Breef

breef

A Brief History of Money: From the Economy of Favors to Bitcoin

A Brief Guide to the Governance of Apache Iceberg Tables

Authors:
(1) Lenore Blum (lblum@cs.cmu.edu);
(2) Manuel Blum (mblum@cs.cmu.edu). Table of Links Abstract and 1 Introduction 2 Brief Overview of CtmR, a Robot with a CTM Brain 2.1 Formal Definition of CtmR 2.2 Conscious Attention in CtmR 2.3 Conscious Awareness and the Feeling of Consciousness in CtmR 2.4 CtmR as a Framework for Artificial General Intelligence (AGI) 3 Alignment of CtmR with Other Theories of Consciousness 4 Addressing Kevin Mitchell’s questions from the perspective of CtmR 5 Summary and Conclusions 6 Acknowledgements 7 Appendix 7.1 A Brief History of the Theoretical Computer Science Approach to Computation 7.2 The Probabilistic Competition for Conscious Attention and the Influence of Disposition on it References 7.1 A Brief History of the Theoretical Computer Science Approach to Computation The theoretical computer science approach to computation starts with Alan Turing in the 1930’s and focuses on the question, “What is computable (decidable) and what is not?” (Turing, 1937). Turing defined a simple formal model of computation, which we now call the Turing Machine (TM) and defined a function to be computable if and only if it can be realized as the input-output map of a TM. The formal definition of a TM (program) also provides a formal definition of the informal concept of algorithm. Using his model, Turing proved properties (theorems) of computable functions, including the existence of universal computable functions (universal Turing machines) and the fact that some functions are not computable. The former foresees the realization of general purpose programmable computers; the latter that some problems cannot be decided even by the most powerful computers. For example, Turing shows there is no Turing machine (Turing computable function) that given the description of a TM M and an input x, outputs 1 if M on input x (eventually) halts, and 0 if not. This is known as the “halting problem” and is equivalent to Gödel’s theorem on the undecidability of arithmetic. But why should we believe the Church-Turing Thesis, suggested first in (Turing, 1937), that the TM embodies the informal notion of computability (decidability)? That’s because each of a great many very different independently defined models of discrete computation, including TMs and Alonzo Church’s effective calculability (Church, 1936), define exactly the same class of functions, the computable functions. In programming parlance, all sufficiently powerful practical programming languages are equivalent in that any one can simulate (be compiled into) any other. The ensuing mathematical theory is often called the Theory of Computation (TOC). In the 1960’s, with the wider accessibility of computers, newly minted theoretical computer scientists such as Jack Edmonds (Edmonds, 1965) and Richard Karp (Karp, 1972), pointed out that resources matter. Certain problems that in principle were decidable, were seemingly intractable given feasible time and space resources. Even more, intractability seemed to be an intrinsic property of the problem, not the method of solution or the implementing machine. The ensuing sub-theory of TOC, which introduces resource constraints into what is or is not computable efficiently, is called Theoretical Computer Science (TCS). TCS focuses on the question, “What is or is not computable (decidable) given limited resources?” A key problem here is the deceivingly simple “SAT problem”: Given a boolean formula �, is it satisfiable, meaning is there a truth assignment to its variables that makes formula � true? This problem is decidable. Here is a decision procedure: Given a boolean formula � with n variables, systematically check to see if any of the 2n possible truth assignments makes the formula true. If yes, output 1, otherwise output 0. This brute force procedure takes exponential(n) time in general. But is the “SAT problem” tractable, meaning decidable efficiently, i.e., in polynomial(n) time? This is equivalent to the well-known P = NP? problem of (Cook, 1971), (Karp, 1972), (Levin, 1973). The design of novel and efficient algorithms is a key focus of TCS. Turning the table on its head, the ability to exploit the power of hard problems, problems that cannot be solved efficiently, has been a key insight of TCS. An example is the definition of pseudorandomness. This ability to exploit the power of hardness is novel for mathematics. This paper is available on arxiv under CC BY 4.0 DEED license. Authors: (1) Lenore Blum (lblum@cs.cmu.edu); (2) Manuel Blum (mblum@cs.cmu.edu). Authors: Authors: (1) Lenore Blum (lblum@cs.cmu.edu); (2) Manuel Blum (mblum@cs.cmu.edu). Table of Links Abstract and 1 Introduction Abstract and 1 Introduction 2 Brief Overview of CtmR, a Robot with a CTM Brain 2.1 Formal