/talk/299.

We're just over two weeks out from TWIMLcon: AI Platforms! You definitely want to be there. Visit twimlcon.com for more info, or to register. 

2019-09-13
Länk till avsnitt

Rebooting AI: What's Missing, What's Next with Gary Marcus - TWIML Talk #298

Today we are joined by Gary Marcus, CEO and Founder at Robust.AI, former CEO and Founder of Geometric Intelligence (acquired by Uber) and well-known scientist, bestselling author, professor and entrepreneur. In this episode hear Gary discuss:

His latest book, ?Rebooting AI: Building Artificial Intelligence We Can Trust?, an extensive look into the current gaps, pitfalls and areas for improvement in the field of machine learning and AI  A break down of the difference between reinforcement learning and real learning  Why we need machines with both automation and autonomy to be truly usable in the world today  Examples from his book, including Teslas driving into tow trucks and Microsoft?s SQuAD reading test results Insight into what we should be talking and thinking about to make even greater (and safer) strides in AI

The complete show notes for this episode can be found at twimlai.com/talk/298.

Only 3 weeks left to register for TWIMLcon: AI Platforms! Visit twimlcon.com/register now!

 

2019-09-10
Länk till avsnitt

DeepQB: Deep Learning to Quantify Quarterback Decision-Making with Brian Burke - TWIML Talk #297

Today we are joined by Brian Burke, Analytics Specialist with the Stats & Information Group at ESPN. A former Navy pilot and lifelong football fan, Brian saw the correlation between fighter pilots and quarterbacks in the quick, pressure-filled decisions both roles have to make on a regular basis. In this episode, we discuss:

Brian?s self-taught modeling techniques and his journey finding and handling vast amounts of sports data  His findings in the paper, ?DeepQB: Deep Learning with Player Tracking to Quantify Quarterback Decision-Making & Performance? Brian talks through the making of his model, with geometry, algebra and a self-proclaimed ?vanilla? neural network His excitement for the future of machine learning in sports and more!

The complete show notes for this episode can be found at twimlai.com/talk/297.

2019-09-05
Länk till avsnitt

Measuring Performance Under Pressure Using ML with Lotte Bransen - TWIML Talk #296

Today we are joined by Lotte Bransen, Scientific Researcher at SciSports. With a background in mathematics, econometrics and soccer, Lotte has honed her research on analytics of the game and its players. More specifically, using trained models to understand the impact of mental pressure on a player?s performance. In this episode, Lotte discusses:

Her latest paper, ?Choke or Shine? Quantifying Soccer Players' Abilities to Perform Under Mental Pressure? and shares  The basis of the models through two aspects of mental pressure: pre-game and in-game, and three performance metrics: the chance of a goal with every action a player takes (contribution), the quality of that decision and the quality of the execution The implications of her research in the world of sports Just a few of the exponential applications for her work - check it out!

Check out the full show notes at twimlai.com/talk/296.

2019-09-03
Länk till avsnitt

Managing Deep Learning Experiments with Lukas Biewald - TWIML Talk #295

Today we are joined by Lukas Biewald, CEO and Co-Founder of Weights & Biases. Lukas, previously CEO and Founder of Figure Eight (CrowdFlower), has a straightforward goal: provide researchers with SaaS that is easy to install, simple to operate, and always accessible. Seeing a need for reproducibility in deep learning experiments, Lukas founded Weights & Biases. In this episode we discuss:

The experiment tracking tool, how it works, and the components that make it unique in the ML marketplace The open, collaborative culture that Lukas promotes How Lukas got his start in deep learning experiments, what his experiment tracking used to look like,  The current Weights & Biases business success strategy and what his team is working on today

The complete show notes for this episode can be found at twimlai.com/talk/295

Thanks to our friends at Weights & Biases for their support of the show, their sponsorship of this episode, and our upcoming event, TWIMLcon: AI Platforms. 

Registration for TWIMLcon is still open! Visit twimlcon.com/register today! 

2019-08-29
Länk till avsnitt

Re-Architecting Data Science at iRobot with Angela Bassa - TWIML Talk #294

Today we?re joined by Angela Bassa, Director of Data Science at iRobot. In our conversation, Angela and I discuss:

? iRobot's re-architecture, and a look at the evolution of iRobot.

? Where iRobot gets its data from and how they taxonomize data science.

? The platforms and processes that have been put into place to support delivering models in production.

?The role of DevOps in bringing these various platforms together, and much more!

The complete show notes can be found at twimlai.com/talk/294.

