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.

We’re releasing Triton 1.0, an open-source Python-like programming language which enables researchers with no CUDA experience to write highly efficient GPU code—most of the time on par with what an expert would be able to produce.

OpenAI continues to deliver the goods for the AI community.

GAN Theft Auto is a Generative Adversarial Network that recreates the Grand Theft Auto 5 environment. It is created using a GameGAN fork based on NVIDIA’s GameGAN research.

Source code here.

This API supports multiple deep learning frameworks (TensorFlow, PyTorch, etc), supports multiple hardware accelerators (CPU, GPU, egdeTPU), and is based on open source models. You can think of it a bit like the Google’s Cloud Vision API, only open source and self-hosted.

This article starts with a concise description of the relationship and differences of these 3 commonly used industry terms. Then it digs into the history.

Deep learning is a subset of machine learning, which in turn is a subset of artificial intelligence, but the origins of these names arose from an interesting history. In addition, there are fascinating technical characteristics that can differentiate deep learning from other types of machine learning…essential working knowledge for anyone with ML, DL, or AI in their skillset.

geared towards building search systems for any kind of data, including text, images, audio, video and many more. With the modular design & multi-layer abstraction, you can leverage the efficient patterns to build the system by parts, or chaining them into a Flow for an end-to-end experience.

SpeechBrain is an open-source and all-in-one speech toolkit based on PyTorch.

The goal is to create a single, flexible, and user-friendly toolkit that can be used to easily develop state-of-the-art speech technologies, including systems for speech recognition, speaker recognition, speech enhancement, multi-microphone signal processing and many others.

Currently in beta.

Use OpenAI’s CLIP neural network to search inside YouTube videos. You can try it by running the notebook on Google Colab.

The README has a bunch of examples of things you might search for and the results you’d get back. (“The Transamerica Pyramid”, anyone?)

The author also has another related project where you can search Unsplash in like manner.

You may recall spaCy from this episode of Practical AI with its creators. If not, now’s a great time to introduce yourself to the project. 3.0 looks like a fantastic new release of the wildly popular NLP library. The list of new and improved things is too long for me to reproduce here, so go check it out for yourself.

There’s also three YouTube videos accompanying the release. That’s evidence of just how much effort and polish went in to this.

This piece by Mark Saroufim on the state of ML starts pretty salty:

Graduate Student Descent is one of the most reliable ways of getting state of the art performance in Machine Learning today and it’s also a fully parallelizable over as many graduate students or employees your lab has. Armed with Graduate Student Descent you are more likely to get published or promoted than if you took on uncertain projects.

and:

BERT engineer is now a full time job. Qualifications include:

• Some bash scripting
• Deep knowledge of pip (starting a new environment is the suckier version of practicing scales)
• Waiting for new HuggingFace models to be released
• Watching Yannic Kilcher’s new Transformer paper the day it comes out
• Repeating what Yannic said at your team reading group

It’s kind of like Dev-ops but you get paid more.

But if you survive through (or maybe even enjoy) the lamentations and ranting, you’ll find some hope and optimism around specific projects that the author believes are pushing the industry through its Great Stagnation.

I learned a few things. Maybe you will too.

Graph neural networks (GNNs) belong to a category of neural networks that operate naturally on data structured as graphs. Despite being what can be a confusing topic, GNNs can be distilled into just a handful of simple concepts.

Practical uses of GNNS include making traffic predictions, search rankings, drug discovery, and more.

Chris Wellons:

Machine learning is a trendy topic, so naturally it’s often used for inappropriate purposes where a simpler, more efficient, and more reliable solution suffices. The other day I saw an illustrative and fun example of this: Neural Network Cars and Genetic Algorithms. The video demonstrates 2D cars driven by a neural network with weights determined by a generic algorithm. However, the entire scheme can be replaced by a first-degree polynomial without any loss in capability. The machine learning part is overkill.

Yet another example of a meta-trend in software: You might not need $X (where $X is a popular tool or technique that is on the upward side of the hype cycle).

