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Practical AI: Machine Learning & Data Science

Practical AI: Machine Learning & Data Science

Making artificial intelligence practical, productive, and accessible to everyone. Practical AI is a show in which technology professionals, business people, students, enthusiasts, and expert guests engage in lively discussions about Artificial Intelligence and related topics (Machine Learning, Deep Learning, Neural Networks, etc). The focus is on productive implementations and real-world scenarios that are accessible to everyone. If you want to keep up with the latest advances in AI, while keeping one foot in the real world, then this is the show for you!

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changelog.com/practicalai

Episodes

Green AI ?

Empirical analysis from Roy Schwartz (Hebrew University of Jerusalem) and Jesse Dodge (AI2) suggests the AI research community has paid relatively little attention to computational efficiency. A focus on accuracy rather than efficiency increases the carbon footprint of AI research and increases research inequality. In this episode, Jesse and Roy advocate for increased research activity in Green AI (AI research that is more environmentally friendly and inclusive). They highlight success stories and help us understand the practicalities of making our workflows more efficient.
2021-03-02
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Low code, no code, accelerated code, & failing code

In this Fully-Connected episode, Chris and Daniel discuss low code / no code development, GPU jargon, plus more data leakage issues. They also share some really cool new learning opportunities for leveling up your AI/ML game!
2021-02-23
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The AI doc will see you now

Elad Walach of Aidoc joins Chris to talk about the use of AI for medical imaging interpretation. Starting with the world?s largest annotated training data set of medical images, Aidoc is the radiologist?s best friend, helping the doctor to interpret imagery faster, more accurately, and improving the imaging workflow along the way. Elad?s vision for the transformative future of AI in medicine clearly soothes Chris?s concern about managing his aging body in the years to come. ;-)
2021-02-16
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Cooking up synthetic data with Gretel

John Myers of Gretel puts on his apron and rolls up his sleeves to show Dan and Chris how to cook up some synthetic data for automated data labeling, differential privacy, and other purposes. His military and intelligence community background give him an interesting perspective that piqued the interest of our intrepid hosts.
2021-02-02
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The nose knows

Daniel and Chris sniff out the secret ingredients for collecting, displaying, and analyzing odor data with Terri Jordan and Yanis Caritu of Aryballe. It certainly smells like a good time, so join them for this scent-illating episode!
2021-01-26
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Accelerating ML innovation at MLCommons

MLCommons launched in December 2020 as an open engineering consortium that seeks to accelerate machine learning innovation and broaden access to this critical technology for the public good. David Kanter, the executive director of MLCommons, joins us to discuss the launch and the ambitions of the organization. In particular we discuss the three pillars of the organization: Benchmarks and Metrics (e.g. MLPerf), Datasets and Models (e.g. People?s Speech), and Best Practices (e.g. MLCube).
2021-01-19
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The $1 trillion dollar ML model ?

American Express is running what is perhaps the largest commercial ML model in the world; a model that automates over 8 billion decisions, ingests data from over $1T in transactions, and generates decisions in mere milliseconds or less globally. Madhurima Khandelwal, head of AMEX AI Labs, joins us for a fascinating discussion about scaling research and building robust and ethical AI-driven financial applications.
2021-01-11
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Getting in the Flow with Snorkel AI

Braden Hancock joins Chris to discuss Snorkel Flow and the Snorkel open source project. With Flow, users programmatically label, build, and augment training data to drive a radically faster, more flexible, and higher quality end-to-end AI development and deployment process.
2020-12-21
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Engaging with governments on AI for good

At this year?s Government & Public Sector R Conference (or R|Gov) our very own Daniel Whitenack moderated a panel on how AI practitioners can engage with governments on AI for good projects. That discussion is being republished in this episode for all our listeners to enjoy! The panelists were Danya Murali from Arcadia Power and Emily Martinez from the NYC Department of Health and Mental Hygiene. Danya and Emily gave some great perspectives on sources of government data, ethical uses of data, and privacy.
2020-12-14
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From research to product at Azure AI