Check out the recently released speaker list for TWIMLcon: AI Platforms now! twimlcon.com/speakers.

2019-08-26
Länk till avsnitt

Disentangled Representations & Google Research Football with Olivier Bachem - TWIML Talk #293

Today we?re joined by Olivier Bachem, a research scientist at Google AI on the Brain team.

Initially, Olivier joined us to discuss his work on Google?s research football project, their foray into building a novel reinforcement learning environment, but we spent a fair amount of time exploring his research in disentangled representations. Olivier and Sam also discuss what makes the football environment different than other available reinforcement learning environments like OpenAI Gym and PyGame, what other techniques they explored while using this environment, and what?s on the horizon for their team and Football RLE.

Check out the full show notes at twimlai.com/talk/293

2019-08-22
Länk till avsnitt

Neural Network Quantization and Compression with Tijmen Blankevoort - TWIML Talk #292

Today we?re joined by Tijmen Blankevoort, a staff engineer at Qualcomm, who leads their compression and quantization research teams. Tijmen is also co-founder of ML startup Scyfer, along with Qualcomm colleague Max Welling, who we spoke with back on episode 267. In our conversation with Tijmen we discuss: 

? The ins and outs of compression and quantization of ML models, specifically NNs,

? How much models can actually be compressed, and the best way to achieve compression, 

? We also look at a few recent papers including ?Lottery Hypothesis."  

Check out the full show notes at twimlai.com/talk/292.

 

2019-08-19
Länk till avsnitt

Identifying New Materials with NLP with Anubhav Jain - TWIML Talk #291

Today we are joined by Anubhav Jain, Staff Scientist & Chemist at Lawrence Berkeley National Lab. Anubhav leads the Hacker Materials Research Group, where his research focuses on applying computing to accelerate the process of finding new materials for functional applications. With the immense amount of published scientific research out there, it can be difficult to understand how that information can be applied to future studies, let alone find a way to read it all. In this episode we discuss:

- His latest paper, ?Unsupervised word embeddings capture latent knowledge from materials science literature?

- The design of a system that takes the literature and uses natural language processing to analyze, synthesize and then conceptualize complex material science concepts

- How the method is shown to recommend materials for functional applications in the future - scientific literature mining at its best.

Check out the complete show notes at twimlai.com/talk/291.

2019-08-15
Länk till avsnitt

The Problem with Black Boxes with Cynthia Rudin - TWIML Talk #290

You asked, we listened! Today, by listener request, we are joined by Cynthia Rudin, Professor of Computer Science, Electrical and Computer Engineering, and Statistical Science at Duke University. Cynthia is passionate about machine learning and social justice, with extensive work and leadership in both areas. In this episode we discuss:

Her paper, ?Please Stop Explaining Black Box Models for High Stakes Decisions? How interpretable models make for less error-prone and more comprehensible decisions - and why we should care A break down of black box and interpretable models, including their development, sample use cases, and more!

Check out the complete show notes at https://twimlai.com/talk/290

2019-08-14
Länk till avsnitt

Human-Robot Interaction and Empathy with Kate Darling - TWIML Talk #289

Today we?re joined by Dr. Kate Darling, Research Specialist at the MIT Media Lab. Kate?s focus is on robot ethics and interaction, namely the social implication of how people treat robots and the purposeful design of robots in our daily lives. This episode is a fascinating look into the intersection of psychology and how we are using technology. We cover topics like:

How to measure empathy The impact of robot treatment on kids behavior The correlation between animals and robots  Why ?successful? robots aren?t always humanoid and so much more!
2019-08-08
Länk till avsnitt

Automated ML for RNA Design with Danny Stoll - TWIML Talk #288

Today we?re joined by Danny Stoll, Research Assistant at the University of Freiburg. Since high school, Danny has been fascinated by Deep Learning which has grown into a desire to make machine learning available to anyone with interest. Danny?s current research can be encapsulated in his latest paper, ?Learning to Design RNA?. Designing RNA molecules has become increasingly popular as RNA is responsible for regulating biological process, even connected to diseases like Alzheimers and Epilepsy. In this episode, Danny discusses:

The RNA design process through reverse engineering How his team?s deep learning algorithm is applied to train and design sequences Transfer learning & multitask learning Ablation studies, hyperparameter optimization, the difference between chemical and statistical based approaches and more!
2019-08-05
Länk till avsnitt

Developing a brain atlas using deep learning with Theofanis Karayannis - TWIML Talk #287

