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Machine Learning

Machine Learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.
196 episodes
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Practical AI Practical AI #158

Zero-shot multitask learning

In this Fully-Connected episode, Daniel and Chris ponder whether in-person AI conferences are on the verge of making a post-pandemic comeback. Then on to BigScience from Hugging Face, a year-long research workshop on large multilingual models and datasets. Specifically they dive into the T0, a series of natural language processing (NLP) AI models specifically trained for researching zero-shot multitask learning. Daniel provides a brief tour of the possible with the T0 family. They finish up with a couple of new learning resources.

Practical AI Practical AI #157

Analyzing the 2021 AI Index Report

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2021-11-10T20:00:00Z #fully-connected +3 🎧 20,467

Each year we discuss the latest insights from the Stanford Institute for Human-Centered Artificial Intelligence (HAI), and this year is no different. Daniel and Chris delve into key findings and discuss in this Fully-Connected episode. They also check out a study called ‘Delphi: Towards Machine Ethics and Norms’, about how to integrate ethics and morals into AI models.

Practical AI Practical AI #156

Photonic computing for AI acceleration

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2021-11-02T20:00:00Z #ai +2 🎧 19,571

There are a lot of people trying to innovate in the area of specialized AI hardware, but most of them are doing it with traditional transistors. Lightmatter is doing something totally different. They’re building photonic computers that are more power efficient and faster for AI inference. Nick Harris joins us in this episode to bring us up to speed on all the details.

Practical AI Practical AI #154

🌍 AI in Africa - Makerere AI Lab

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2021-10-19T20:15:00Z #ai +2 🎧 18,485

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.

Practical AI Practical AI #153

Federated Learning 📱

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2021-10-12T18:20:00Z #ai +3 🎧 19,782

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.

Practical AI Practical AI #152

The mathematics of machine learning

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2021-10-05T20:15:00Z #ai +2 🎧 23,601

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!

Practical AI Practical AI #151

Balancing human intelligence with AI

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2021-09-28T21:20:00Z #ai +3 🎧 19,332

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.

Practical AI Practical AI #150

From notebooks to Netflix scale with Metaflow

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2021-09-21T14:45:00Z #ai +2 🎧 33,362

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.

Practical AI Practical AI #149

Trends in data labeling

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2021-09-14T20:30:00Z #ai +2 🎧 19,548

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.

Practical AI Practical AI #147

Anaconda + Pyston and more

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2021-09-01T14:40:00Z #ai +2 🎧 18,432

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).

Practical AI Practical AI #146

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.

Practical AI Practical AI #142

Building a data team

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2021-07-27T14:45:00Z #ai +3 🎧 18,005

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.

Practical AI Practical AI #139

Vector databases for machine learning

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2021-06-22T16:00:00Z #ai +3 🎧 18,391

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.

Practical AI Practical AI #138

Multi-GPU training is hard (without PyTorch Lightning)

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2021-06-15T14:45:00Z #ai +3 🎧 15,084

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.

Practical AI Practical AI #137

Learning to learn deep learning 📖

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2021-06-08T18:00:00Z #ai +3 🎧 16,880

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.

Practical AI Practical AI #135

Elixir meets machine learning

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2021-05-26T14:45:00Z #elixir +2 🎧 12,799

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.

Practical AI Practical AI #134

Apache TVM and OctoML

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2021-05-18T20:45:00Z #ai +2 🎧 11,534

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.

Practical AI Practical AI #133

25 years of speech technology innovation

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2021-05-11T19:00:00Z #ai +2 🎧 11,730

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.

Changelog Interviews Changelog Interviews #439

Elixir meets machine learning

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2021-05-07T21:00:00Z #elixir +2 🎧 27,392

This week Elixir creator José Valim joins Jerod and Practical AI’s Daniel Whitenack to discuss Numerical Elixir, his new project that’s bringing Elixir into the world of machine learning. We 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 project that’s built on Phoenix LiveView.

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