Blockchain

Blockchain-based Machine Learning Marketplaces

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Despite the fact that large tech is growing due to the abundance of data and is reporting higher-than-expected profits, mid-sized businesses and startups lack resources for AI-based computing capacity.

Big data access is monopolized by the American IT giant and China’s BAT (Baidu, Alibaba, and Tencent) trinity. 

According to experts, access to cloud computing for ML activities is heavily concentrated by a small group of companies. And, as the hype around machine learning and deep learning increases by the day, data — the critical need that underpins AI systems — is absent from the picture.

In this post, we will also look at the best companies that use Artificial Intelligence- AI and blockchain for their technological goods.

At the present, blockchain and artificial intelligence (AI) are two of the most widely discussed cutting-edge technologies, having an effect across a variety of business sectors. While the technologies are often used separately, several companies are merging the two to create strong commercial solutions.

Startups are developing innovative solutions that integrate blockchain and AI, ranging from data markets to decentralized machine learning models and crowdsourcing. The primary argument is that by combining the two technologies, data may be stored, retrieved, and processed without the need for a centralized server/database, with a focus on traceability and data immutability to achieve trust.

What exactly is the AI Marketplace?

The AI marketplace, in our vision, should enable two high-level operations: 1) a developer should be able to sell their pre-trained AI models in the marketplace, and 2) a customer should be able to request a custom AI model that meets their specific needs, and the marketplace should be able to match such customers with developers who could build such a model.

In addition, the marketplace should enable data exchange (selling and purchasing) and involvement of marketplace players in, for example, federated learning in support of the aforementioned activities when required.

Machine learning models which are trained on blockchain-based market data can build the world’s most powerful artificial intelligence. They are a combination of  two potent primitives: 

-Private machine learning: This allows training on sensitive private data without revealing it.

-Blockchain-based incentives: They allow these systems to attract the best data and models to make them smarter. As a result, open marketplaces exist in which anybody may sell off their data while keeping it private, and developers can make use of incentives to recruit the appropriate information for their algorithms.

It is difficult to construct these systems, and the required building parts are still under development. However, simpler initial versions seem to be becoming possible. These marketplaces, in my opinion, will head us out of the present period of Web 2.0 data monopolies and into a Web 3.0 era of free competition for data and algorithms, both of which will be directly monetized.

Origin

Numerai is a hedge fund that makes encrypted market data available to any data scientist interested in stock market modeling. Numerai compiles the best model submissions into a “metamodel,” trades that metamodel, and pays data scientists whose models outperform the competition.

It sounded like a good idea to have data scientists compete. Could you create a fully decentralized version of this system that could be applied to any problem? Probably, there is a possibility of that.

Construction

Consider creating a fully decentralized system for exchanging bitcoins on decentralized exchanges as an example. This is one of many structures that may be used.

Data providers stake data and make it available to modelers.

Building a model Modelers choose the data to be used and then construct models. Training is done using a secure computing approach that allows models to be trained without revealing the underlying data.

Developing a Metamodel

A method that takes into account the characteristics of each model is used to create a metamodel.

It is not essential to build a metamodel; you may develop models that are used without incorporating them into a metamodel.

Utilizing the Metamodel

The metamodel is used by smart contracts to trade programmatically on-chain through decentralized exchange systems.

Gains and losses are allocated

When a specific period of time has elapsed, trading results in a profit or loss. This advantage or disadvantage is divided among metamodel contributors based on how much smarter they made it. Models that made poor contributions will have some or all of their staked money taken away. After then, the models return to their data sources and do identical distributions/stake trimming.

Calculation that is verifiable The computation for each step may be centralized but verifiable and challengeable using a verification game like Truebit or decentralized via secure multiparty computing.

Hosting

Data and models are saved on IPFS or with nodes in a secure multiparty computing network since on-chain storage would be prohibitively expensive.

Challenges

For starters, secure computing methods are now extremely slow, and machine learning is already computationally expensive. On the other hand, there is a growing interest in safe computing methods, and performance is increasing. I’ve seen new techniques with significant performance improvements to HE, MPC, and ZKPs in the last 6 months.

It is difficult to quantify the value that a particular piece of data or model adds to the metamodel.

Cleaning and formatting crowdsourced data is challenging. To solve this, we’re likely to see the emergence of a combination of tools, standards, and small businesses.

Finally, and ironically, the business model for creating a generalized design of this kind of system is less apparent than the economic strategy for creating a single instance of it. This seems to be true for many emerging crypto primitives, including curation markets.

Companies that are merging blockchain and artificial intelligence

Finalize

Finalize is a software platform that combines blockchain and machine learning to create apps that seek to improve civil infrastructure. The company’s solutions simplify and accelerate workflow, management, and verification processes in the construction sector, and its technology also interacts with wearables to satisfy safety standards.

Blackbox AI

Blackbox AI is a company that creates artificial intelligence solutions for new technologies. Engineers at the business provide bespoke information architecture that supports anything from machine learning and natural language processing to blockchain technologies. Aside from building blockchain infrastructure, the business also provides consulting services that concentrate on how their products may leverage the potential of a blockchain.

Core Scientific

Core Scientific combines customized blockchain and AI infrastructure with existing business networks, updating a company’s infrastructure, servers, and software that handles everything from supply chain monitoring to real-time data reporting.

Bext360 

Bext360  has used mobile applications, bots, and blockchain to make the coffee supply chain more transparent and ethical from bean to cup. The company’s AI assesses the quality of coffee beans and forecasts weather and growth trends, while the blockchain records the farm where the beans were grown and the precise specs of a coffee shipment in perpetuity. It also functions as a payment ledger, ensuring that all parties are paid on time and equitably.

BurstIQ 

BurstIQ developed a “Health Wallet,” which integrates AI, blockchain, and big data to handle a patient’s data holistically. The Burst IQ wallet grants access to a patient’s health data and wellness programs to the patient’s team of healthcare providers. Healthcare practitioners may then decide whether to purchase, sell, or exchange patient data for various scientific projects or to learn more about a particular illness. However, the blockchain allows patients to keep their personal identifying information secret while sharing just aggregate health data.

Conclusion

You are now aware of the potentials of this movement if you have read our articles on the usage of blockchain and AI. These are the most advanced technologies in the contemporary world, and their integration has the potential to enhance them both. If you’re interested in creating decentralized artificial intelligence, our talented engineers would happily assist you.

About the author: Rachita Nayar

Rachita Nayar is a professional writer. She has a penchant for writing and is involved in many projects throughout the world. Currently, she works with a blockchain development company in the USA that allowed her to explore the domain and hone her skills further by learning about blockchain and spreading the knowledge.

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