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AI (Artificial Intelligence)

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Practical AI Practical AI #147

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).

AI (Artificial Intelligence) github.com

Jina – build search-as-a-service powered by deep learning in just minutes

Jina calls itself a “cloud-native neural search framework”. What is neural search, exactly?

The core idea of neural search is to leverage state-of-the-art deep neural networks to build every component of a search system. In short, neural search is deep neural network-powered information retrieval. In academia, it’s often called neural IR.

And what can it do for you?

Thanks to recent advances in deep neural networks, a neural search system can go way beyond simple text search. It enables advanced intelligence on all kinds of unstructured data, such as images, audio, video, PDF, 3D mesh, you name it.

For example, retrieving animation according to some beats; finding the best-fit memes according to some jokes; scanning a table with your iPhone’s LiDAR camera and finding similar furniture at IKEA. Neural search systems enable what traditional search can’t: multi/cross-modal data retrieval.

This project looks quite established and collaborative. 172 contributors and counting…

The Verge Icon The Verge

OpenAI Codex translates english into code

Codex is a descendant of GPT-3 – its training data contains both natural language and billions of lines of source code from publicly available sources, including code in public GitHub repositories.

“We see this as a tool to multiply programmers,” OpenAI’s CTO and co-founder Greg Brockman told The Verge. “Programming has two parts to it: you have ‘think hard about a problem and try to understand it,’ and ‘map those small pieces to existing code, whether it’s a library, a function, or an API.’” The second part is tedious, he says, but it’s what Codex is best at. “It takes people who are already programmers and removes the drudge work.”

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 #145

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.

Practical AI Practical AI #144

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.

SLICED on Practical AI

Mozilla Icon Mozilla

Mozilla Common Voice adds 16 new languages and 4,600 new hours of speech

That’s a big addition. Here’s what Hillary Juma (Common Voice’s community mgr) had to say about it:

Internet access is increasingly mediated through speech: Voice assistants and smart speakers give us directions, search for information, connect us to friends, used in assistive technology and much more. Yet this technology doesn’t work for millions of people. For example, neither Amazon’s Alexa, Apple’s Siri, nor Google Home support a single native African language.

By giving individuals the ability to share their speech, we can help ensure all communities have access to voice technology and the opportunity it unlocks.

What a great initiative! (I first heard about Common Voice on Practical AI.)

Practical AI Practical AI #143

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.

Licensing fsf.org

Free Software Foundations declares GitHub Copilot "unacceptable and unjust"

The FSF is funding white papers on “philosophical and legal questions around Copilot”. In their post announcing the fund, Donald Robertson states:

The Free Software Foundation has received numerous inquiries about our position on these questions. We can see that Copilot’s use of freely licensed software has many implications for an incredibly large portion of the free software community. Developers want to know whether training a neural network on their software can really be considered fair use. Others who may be interested in using Copilot wonder if the code snippets and other elements copied from GitHub-hosted repositories could result in copyright infringement. And even if everything might be legally copacetic, activists wonder if there isn’t something fundamentally unfair about a proprietary software company building a service off their work.

One thing is for sure: there are many open questions that need answering. How we (as a community / industry) go about answering those questions is much less clear. But it’ll probably take place on blogs, forums, GitHub Issues, and even court rooms over the next decade.

Practical AI Practical AI #142

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.

Practical AI Practical AI #139

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.

Practical AI Practical AI #138

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.

Practical AI Practical AI #137

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.

Practical AI Practical AI #135

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.

Practical AI Practical AI #133

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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