Bharat Sandhu, Director of Azure AI and Mixed Reality at Microsoft, joins Chris and Daniel to talk about how Microsoft is making AI accessible and productive for users, and how AI solutions can address real world challenges that customers face. He also shares Microsoft?s research-to-product process, along with the advances they have made in computer vision, image captioning, and how researchers were able to make AI that can describe images as well as people do.
2020-12-07
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The world's largest open library dataset

Unsplash has released the world?s largest open library dataset, which includes 2M+ high-quality Unsplash photos, 5M keywords, and over 250M searches. They have big ideas about how the dataset might be used by ML/AI folks, and there have already been some interesting applications. In this episode, Luke and Tim discuss why they released this data and what it take to maintain a dataset of this size.
2020-12-01
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A casual conversation concerning causal inference

Lucy D?Agostino McGowan, cohost of the Casual Inference Podcast and a professor at Wake Forest University, joins Daniel and Chris for a deep dive into causal inference. Referring to current events (e.g. misreporting of COVID-19 data in Georgia) as examples, they explore how we interact with, analyze, trust, and interpret data - addressing underlying assumptions, counterfactual frameworks, and unmeasured confounders (Chris?s next Halloween costume).
2020-11-24
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Building a deep learning workstation

What?s it like to try and build your own deep learning workstation? Is it worth it in terms of money, effort, and maintenance? Then once built, what?s the best way to utilize it? Chris and Daniel dig into questions today as they talk about Daniel?s recent workstation build. He built a workstation for his NLP and Speech work with two GPUs, and it has been serving him well (minus a few things he would change if he did it again).
2020-11-17
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Killer developer tools for machine learning

Weights & Biases is coming up with some awesome developer tools for AI practitioners! In this episode, Lukas Biewald describes how these tools were a direct result of pain points that he uncovered while working as an AI intern at OpenAI. He also shares his vision for the future of machine learning tooling and where he would like to see people level up tool-wise.
2020-11-09
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Reinforcement Learning for search

Hamish from Sajari blows our mind with a great discussion about AI in search. In particular, he talks about Sajari?s quest for performant AI implementations and extensive use of Reinforcement Learning (RL). We?ve been wanting to make this one happen for a while, and it was well worth the wait.
2020-10-26
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When data leakage turns into a flood of trouble

Rajiv Shah teaches Daniel and Chris about data leakage, and its major impact upon machine learning models. It?s the kind of topic that we don?t often think about, but which can ruin our results. Raj discusses how to use activation maps and image embedding to find leakage, so that leaking information in our test set does not find its way into our training set.
2020-10-20
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Productionizing AI at LinkedIn

Suju Rajan from LinkedIn joined us to talk about how they are operationalizing state-of-the-art AI at LinkedIn. She sheds light on how AI can and is being used in recruiting, and she weaves in some great explanations of how graph-structured data, personalization, and representation learning can be applied to LinkedIn?s candidate search problem. Suju is passionate about helping people deal with machine learning technical debt, and that gives this episode a good dose of practicality.
2020-10-13
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R, Data Science, & Computational Biology

We?re partnering with the upcoming R Conference, because the R Conference is well? amazing! Tons of great AI content, and they were nice enough to connect us to Daniel Chen for this episode. He discusses data science in Computational Biology and his perspective on data science project organization.
2020-10-06
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Learning about (Deep) Learning

In anticipation of the upcoming NVIDIA GPU Technology Conference (GTC), Will Ramey joins Daniel and Chris to talk about education for artificial intelligence practitioners, and specifically the role that the NVIDIA Deep Learning Institute plays in the industry. Will?s insights from long experience are shaping how we all stay on top of AI, so don?t miss this ?must learn? episode.
2020-09-21
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When AI goes wrong

So, you trained a great AI model and deployed it in your app? It?s smooth sailing from there right? Well, not in most people?s experience. Sometimes things goes wrong, and you need to know how to respond to a real life AI incident. In this episode, Andrew and Patrick from BNH.ai join us to discuss an AI incident response plan along with some general discussion of debugging models, discrimination, privacy, and security.
2020-09-15
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Speech tech and Common Voice at Mozilla