Definition of CtmR 2.1 Formal Definition of CtmR 2.2 Conscious Attention in CtmR 2.2 Conscious Attention in CtmR 2.3 Conscious Awareness and the Feeling of Consciousness in CtmR 2.3 Conscious Awareness and the Feeling of Consciousness in CtmR 2.4 CtmR as a Framework for Artificial General Intelligence (AGI) 2.4 CtmR as a Framework for Artificial General Intelligence (AGI) 3 Alignment of CtmR with Other Theories of Consciousness 3 Alignment of CtmR with Other Theories of Consciousness 4 Addressing Kevin Mitchell’s questions from the perspective of CtmR 4 Addressing Kevin Mitchell’s questions from the perspective of CtmR 5 Summary and Conclusions 5 Summary and Conclusions 6 Acknowledgements 6 Acknowledgements 7 Appendix 7.1 A Brief History of the Theoretical Computer Science Approach to Computation 7.1 A Brief History of the Theoretical Computer Science Approach to Computation 7.2 The Probabilistic Competition for Conscious Attention and the Influence of Disposition on it 7.2 The Probabilistic Competition for Conscious Attention and the Influence of Disposition on it References References 7.1 A Brief History of the Theoretical Computer Science Approach to Computation The theoretical computer science approach to computation starts with Alan Turing in the 1930’s and focuses on the question, “What is computable (decidable) and what is not?” (Turing, 1937). Turing defined a simple formal model of computation, which we now call the Turing Machine (TM) and defined a function to be computable if and only if it can be realized as the input-output map of a TM. The formal definition of a TM (program) also provides a formal definition of the informal concept of algorithm. Using his model, Turing proved properties (theorems) of computable functions, including the existence of universal computable functions (universal Turing machines) and the fact that some functions are not computable. The former foresees the realization of general purpose programmable computers; the latter that some problems cannot be decided even by the most powerful computers. For example, Turing shows there is no Turing machine (Turing computable function) that given the description of a TM M and an input x, outputs 1 if M on input x (eventually) halts, and 0 if not. This is known as the “halting problem” and is equivalent to Gödel’s theorem on the undecidability of arithmetic. But why should we believe the Church-Turing Thesis, suggested first in (Turing, 1937), that the TM embodies the informal notion of computability (decidability)? That’s because each of a great many very different independently defined models of discrete computation, including TMs and Alonzo Church’s effective calculability (Church, 1936), define exactly the same class of functions, the computable functions. In programming parlance, all sufficiently powerful practical programming languages are equivalent in that any one can simulate (be compiled into) any other. The ensuing mathematical theory is often called the Theory of Computation (TOC). In the 1960’s, with the wider accessibility of computers, newly minted theoretical computer scientists such as Jack Edmonds (Edmonds, 1965) and Richard Karp (Karp, 1972), pointed out that resources matter. Certain problems that in principle were decidable, were seemingly intractable given feasible time and space resources. Even more, intractability seemed to be an intrinsic property of the problem, not the method of solution or the implementing machine. The ensuing sub-theory of TOC, which introduces resource constraints into what is or is not computable efficiently, is called Theoretical Computer Science (TCS). TCS focuses on the question, “What is or is not computable (decidable) given limited resources?” A key problem here is the deceivingly simple “SAT problem”: Given a boolean formula �, is it satisfiable, meaning is there a truth assignment to its variables that makes formula � true? This problem is decidable. Here is a decision procedure: Given a boolean formula � with n variables, systematically check to see if any of the 2n possible truth assignments makes the formula true. If yes, output 1, otherwise output 0. This brute force procedure takes exponential(n) time in general. But is the “SAT problem” tractable, meaning decidable efficiently, i.e., in polynomial(n) time? This is equivalent to the well-known P = NP? problem of (Cook, 1971), (Karp, 1972), (Levin, 1973). The design of novel and efficient algorithms is a key focus of TCS. Turning the table on its head, the ability to exploit the power of hard problems, problems that cannot be solved efficiently, has been a key insight of TCS. An example is the definition of pseudorandomness. This ability to exploit the power of hardness is novel for mathematics. This paper is available on arxiv under CC BY 4.0 DEED license. This paper is available on arxiv under CC BY 4.0 DEED license. available on arxiv

