Inside Artificialis - #9
Sep 30, 2022 9:19 am
Hey, . Welcome to the monthly version of the Artificialis newsletter!
A summary of what happened during the month, the best blogs of our Medium publication, latest news of our Discord server events, and recent news in the world of Artificial Intelligence and Machine Learning!
So seat back, take a cup of coffee, let's go over what happened this month.
First of all, a list of this month most viewed blog articles:
- GTC 2022 Keynote summary, by Maximilian
- Multi-task Deep Learning with Pytorch, by Alessandro
- Review: Learning to learn, by Si Thu Khant
A little bit from me:
Lately, I've been very fascinated by the concept of Multi-task learning, you might be familiar with Tesla's Hydranets.
The concept is very simple yet effective, using a shared backbone and several independent "heads", each one dedicated for a single task. It's a relatively new concept, there's not very much online, yet.
I'll probably dive into it even more, let me know if you're interested in more concise and in-depth tutorials or other resources!
AI in the world
Introducing Make-A-Video: An AI system that generates videos from text
Generative AI research is pushing creative expression forward by giving people tools to quickly and easily create new content. With just a few words or lines of text, Make-A-Video can bring imagination to life and create one-of-a-kind videos full of vivid colors, characters, and landscapes. The system can also create videos from images or take existing videos and create new ones that are similar. [...]
Meta Built an AI That Can Guess the Words You’re Hearing by Decoding Your Brainwaves
Being able to decode brainwaves could help patients who have lost the ability to speak to communicate again, and could ultimately provide novel ways for humans to interact with computers. Now Meta researchers have shown they can tell what words someone is hearing using recordings from non-invasive brain scans. [...]
PyTorch strengthens its governance by joining the Linux Foundation
As PyTorch grew, many companies have made foundational investments around it. While Meta remains the largest contributor to PyTorch, companies such as AMD, Amazon Web Services (AWS), Google Cloud, HuggingFace, and many others have made significant investments. They’ve established teams around PyTorch or filled significant voids within the PyTorch community and sent countless contributions to the PyTorch core and to the ecosystem around it — PyTorch is an important part of their future. With PyTorch continuing to grow as a multi-stakeholder project, it’s time to move to a broader open-source foundation. [...]
TensorStore for High-Performance, Scalable Array Storage
Today we are introducing TensorStore, an open-source C++ and Python software library designed for storage and manipulation of n-dimensional that [...]
We’ve trained and are open-sourcing a neural net called Whisper that approaches human level robustness and accuracy on English speech recognition. [...]
DALL-E is now available without waitlist
New users can start creating straight away. Lessons learned from deployment and improvements to our safety systems make wider availability possible. [...]
FindIt: Generalized Object Localization with Natural Language Queries
Here we propose a unified, general-purpose and multitask visual grounding model, called FindIt, that can flexibly answer different types of grounding and detection queries [...]
Tip of the month
This month's tip is:
Throughout your optimization process, consider using profilers!
Both Tensorflow and Pytorch have profilers. They're used to check what's running slow inside your model:
And you'll see something like:
You'll notice most of the time is - for example - spent during convolution operations.
'Till next month, you can find everyone here:
Have a fantastic month, !
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