Many people are excited about creating usable speech technology. However, most of the audio data used by large companies isn?t available to the majority of people, and that data is often biased in terms of language, accent, and gender. Jenny, Josh, and Remy from Mozilla join us to discuss how Mozilla is building an open-source voice database that anyone can use to make innovative apps for devices and the web (Common Voice). They also discuss efforts through Mozilla fellowship program to develop speech tech for African languages and understand bias in data sets.
2020-09-09
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Getting Waymo into autonomous driving

Waymo?s mission is to make it safe and easy for people and things to get where they?re going. After describing the state of the industry, Drago Anguelov - Principal Scientist and Head of Research at Waymo - takes us on a deep dive into the world of AI-powered autonomous driving. Starting with Waymo?s approach to autonomous driving, Drago then delights Daniel and Chris with a tour of the algorithmic tools in the autonomy toolbox.
2020-09-01
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Hidden Door and so much more

Hilary Mason is building a new way for kids and families to create stories with AI. It?s called Hidden Door, and in her first interview since founding it, Hilary reveals to Chris and Daniel what the experience will be like for kids. It?s the first Practical AI episode in which some of the questions came from Chris?s 8yo daughter Athena. Hilary also shares her insights into various topics, like how to build data science communities during the COVID-19 Pandemic, reasons why data science goes wrong, and how to build great data-based products. Don?t miss this episode packed with hard-won wisdom!
2020-08-24
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Building the world's most popular data science platform

Everyone working in data science and AI knows about Anaconda and has probably ?conda? installed something. But how did Anaconda get started and what are they working on now? Peter Wang, CEO of Anaconda and creator of PyData and popular packages like Bokeh and DataShader, joins us to discuss that and much more. Peter gives some great insights on the Python AI ecosystem and very practical advice for scaling up your data science operation.
2020-08-17
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Practical AI turns 100!!! ?

We made it to 100 episodes of Practical AI! It has been a privilege to have had so many great guests and discussions about everything from AGI to GPUs to AI for good. In this episode, we circle back to the beginning when Jerod and Adam from The Changelog helped us kick off the podcast. We discuss how our perspectives have changed over time, what it has been like to host an AI podcast, and what the future of AI might look like. (GIVEAWAY!)
2020-08-11
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Attack of the C?l?o?n?e?s? Text!

Come hang with the bad boys of natural language processing (NLP)! Jack Morris joins Daniel and Chris to talk about TextAttack, a Python framework for adversarial attacks, data augmentation, and model training in NLP. TextAttack will improve your understanding of your NLP models, so come prepared to rumble with your own adversarial attacks!
2020-08-04
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? All things transformers with Hugging Face

Sash Rush, of Cornell Tech and Hugging Face, catches us up on all the things happening with Hugging Face and transformers. Last time we had Clem from Hugging Face on the show (episode 35), their transformers library wasn?t even a thing yet. Oh how things have changed! This time Sasha tells us all about Hugging Face?s open source NLP work, gives us an intro to the key components of transformers, and shares his perspective on the future of AI research conferences.
2020-07-27
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MLOps and tracking experiments with Allegro AI

DevOps for deep learning is well? different. You need to track both data and code, and you need to run multiple different versions of your code for long periods of time on accelerated hardware. Allegro AI is helping data scientists manage these workflows with their open source MLOps solution called Trains. Nir Bar-Lev, Allegro?s CEO, joins us to discuss their approach to MLOps and how to make deep learning development more robust.
2020-07-20
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Practical AI Ethics

The multidisciplinary field of AI Ethics is brand new, and is currently being pioneered by a relatively small number of leading AI organizations and academic institutions around the world. AI Ethics focuses on ensuring that unexpected outcomes from AI technology implementations occur as rarely as possible. Daniel and Chris discuss strategies for how to arrive at AI ethical principles suitable for your own organization, and what is involved in implementing those strategies in the real world. Tune in for a practical AI primer on AI Ethics!
2020-07-14
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The ins and outs of open source for AI

