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Practical AI Practical AI #195

Production data labeling workflows

It’s one thing to gather some labels for your data. It’s another thing to integrate data labeling into your workflows and infrastructure in a scalable, secure, and useful way. Mark from Xelex joins us to talk through some of what he has learned after helping companies scale their data annotation efforts. We get into workflow management, labeling instructions, team dynamics, and quality assessment. This is a super practical episode!

Practical AI Practical AI #191

Privacy in the age of AI

In this Fully-Connected episode, Daniel and Chris discuss concerns of privacy in the face of ever-improving AI / ML technologies. Evaluating AI’s impact on privacy from various angles, they note that ethical AI practitioners and data scientists have an enormous burden, given that much of the general population may not understand the implications of the data privacy decisions of everyday life.

This intentionally thought-provoking conversation advocates consideration and action from each listener when it comes to evaluating how their own activities either protect or violate the privacy of those whom they impact.

Chip Huyen huyenchip.com

Introduction to streaming for data scientists

Chip Huyen:

As machine learning moves towards real-time, streaming technology is becoming increasingly important for data scientists. Like many people coming from a machine learning background, I used to dread streaming. In our recent survey, almost half of the data scientists we asked said they would like to move from batch prediction to online prediction but can’t because streaming is hard, both technically and operationally…

Over the last year, working with a co-founder who’s super deep into streaming, I’ve learned that streaming can be quite intuitive. This post is an attempt to rephrase what I’ve learned.

Practical AI Practical AI #187

AI IRL & Mozilla's Internet Health Report

Every year Mozilla releases an Internet Health Report that combines research and stories exploring what it means for the internet to be healthy. This year’s report is focused on AI. In this episode, Solana and Bridget from Mozilla join us to discuss the power dynamics of AI and the current state of AI worldwide. They highlight concerning trends in the application of this transformational technology along with positive signs of change.

Sean Moriarity dockyard.com

Elixir versus Python for data science

Sean Moriarity:

A common argument against using Nx for a new machine learning project is its perceived lack of a library/support for some common task that is available in Python. In this post, I’ll do my best to highlight areas where this is not the case, and compare and contrast Elixir projects with their Python equivalents. Additionally, I’ll discuss areas where the Elixir ecosystem still comes up short, and using Nx for a new project might not be the best idea.

Sean is a prominent member of the Elixir community, so that’s the perspective on display here, but it’s a thorough and well-reasoned comparison. He concludes:

While there are still many gaps in the Elixir ecosystem, the progress over the last year has been rapid. Almost every library I’ve mentioned in this post is less than two years old, and I suspect there will be many more projects to fill some of the gaps I’ve mentioned in the coming months.

Practical AI Practical AI #183

AI's role in reprogramming immunity

Drausin Wulsin, Director of ML at Immunai, joins Daniel & Chris to talk about the role of AI in immunotherapy, and why it is proving to be the foremost approach in fighting cancer, autoimmune disease, and infectious diseases.

The large amount of high dimensional biological data that is available today, combined with advanced machine learning techniques, creates unique opportunities to push the boundaries of what is possible in biology.

To that end, Immunai has built the largest immune database called AMICA that contains tens of millions of cells. The company uses cutting-edge transfer learning techniques to transfer knowledge across different cell types, studies, and even species.

Practical AI Practical AI #171

Clothing AI in a data fabric

What happens when your data operations grow to Internet-scale? How do thousands or millions of data producers and consumers efficiently, effectively, and productively interact with each other? How are varying formats, protocols, security levels, performance criteria, and use-case specific characteristics meshed into one unified data fabric? Chris and Daniel explore these questions in this illuminating and Fully-Connected discussion that brings this new data technology into the light.

Practical AI Practical AI #166

Exploring deep reinforcement learning

In addition to being a Developer Advocate at Hugging Face, Thomas Simonini is building next-gen AI in games that can talk and have smart interactions with the player using Deep Reinforcement Learning (DRL) and Natural Language Processing (NLP). He also created a Deep Reinforcement Learning course that takes a DRL beginner to from zero to hero. Natalie and Chris explore what’s involved, and what the implications are, with a focus on the development path of the new AI data scientist.

Python kaggle.com

Get the daily Wordle on the first try using the tweet distribution

I love how much hacking has been inspired by Wordle.

The Wordle source code contains 2,315 days of answers (all common 5-letter English words) and 10,657 other valid, less-common 5-letter English words.

We combine these to form a set of 12,972 possible words/answers.

We then simulate playing 1,000 Wordle games for each of these possible words, guessing based on the frequency of the word in the English language and the feedback received.

Then we take three measures to evaluate the observed distribution of ⬛🟨🟩 squares on Twitter according to our valid words.

The resulting code is included in the article.

Alex Strick van Linschoten github.com

ZenML helps data scientists work across the full stack

ZenML is an extensible MLOps framework to create production-ready machine learning pipelines. Built for data scientists, it has a simple, flexible syntax, is cloud and tool agnostic, and has interfaces/abstractions that are catered towards ML workflows.

The code base was recently completely rewritten with better abstractions and to set us up for our ongoing growth and inclusion of more integrations with tools that data scientists love to use.

Practical AI Practical AI #160

Friendly federated learning 🌼

This episode is a follow up to our recent Fully Connected show discussing federated learning. In that previous discussion, we mentioned Flower (a “friendly” federated learning framework). Well, one of the creators of Flower, Daniel Beutel, agreed to join us on the show to discuss the project (and federated learning more broadly)! The result is a really interesting and motivating discussion of ML, privacy, distributed training, and open source AI.

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.

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