A Brief History of the Theoretical Computer Science Approach to Computation

A Brief Introduction to The Boltzmann Brain Theory

বোল্টজম্যান ব্রেন থিওরির একটি সংক্ষিপ্ত ভূমিকা

Eine kurze Einführung in die Boltzmann-Gehirntheorie

Una breve introducción a la teoría del cerebro de Boltzmann

Une brève introduction à la théorie du cerveau de Boltzmann

बोल्ट्ज़मैन मस्तिष्क सिद्धांत का संक्षिप्त परिचय

ボルツマン脳理論の簡単な紹介

볼츠만 뇌 이론에 대한 간략한 소개

Uma breve introdução à teoria do cérebro de Boltzmann

Краткое введение в теорию мозга Больцмана

Boltzmann Beyin Teorisine Kısa Bir Giriş

Giới thiệu tóm tắt về Lý thuyết bộ não của Boltzmann

玻尔兹曼脑理论简介

This paper is available on arxiv under CC 4.0 license.
Authors:
(1) A. Oliveros, Programa de F´ısica, Universidad del Atl´antico;
(2) Mario A. Acero, Programa de F´ısica, Universidad del Atl´antico. Table of Links Abstract and Intro
The f(Q) gravity: a brief review
Cosmological dynamics in late-time
Parameter constraints
Conclusions
Acknowledgments and References 2. The f(Q) gravity: a brief review In general, the action for an f(Q) gravity model in the presence of matter components is given by with the Levi-Civita connection of the metric given by the contortion tensor is Considering the flat Friedman-Robertson-Walker (FRW) metric, with a(t) representing the scale factor, the time and spatial components of Eq. (10) are given, respectively, by [9, 10] and It should be noted that in this scenario, the standard Friedmann equations of General Relativity (GR) plus the cosmological constant are recovered when assuming that f(Q) = Q + 2Λ. If there is no interaction between non-relativistic matter and radiation, then these components obey separately the conservation laws As usual in the literature, it is possible to rewrite the field equations (13) and (14) in the Einstein-Hilbert form: In this case, we assume that the equation of state parameter for this effective dark energy satisfies the following relation: This paper is available on arxiv under CC 4.0 license. Authors: (1) A. Oliveros, Programa de F´ısica, Universidad del Atl´antico; (2) Mario A. Acero, Programa de F´ısica, Universidad del Atl´antico. This paper is available on arxiv under CC 4.0 license. Authors: Authors: (1) A. Oliveros, Programa de F´ısica, Universidad del Atl´antico; (2) Mario A. Acero, Programa de F´ısica, Universidad del Atl´antico. Table of Links Abstract and Intro The f(Q) gravity: a brief review Cosmological dynamics in late-time Parameter constraints Conclusions Acknowledgments and References Abstract and Intro Abstract and Intro The f(Q) gravity: a brief review The f(Q) gravity: a brief review Cosmological dynamics in late-time Cosmological dynamics in late-time Parameter constraints Parameter constraints Conclusions Conclusions Acknowledgments and References Acknowledgments and References 2. The f(Q) gravity: a brief review In general, the action for an f(Q) gravity model in the presence of matter components is given by with the Levi-Civita connection of the metric given by the contortion tensor is Considering the flat Friedman-Robertson-Walker (FRW) metric, with a(t) representing the scale factor, the time and spatial components of Eq. (10) are given, respectively, by [9, 10] and It should be noted that in this scenario, the standard Friedmann equations of General Relativity (GR) plus the cosmological constant are recovered when assuming that f(Q) = Q + 2Λ. If there is no interaction between non-relativistic matter and radiation, then these components obey separately the conservation laws As usual in the literature, it is possible to rewrite the field equations (13) and (14) in the Einstein-Hilbert form: In this case, we assume that the equation of state parameter for this effective dark energy satisfies the following relation:

Cosmological dynamics and observational constraints: The f(Q) gravity, a brief review

মহাজাগতিক গতিবিদ্যা এবং পর্যবেক্ষণের সীমাবদ্ধতা: f(Q) মাধ্যাকর্ষণ, একটি সংক্ষিপ্ত পর্যালোচনা

Kosmologische Dynamik und Beobachtungsbeschränkungen: Die f(Q)-Gravitation, eine kurze Übersicht

Dinámica cosmológica y restricciones de observación: la gravedad f(Q), una breve reseña

Dynamique cosmologique et contraintes d'observation : la gravité f(Q), un bref aperçu

ब्रह्माण्ड संबंधी गतिशीलता और अवलोकन संबंधी बाधाएं: f(Q) गुरुत्वाकर्षण, एक संक्षिप्त समीक्षा

宇宙論的ダイナミクスと観測的制約: f(Q) 重力の簡単なレビュー

우주론적 역학 및 관측 제약: f(Q) 중력, 간략한 검토

Dinâmica cosmológica e restrições observacionais: A gravidade f(Q), uma breve revisão

Космологическая динамика и ограничения наблюдений: гравитация f(Q), краткий обзор

Kozmolojik dinamikler ve gözlemsel kısıtlamalar: f(Q) yerçekimi, kısa bir inceleme

