PoV-RO : How OpenAI can save its business (and its proprietary data), and how you can too! Part 2 : The Finale!?
Checkpoints. Checkpoints. Checkpoints. It is all about Checkpoints!
The world is changing, generative art, text and ideas are slowly taking over and not in a good way, so what is the solution?
Before we introduce our solution we must first remind our readers on how we arrived here :
- We first introduced how blockchain and vertical farming can work hand-in-hand in order to give the highest level of transparency within the food quality we all consume! (https://medium.com/@elever-group/personal-vertical-farms-blockchain-technology-will-change-how-we-grow-consume-and-sell-4cffdbd21eaf)
- We then dove into the basics of how Protocols such as Proof of Growth & Proof of Production can aid communities to become sustainable and decentralised in order to self govern the people’s interest who are contributing to their society! (https://medium.com/@elever-group/pop-pog-how-the-mixture-of-dag-nfts-will-boost-the-fourth-industrial-revolution-and-save-b1da8d050cc1)
- Finally, we published what may be the most mundane yet crucial article, which explains how the HGTP from the Constellation Network will be utilized to construct the future of logging various types of data. This will occur in parallel with analytical computation, ensuring the security of each data interpolation transaction!
OpenAI summary*
OpenAI is a leading artificial intelligence research organization founded in 2015 with the mission of ensuring that artificial general intelligence (AGI) benefits all of humanity. Comprising a team of world-class researchers and engineers, OpenAI tends to focus on developing advanced AI technologies with long-term safety and “social impact in mind” .
Some notable achievements by OpenAI include the development of AI models like GPT-3, which has revolutionized natural language processing, and AI systems capable of mastering games like Dota 2. As OpenAI continues to advance the AI frontier, it remains committed to fostering cooperation, sharing knowledge, and prioritising the well-being of society.
*generated by GPT-4 from OpenAI
LLMs and LangChain!*
Large Language Models (LLMs) are advanced AI systems designed to understand and generate human-like text by processing vast amounts of data. They are transforming industries by offering enhanced natural language processing capabilities. LangChain is an innovative platform that leverages LLMs to facilitate seamless language translation and communication. By harnessing the power of LLMs, LangChain delivers accurate translations across multiple languages, bridging linguistic barriers and promoting global understanding. In essence, LangChain combines the strengths of LLMs with its unique framework to offer users an efficient and reliable language translation solution.
What is LangChain?*
LangChain, a blockchain-based platform, leverages these LLMs to provide decentralized, secure, and efficient translation services, bridging the gap between human language and technology while ensuring data integrity and privacy.
*again, generated by GPT-4
LangChain and Blockchain
We mentioned checkpoints at the beginning of the article, and now we present our first set of checkpoints, which outline the connections between AI (specifically, Natural Language Processing or NLP) and blockchain technologies and principles. These intersections demonstrate how the two fields can complement and enhance each other, fostering innovation and new possibilities for their applications.
- Decentralization: LangChain utilizes blockchain’s decentralized nature to distribute translation tasks, ensuring no single point of control or failure.
- Security: Blockchain technology provides a secure and tamper-proof environment for data storage and transactions, which LangChain adopts for its translation services.
- Consensus Mechanisms: LangChain leverages blockchain’s consensus mechanisms to validate and verify translation tasks, ensuring accuracy and quality.
- Tokenization: LangChain may use a native cryptocurrency or token for incentivizing users, rewarding translators, and enabling transactions within the platform.
- Trustless Environment: Blockchain’s inherent trustless nature allows LangChain to facilitate transactions and interactions without requiring a central authority, increasing transparency and efficiency in the translation process.*
*another one by GPT-4
OpenAI : visual ideas into code
Here is what GPT-4 generated when asked about this idea of turning visual ideas sketched by a human into real code that can be published within seconds :
- Analyze the visual sketch generated by GPT-4 and identify key components such as layout, typography, color schemes, and UI elements.
- Choose an appropriate front-end framework (e.g., Bootstrap, Material-UI) that aligns with the design style and facilitates responsive web development.
- Convert the visual sketch into HTML structure, creating a basic skeleton of the web page and defining the placement of various elements.
- Apply CSS styling to refine the appearance of the page, following the design guidelines from the GPT-4 visual sketch. This includes setting font styles, colors, margins, and other visual aspects.
- Incorporate JavaScript or a JavaScript framework (e.g., React, Vue.js) to add interactivity and dynamic content to the website, as needed.
It is kind of like GPT-4 is setting cookies and milk for itself, in other words patting itself on the back. Surely no bias there (even in ML terms…)
PoV-RO : THE SOLUTION
Proof- of- Vision — through — Reputable-Observation
The importance of reputable direct observation!
Direct Observation in the Islamic Golden Age.
Direct (Reputable) observation is a fundamental aspect of the scientific method, where researchers observe and analyze phenomena to collect data and draw conclusions. It played a crucial role in the Islamic Golden Age (8th to 14th century) by facilitating many scientific breakthroughs in various fields, such as astronomy, medicine, chemistry, and mathematics.
— Astronomy: Islamic scholars built observatories to study celestial bodies and their movements. Direct observation allowed them to create accurate star catalogs, like the one by Al-Sufi, as well as improve the existing astronomical models. Al-Biruni and Al-Battani made precise measurements of the Earth’s axial tilt and the precession of the equinoxes, which contributed to the development of more accurate calendars.
