The concept of Decentralized Science Transforming science, or DeSci, has gained traction in recent years as a potential answer to the systemic problems that conventional scientific inquiry faces. Based on decentralized technology. DeSci seeks to address issues like academic fraud and paywalled knowledge while also democratizing research and increasing transparency. The idea of decentralized science first emerged in the larger decentralization movement, which blockchain enthusiasts spearheaded in the early 2010s.
The advent of dynamic smart contract platforms, such as Ethereum (ETH), brought blockchains’ potential uses outside the realm of cryptocurrencies like Bitcoin (BTC). As pioneers realized that decentralization could solve some of the problems with the traditional scientific research method, the term DeSci started to catch on in 2018.
Renown initiatives and policies
At the forefront of the DeSci revolution are a number of groundbreaking projects. An alternative to the traditional, centralized grant and reward system is emerging on platforms such as VitaDAO, BIO Protocol, and Molecule. In an interview with VitaDAO, Professor David M. Wilson III discussed his vision for the future of decentralized finance methods and provided the following explanation:
I believe that Decentralized Science Transforming funding gives everyone an opportunity to participate. The medication development process and share in the rewards that come with it. This adds to the growing body of evidence that can guide the development of novel treatments and interventions. Considering the widespread pessimism in modern society, it presents a genuine chance for anyone who is inspired. For whatever reason, to participate in and witness the drug development journey up close.
The emerging standards in the artificial intelligence field
Blockchain-based DeSci solutions can permanently record all research efforts, from data collection to publication. This transparency mitigates the difficulty in manipulating or falsifying results, undermining faith in scientific findings.
The problem is particularly acute in the field of artificial intelligence (AI) development. Where a research tradition formerly dedicated to open-source code and transparency has been transformed by commercial interests and an increasing culture of secrecy.
Computer scientist Tiancheng Xie explains why most of the conclusions in the thousands of AI research papers published this year cannot be confirmed. Either because it’s closed source or they have some private data that they don’t want to share.
He went on to say that this means that many AI studies aren’t. Scientifically rigorous because they can’t be replicated weakens peer review and makes academic fraud more likely. The scientist pointed out that others cannot verify their model’s or algorithm’s claimed performance without repeatability.
Looking Past Blockchain
Even though blockchains can make data more transparent, they aren’t the sole part of the new DeSci tech stack. Take Xie’s work as an example. He proposed zero-knowledge (ZK) proofs to solve the problem of result verification without revealing trade secrets.
He noted that the idea is still in its speculative early stages. However, thanks to projects like Polyhedra Network, the use of blockchain technology in conjunction with ZK proofs is increasing the verifiability of AI development.
FAQs
How does blockchain improve scientific research?
Blockchain enables transparent, tamper-proof records of research data, making it easier to verify results, prevent manipulation, and ensure reproducibility.
What are some notable DeSci projects?
Projects like VitaDAO, BIO Protocol, and Molecule provide decentralized funding and reward systems for scientific research, democratizing access to drug development and innovation.
Why is DeSci important for AI research?
DeSci helps combat secrecy in AI development by promoting open-source models and ensuring that AI research is transparent and replicable, reducing the risk of fraud.
What role do zero-knowledge (ZK) proofs play in DeSci?
ZK proofs allow researchers to verify findings without exposing sensitive data, enhancing trust and security while protecting proprietary information.
