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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.
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Machine Learning github.com

A collection of resources to learn about MLOps

While still in its infancy, MLOps has attracted machine learning engineers and software engineers in general. With every new paradigm comes new challenges and opportunities to learn. In this primer, we highlight a few available resources to upskill and inform yourself on the latest in the world of MLOps.

Good resources, regardless of whether you think MLOps is its own thing or should be rolled into DevOps.

AI (Artificial Intelligence) github.com

A human-in-the-loop workflow for creating HD images from text

DALL-E can generate some amazing results, but we’re still in a phase of AI’s progress where having humans involved in the process is just better. Here’s how the authors of this workflow explain it:

Generative art is a creative process. While recent advances of DALL·E unleash people’s creativity, having a single-prompt-single-output UX/UI locks the imagination to a single possibility, which is bad no matter how fine this single result is. DALL·E Flow is an alternative to the one-liner, by formalizing the generative art as an iterative procedure.

A human-in-the-loop workflow for creating HD images from text

Security github.com

The Deepfake Offensive Toolkit

dot (aka Deepfake Offensive Toolkit) makes real-time, controllable deepfakes ready for virtual cameras injection. dot is created for performing penetration testing against e.g. identity verification and video conferencing systems, for the use by security analysts, Red Team members, and biometrics researchers.

What’s crazy is dot deepfakes don’t require any additional training. 🤯

The Deepfake Offensive Toolkit

Python github.com

Imagen (Google's text-to-image neural net) implemented in Pytorch

Last week I logged the very impressive Imagen project, which smarter people than me have said is the SOTA for text-to-image synthesis. Now a WIP implementation is just a pip install imagen-pytorch away.

Architecturally, it is actually much simpler than DALL-E2. It consists of a cascading DDPM conditioned on text embeddings from a large pretrained T5 model (attention network). It also contains dynamic clipping for improved classifier free guidance, noise level conditioning, and a memory efficient unet design.

Google Icon Google

A text-to-image diffusion model with an unprecedented degree of photorealism

Google researchers are giving DALL-E a run for its money:

Our key discovery is that generic large language models (e.g. T5), pretrained on text-only corpora, are surprisingly effective at encoding text for image synthesis: increasing the size of the language model in Imagen boosts both sample fidelity and image-text alignment much more than increasing the size of the image diffusion model.

A text-to-image diffusion model with an unprecedented degree of photorealism

Clément Delangue huggingface.co

Hugging Face raised $100 million for open/collaborative machine learning

Big news from our friends at Hugging Face:

Hugging Face is now the fastest growing community & most used platform for machine learning! With 100,000 pre-trained models & 10,000 datasets hosted on the platform for NLP, computer vision, speech, time-series, biology, reinforcement learning, chemistry and more, the Hugging Face Hub has become the Home of Machine Learning to create, collaborate, and deploy state-of-the-art models.

What will they spend the money on? Good stuff:

Thanks to the new funding, we’ll be doubling down on research, open-source, products and responsible democratization of AI.

Career evjang.com

The machine learning job market in 2022

Eric Jang was recently on the job market (finally landing at [Halodi Robotics])(https://halodi.com/) and in this post he shares his process and view of the job market today. He also has some insights on where it’s headed. In brief:

In the future, every successful tech company will use their data moats to build some variant of an Artificial General Intelligence.

AI (Artificial Intelligence) nautil.us

What would it take for artificial intelligence to make real progress?

Gary Marcus makes the case that deep learning has hit a wall:

Let me start by saying a few things that seem obvious,” Geoffrey Hinton, “Godfather” of deep learning, and one of the most celebrated scientists of our time, told a leading AI conference in Toronto in 2016. “If you work as a radiologist you’re like the coyote that’s already over the edge of the cliff but hasn’t looked down.” Deep learning is so well-suited to reading images from MRIs and CT scans, he reasoned, that people should “stop training radiologists now” and that it’s “just completely obvious within five years deep learning is going to do better.”

Fast forward to 2022, and not a single radiologist has been replaced.

But he doesn’t stop there. After laying out multiple examples of deep learning failures, he change tone:

For the first time in 40 years, I finally feel some optimism about AI.

Read the article to find out why that is.

Machine Learning nyckel.com

Machine learning is still too hard for software engineers

George Mathew:

For a software engineer, the hardest thing about developing Machine Learning functionality should be finding clean and representative ground-truth data, but it often isn’t. If you already have a source of good quality data (perhaps because it is already gathered by your application), here are some obstacles that still lay ahead of you

From the difficult to grasp concepts to the varied (and variable quality) software you’ll need to explore and manage the data, there’s still a lot of obstacles in our path before we can put a model to good use.

Chip Huyen huyenchip.com

Real-time machine learning: challenges and solutions

Chip Huyen:

In the last year, I’ve talked to ~30 companies in different industries about their challenges with real-time machine learning. I’ve also worked with quite a few to find the solutions. This post outlines the solutions for (1) online prediction and (2) continual learning, with step-by-step use cases, considerations, and technologies required for each level.

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.

AI (Artificial Intelligence) deepmind.com

A 280 billion parameter language model named Gopher

In the quest to explore language models and develop new ones, we trained a series of transformer language models of different sizes, ranging from 44 million parameters to 280 billion parameters.

Our research investigated the strengths and weaknesses of those different-sized models, highlighting areas where increasing the scale of a model continues to boost performance – for example, in areas like reading comprehension, fact-checking, and the identification of toxic language. We also surface results where model scale does not significantly improve results — for instance, in logical reasoning and common-sense tasks.

Sometimes size matters, sometimes it doesn’t as much. Fascinating analysis.

A 280 billion parameter language model named Gopher

Machine Learning cerebralab.com

Boring machine learning is where it's at

It surprises me that when people think of “software that brings about the singularity” they think of text models, or of RL agents. But they sneer at decision tree boosting and the like as boring algorithms for boring problems.

To me, this seems counter-intuitive, and the fact that most people researching ML are interested in subjects like vision and language is flabbergasting. For one, because getting anywhere productive in these fields is really hard, for another, because their usefulness seems relatively minimal.

Practices eugeneyan.com

The first rule of X: start without X

Eugene Yan, in a post titled The first rule of machine learning: start without machine learning:

Applying machine learning effectively is tricky. You need data. You need a robust pipeline to support your data flows. And most of all, you need high-quality labels. As a result, most of the time, my first iteration doesn’t involve machine learning at all.

Eugene is stating the obvious with this post, but hey sometimes you just gotta state it. What’s even more interesting to me is how nicely the format generalizes! Let’s pattern match this sucker:

The first rule of X: start without X

Now, apply the pattern a few times and see if it holds:

  1. The first rule of Kubernetes: start without Kubernetes
  2. The first rule of goroutines: start without goroutines
  3. The first rule of coding: start without coding

Yeah, that abstraction holds pretty true. Surely there will be cases where it falls flat on its face, though. Can you think of any examples?

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.”

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.)

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