— Medicine: Islamic physicians, like Al-Razi and Ibn Sina, relied on direct observation during clinical practice. They recorded patients’ symptoms, responses to treatments, and outcomes, which helped them develop a better understanding of diseases and their treatments. Their emphasis on clinical observation and recording laid the foundation for evidence-based medicine.
— Chemistry: Direct observation played a crucial role in the development of experimental chemistry during the Islamic Golden Age. Alchemists like Jabir ibn Hayyan and Al-Razi observed chemical reactions and recorded their findings, leading to the discovery of new chemical substances and the development of early laboratory techniques. Their works laid the groundwork for modern chemistry.
— Mathematics: Islamic mathematicians, like Al-Khwarizmi and Al-Kindi, applied observational data to develop mathematical models and methods. They made significant advancements in algebra, trigonometry, and geometry, often drawing from their observations of nature and real-world problems. Al-Khwarizmi’s (AKA Algorithms) work on algebra and algorithms, based on his observations of practical problems, had a profound impact on the development of mathematics.
Direct Observation in the Post-Maxwell Era.
Direct observation also played a critical role in Albert Einstein’s work, particularly in the development of his two most influential theories: the Special Theory of Relativity (1905) and the General Theory of Relativity (1915).
- Special Theory of Relativity: One of the key observations that influenced Einstein’s development of the Special Theory of Relativity was the Michelson-Morley experiment (1887). This experiment attempted to detect the motion of Earth through the hypothetical “luminiferous aether” by observing the speed of light in different directions. The experiment’s results showed that the speed of light remained constant regardless of Earth’s motion. This direct observation led Einstein to postulate that the speed of light is the same for all observers, regardless of their relative motion, and ultimately resulted in the development of the Special Theory of Relativity.
- General Theory of Relativity: Einstein’s General Theory of Relativity was a more comprehensive theory of gravitation, which incorporated the principles of the Special Theory of Relativity. Direct observations played a crucial role in validating the predictions of the General Theory of Relativity. The most famous of these observations was the solar eclipse experiment of 1919, led by Arthur Eddington. Eddington’s team observed the positions of stars near the Sun during a total solar eclipse, and their measurements confirmed that the Sun’s gravity bends light from distant stars, as predicted by Einstein’s theory.
Additionally, direct observation of the perihelion precession of Mercury’s orbit provided further evidence for the General Theory of Relativity. Before Einstein’s theory, astronomers could not account for the slight discrepancy between the observed precession and the predictions of Newton’s theory of gravity. The General Theory of Relativity, however, was able to explain this discrepancy, further cementing its validity.
In both cases, direct observation played a significant role in shaping Einstein’s theories and confirming their predictions.
So what is PoV-RO?
In one sentence : Proof-of-Vision-through-Reputable-Observations is the framework that the human race needs in the time of exponentially rising generative AI!
PoV-RO describes all the influences of light and enables automatic consensus between humans and machines, and between machines, which combines the principles of physics, computer vision, and communication protocols.
PoV-RO in details :
- Physics of Light: Understand properties, behavior, and interaction of light with matter for accurate models.
- Encoding Information: Utilize visible light communication (VLC) and Li-Fi for transmitting data between devices.
- Computer Vision: Implement fusion of low-level, mid-level, and high-level computer vision algorithms to interpret light-based information from images and videos in varying conditions.
- Communication Protocols: Standardised protocols for seamless interaction between humans to machines, and machines to machines.
- Machine Learning and AI: Incorporates adaptive and responsive communication through optimization and real-time decision-making.
- Security and Privacy: Addresses concerns by implementing secure communication protocols and data encryption methods that are resistant to quantum computer decryption, while continuously updating and adapting to advancements in quantum computing.
Why PoV-RO is needed?
Generative AI, which has been gradually evolving within the realm of the internet (interconnected networks), has intrigued many due to its relatively obscure history. This branch of artificial intelligence, responsible for creating new content such as text, images, or music, has seen significant advancements in recent years, especially with the introduction of models like OpenAI’s GPT-3 in 2020. However, its roots can be traced back to earlier machine learning algorithms and neural networks that started emerging in the late 20th century, paving the way for the sophisticated generative AI systems we see today.
So how can we protect ourselves, from every selfie taken and every data ever generated from our everyday interaction on the social applications that are deliberately free?
Integrating PoV-RO with OpenAI based technology
At ÉLEVER GROUP we have developed the PoV-RO framework under one principle we believe in, which is :
- Transparency of socially responsible contribution.
This will leverage the usage of GPTs with automatic consensus in order to power protocols such as Proof-of-Growth (PoG), Proof-of-Production (PoP), and Proof-of-Contribution (PoC)!
The first step we took to build the PoV-RO is by building the MHQQ module, which in Arabic “محقق” means to investigate.
MHQQ stands for Multi-Headed Qualitative Quantum-analysis, but we must take a break now and explain how this module works and when it will be published in a Youtube video introduced by our CEO and CTO.
The last tease!
The MHQQ module has the following functions :
- mhqq.sabq (selective-attribute on bi-quantum counting)
- mhqq.lahq (leveraged-attribute on headless-quantum counting)
- mhqq.thqq (tensor-based hierarichal qualitqtive query)
- mhqq.thbt (tensor-headless bit-transformers)
What are you waiting for? Follow us to learn how you can protect yourself and the data you generate!