Daniel and Chris get you Fully-Connected with open source software for artificial intelligence. In addition to defining what open source is, they discuss where to find open source tools and data, and how you can contribute back to the open source AI community.
2020-07-07
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Operationalizing ML/AI with MemSQL

A lot of effort is put into the training of AI models, but, for those of us that actually want to run AI models in production, performance and scaling quickly become blockers. Nikita from MemSQL joins us to talk about how people are integrating ML/AI inference at scale into existing SQL-based workflows. He also touches on how model features and raw files can be managed and integrated with distributed databases.
2020-06-29
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Roles to play in the AI dev workflow

This full connected has it all: news, updates on AI/ML tooling, discussions about AI workflow, and learning resources. Chris and Daniel breakdown the various roles to be played in AI development including scoping out a solution, finding AI value, experimentation, and more technical engineering tasks. They also point out some good resources for exploring bias in your data/model and monitoring for fairness.
2020-06-22
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The long road to AGI

Daniel and Chris go beyond the current state of the art in deep learning to explore the next evolutions in artificial intelligence. From Yoshua Bengio?s NeurIPS keynote, which urges us forward towards System 2 deep learning, to DARPA?s vision of a 3rd Wave of AI, Chris and Daniel investigate the incremental steps between today?s AI and possible future manifestations of artificial general intelligence (AGI).
2020-06-15
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Explaining AI explainability

The CEO of Darwin AI, Sheldon Fernandez, joins Daniel to discuss generative synthesis and its connection to explainability. You might have heard of AutoML and meta-learning. Well, generative synthesis tackles similar problems from a different angle and results in compact, explainable networks. This episode is fascinating and very timely.
2020-06-08
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Exploring NVIDIA's Ampere & the A100 GPU

On the heels of NVIDIA?s latest announcements, Daniel and Chris explore how the new NVIDIA Ampere architecture evolves the high-performance computing (HPC) landscape for artificial intelligence. After investigating the new specifications of the NVIDIA A100 Tensor Core GPU, Chris and Daniel turn their attention to the data center with the NVIDIA DGX A100, and then finish their journey at ?the edge? with the NVIDIA EGX A100 and the NVIDIA Jetson Xavier NX.
2020-05-26
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AI for Good: clean water access in Africa

Chandler McCann tells Daniel and Chris about how DataRobot engaged in a project to develop sustainable water solutions with the Global Water Challenge (GWC). They analyzed over 500,000 data points to predict future water point breaks. This enabled African governments to make data-driven decisions related to budgeting, preventative maintenance, and policy in order to promote and protect people?s access to safe water for drinking and washing. From this effort sprang DataRobot?s larger AI for Good initiative.
2020-05-11
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Ask us anything (about AI)

Daniel and Chris get you Fully-Connected with AI questions from listeners and online forums: What do you think is the next big thing? What are CNNs? How does one start developing an AI-enabled business solution? What tools do you use every day? What will AI replace? And more?
2020-05-04
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Reinforcement learning for chip design

Daniel and Chris have a fascinating discussion with Anna Goldie and Azalia Mirhoseini from Google Brain about the use of reinforcement learning for chip floor planning - or placement - in which many new designs are generated, and then evaluated, to find an optimal component layout. Anna and Azalia also describe the use of graph convolutional neural networks in their approach.
2020-04-27
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Exploring the COVID-19 Open Research Dataset

In the midst of the COVID-19 pandemic, Daniel and Chris have a timely conversation with Lucy Lu Wang of the Allen Institute for Artificial Intelligence about COVID-19 Open Research Dataset (CORD-19). She relates how CORD-19 was created and organized, and how researchers around the world are currently using the data to answer important COVID-19 questions that will help the world through this ongoing crisis.
2020-04-20
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Achieving provably beneficial, human-compatible AI

AI legend Stuart Russell, the Berkeley professor who leads the Center for Human-Compatible AI, joins Chris to share his insights into the future of artificial intelligence. Stuart is the author of Human Compatible, and the upcoming 4th edition of his perennial classic Artificial Intelligence: A Modern Approach, which is widely regarded as the standard text on AI. After exposing the shortcomings inherent in deep learning, Stuart goes on to propose a new practitioner approach to creating AI that avoids harmful unintended consequences, and offers a path forward towards a future in which humans can safely rely of provably beneficial AI.
2020-04-13
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COVID-19 Q&A and CORD-19

