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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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? AI in Africa - Makerere AI Lab

This is the first episode in a special series we are calling the ?Spotlight on AI in Africa?. To kick things off, Joyce and Mutembesa from Makerere University?s AI Lab join us to talk about their amazing work in computer vision, natural language processing, and data collection. Their lab seeks out problems that matter in African communities, pairs those problems with appropriate data/tools, and works with the end users to ensure that solutions create real value.
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Federated Learning ?

Federated learning is increasingly practical for machine learning developers because of the challenges we face with model and data privacy. In this fully connected episode, Chris and Daniel dive into the topic and dissect the ideas behind federated learning, practicalities of implementing decentralized training, and current uses of the technique.
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The mathematics of machine learning

Tivadar Danka is an educator and content creator in the machine learning space, and he is writing a book to help practitioners go from high school mathematics to mathematics of neural networks. His explanations are lucid and easy to understand. You have never had such a fun and interesting conversation about calculus, linear algebra, and probability theory before!
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Balancing human intelligence with AI

Polarity Mapping is a framework to ?help problems be solved in a realistic and multidimensional manner? (see here for more info). In this week?s fully connected episode, Chris and Daniel use this framework to help them discuss how an organization can strike a good balance between human intelligence and AI. AI can?t solve everything and humans need to be in-the-loop with many AI solutions.
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From notebooks to Netflix scale with Metaflow

As you start developing an AI/ML based solution, you quickly figure out that you need to run workflows. Not only that, you might need to run those workflows across various kinds of infrastructure (including GPUs) at scale. Ville Tuulos developed Metaflow while working at Netflix to help data scientists scale their work. In this episode, Ville tells us a bit more about Metaflow, his new book on data science infrastructure, and his approach to helping scale ML/AI work.
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Trends in data labeling

Any AI play that lacks an underlying data strategy is doomed to fail, and a big part of any data strategy is labeling. Michael, from Label Studio, joins us in this episode to discuss how the industry?s perception of data labeling is shifting. We cover open source tooling, validating labels, and integrating ML/AI models in the labeling loop.
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Stellar inference speed via AutoNAS

Yonatan Geifman of Deci makes Daniel and Chris buckle up, and takes them on a tour of the ideas behind his amazing new inference platform. It enables AI developers to build, optimize, and deploy blazing-fast deep learning models on any hardware. Don?t blink or you?ll miss it!
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Anaconda + Pyston and more

In this episode, Peter Wang from Anaconda joins us again to go over their latest ?State of Data Science? survey. The updated results include some insights related to data science work during COVID along with other topics including AutoML and model bias. Peter also tells us a bit about the exciting new partnership between Anaconda and Pyston (a fork of the standard CPython interpreter which has been extensively enhanced to improve the execution performance of most Python programs).
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Exploring a new AI lexicon

We?re back with another Fully Connected episode ? Daniel and Chris dive into a series of articles called ?A New AI Lexicon? that collectively explore alternate narratives, positionalities, and understandings to the better known and widely circulated ways of talking about AI. The fun begins early as they discuss and debate ?An Electric Brain? with strong opinions, and consider viewpoints that aren?t always popular.
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NLP to help pregnant mothers in Kenya

In Kenya, 33% of maternal deaths are caused by delays in seeking care, and 55% of maternal deaths are caused by delays in action or inadequate care by providers. Jacaranda Health is employing NLP and dialogue system techniques to help mothers experience childbirth safely and with respect and to help newborns get a safe start in life. Jay and Sathy from Jacaranda join us in this episode to discuss how they are using AI to prioritize incoming SMS messages from mothers and help them get the care they need.
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SLICED - will you make the (data science) cut?

SLICED is like the TV Show Chopped but for data science. Competitors get a never-before-seen dataset and two-hours to code a solution to a prediction challenge. Meg and Nick, the SLICED show hosts, join us in this episode to discuss how the show is creating much needed data science community. They give us a behind the scenes look at all the datasets, memes, contestants, scores, and chat of SLICED.
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AI is creating never before heard sounds! ?

AI is being used to transform the most personal instrument we have, our voice, into something that can be ?played.? This is fascinating in and of itself, but Yotam Mann from Never Before Heard Sounds is doing so much more! In this episode, he describes how he is using neural nets to process audio in real time for musicians and how AI is poised to change the music industry forever.
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Building a data team

Inspired by a recent article from Erik Bernhardsson titled ?Building a data team at a mid-stage startup: a short story?, Chris and Daniel discuss all things AI/data team building. They share some stories from their experiences kick starting AI efforts at various organizations and weight the pro and cons of things like centralized data management, prototype development, and a focus on engineering skills.
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Towards stability and robustness

9 out of 10 AI projects don?t end up creating value in production. Why? At least partly because these projects utilize unstable models and drifting data. In this episode, Roey from BeyondMinds gives us some insights on how to filter garbage input, detect risky output, and generally develop more robust AI systems.
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From symbols to AI pair programmers ?