Today we?re joined by Theofanis Karayannis, Assistant Professor at the Brain Research Institute of the University of Zurich. Theo?s research is currently focused on understanding how circuits in the brain are formed during development and modified by experiences. Working with animal models, Theo segments and classifies the brain regions, then detects cellular signals that make connections throughout and between each region. How? The answer is (relatively) simple: Deep Learning. In this episode we discuss:

Adapting DL methods to fit the biological scope of work The distribution of connections that makes neurological decisions in both animals and humans every day The way images of the brain are collected Genetic trackability, and more!
2019-08-01
Länk till avsnitt

Environmental Impact of Large-Scale NLP Model Training with Emma Strubell - TWIML Talk #286

Today we?re joined by Emma Strubell, currently a visiting scientist at Facebook AI Research. Emma?s focus is on NLP and bringing state of the art NLP systems to practitioners by developing efficient and robust machine learning models. Her paper, Energy and Policy Considerations for Deep Learning in NLP, hones in on one of the biggest topics of the generation: environmental impact. In this episode we discuss:

How training neural networks have resulted in an increase in accuracy, however the computational resources required to train these models is staggering - and carbon footprints are only getting bigger Emma?s research methods for determining carbon emissions How companies are reacting to environmental concerns What we, as an industry, can be doing better
2019-07-29
Länk till avsnitt

?Fairwashing? and the Folly of ML Solutionism with Zachary Lipton - TWIML Talk #285

Today we?re joined by Zachary Lipton, Assistant Professor in the Tepper School of Business. With an overarching theme of data quality and interpretation, Zachary's research and work is focused on machine learning in healthcare, with the goal of not replacing doctors, but to assist through an understanding of the diagnosis and treatment process. Zachary is also working on the broader question of fairness and ethics in machine learning systems across multiple industries. We delve into these topics today, discussing: 

Supervised learning in the medical field,  Guaranteed robustness under distribution shifts,  The concept of ?fairwashing?, How there is insufficient language in machine learning to encompass abstract ethical behavior, and much, much more
2019-07-25
Länk till avsnitt

Retinal Image Generation for Disease Discovery with Stephen Odaibo - TWIML Talk #284

Today we?re joined by Dr. Stephen Odaibo, Founder and CEO of RETINA-AI Health Inc. Stephen?s unique journey to machine learning and AI includes degrees in math, medicine and computer science, which led him to an ophthalmology practice before taking on the ultimate challenge as an entrepreneur. In this episode we discuss:

How RETINA-AI Health harnesses the power of machine learning to build autonomous systems that diagnose and treat retinal diseases  The importance of domain experience and how Stephen?s expertise in ophthalmology and engineering along with the current state of both industries that led to the founding of his company His work with GANs to create artificial retinal images and why more data isn?t always better!
2019-07-22
Länk till avsnitt

Real world model explainability with Rayid Ghani - TWiML Talk #283

Today we?re joined by Rayid Ghani, Director of the Center for Data Science and Public Policy at the University of Chicago. Rayid?s goal is to combine his skills in machine learning and data with his desire to improve public policy and the social sector. Drawing on his range of experience from the corporate world to Chief Scientist for the 2012 Obama Campaign, we delve into the world of automated predictions and explainability methods. Here we discuss:

How automated predictions can be helpful, but they don?t always paint a full picture  When dealing with public policy and the social sector, the key to an effective explainability method is the correct context Machine feedback loops that help humans override the wrong predictions and reinforce the right ones Supporting proactive intervention through complex explanability tools
2019-07-18
Länk till avsnitt

Inspiring New Machine Learning Platforms w/ Bioelectric Computation with Michael Levin - TWiML Talk #282

Today we?re joined by Michael Levin, Director of the Allen Discovery Institute at Tufts University. Michael joined us back at NeurIPS to discuss his invited talk ?What Bodies Think About: Bioelectric Computation Beyond the Nervous System as Inspiration for New Machine Learning Platforms.? In our conversation, we talk about:

Synthetic living machines, novel AI architectures and brain-body plasticity How our DNA doesn?t control everything like we thought and how the behavior of cells in living organisms can be modified and adapted Biological systems dynamic remodeling in the future of developmental biology and regenerative medicine...and more!

The complete show notes for this episode can be found at twimlai.com/talk/282

Register for TWIMLcon: AI Platforms now at twimlcon.com!

