Artificial intelligence (AI), data and blockchain: what does the future hold?

By Derliz Machado25 March, 2022 in Technology

Rostro de mujer en formato digital.

Key facts
  • By 2025, the artificial intelligence market is expected to exceed USD 6 trillion.
  • Bitcoin technology has been instrumental in the development of artificial intelligence.

Content sponsored by PlatON Network

When the popularization of computers began 70 years ago, we would never have imagined the level of digitization the world is experiencing today. With the advent of the metaverse and the development of artificial intelligence (AI), it is becoming increasingly difficult to distinguish between the physical and the digital.

On the other hand, data has become a highly valuable resource in the digital age. Generated from recording the characteristics and behaviors of observed objects, data can take many forms, including words, numbers, graphs, audio and video.  

According to Statista, it is estimated that connected devices worldwide will total 30.9 billion units by 2025. These devices generate massive data. IDC predicts that, by the same year, the global data circle will expand to 163ZB (1 trillion GB), which is ten times the 16.1ZB of data generated in 2016.

How can we harness the value of data at such a scale? Artificial intelligence may be the answer.

60 years ago Artificial Intelligence appears on the map

Gorithms based on big data and high computing power have achieved breakthroughs in several fields During a six-month seminar at Dartmouth College in the summer of 1956, a group of young scientists, including Minsky, coined the term “artificial intelligence.” Since then, machine learning algorithms based on big data and great computing power have made breakthroughs in several fields of artificial intelligence.x`

Today, most people are familiar with artificial intelligence. This technology has been integrated into our daily lives. From online shopping to industrial production, we see the convenience and progress that artificial intelligence generates.

Deloitte’s 2019 Global Artificial Intelligence Development White Paper predicted that the global AI market is expected to exceed $6 trillion by 2025, with a compound growth rate of 30% from 2017 to 2025. A research report published by PwC suggests that global GDP will be 14% higher by 2030 as a result of AI adoption, contributing an additional $15.7 trillion to the world economy.

As we move towards the fourth industrial revolution, the technological revolution, the power of this technology has become increasingly evident.

Data privacy: an obstacle to artificial intelligence

For artificial intelligence to become the core technology, three elements are indispensable: data, algorithms and computational power.

The widespread adoption of the mobile Internet has contributed to the incredible growth of global data. This data provides the “production material” for AI. While we cannot deny the breakthrough of artificial intelligence, there are still hurdles to overcome for mass adoption.

The first challenge is the pressure of data governance and privacy. In 2018, the European Union introduced the General Data Protection Regulation (GDPR). In 2021, China’s Data Security Law and Personal Information Protection Law came into force. All of these stricter privacy and personal data regulations seek to effectively prevent data misuse.

In addition, data privacy pressures are also coming from within. Companies face a major dilemma: while sharing data and interacting with other companies clearly improves the performance of artificial intelligence algorithms, they must also ensure that their data is not disclosed as such.

Additionally, developing artificial intelligence is expensive. Some institutions believe that the cost of training next-generation artificial intelligence models could increase by a factor of 100 by 2025, from around $1 million today to more than $100 million.

Faced with challenges such as data privacy, high costs and centralization of technologies, how can artificial intelligence overcome these obstacles and move forward? Research and application of certain cutting-edge technologies have paved the way.

Blockchain comes to the rescue

Ingenious data fabric has catalyzed the interplay between blockchain, privacy-preserving computing and AI in different ways. When these technologies are combined, data processing reaches a new level.

For example, Blockchain consensus algorithms help to complete subject collaboration tasks in AI systems. At the same time, its technical features enable data valorization. In that way, it can incentivize the addition of a wider range of data, algorithms and computing power to create more efficient AI models.

There are a variety of platforms developed to preserve privacy. AntChain Morse MPC and Baidu MesaTEE are an example. However, most of these platforms provide their services to other companies. The reason is simple: inter-enterprise data business is the most fundamental business need, which solves the basic contradiction between companies for data sharing, interaction and AI algorithm improvement.

Enterprise services are just the beginning of what AI can achieve so far. In the foreseeable future, data ownership will eventually be returned to people.

Recently, a product launched by a company that focuses on cutting-edge technology has shown users and the market a new direction in the application of Universal Artificial Intelligence (UAI).

A solution to the data privacy problem

A network innovatively designed to integrate the three elements of artificial intelligence (computing power, algorithm and data) is PlatON Privacy-preserving Computation Network (tentative name). It is a decentralized computing infrastructure network for data sharing and privacy preservation.

PlatON is not only useful for companies, but also for individuals. Individuals and institutions can aggregate data and participate in computational tasks published on the platform, as well as provide computing power on the platform to complete the computational tasks of others. Individual AI developers can develop desired AI algorithms and the computational tasks completed with their algorithms will also generate profits for them.

This innovative approach enables effective ownership identification, pricing and data protection, as well as privacy-preserving asset creation.

The network has multiple data privacy preservation measures. Secure collaborative computing, zeroknowledge proof, homomorphic encryption, verifiable computing, federal learning and other cryptographic technologies protect data well.

In addition, computational outputs, such as trained AI models, are also protected against leakage. The products can run smart contracts efficiently and popular deep learning frameworks smoothly, ensuring versatility, compatibility and high availability.

PlatON is going through closed beta testing, and such a large and complex platform will inevitably face challenges. How to price data by multiple parties? How to accurately capture and apply data as it flows between multiple parties? How to attract AI developers to provide basic algorithms?

Clearly, this is an unprecedented amount of data. The integration and application of new technologies takes time, but PlatON has already taken a step forward in data commercialization.

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