This AI newsletter is all you need #7

What happened this week in AI

This week’s newsletter iteration is all about how AI has been used to help society. As you know, there are many, many possible use cases of AI, and more are discovered daily. Fortunately, many of these use cases help take research forward in important fields like medicine or climate science to improve our quality of life. Last week, DeepMind announced that they predicted structures for nearly all cataloged proteins known to science. What does this mean? Researchers can already try new opportunities and use AlphaFold (DeepMind’s model and database) to advance their work on important issues, including sustainability, food insecurity, and neglected diseases. This focus on high impact is becoming more and more common, as a new model called ProtGPT2 is now capable of designing new proteins capable of stable folding. This area of research is no longer only accessible to big and heavily financed organizations! This protein folding progress is mainly thanks to the similarity between proteins and language. As they mention, “Natural languages and proteins are actually similar in structure. Amino acids arrange themselves in a multitude of combinations to form structures that have specific functions in the living organism—similar to the way words form sentences in different combinations that express certain facts.”

So what’s the moral of this story? Don’t think too much about your research area and keep focusing on NLP models, as it generalizes to other important fields anyway! ;)?

Don’t forget, if you are interested in the MineRL competition, or OpenAI/DeepMind, but do not know much about what it’s like to work there, join the conversation and ask your question on our discord channel!

Hottest News

1. DeepMind predicted structures for nearly all cataloged proteins known to science

DeepMind predicted structures for nearly all cataloged proteins known to science. It will expand the AlphaFold database by over 200x - from nearly 1 million structures to over 200 million structures.

2. Artificial intelligence enables the design of novel proteins

Artificial intelligence (AI) has created new possibilities for designing tailor-made proteins to solve everything from medical to ecological problems. The ProtGPT2 model designs new proteins that are capable of stable folding and could take over defined functions in larger molecular contexts.

3. Louis’ video was featured on PetaPixel and a couple of other news websites thanks to them!

Louis, co-founder and Head of the Community at Toward’s AI had his video “Impressive restoration of memories by AI !” featured on PetaPixal, the largest news website for photographs. Read more on Toward’s AI.

Most interesting papers of the week

1. A Fast Text-Driven Approach for Generating Artistic Content

Yes, another image generative model! As shown in the image above, the image (a) can be stylized according to the user’s requirements with a text prompt, a style image (b) and (c), or a combination of style parameters.

2. High Dynamic Range and Super-Resolution from Raw Image Bursts

This paper introduces the first approach to the reconstruction of high-resolution, high-dynamic range color images from raw photographic bursts captured by a handheld camera (phone) with exposure bracketing.

3. 3D Cartoon Face Generation with Controllable Expressions from a Single GAN Image

From 2D human face images to 3D cartoon-styled avatars using a single GAN-generated human face image and without 3D annotations.

Enjoy these papers and news summaries? Get a daily recap in your inbox!

The Learn AI Together Community section!

Meme of the week!

Meme shared by 0wnlife#8511. Join the conversation and share your memes with us!

Featured Community post from the Discord

A member of the community, Tomi, has started a YouTube channel where he explains AI-related topics using shorts. If you enjoy quick information-dense videos, check out his channel and support a fellow member of this community giving him a little thumbs up and a subscription!

Check out his video explaining ResNets in 60 seconds and let him know what you think of his video. Feedback is hard to have when you start a YouTube channel, blog, or similar adventure and we’d be grateful if a handful of you guys click on his video and give honest feedback. :)

AI poll of the week!

Join the discussion on Discord.

TAI Curated section

Article of the week

How to Verify the Assumptions of Linear Regression: Linear regression is the most basic approach for getting started with ML, and it is frequently underrated. Every article discusses dealing with Linear Regression and ways for overcoming overfitting rather than preventing overfitting by adhering to a few fundamental Linear Regression assumptions. This article discusses the less-known and underestimated assumptions of Linear Regression, how to check them, and several approaches to adhere to them.

If you are interested in writing for us at Towards AI, please sign up here and we will publish your blog to our network if it meets our editorial policies and standards. https://contribute.towardsai.net/

Lauren’s Ethical Take on Collaboration as a Tool for Innovation

To match this issue’s focus on improving society with AI, I wanted to wander off path a bit to highlight how collaboration provides the foundation for groundbreaking interdisciplinary work. Many of the improvements in society are the result of a collaboration of a complicated problem and a novel solution, for example, unique genetic conditions and targeted pharmaceuticals developed with AI tools. In AI contexts, AI ethics is the precursor to this: Complicated problems posed in AI, such as moral responsibility for a death caused by AI or how personal information should be used and treated, are met with a novel ethical solution to provide great societal benefit.?

This benefit forms the foundation for more collaborations and advancements to happen, and our ability to work together and diversify our ways of thinking and working makes this possible. AI ethics helps ensure that AI developments benefit everyone. The best way to do that is to continue to expand our understanding of other disciplines so we can solve problems through teamwork, because no one knows everything. So don’t be afraid to branch out of your comfort zone – this is how we make great progress!

Job offers

Senior Computer Vision Engineer @ Neurolabs (London & Remote)

Principal Machine Learning Engineer | AI Product @ Jasper.ai (Remote)

Machine Learning Engineer @ Runway (Remote)

Senior ML Engineer - Algolia AI @ Algolia (Hybrid remote)

Senior ML Engineer - Semantic Search @ Algolia (Hybrid remote)

Interested in sharing a job opportunity here? Contact [email protected] or post the opportunity in our #hiring channel on discord!