Binance, a cryptocurrency exchange, has unveiled a new website that details its proof-of-reserves architecture. Beginning with reserves in BTC, the business. Binance currently has a 101% reserve ratio. This indicates that the business has enough bitcoins on hand to cover every user’s balance.
This action was taken a few weeks after another well-known cryptocurrency exchange, FTX, went under. In the case of FTX, the business experienced a liquidity issue. It ceased processing withdrawals because it was unable to satisfy end users’ and investors’ requirements.
Since then, cryptocurrency businesses, and exchanges in particular, have worked to increase user fund transparency. More knowledge on hot and cold wallets has to be shared. But before you can fully trust cryptocurrency exchanges and how they handle assets, there is still a lot of work to be done.
A few weeks ago, Binance began by disclosing wallet addresses for cryptocurrency holdings worth billions of dollars. The corporation demonstrated with this action that it does, in fact, own a sizable asset base and is capable of handling a sizable volume of withdrawals. However, the business didn’t make it clear whether those assets are on the balance sheets of users, Binance, or a combination of both.
Binance confirmed this by stating that the BTC wallets contained in the proof-of-reserves system do not include Binance’s own assets with today’s launch of the new proof-of-reserves site.
The corporation notes that its corporate holdings, which are maintained on a totally different ledger, are not included in this. Since a blockchain explorer can’t be used to confirm this, you will have to take Binance at their word.
Binance’s initial assets are in bitcoin. It’s simple to add up the balances in each wallet owned by Binance. The business creates a cryptographic seal for all individual user accounts and includes them in a Merkle tree when it comes to user assets.
Users of Binance possessed 575742.4228 BTC as of November 22 at 23:59 UTC, which is around $9.5 billion at the current exchange rate. And Binance has 101% of these funds in bitcoins in its own wallets. In other words, Binance would have enough BTC to handle all withdrawals if everyone withdrew their BTC at once.
Individual users can utilize the root hash to determine whether their accounts are included in the snapshot of user balances thanks to the Merkle tree. According to Binance, user balances are included for a variety of products, including the Spot, Funding, Margin, Futures, Earn, and Options Wallet. The business also provides a little Python script for you to check on your own.
“In light of recent events, it makes sense that the community will demand more from cryptocurrency exchanges—far more than is now expected of traditional financial institutions. Because of this, we’re happy to provide our users this most recent functionality that allows them to confirm their funds, according to Changpeng Zhao, founder and CEO of Binance. It will take a few weeks to produce the data for the majority of our assets in custody because Binance has a significantly greater user base than the next largest exchange. In order to meet the community’s expectations, we are striving to release the following update as soon as feasible.
The business has already announced future plans to publish comparable proof-of-reserves data for ETH, USDT, USDC, BUSD, and BNB. We can only hope that Binance will be able to cover withdrawals for less well-known cryptocurrencies given the hundreds of different crypto assets they offer.
In a similar vein, the company should collaborate with impartial financial and security auditing companies so that you are not forced to have blind faith in the business. Even if there is still a long way to go, the new proof-of-reserves mechanism implemented today is a positive start.
Gaming models are created by Auctoria using generative AI
Aleksander Caban, co-founder of Polish VR game developer Carbon Studio, noticed a major problem in modern game design several years ago. He manually created rocks, hills, paths, and other video game environment elements, which was time-consuming and laborious.
Caban created tech to automate the process.
In collaboration with Michal Bugała, Joanna Zając, Karolina Koszuta, and Błażej Szaflik, he founded Auctoria, an AI-powered platform for creating 3D game assets. Auctoria, from Gliwice, Poland, is in Startup Battlefield 200 at Disrupt 2023.
Auctoria was founded on a passion for limitless creativity, according to Zając in an email interview. It was designed to help game developers, but anyone can use it. Few advanced tools exist for professionals; most are for hobbyists and amateurs. We want to change that.”
Using generative AI, Auctoria creates various video game models. One feature generates basic 3D game levels with pathways, while another converts uploaded images and textures of walls, floors, and columns into 3D versions.
Like DALL-E 2 and Midjourney, Auctoria can generate assets from text prompts. Or they can submit a sketch, which the platform will try to turn into a digital model.
All AI algorithms and training data for Auctoria were developed in-house, according to Zając.
She said “Auctoria is based 100% on our content, so we’re not dependent on any other provider.” It’s independent—Auctoria doesn’t use open source or external engines.
In the emerging market for AI game asset generation tools, Auctoria isn’t alone. The 3DFY, Scenario, Kaedim, Mirage, and Hypothetic startups create 3D models. Even Nvidia and Autodesk are entering the space with apps like Get3D, which converts images to 3D models, and ClipForge, which generates models from text descriptions.
Meta also tried tech to create 3D assets from prompts. In December, OpenAI released Point-E, an AI that synthesizes 3D models for 3D printing, game design, and animation.
Given the size of the opportunity, the race to market new solutions isn’t surprising. According to Proficient Market Insights, 3D models could be worth $3.57 billion by 2028.
According to Zając, Auctoria’s two-year R&D cycle has led to a more robust and comprehensive toolset than rivals.
“Currently, AI-based software is lacking for creating complete 3D world models,” Zając stated. “3D editors and plugins offer only a fraction of Auctoria’s capabilities. Our team started developing the tool two years ago, giving us a ready-to-use product.”
