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AI (Artificial Intelligence)

Machines simulating human characteristics and intelligence.
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Python github.com

A PyTorch-based speech toolkit

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.

Tooling github.com

Search inside YouTube videos using natural language

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.

Ines Montani github.com

Introducing spaCy 3.0

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.

Machine Learning marksaroufim.substack.com

Machine Learning: The Great Stagnation

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.

Machine Learning blog.exxactcorp.com

A friendly introduction to Graph Neural Networks

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.

AI (Artificial Intelligence) nullprogram.com

You might not need machine learning

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).

Learn github.com

A roadmap to becoming an AI expert in 2020

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. 😉

InfoQ Icon InfoQ

AI training method exceeds GPT-3 performance with 99.9% fewer parameters

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.

NVIDIA Developer Blog Icon NVIDIA Developer Blog

NVIDIA's new GAN reduces video bandwidth by orders of magnitude

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?).

Microsoft github.com

Microsoft's deep learning approach to restoring old photos

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!

Microsoft's deep learning approach to restoring old photos

AI (Artificial Intelligence) github.com

Unsplash makes available 2M+ images for research and machine learning

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.

OpenAI Icon OpenAI

OpenAI now has an API

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.

Uber Engineering Icon Uber Engineering

A uniform interface to run deep learning models from multiple frameworks

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.

Python github.com

A research framework for reinforcement learning

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.

Python github.com

A modular toolbox for accelerating meta-learning research

Meta-Blocks is a modular toolbox for research, experimentation, and reproducible benchmarking of learning-to-learn algorithms. The toolbox provides flexible APIs for working with MetaDatasets, TaskDistributions, and MetaLearners (see the figure below). The APIs make it easy to implement a variety of meta-learning algorithms, run them on well-established and emerging benchmarks, and add your own meta-learning problems to the suite and benchmark algorithms on them.

This repo is still under “heavy construction” (a.k.a. unstable) so downloader beware, but it’s worth a star/bookmark for later use.

A modular toolbox for accelerating meta-learning research

Thomas Smith Medium

Clearview AI has a profile on me and 'it freaked me out'

Have you ever posted an image on the public internet and thought, “What if someone used this for something?” Thomas Smith did and what he discovered about Clearview AI is disturbing…

Someone really has been monitoring nearly everything you post to the public internet. And they genuinely are doing “something” with it.

The someone is Clearview AI. And the something is this: building a detailed profile about you from the photos you post online, making it searchable using only your face, and then selling it to government agencies and police departments who use it to help track you, identify your face in a crowd, and investigate you — even if you’ve been accused of no crime.

I realize that this sounds like a bunch of conspiracy theory baloney. But it’s not. Clearview AI’s tech is very real, and it’s already in use.

How do I know? Because Clearview has a profile on me. And today I got my hands on it.

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