The Beauty of the Matrix AI Network
It has been silent around this project since COVID-19 shook the world last year with seemingly few updates, until recently. Despite all this, the Matrix AI team have been working diligently behind the scenes and have once again started to garner worldwide attention. We believe that we should put more effort into introducing the Matrix AI Network to the general public in a more simplified form. For many people, whether they have a general interest or a stake, it can sometimes be difficult to grasp the vision of Matrix AI as a whole. In addition, it may take a certain technical understanding as well as patience to read and understand the Matrix White and Green Papers — The first place you should refer to for a complete technical overview. This article aims to give an overview so that a wider audience can understand how the Matrix AI Network is going to change the whole game of AI. So let’s begin…
How Artificial Intelligence Works
First, let’s understand the basic mechanisms of AI.
Artificial Intelligence is built on 3 pillars:
- Computing power
To solve these basic mechanisms now requires a high calibre scientist/expert in the computing discipline and AI fields. The better he or she understands the task requirements (input), the better the algorithm (AI model) that will be created (output). This algorithm now needs quality data to work from. If the data is of low quality, the result will be expendable. Conversely, the more quality data the algorithm is fed with, the more accurate and usable the resulting data will be. To make the algorithm work effectively now requires a tremendous amount of computing power. Simple problems could be solved on a home computer however, complex problems which require immense computational power need high-performance, costly machines in order to achieve the desired result.
AI is still in it’s Infancy
The evolution of AI still stumbles today. AI is highly centralized. Matrix AI Network’s own estimates show that over 50% of AI companies are limited by a chronic lack of computing power. These companies would have to invest huge sums of money on expensive hardware if they want to retain complete control of their own data and algorithms, avoiding the possibility of third party exploitation, plagiarism and so on. Unfortunately, the big players such as Google, Alibaba, Microsoft etc (I will refer to them as the ‘Biggies’ in this article) have the financial clout to monopolize this area.
The same applies for data. Endless data is created every day but there is no effective, safe way to share it without the aid of a central entity, so ‘data islands’ are formed. Each entity is keeping data for their own narrative and financial gain. The ‘Biggies’ are in possession of huge amounts of personal data. They collect it each time we use applications, software etc, every second of every day. But this data is rather one-dimensional. Amazon collects Amazon-related client data, Google collects Google-related client data and so on. But it’s ‘multi-dimensional’ data which will support truly transformative growth in AI. How can multi-dimensional data be created and shared? We’ll find the solution in the next chapter…
Let’s continue talking about the problems of the current situation of AI.
A manufacturing company is holding their own data, employing their own AI scientists and having complete control of their own computing power. The amount of data is limited, the scientists might not create the most effective AI models and the total computing power is dictated by the financial limitations of the company. In this case, AI is comparatively very expensive and ineffective. Small-sized companies or individuals are not financially equipped and as a result, it is unrealistic for them to harness the use of high performance computational power to achieve their AI goals, which leaves them no choice but to use a third party. In most cases, this would be one of the ‘Biggies’ AI services.
At this point we discover the next big problem of AI: privacy and protection. Using ‘Biggies’ AI services means you would have to expose your proprietary data and AI models to their clouds and services, but they do not provide a risk free mechanism to protect this data. How do you truly know that the data you provided is not plagiarised, stolen, or leaked? There is no guarantee that your data is 100% safe and secure on their platform.
Imagine you are a top scientist and you’ve created a high quality algorithm to solve a certain technical issue. How is it possible that you can achieve an adequate and deserving financial reward for your efforts without having to expose your knowledge and data to a third party or an AI cloud provider?
Imagine you are an app development company. You would like to integrate existing AI models and existing data into your products, assuming that you don’t have an AI department and you need exposure to external computing power at minimal cost. How can you achieve this and be sure of data integrity?
Imagine you are a coal mining company. You want to use fully automated robots without human interference. Undoubtedly, this would require you to spend millions of dollars on R&D every year. Could there be another option?
Matrix AI’s Solution: AI on the Blockchain
The Matrix AI network combines the two upcoming key technologies, AI and Blockchain, to build a perfect symbiosis:
Matrix AI have created the Matrix 1.0 platform, an AI-optimised public blockchain platform using AI technology to overcome four fundamental problems in blockchain:
- Low transaction speeds
- Lack of security
- Difficulty of use
- Wasted resources
On top of this platform, Matrix AI is building the Matrix 2.0 ecosystem based on the three pillars of AI; Data, computing power and AI models. All participating nodes will form a worldwide supercomputing network providing:
- Aggregated computing power
- Fully encrypted secure data management
- Safe and secure data sharing
- Access to on-chain data, AI models and intelligent applications
Mining and validator nodes, security and monitoring nodes, AI calculating nodes, data/model storing nodes, AI scientists, data holders, app developers and finally AI clients will form an entire AI ecosystem in which each entity can participate, whether an individual, a company, a society, a platform, a government or an institution. Matrix AI is going to break down the boundaries of AI to democratize it. AI scientists and data owners will choose the Matrix AI Network as their operating platform because the Matrix blockchain will secure their ownership at significantly less cost and thus, get paid fairly. Companies working in the same fields will contribute their data to create a greater pool of data and thus profit from each other, creating “multidimensional data”, data that contains the diversity needed to let AI models solve complex problems the best way possible. High quality AI models will compete against each other and will finally be available to all participants. Individual app developers as well as big institutions will equally integrate AI to their products and services. Companies will not need bespoke AI algorithms, but instead they can save a lot of time and resources using existing AI models and data. AI will grow and become very competitive whilst at the same time create a social ecosystem of sharing value and fairness. The gas of the entire Matrix AI ecosystem will be the MAN token. Each contribution will be paid in MAN, much like ETH in the Ethereum ecosystem.
In the end
It would take a grand series of articles to describe how Matrix AI is going to realize its technological AI goals. This article, as mentioned before, is just a glimpse into the future of the Matrix AI network. Further technical articles will follow soon.