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Generative AI

Protein programmers get a helping hand from Cradle’s generative AI • ZebethMedia

Proteins are the molecules that get work done in nature, and there’s a whole industry emerging around successfully modifying and manufacturing them for various uses. But doing so is time consuming and haphazard; Cradle aims to change that with an AI-powered tool that tells scientists what new structures and sequences will make a protein do what they want it to. The company emerged from stealth today with a substantial seed round. AI and proteins have been in the news lately, but largely because of the efforts of research outfits like DeepMind and Baker Lab. Their machine learning models take in easily collected RNA sequence data and predict the structure a protein will take — a step that used to take weeks and expensive special equipment. But as incredible as that capability is in some domains, it’s just the starting point for others. Modifying a protein to be more stable or bind to a certain other molecule involves much more than just understanding its general shape and size. “If you’re a protein engineer, and you want to design a certain property or function into a protein, just knowing what it looks like doesn’t help you. It’s like, if you have a picture of a bridge, that doesn’t tell you whether it’ll fall down or not,” explained Cradle CEO and co-founder Stef van Grieken. “Alphafold takes a sequence and predicts what the protein will look like,” he continued. “We’re the generative brother of that: you pick the properties you want to engineer, and the model will generate sequences you can test in your laboratory.” Predicting what proteins — especially ones new to science — will do in situ is a difficult task for lots of reasons, but in the context of machine learning the biggest issue is that there isn’t enough data available. So Cradle originated much of its own data set in a wet lab, testing protein after protein and seeing what changes in their sequences seemed to lead to which effects. Interestingly the model itself is not biotech-specific exactly but a derivative of the same “large language models” that have produced text production engines like GPT-3. Van Grieken noted that these models are not limited strictly to language in how they understand and predict data, an interesting “generalization” characteristic that researchers are still exploring. Examples of the Cradle UI in action. The protein sequences Cradle ingests and predicts are not in any language we know, of course, but they are relatively straightforward linear sequences of text that have associated meanings. “It’s like an alien programming language,” van Grieken said. Protein engineers aren’t helpless, of course, but their work necessarily involves a lot of guessing. One may know for sure that among the 100 sequences they are modifying is the combination that will produce The model works in three basic layers, he explained. First it assesses whether a given sequence is “natural,” i.e. whether it is a meaningful sequence of amino acids or just random ones. This is akin to a language model just being able to say with 99 percent confidence that a sentence is in English (or Swedish, in van Grieken’s example), and the words are in the correct order. This it knows from “reading” millions of such sequences determined by lab analysis. Next it looks at the actual or potential meaning in the protein’s alien language. “Imagine we give you a sequence, and this is the temperature at which this sequence will fall apart,” he said. “If you do that for a lot of sequences, you can say not just, ‘this looks natural,’ but ‘this looks like 26 degrees Celsius.’ that helps the model figure out what regions of the protein to focus on.” The model can then suggest sequences to slot in — educated guesses, essentially, but a stronger starting point than scratch. And the engineer or lab can then try them and bring that data back to the Cradle platform, where it can be re-ingested and used to fine tune the model for the situation. The Cradle team on a nice day at their HQ (van Grieken is center). Modifying proteins for various purposes is useful across biotech, from drug design to biomanufacturing, and the path from vanilla molecule to customized, effective and efficient molecule can be long and expensive. Any way to shorten it will likely be welcomed by, at the very least, the lab techs who have to run hundreds of experiments just to get one good result. Cradle has been operating in stealth, and now is emerging having raised $5.5 million in a seed round co-led by Index Ventures and Kindred Capital, with participation from angels John Zimmer, Feike Sijbesma, and Emily Leproust. Van Grieken said the funding would allow the team to scale up data collection — the more the better when it comes to machine learning — and work on the product to make it “more self-service.” “Our goal is to reduce the cost and time of getting a bio-based product to market by an order of magnitude,” said van Grieken in the press release, “so that anyone – even ‘two kids in their garage’ – can bring a bio-based product to market.”