2019-07-15
Länk till avsnitt

Simulation and Synthetic Data for Computer Vision with Batu Arisoy - TWiML Talk #281

Today we?re joined by Batu Arisoy, Research Manager with the Vision Technologies & Solutions team at Siemens Corporate Technology. Currently, Batu?s research focus is solving limited data computer vision problems, providing R&D for many of the business units throughout the company. In our conversation we discuss:

An emulation of a teacher teaching students information without the use of memorization Discerning which parts of our neural network are required to make decisions An activity recognition project with the Office of Naval Research that keeps ?humans in the loop? and more.

 The complete show notes for this episode can be found at twimlai.com/talk/281

Register for TWIMLcon: AI Platforms now at twimlcon.com!

Thanks to Siemens for their sponsorship of today's episode! Check out what they?re up to, visit twimlai.com/siemens.

2019-07-09
Länk till avsnitt

Spiking Neural Nets and ML as a Systems Challenge with Jeff Gehlhaar - TWIML Talk #280

Today we?re joined by Jeff Gehlhaar, VP of Technology and Head of AI Software Platforms at Qualcomm. As we?ve explored in our conversations with both Gary Brotman and Max Welling, Qualcomm has a hand in tons of machine learning research and hardware, and our conversation with Jeff is no different. We discuss:

? How the various training frameworks fit into the developer experience when working with their chipsets.

? Examples of federated learning in the wild.

? The role inference will play in data center devices and more.

The complete show notes for this episode can be found at twimlai.com/talk/280

Register for TWIMLcon now at twimlcon.com.

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

2019-07-08
Länk till avsnitt

Transforming Oil & Gas with AI with Adi Bhashyam and Daniel Jeavons - TWIML Talk #279

Today we?re joined by return guest Daniel Jeavons, GM of Data Science at Shell, and Adi Bhashyam, GM of Data Science at C3, who we had the pleasure of speaking to at this years C3 Transform Conference. In our conversation, we discuss:

? The progress that Dan and his team has made since our last conversation, including an overview of their data platform.

? We explore the various types of users of the platform, and how those users informed the decision to use C3?s out-of-the-box platform solution instead of building their own internal platform.

? Adi gives us an overview of the evolution of C3 and their platform, along with a breakdown of a few Shell-specific use cases. 

The complete show notes can be found at twimlai.com/talk/279.

Visit twimlcon.com to learn more about the TWIMLcon: AI Platforms conference! Early-bird registration has been extended until this Wednesday, 7/3, register today for the lowest possible price!!

2019-07-01
Länk till avsnitt

Fast Radio Burst Pulse Detection with Gerry Zhang - TWIML Talk #278

Today we?re joined by Yunfan Gerry Zhang, a PhD student in the Department of Astrophysics at UC Berkely, and an affiliate of Berkeley?s SETI research center. In our conversation, we discuss: 

? Gerry's research on applying machine learning techniques to astrophysics and astronomy.

? His paper ?Fast Radio Burst 121102 Pulse Detection and Periodicity: A Machine Learning Approach?.

? We explore the types of data sources used for this project, challenges Gerry encountered along the way, the role of GANs and much more.

The complete show notes can be found at twimlai.com/talk/278.

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

2019-06-27
Länk till avsnitt

Tracking CO2 Emissions with Machine Learning with Laurence Watson - TWIML Talk #277

Today we?re joined by Laurence Watson, Co-Founder and CTO of Plentiful Energy and a former data scientist at Carbon Tracker. In our conversation, we discuss:

? Carbon Tracker's goals, and their report ?Nowhere to hide: Using satellite imagery to estimate the utilisation of fossil fuel power plants?.

? How they're using computer vision to process satellite images of coal plants, including how the images are labeled

?Various challenges with the scope and scale of this project, including dealing with varied time zones and imbalanced training classes.

The complete show notes can be found at twimlai.com/talk/277.

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

2019-06-24
Länk till avsnitt

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
Länk till avsnitt

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
Länk till avsnitt

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
Länk till avsnitt

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
Länk till avsnitt

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
Länk till avsnitt

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
Länk till avsnitt

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
Länk till avsnitt

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
Länk till avsnitt

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
Länk till avsnitt

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
Länk till avsnitt

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
Länk till avsnitt

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
Länk till avsnitt

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
Länk till avsnitt

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
Länk till avsnitt

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
Länk till avsnitt

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
Länk till avsnitt

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
Länk till avsnitt

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
Länk till avsnitt

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
Länk till avsnitt

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
Länk till avsnitt

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
Länk till avsnitt

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
Länk till avsnitt

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
Länk till avsnitt

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
Länk till avsnitt

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
Länk till avsnitt
En liten tjänst av I'm With Friends. Finns även på engelska.
Uppdateras med hjälp från iTunes.