Updated: Feb 8
Why blockchain? A very valid and relevant question that is often posed to those pioneers attempting to add a currently existing technology to the immutable record of data secured by cryptographic principles known as “The Blockchain”.
To help answer a question such as this it is helpful to look at it from a number of angles. On this occasion we will firstly look to the views of a representative of the GNY.io team, and secondly we discover the views of a representative of the Microsoft team working on researching AI and Machine Learning.
Cosmas Wong, CEO of GNY.io, the first company to decentralize advanced machine learning software across the nodes of a blockchain.
“The reason why we wanted to put our already existing machine learning capabilities on the blockchain was because fundamentally we believe that data is going to become decentralized no matter what. We’re going to have a situation soon where people are no longer going to trust huge pools of data being contained at one place because of hacking probabilities and because of concerns over data protection policies. So if we believe in data becoming decentralized then individuals and businesses are going to need a way to communicate with all of these different decentralized parts; learn from them, make predictions and suggestion based on this data, and flag inconsistencies where they occur.”
“The other reason obviously is why not? Of course, you could be a contrarian and say we don’t need Airbnb because we already have hotels, or Uber because we already use taxis. What happens though when we decentralize our machine learning platform on the blockchain so anyone could access it? The thing is that I think it required a certain level of “you know we’re just gonna do this, right, and see what happens when we take an already working and patented project and transfer it into the brave new world of blockchain”.
The big takeaways so far from this are numerous, but four main ones stand out.
- By utilizing a decentralized blockchain it leverages this power to securely record and maintain consistent data.
- This encourages secure data sharing, so data does not leave the security of the chain.
- GNY’s Machine Learning can respond In real-time and flag issues.
- GNY Is learning constantly, and so keeping pace with buyers in its retail prediction model, fraudsters in its fraud prediction models, keywords in its keyword prediction model etc.
This constant learning and responding in real-time is aided by GNY leveraging data sets from the hits and misses of past communications to build a predictive model for each individual item and each action.
In an article published on Investing.com titled “Bringing Machine Learning Closer to Clients, Will Blockchain Eliminate Barriers?”, Cosmas spoke to Andrey Sergeenkov about the further objectives for their Machine learning on a blockchain project. He said “We are letting a company or a group or consortium of companies that normally share data, to build a side chain so that the necessary data is shared and used securely within that group, and all processing is done on-chain. Nothing leaves the security of the chain. This is extremely important for us as it promotes the use of the technology and “democratizes” it so it can be used by not just the very large corporations.”
Justin D. Harris, Senior Software Development Engineer working in the field of research for the Microsoft Corporation.
Microsoft Corporation, the American multinational technology company is known for developing, manufacturing, licensing, and supporting computer software, consumer electronics, personal computers etc. On Jan 13, 2017 in a bid to help Microsoft advance their strategy to democratize AI and to make it accessible and valuable to everyone they acquired Maluuba, a Montreal-based company with one of the world’s most impressive deep learning research labs for natural language understanding. Justin D. Harris was one of the initial employees at Maluuba. When Maluuba was acquired, Justin stayed on as a software developer working alongside researchers.
On July 12, 2019, in the Artificial intelligence section of the Microsoft Research Blog, in an article titled “Leveraging blockchain to make machine learning models more accessible”, Justin wrote the following in reply to the question “Why blockchain?”
“Significant advances are being made in artificial intelligence, but accessing and taking advantage of the machine learning systems making these developments possible can be challenging, especially for those with limited resources. These systems tend to be highly centralized, their predictions are often sold on a per-query basis, and the datasets required to train them are generally proprietary and expensive to create on their own. Additionally, published models run the risk of becoming outdated if new data isn’t regularly provided to retrain them.”
“We envision a slightly different paradigm, one in which people will be able to easily and cost-effectively run machine learning models with technology they already have, such as browsers and apps on their phones and other devices. In
the spirit of democratizing AI, we’re introducing Decentralized & Collaborative AI on Blockchain.”
“Through this new framework, participants can collaboratively and continually train and maintain models, as well as build datasets, on public blockchains, where models are generally free to use for evaluating predictions. The framework is ideal for AI-assisted scenarios people encounter daily, such as interacting with personal assistants, playing games, or using recommender systems.”
“Leveraging blockchain technology allows us to do two things that are integral to the success of the framework: offer participants a level of trust and security and reliably execute an incentive-based system to encourage participants to contribute data that will help improve a model’s performance.”
“With current web services, even if code is open source, people can’t be 100 percent sure of what they’re interacting with, and running the models generally requires specialized cloud services. In our solution, we put these public models into smart contracts, code on a blockchain that helps ensure the specifications of agreed upon terms are upheld. In our framework, models can be updated on-chain, meaning within the blockchain environment, for a small transaction fee or used for inference off-chain, locally on the individual’s device, with no transaction costs.”
“Smart contracts are unmodifiable and evaluated by many machines, helping to ensure the model does what it specifies it will do. The immutable nature and permanent record of smart contracts also allows us to reliably compute and deliver rewards for good data contributions. Trust is important when processing payments, especially in a system like ours that seeks to encourage positive participation via incentives (more to come on that later). Additionally, blockchains such as Ethereum have thousands of decentralized machines all over the world, making it less likely a smart contract will become completely unavailable or taken offline.”
You can read the full piece by Justin on the Microsoft Research Blog here
Taking into consideration both the GNY.io and Microsoft viewpoints on their reasons as to “Why Blockchain”, I believe the common ground that we find can be summed up in the following five words… Decentralized, Secure, Permanent, Democratizing, and Affordable. To finish up we would like to add two more of our own…. Positive, and Progressive.