Artificial Intelligence — A case for Epik Protocol

7 min readAug 19, 2021

The methods by which humans acquire knowledge has been dynamic with increasing development that comes with different eras. Ranging from writings on the walls to tree backs to papers and now the internet. Notwithstanding, it has not been easy for machines to understand the traditional methodologies of human knowledge such as texts , pictures and videos and thanks to Google this problem has being solved via the introduction of the knowledge graph technology which has given rise to the progress in Artificial Intelligence today. The building of a standard KG that can be put into use by AI appropriately is faced with a good number of problems which includes a long span in converting all knowledge into right structures, the great amount of work needed to carry out tasks and the risk of corrupted data under a fixed control system.Epik Protocol supports creating a flexible KG using block chain technology to widen the scopes of contemporary A.I and taking advantage of the resources in terms of flexible storage technology whose source is traced to Filecoin

AI in modern times could place identification but defects arise when it comes to comprehension.

Humans can recognise an object from it’s image they have seen before but A.I is dependent on on the data it is given to be able to identify the object.

The development of A.I is classified into four stages which are; computational intelligence, perceptual intelligence, cognitive intelligence and conscious intelligence.

Hitherto, applaudable growth had been identified in AI’s perceptual intelligence, since face and voice recognition is not so far fetched via our mobile devices.

To achieve the cognitive intelligence stage , which will allow AI to comprehend and break down information into phases , alot of research is needed to reaching this milestone. This knowledge would be different of course from the conventional knowledge aquisition by humans instead it would be in a well arranged pattern the machine would understand.

The introduction of the knowledge graph technology in 2012 by Google which utilizes well patterned methods, integrations and other strings to represent human knowledge

Challenges faced in creating a high quality KG

Managing identity

In its simplest form, the challenge is in assigning a unique normalized identity and a type to an utterance or a mention of an entity. Basically. the challenge comes in play when placing a specified imagery to a voice or mention of an individual.

Type membership

In current times, the knowledge graph systems gives room for entity to to have various types and a unique type could matter with regards the scenario it is in play. Arnold Schwarzenegger is a governor and an actor but he is more known as an actor.

Managing changing knowledge.

A functional entity joined system needs to develop from the grassroot with all the support necessary on it’s dynamic data. A company merging with another company might bring about complications in rights to owning brand names.

EpiK Protocol

EpiK Protocol plans on creating a flexible KG with the aid of blockchain technology. This builds a reliable interface where all participants are justly rewarded while keeping human knowledge at a considerable fixed rate and widening the scope of modern AI and will bring into existence cognitive intelligence.

Trusted Storage

EpiK Protocol took advantage of Filecoin’s storage technology to acquire trusted storage. With Filecoin’s IPFS, a pair to pair network for storing and sharing data in a distributed system, keeping of copied data blocks is will come to a huge decline. Though, IPFS comes short in the area of preventing fraud. A good advantage of Filecoin is that it grants foreign parties the liberty to store and access data under discretion.

Trusted Incentives

Tokenization in EpiK Protocol’s Token Economy builds a contemporary way for the transfer of value based on blockchain technology.

The movement of EpiK Protocol’s native token , EPK, serves as the grassroot of Epik’s Token Economy. Whilst other interests are at hand, the protocol builds an unalloyed relationship for trusted partners of the KG network to collaboratively build a KG .

Trusted Governance

Decentralized Autonomous Organization Technology (DAO), works like the Filecoin but it includes immutability. Issues involving administrations are put in order through DAO where core participants decide on outcomes and through this method, the normal manual ways of carrying out processes like counting and monitoring are discarded. With regards to this, EpiK Protocol classifies this core participants into three which are; Global Group, the Storage Group and the Knowledge Group.

The Global Group takes responsibility for the administration of global effects which are put in place to standby the interest of all EPK token holders. By this reason, the core participants level of collaboration with the Global Group is determined by the amount of EPK locked. The variation in the amount of stored data, back up size and similar requirements has made it necessary for this elements to be well adjusted and this elements are the responsibilities of the Storage Group in as much it is in the miner’s interest. The Knowledge Group takes care of the elements in controlling the Domain Experts. They have administrative powers and commensurates the amount of data they have put in post log information.