After 40 million app downloads, PhotoRoom raises $19 million • ZebethMedia

French startup PhotoRoom has raised a $19 million Series A funding round. The company develops a popular photo editing app for e-commerce vendors and small businesses. In particular, it helps you remove the background behind objects you are about to sell so that your photos look more professional. Balderton Capital is leading the Series A round with angels from Facebook, Hugging Face and Disney+ also participating. Existing investor Adjacent is also putting more money in the company. PhotoRoom isn’t the only app that helps you remove photo backgrounds. Another popular app in the “Graphics & Design” category on the App Store is Pixelcut. More generic apps, such as Picsart also have background removal features. But PhotoRoom has focused on one niche in particular — small businesses reselling objects on eBay, Poshmark or Depop. In just a few taps, these marketplace experts can process a batch of photos and create images that are ready to be used. It can help them save a lot of time and sell more products. Behind the scenes, PhotoRoom uses deep learning to identify different objects and elements on a photo. A flat photo becomes a multi-layer images, which means that users can delete the photo background, blur it or replace it with something else entirely. The same technology can be used to retouch image and remove unwanted objects. PhotoRoom has a large library of background templates and helps you export images in different formats with each platform constraints in mind. And it’s been working remarkably well as the company managed to attract 40 million downloads on iOS and Android. There are 7 million monthly active users and hundreds of thousands of users pay a subscription fee to unlock all the features in the app. It currently costs $9.99 a month or $69.99 a year. Up next, PhotoRoom wants to bring generative AI to its app, starting with Stable Diffusion. That’s why it is raising money as the company has been profitable with its current team. “Until now, we grabbed an object and we erased the background. But then users have been looking for a background to put the object back in front of a template,” co-founder and CEO Matthieu Rouif told me. With a text prompt, PhotoRoom will generate a marketing product photo based on your object. The feature will be available in beta for some users at first. It will be rolled out to all users progressively. “By focusing intently on user needs, Matthieu and Eliot have created a product that stands out from the rest. The importance of online photography is immense and PhotoRoom has both the traction and the ambition to become a market leader,” Balderton Capital partner Bernard Liautaud said in a statement. Image Credits: PhotoRoom

Digital assets marketplace Creative Fabrica launches generative AI tool • ZebethMedia

Creative Fabrica, a marketplace for digital files like print-on-demand assets, fonts and graphics, announced today it will launch its own generative AI tool. Called CF Spark, it’s already seen three million prompts generated, and more than 500,000 published by Creative Fabrica creators over the past three weeks. Like other digital assets on the platform, users can put up their generative AI files for paid use by other members, which Creative Fabrica says makes it the first generative AI that also allows creators to make money. Backed by investors like Felix Capital, FJ Labs and Peak Capital, Creative Fabrica has an agreement with Stable Diffusion, the image-generating AI system by Stability AI and is working on a partnership with OpenAI to include Dalle 2 in its ecosystem. CF Spark also uses the Dreamstudio API. Creative Fabrica CEO Roemie Hillenaar said this mix allows users to get different results and covers a broader range of styles. Creative Spark started building CF Spark before Stable Diffusion was released open source, Hillenaar told ZebethMedia. “We saw that DALLE and MidJourney where opening up their gates towards more beta users and we took the bet that OpenAI (make DALLE) will open up their API at any time. At the time we were betting towards the end of this year and we thought to build already the product as if the API would be available,” he added. But in the middle of that, Stability AI released Stable Diffusion open source and since Creative Fabrica was already in the process of developing CF Spark, it allowed it to take the new tool live more quickly. To use CF Spark, creators enter a prompt, which generates four images that they can chose to publish on their page. Other Creative Fabrica users can re-prompt the AI images to get different results and upload images of what they create with AI-generated art (for example, a T-shirt). CF Spark is available to Creative Fabrica’s four million users for free. In total, the platform has a library of almost six million fonts and graphics.

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