Below you find a set of charts demonstrating the paths that you can take and the technologies that you would want to adopt in order to become a data scientist, machine learning or an ai expert. We made these charts for our new employees to make them AI Experts but we wanted to share them here to help the community.

I didn’t embed the roadmap images because they are too many and too vertical to fit. It sound like an interactive version is Coming Soon™️, but don’t wait on that to get started here. 2020 is almost over. 😉

A team of scientists at LMU Munich have developed Pattern-Exploiting Training (PET), a deep-learning training technique for natural language processing (NLP) models. Using PET, the team trained a Transformer NLP model with 223M parameters that out-performed the 175B-parameter GPT-3 by over 3 percentage points on the SuperGLUE benchmark.

Daniel Jeffries’ wildly popular Learning AI If You Suck At Math series is back after a 3-year hiatus. In part 8, Daniel asks (and answers) the question: Can AI make beautiful music?

This is bonkers:

New AI breakthroughs in NVIDIA Maxine, cloud-native video streaming AI SDK, slash bandwidth use while make it possible to re-animate faces, correct gaze and animate characters for immersive and engaging meetings.

Instead of transferring your face at N frames per second, they transfer it once at the beginning of the call and then update key positions over time. The results are super impressive (and just a bit creepy?).

Emily Xie:

Urban legend says that Mona Lisa’s eyes will follow you as you move around the room. This is known as the “Mona Lisa effect.” For fun, I recently programmed an interactive digital portrait that brings this phenomenon to life through your browser and webcam.

What’s linked is the official PyTorch implementation of a paper published in April of this year called Bringing Old Photos Back to Life.

We propose to restore old photos that suffer from severe degradation through a deep learning approach. Unlike conventional restoration tasks that can be solved through supervised learning, the degradation in real photos is complex and the domain gap between synthetic images and real old photos makes the network fail to generalize. Therefore, we propose a novel triplet domain translation network by leveraging real photos along with massive synthetic image pairs. Specifically, we train two variational autoencoders (VAEs) to respectively transform old photos and clean photos into two latent spaces.

The results are impressive!

A formalization and continuation of this old Quora question about the most important research papers which all NLP students “should definitely read”.

They’ve split the dataset up into two bundles:

1. Lite, which you can download w/ a click, but is limited to 25K image
2. Full, which you have to request access to and is limited to non-commercial use

This is interesting for a couple of reasons. First, it’s a great resource for anyone training models for image classification, etc. Second, it’s a nice business model for Unsplash as a startup.

I predict that, unlike its two predecessors (PTB and OpenAI GPT-2), OpenAI GPT-3 will eventually be widely used to pretend the author of a text is a person of interest, with unpredictable and amusing effects on various communities.

If you’re going to read this post, make sure you stick around until the end.

For years now I’ve been asking AI/ML experts when these powerful-yet-complicated tools will become available to average developers like you and me. It’s happening! Just look at how high-level this text generation code sample is:

import openai

prompt = """snipped for brevity's sake"""

response = openai.Completion.create(model="davinci",
  prompt=prompt, 
  stop="\n",
  temperature=0.9,
  max_tokens=100)

They’re oftening all kinds of language tasks: semantic search, summarization, sentiment analysis, content generation, translation, and more. The API is still in beta and there’s a waitlist, but this is exciting news, nonetheless.

Neuropod is a library that provides a uniform interface to run deep learning models from multiple frameworks in C++ and Python. Neuropod makes it easy for researchers to build models in a framework of their choosing while also simplifying productionization of these models.

This looks nice because you can make your inference code framework agnostic and easily switch between frameworks if necessary. Currently supports TensorFlow, PyTorch, TorchScript, and Keras.

Acme is a library of reinforcement learning (RL) agents and agent building blocks. Acme strives to expose simple, efficient, and readable agents, that serve both as reference implementations of popular algorithms and as strong baselines, while still providing enough flexibility to do novel research. The design of Acme also attempts to provide multiple points of entry to the RL problem at differing levels of complexity.

Learn how a CNN model transforms different images into class predictions with all of the intermediate steps along the way. It’s interactive, so you can select individual neurons and inspect the details.