The importance of efforts from micro-collaboration can not be overlooked hence bin-log files are processed during the collaboration and saved in Filecoin Layer 2 network. Anyone can build on these bin-log files from various domains into vast knowledge graph files at an arbitrary time and make a transfer of the files to Filecoin Layer 1 network for fixed preservation. The Filecoin Layer 1 storage network is the bed rock of EpiK’s technical architecture.

Another Technical Architectural feature that cannot be overlooked are components. Built on top of underlying storages, these components are made of Consensus Mechanism , Virtual Mechanism and Ledger on Chain. Consensus Mechanism majorly strengthens the node storage capabilities. Ethereum Virtual machine goes hand in hand with EpiK Protocol hence bring about of smooth running of applications in the Ethereum community.

Furthermore, EpiK Protocol is capable of running a smart contract. With the collaboration with Ethereum Virtual Machine whose contract model gives room for the transfer of administration and financial services from the Ethereum Ecosystem to the KG. This collaboration also brings about monitoring actions of participants and recording them and if a set rule is broken , the corresponding user is brought to book and justly penalised.

Finally, EpiK Protocol encourages the open source knowledge movement. Where one can become a domain expert as far as they obey set down rules and they put in relevant data into the knowledge graph and duly rewarded.Open source knowledge is believed to be able to refine the collaboration between AI and humans, AI and AI.

Knowledge Group and Storage group are main compositions of the EpiK Protocol ecosystem. A collaboration between Filecoin and EpiK Protocol brings about the creation of EPK storage devices , storage tools. cloud computing , cloud node machines e.t.c and all of these contributes to strengthens Filecoin mining ecosystem.

In managing knowledge, EpiK Protocol takes charge of the entire process ranging from conception of knowledge to it’s data to when it would be extracted and applied. The knowledge Graph data is of great importance and popular demand. It is widely sought for by experts cutting across various fields and disciplines.

Knowledge graphs data are majorly generated and outsourced by Domain Experts. As development in technology keeps advancing so does the quality and quantities of Epik’s knowledge graph data becoming crucial applications for various data analysis platforms.

In applying cognitive intelligence , on chain knowledge graph serves as the basic step.

It has proven to be efficient in driving solutions and placing AI products as an emerging substitute for commercial products and well structured data processes which bring about improvements in existing AI products.

Knowledge Graph Applications

Some applications of Knowledge Graphs ranges from healthcare, education, public security, smart construction, smart investment advisor e.t.c

The healthcare industry has been filled with large amount of data and complex sources and having a finite number of professionalism and structure which makes it difficult for data merging. With millions of medical data generated , KGs play a vital role for both patients and medical practitioners. This big data has called for the automation of medical resources , widening of medical knowledge and building up of historical data.

Some of these applications include; Smart Medical Guidance (SMG), Intelligent Medical Auxillary Diagnosis for majorly doctors and nurses and Question Answering System (QA) which aids market expansion especially in pharma industry.

Data skewed in different government departments are brought together and correlated by KGs. The need for the refinement of large stock governance and bringing about development in modern cities cannot be over emphasized. The data sources grows to other part of the public into traffic, video, environment and official data. KGs are trusted to delivering awesome and efficient applications to these effects some of widely used are smart urban utilization applications , data asset management interfaces used for data quantity and quality control and merging and semantic references for knowledge processing and application development

Some of the key components of the manufacturing industry are heterogeneous knowledge, big data. KG technology abet the industry in modelling basic manufacturing data to structuring different data of the whole manufacturing process. This includes from CAD, CAM designs to NC modules to 3D printing , a very complex process it is. Basic data such as heavy duty equipments , workshop tools, defects and labor are well modelled and structured with KG technology and it further extracts the relationship between this data and thereby building a manufacturing technology platform. Example of these applications include;

Making provision for for decision making for new market strategies, Dynamically manage productional floor knowledge for manufacturing and providing guidance and management of staff experience.

Let’s work together to give AI Cognitive abilities.