Project Aiur
initiated by Iris.ai
We envision a world where the right scientific knowledge is available at our fingertips. Where all research is validated and reproducible. Where interdisciplinary connections are the norm. Where unbiased scientific information flows freely. Where research already paid for with our tax money is freely accessible to all. Where massive R&D budgets also benefit contributors to core scientific breakthroughs.
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Democratizing
Science through blockchain-enabled disintermediation.
There are a number of problems in the world of science today hampering global progress. In a highly lucrative oligopolistic industry with terrible incentive misalignments, a radical change is needed.
The only way to change this is with a grassroots movement – of researchers and scientists, librarians, scientific societies, R&D departments, universities, students, and innovators – coming together.
We need to forge an alternative to powerful existing intermediaries, create new incentive structures, build commonly owned tools to validate all research and build a common Validated Repository of human knowledge.
A combination of blockchain and artificial intelligence provides the technology framework, but as with all research, the scientist herself needs to be in the center.
That is what we are proposing with Project Aiur, and we invite you to join us.
Value growth: the Knowledge Validation Engine
The core of the Aiur economy is a community-owned artificial intelligence-based engine for Knowledge Validation – semi-automation of Peer Review, if you like.
All payments for services from the Knowledge Validation Engine will be done to the Aiur Financial Institution. At the same time, new token issuance to community members will be strictly restricted to value contributions. The Aiur Financial Institution will manage AIUR demand and supply flow, burning excess tokens accumulated via a sustained influx of capital. These mechanics govern the value growth of the community economy.
REVENUE STREAM 1:
Direct querying of the KVE
Universities, research institutes and R&D departments spend $128Bn a year on “digital enablers”, and a medium sized department can save millions yearly with tools like the KVE. These organizations have their own internal tools and processes, and will connect these directly to the Aiur API. They pay AIUR to query the engine.
REVENUE STREAM 2:
Third-party applications
A variety of future applications will rely on the Aiur KVE. This will tap into markets such as patent writing and prior art searches, hedge fund technology predictions, research funding and venture capital. 3rd party tools charge their clients and then pay AIUR to query the engine.
The AIUR Token
With clear ’proof-of-human-work’ characteristics in its design, the AIUR token is functional by nature. It’s both the only way to tap into Aiur directly and can be a voucher for significantly discounted prices for products built on top of Aiur, including Iris.ai tools.
Far from an instrument suited to short-term financial speculation, AIUR tokens are designed for natural holders. Value growth occurs over time with core usage of, and 3rd party applications on top of, the community owned Knowledge Validation Engine.
Our token sale will target raising the ETH equivalent of c. € 10,000,000, with a minimum for completion of 60% and a hard cap of 500%. If the minimum is not reached, all ETH will be returned to the original holders.
75% of the amount raised will belong to the community, and will be released subject to development milestones – to anyone who achieves them, subject to community scrutiny. The remaining 25% will be allocated to Iris.ai for the planning and initial execution of the project.
There are two phases in Project Aiur. In ‘Phase 1’ Iris.ai will be holding 50% +1 of the tokens in circulation, and after the transition to ‘Phase 2’ we will renounce all tokens outside of the allowed 2% cap, thus becoming an equal community member.
Iris.ai’s founders will not receive any direct monetary compensation, in either fiat, cryptocurrency or AIUR tokens. Iris.ai, the initiating commercial entity, sees this as a unique strategic opportunity to impact the industry and the world, and commercially to have a first mover advantage on 3rd party applications.
The challenges faced by science
Information overload
Challenge
The amount of scientific knowledge we have as a human species is unprecedented and growing. No human mind can cope with the vast volume of research being generated today. This unmanageable information overload slows down and introduces massive inefficiencies in both academic and corporate research processes, hampering global innovation.
Solution
AI-based tools to assist humans in navigating and connecting the knowledge. Iris.ai’s tools today semi-automate the literature review phase – next we need to do machine hypothesis extraction, hypothesis validation and eventually building new hypotheses.
Access barriers
Challenge
Traditional publisher business models are coming under increased scrutiny. The sustained, abnormally high relationship between economic returns yielded and business risks assumed by these legacy models has faced harsh criticism from scientific researchers, academic institutions, policy-makers and the general public alike.
Solution
Poor reproducibility
Challenge
Substandard reproducibility of published research studies adds to pain points suffered by students, researchers and R&D departments across sectors. And when considered in combination with other problems here, reproducibility deficits make it fundamentally hard to build new knowledge on top of old results.
Solution
Built-in biases
Challenge
Existing tools focused on scientific search have been built with a common keyword and citation-based architecture that incorporates serious issues with learning-over-time and the identification and address of biases including negligence of under-cited research.
Solution
Misaligned incentives
Challenge
Research professionals are currently forced to deliver, publish and review on tight deadlines, with little to no accountability and reward for authors and reviewers, creating perverse incentives towards exaggerating facts and omitting assumptions and constraints.
Solution
Credits
This project has received funding from the European Union’s Horizon 2020 research and innovation programme within the framework of the LEDGER Project funded under grant agreement No825268