So many AI developers are coming up with creative, useful COVID-19 applications during this time of crisis. Among those are Timo from Deepset-AI and Tony from Intel. They are working on a question answering system for pandemic-related questions called COVID-QA. In this episode, they describe the system, related annotation of the CORD-19 data set, and ways that you can contribute!
2020-04-06
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Mapping the intersection of AI and GIS

Daniel Wilson and Rob Fletcher of ESRI hang with Chris and Daniel to chat about how AI powered modern geographic information systems (GIS) and location intelligence. They illuminate the various models used for GIS, spatial analysis, remote sensing, real-time visualization, and 3D analytics. You don?t want to miss the part about their work for the DoD?s Joint AI Center in humanitarian assistance / disaster relief.
2020-03-30
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Welcome to Practical AI

Practical AI is a weekly podcast that?s marking artificial intelligence practical, productive, and accessible to everyone. If world of AI affects your daily life, this show is for you. From the practitioner wanting to keep up with the latest tools & trends? (clip from episode #68) To the AI curious trying to understand the concepts at play and their implications on our lives? (clip from episode #39) Expert hosts Chris Benson and Daniel Whitenack are here to keep you fully-connected with the world of machine learning and data science. Please listen to a recent episode that interests you and subscribe today. We?d love to have you as a listener!
2020-03-25
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Speech recognition to say it just right

Catherine Breslin of Cobalt joins Daniel and Chris to do a deep dive on speech recognition. She also discusses how the technology is integrated into virtual assistants (like Alexa) and is used in other non-assistant contexts (like transcription and captioning). Along the way, she teaches us how to assemble a lexicon, acoustic model, and language model to bring speech recognition to life.
2020-03-23
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Building a career in Data Science

Emily Robinson, co-author of the book Build a Career in Data Science, gives us the inside scoop about optimizing the data science job search. From creating one?s resume, cover letter, and portfolio to knowing how to recognize the right job at a fair compensation rate. Emily?s expert guidance takes us from the beginning of the process to conclusion, including being successful during your early days in that fantastic new data science position.
2020-03-16
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What exactly is "data science" these days?

Matt Brems from General Assembly joins us to explain what ?data science? actually means these days and how that has changed over time. He also gives us some insight into how people are going about data science education, how AI fits into the data science workflow, and how to differentiate yourself career-wise.
2020-03-09
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TensorFlow in the cloud

Craig Wiley, from Google Cloud, joins us to discuss various pieces of the TensorFlow ecosystem along with TensorFlow Enterprise. He sheds light on how enterprises are utilizing AI and supporting AI-driven applications in the Cloud. He also clarifies Google?s relationship to TensorFlow and explains how TensorFlow development is impacting Google Cloud Platform.
2020-03-02
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NLP for the world's 7000+ languages

Expanding AI technology to the local languages of emerging markets presents huge challenges. Good data is scarce or non-existent. Users often have bandwidth or connectivity issues. Existing platforms target only a small number of high-resource languages. Our own Daniel Whitenack (data scientist at SIL International) and Dan Jeffries (from Pachyderm) discuss how these and related problems will only be solved when AI technology and resources from industry are combined with linguistic expertise from those on the ground working with local language communities. They have illustrated this approach as they work on pushing voice technology into emerging markets.
2020-02-24
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Real-time conversational insights from phone call data

Daniel and Chris hang out with Mike McCourt from Invoca to learn about the natural language processing model architectures underlying Signal AI. Mike shares how they process conversational data, the challenges they have to overcome, and the types of insights that can be harvested.
2020-02-17
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AI-powered scientific exploration and discovery

Daniel and Chris explore Semantic Scholar with Doug Raymond of the Allen Institute for Artificial Intelligence. Semantic Scholar is an AI-backed search engine that uses machine learning, natural language processing, and machine vision to surface relevant information from scientific papers.
2020-02-10
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