How did we get from symbolic AI to deep learning models that help you write code (i.e., GitHub and OpenAI?s new Copilot)? That?s what Chris and Daniel discuss in this episode about the history and future of deep learning (with some help from an article recently published in ACM and written by the luminaries of deep learning).
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Vector databases for machine learning

Pinecone is the first vector database for machine learning. Edo Liberty explains to Chris how vector similarity search works, and its advantages over traditional database approaches for machine learning. It enables one to search through billions of vector embeddings for similar matches, in milliseconds, and Pinecone is a managed service that puts this capability at the fingertips of machine learning practitioners.
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Multi-GPU training is hard (without PyTorch Lightning)

William Falcon wants AI practitioners to spend more time on model development, and less time on engineering. PyTorch Lightning is a lightweight PyTorch wrapper for high-performance AI research that lets you train on multiple-GPUs, TPUs, CPUs and even in 16-bit precision without changing your code! In this episode, we dig deep into Lightning, how it works, and what it is enabling. William also discusses the Grid AI platform (built on top of PyTorch Lightning). This platform lets you seamlessly train 100s of Machine Learning models on the cloud from your laptop.
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Learning to learn deep learning ?

Chris and Daniel sit down to chat about some exciting new AI developments including wav2vec-u (an unsupervised speech recognition model) and meta-learning (a new book about ?How To Learn Deep Learning And Thrive In The Digital World?). Along the way they discuss engineering skills for AI developers and strategies for launching AI initiatives in established companies.
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The fastest way to build ML-powered apps

Tuhin Srivastava tells Daniel and Chris why BaseTen is the application development toolkit for data scientists. BaseTen?s goal is to make it simple to serve machine learning models, write custom business logic around them, and expose those through API endpoints without configuring any infrastructure.
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Elixir meets machine learning

Today we?re sharing a special crossover episode from The Changelog podcast here on Practical AI. Recently, Daniel Whitenack joined Jerod Santo to talk with José Valim, Elixir creator, about Numerical Elixir. This is José?s newest project that?s bringing Elixir into the world of machine learning. They discuss why José chose this as his next direction, the team?s layered approach, influences and collaborators on this effort, and their awesome collaborative notebook that?s built on Phoenix LiveView.
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Apache TVM and OctoML

90% of AI / ML applications never make it to market, because fine tuning models for maximum performance across disparate ML software solutions and hardware backends requires a ton of manual labor and is cost-prohibitive. Luis Ceze and his team created Apache TVM at the University of Washington, then left founded OctoML to bring the project to market.
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25 years of speech technology innovation

To say that Jeff Adams is a trailblazer when it comes to speech technology is an understatement. Along with many other notable accomplishments, his team at Amazon developed the Echo, Dash, and Fire TV changing our perception of how we could interact with devices in our home. Jeff now leads Cobalt Speech and Language, and he was kind enough to join us for a discussion about human computer interaction, multimodal AI tasks, the history of language modeling, and AI for social good.
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Generating "hunches" using smart home data ?

Smart home data is complicated. There are all kinds of devices, and they are in many different combinations, geographies, configurations, etc. This complicated data situation is further exacerbated during a pandemic when time series data seems to be filled with anomalies. Evan Welbourne joins us to discuss how Amazon is synthesizing this disparate data into functionality for the next generation of smart homes. He discusses the challenges of working with smart home technology, and he describes how they developed their latest feature called ?hunches.?
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Mapping the world

Ro Gupta from CARMERA teaches Daniel and Chris all about road intelligence. CARMERA maintains the maps that move the world, from HD maps for automated driving to consumer maps for human navigation.
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Data science for intuitive user experiences

Nhung Ho joins Daniel and Chris to discuss how data science creates insights into financial operations and economic conditions. They delve into topics ranging from predictive forecasting to aid small businesses, to learning about the economic fallout from the COVID-19 Pandemic.
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Going full bore with Graphcore!

Dave Lacey takes Daniel and Chris on a journey that connects the user interfaces that we already know - TensorFlow and PyTorch - with the layers that connect to the underlying hardware. Along the way, we learn about Poplar Graph Framework Software. If you are the type of practitioner who values ?under the hood? knowledge, then this is the episode for you.
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Next-gen voice assistants

Nikola Mrk?i?, CEO & Co-Founder of PolyAI, takes Daniel and Chris on a deep dive into conversational AI, describing the underlying technologies, and teaching them about the next generation of voice assistants that will be capable of handling true human-level conversations. It?s an episode you?ll be talking about for a long time!
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Women in Data Science (WiDS)

Chris has the privilege of talking with Stanford Professor Margot Gerritsen, who co-leads the Women in Data Science (WiDS) Worldwide Initiative. This is a conversation that everyone should listen to. Professor Gerritsen?s profound insights into how we can all help the women in our lives succeed - in data science and in life - is a ?must listen? episode for everyone, regardless of gender.
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Recommender systems and high-frequency trading

David Sweet, author of ?Tuning Up: From A/B testing to Bayesian optimization?, introduces Dan and Chris to system tuning, and takes them from A/B testing to response surface methodology, contextual bandit, and finally bayesian optimization. Along the way, we get fascinating insights into recommender systems and high-frequency trading!
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Deep learning technology for drug discovery

Our Slack community wanted to hear about AI-driven drug discovery, and we listened. Abraham Heifets from Atomwise joins us for a fascinating deep dive into the intersection of deep learning models and molecule binding. He describes how these methods work and how they are beginning to help create drugs for ?undruggable? diseases!
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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.
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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!
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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. ;-)
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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.
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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!
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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).
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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.
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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.
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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.
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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.
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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.
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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).
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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).
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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.
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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.
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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.
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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.
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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.
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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.
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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 join us to discuss an AI incident response plan along with some general discussion of debugging models, discrimination, privacy, and security.
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