Mind AI is a venture-backed AI company that has more than a decade of research and development under its belt. This project is part of the third wave of AI, which has a focus on reaching human-level reasoning via crowdsourced artificial intelligence.
The ecosystem and artificial intelligence engine in Mind AI were developed to create a new and an innovative approach to AI. Previous AI architectures rely on large quantities of data, supercomputers, and parallel processing. By contrast, Mind AI has a core reasoning engine that is based on new data structures, which are internationally patented and known as canonicals.
How Did Mind AI Begin?
- 1 How Did Mind AI Begin?
- 2 Why Is a New Type of AI System Necessary?
- 3 How Does Mind AI Resolve These Issues?
- 4 What Is the Mind AI Reasoning Engine?
- 5 How Else Does Mind AI Stand Out from Other AI Solutions?
- 6 Why Does Mind AI Use the Blockchain?
- 7 What Are Some Use Cases for Mind AI?
- 8 Conclusion
- 9 Useful Links
The co-founders of Mind AI are CEO Paul Lee and Joshua Hong, who entered the IBM Watson ecosystem while working on their telehealth startup. They soon realized that the artificial intelligence used by the system was not yet robust enough for general applications. This spurred them onto creating an in-house AI system that would help patients and doctors. The two met John Doe, who is an engineer who was developing a new AI approach. The three decided to make an AI engine by itself, so it could be used in telehealth and other applications. In 2017, they patented their novel AI data structures and incorporated their Mind AI project.
Why Is a New Type of AI System Necessary?
The team behind Mind AI feels that a new type of artificial intelligence is needed for many reasons. This includes the fact that the currently common AI based on machine learning is computationally wasteful since it requires significant data and calculations. The team also dislikes that the common deep learning systems follow data analysis and quantitative narratives instead of human thought principles, such as reasoning. Additionally, they dislike that AI tends to be inaccessible, with only high-end tech ventures and governments using it.
Overall, computationally intense artificial intelligence is expensive and wasteful. Mind AI also feels that these systems are inelegant since “intelligent” beings should not require viewing tens of thousands of images to correctly learn to recognize an object. These AI models also tend to be invasive because of the vast amount of data needed, as well as inaccessible due to some of the previously mentioned factors.
How Does Mind AI Resolve These Issues?
To overcome those hurdles, Mind AI developed an artificial reasoning engine that uses natural language as a way to reason in the same manner that humans do. Mind AI is also domain agnostic, meaning it can work in any industry; crowdsourced or collaboratively built; and not owned by a single company. This last point comes from the fact that Mind AI will operate as a DAO. Mind AI uses a proprietary canonical data structure that lets contributors feed system ontologies that are relevant to any and all industries and domains.
What Is the Mind AI Reasoning Engine?
The basis of the Mind AI reasoning engine functionality is the ontologies that contributors input. At the moment, there is nothing like this reasoning engine on the market since it lets the AI learn and think in the same way as humans.
The reasoning engine includes three types of human reasoning. Inductive reasoning covers specific to general generalizations, while deductive reasoning covers general to specific generalizations and abductive reasoning creates logical assumptions. Thanks to these types of reasoning and the ontologies fed by the community, the Mind AI can reason like humans without requiring massive quantities of data.
How Else Does Mind AI Stand Out from Other AI Solutions?
Mind AI is unique from other artificial intelligence solutions because it creates a new paradigm that lets the core engine work with incomplete information and uncertainty. It can also use natural language, parsing it then converting it into the canonicals or data structures. Those canonicals allow for the full integration of each type of reasoning into every canonical. Mind AI also becomes smarter as general knowledge is input in natural language.
Mind AI will accumulate its ontologies and eventually reach critical mass. At this point, the system can learn how it will learn. This means it can complete its own research, including hypothesizing and testing those hypotheses.
Why Does Mind AI Use the Blockchain?
The team behind Mind AI feels that it is necessary for this project to use the blockchain to allow for immutability, a global reward system, transparency, and democracy. Immutability is crucial for the ontologies. The team expects contributors to submit low-quality and inaccurate ontologies but recognizes those as useful due to Mind AI’s ability to learn via contextualization. In other words, outdated ontologies serve as a unique context instead of becoming obsolete, and the immutability of the blockchain lets Mind AI access the history in a way that is unaltered.
The global reward system is key since a tokenized economy delivers transparent, inexpensive, and quick rewards. Since the AI engine requires crowdsourcing for ontologies, this tokenized economy helps incentivize contributions from those of all backgrounds, specialties, and geographical locations.
The transparency from the use of blockchain is crucial for a fair reward distribution and for community voting. This latter use becomes particularly important when voting on ethical upper ontologies, triggering the kill switch, or prioritizing ontological bounties. The blockchain also makes it possible for the community to see what the artificial intelligence has already learned and allows for data layering.
What Are Some Use Cases for Mind AI?
Mind AI’s blockchain layer of transparency allows users to backtrack on how the AI reached a certain conclusion and see exactly how the decision was reached. This is achieved using two components – the core reasoning engine and ontologies. The core reasoning engine is their own patented technology which has been in development for over a decade. Ontologies are the crowd-sourced rules which the engine uses to to perform its process.
Mind AI aims to democratize the power of AI by storing the database of Ontologies on the blockchain which is owned by it’s community of contributors, know as “Ontologists”. Ontologists will have a say in what applications are created by Mind AI by providing Ontologies in certain subject areas. They will also have the power to prevent malicious entities from using the Ontologies and Mind AI.
The use of Blockchain ensures everything is immutable and transparent and will prevent tampering with the Database and altering the AI’s knowledge.
Mind AI is inherently “industry agnostic” – so any industry can eventually run its AI depending on user participation/contribution of ontologies. Some industries which would benefit are healthcare ( providing diagnosis via AI), areas of Law, Education and many more.
One use case for Mind AI is universal holistic education. The ecosystem could gather the curriculum from one country and share it with those in other countries that do not have access to the same resources. This way, teachers and students can immediately access the latest, most effective curricula and do so in their native language.
Mind AI is language-agnostic, so all updates are automatic across languages. Mind AI can even be used to create customized curricula for each individual as a way to maximize their learning.
Mind AI can also help maximize social impacts. There are applications for policymakers, philanthropists, and investors. The use cases are extensive since Mind AI will make artificial intelligence more accessible to everyone by overcoming the limitations of deep-learning AI.
Mind AI is a new AI engine and ecosystem designed to be part of the third wave of artificial intelligence. It takes a new approach to artificial intelligence that is not as wasteful in terms of resource consumption and has applications across various industries.