Auctoria, like all generative AI startups, must deal with AI-generated media legal issues. Not yet clear how AI-generated works can be copyrighted in the U.S.
However, the Auctoria team of seven employees and five co-founders is delaying answering those questions. Instead, they’re piloting the tooling with game development studios like Caban’s Carbon Studio.
Before releasing Auctoria in the coming months, the company hopes to raise $5 million to “speed up the process” of creating back-end cloud services to scale the platform.
Zając stated that the funding would reduce the computing time required for creating worlds or 3D models with Auctoria. Achieving a software-as-a-service model requires both infrastructure and user experience enhancements, such as a simple UI, excellent customer service, and effective marketing. We’ll keep our core team small, but we’ll hire more by year’s end.”
DALL-E 3, from OpenAI, lets artists skip training
Today, OpenAI released an updated version of DALL-E, its text-to-image tool that uses ChatGPT, its viral AI chatbot, to make prompting easier.
Most modern, AI-powered image generation tools turn prompts—image descriptions—into photorealistic or fantastical artwork. However, writing the right prompt is so difficult that “prompt engineering” is becoming a profession.
New OpenAI tool DALL-E 3 uses ChatGPT to fill prompts. OpenAI’s premium ChatGPT plans, ChatGPT Plus and ChatGPT Enterprise, allow users to type in an image request and refine it with the chatbot, receiving the results in the chat app.
ChatGPT can make a few-word prompt more descriptive, guiding the DALL-E 3 model.
DALL-E 3 adds more than ChatGPT integration. OpenAI claims that DALL-E 3 produces better images that better reflect prompts, especially for longer prompts. It handles text and human hands better, which have previously hampered image-generating models.
OpenAI claims DALL-E 3 has new algorithmic bias-reduction and safety mechanisms. For instance, DALL-E 3 will reject requests to depict living artists or public figures. Artists can now choose not to train future OpenAI text-to-image models with their work. (OpenAI and its rivals are being sued for using copyrighted artists’ work to train their generative AI image models.)
As the image-synthesizing generative AI race heats up, DALL-E 3 launches. Midjourney and Stability AI keep improving their image-generating models, putting pressure on OpenAI to keep up.
OpenAI will release DALL-E 3 to premium ChatGPT users in October, then research labs and API customers. The company did not say when or if it would release a free web tool like DALL-E 2 and the original model.
Open-source Microsoft Novel protein-generating AI EvoDiff
All diseases are based on proteins, natural molecules that perform vital cellular functions. Characterizing proteins can reveal disease mechanisms and ways to slow or reverse them, while creating proteins can lead to new drug classes.
The lab’s protein design process is computationally and human resource-intensive. It involves creating a protein structure that could perform a specific function in the body and then finding a protein sequence that could “fold” into that structure. To function, proteins must fold correctly into three-dimensional shapes.
Not everything has to be complicated.
Microsoft introduced EvoDiff, a general-purpose framework that generates “high-fidelity,” “diverse” proteins from protein sequences, this week. Unlike other protein-generating frameworks, EvoDiff doesn’t need target protein structure, eliminating the most laborious step.
Microsoft senior researcher Kevin Yang says EvoDiff, which is open source, could be used to create enzymes for new therapeutics, drug delivery, and industrial chemical reactions.
Yang, one of EvoDiff’s co-creators, told n an email interview that the platform will advance protein engineering beyond structure-function to sequence-first design. EvoDiff shows that ‘protein sequence is all you need’ to controllably design new proteins.
A 640-million-parameter model trained on data from all protein species and functional classes underpins EvoDiff. “Parameters” are the parts of an AI model learned from training data that define its skill at a problem, in this case protein generation. The model was trained using OpenFold sequence alignment data and UniRef50, a subset of UniProt, the UniProt consortium’s protein sequence and functional information database.
Modern image-generating models like Stable Diffusion and DALL-E 2 are diffusion models like EvoDiff. EvoDiff slowly subtracts noise from a protein made almost entirely of noise to move it closer to a protein sequence.
Beyond image generation, diffusion models are being used to design novel proteins like EvoDiff, create music, and synthesize speech.
“If there’s one thing to take away [from EvoDiff], I think it’s this idea that we can — and should — do protein generation over sequence because of the generality, scale, and modularity we can achieve,” Microsoft senior researcher Ava Amini, another co-contributor, said via email. “Our diffusion framework lets us do that and control how we design these proteins to meet functional goals.”
EvoDiff can create new proteins and fill protein design “gaps,” as Amini noted. A protein amino acid sequence that meets criteria can be generated by the model from a part that binds to another protein.
EvoDiff can synthesize “disordered proteins” that don’t fold into a three-dimensional structure because it designs proteins in “sequence space” rather than structure. Disordered proteins enhance or decrease protein activity in biology and disease, like normal proteins.
EvoDiff research isn’t peer-reviewed yet. Microsoft data scientist Sarah Alamdari says the framework needs “a lot more scaling work” before it can be used commercially.
“This is just a 640-million-parameter model, and we may see improved generation quality if we scale up to billions,” Alamdari emailed. WeAI emonstrated some coarse-grained strategies, but to achieve even finer control, we would want to condition EvoDiff on text, chemical information, or other ways to specify the desired function.”
Next, the EvoDiff team will test the model’s lab-generated proteins for viability. Those who are will start work on the next framework.
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