Động lực vũ trụ và các ràng buộc quan sát: Lực hấp dẫn f(Q), đánh giá ngắn gọn

宇宙动力学和观测约束：f(Q) 引力的简要回顾

When most people think of NFTs, they think of Bored Ape monkeys and digital artworks, but in reality, it is a universal standard meant for much more. For example, NFTs perfectly represent real-world entities on the blockchain. An NFT can represent the ownership of a real piece of art (such as a painting), allowing it to be traded on the blockchain while the painting remains in a well-protected safe. Similarly, an NFT can represent raw materials, such as the steel needed for car manufacturing, or a container of berries that will eventually be made into smoothies. To these NFTs that represent real entities, we can also assign metadata that provides precise information about the entity associated with the NFT. For instance, in the case of a piece of art, this could include certificates proving authenticity and ownership, or in the case of raw materials, quality statements, and unique attributes like the place of origin. If the metadata is stored on immutable storage, then every change requires updating the metadata reference in the NFT, making the entity's history traceable throughout the blockchain. This history is unchangeable due to the nature of blockchain and immutable storage systems! This brings us to the trace.market project, which we are currently working on in close collaboration with the Swarm Foundation. The system's goal is to allow anyone to freely create NFTs representing physical entities on the Polygon blockchain and to assign metadata to them on Swarm. The blockchain ensures a reliable product history, which can be accessed on Swarm. From the end-users perspective, this looks like scanning a QR code (which contains the NFT ID) on the final product (such as a garment, a box of smoothies, or a car service book) using an app, where they can view the metadata and the complete history. For example, they can verify that the t-shirt was made by recycled textiles, or that the price of refurbished electronics is in balance with its defects. The project uses standard ERC721 NFTs, which are supplemented by two methods for storing and retrieving metadata: function updateMetadata(uint256 tokenId, uint256 swarmHash)
function metadata(uint256 tokenId) The updateMetadata method allows us to modify the metadata assigned to the token. Since the address of content on Swarm is the keccak256 hash of that content, we use uint256 for storage. As the content stored on Swarm is immutable, any change requires us to modify the metadata hash. This ensures that the complete history can be traced on the blockchain. To facilitate traceability, we are also introducing a new event: event metadataUpdated(uint256 indexed _tokenId, uint256 _swarmHash); The event is emitted with every change in metadata, allowing the complete history to be collected based on the tokenId. Since Swarm has its storage incentive system, the storage fee must be covered. This responsibility always falls on the current owner of the NFT. It is in their interest to do so because if the metadata is not accessible, significantly more trust will be needed to sell the product/ NFT. Once the NFT is transferred to a new owner, it then becomes the new owner's responsibility to pay for the storage. During the life cycle of a product, there may be events when the nature of the product changes. For example, locally grown berries are freeze-dried to serve them off-season, or a certified raw material is built into a premium product. Each activity output is detailed in a new NFT, so Industry 5.0’s smart manufacturing can be customized at scale. We refer to these in the system as transformations, and they can occur with the assistance of smart contracts. We distinguish three fundamental types of transformations: 1-1 transformation: In this case, one NFT transforms into another NFT. For example, a container of strawberries becomes a barrel of juice.
1-N transformation (split): In this case, one NFT splits into multiple NFTs. For example, a container of strawberries is divided into two containers, which are then shipped to different countries.
N-1 transformation (merge): In this case, component  NFTs merge into one NFT. This is like when parts are shipped or assembled into a complete car. With these three types, all transformations can be covered. The smart contract executing the transformation locks the source NFT (removes it from circulation) and mints new NFTs in its place. To execute the transformation, the smart contract can verify certain conditions. For example, in the case of a container of berries, a split operation is only permitted if the mass of the resulting new NFTs matches the mass of the source NFT. The mass measurement is performed by reliable external oracles. We store the NFT metadata in the Beeson format developed for Swarm. The advantage of this is that it is verifiable on the blockchain, enabling the transformation smart contract to perform basic Merkle proof checks on the metadata based solely on the Swarm hash (Merkle root). For example, the full price is paid only if the product is manufactured according to a recipe that is confidential but its hash is known. Of course, there are many factors in a transformation that may be difficult or impossible to verify with a smart contract. In such cases, permission from external oracles is required for the transformation (e.g. the location of a vessel). This could be a single oracle or a group of oracles. In the latter case, the transformation can be executed if the majority of the group approves it. Finally, for the sake of example, let's follow this short supply chain: First, the producer picks the bio berries and packs them into a container. Subsequently, an independent validator checks the field and its bio certificate. Photos of the Real-World Asset are uploaded to Swarm, which is linked to the NFT as metadata. Thus, the NFT representing the food is created. Tons of berries go bad or require huge energy during logistics, so a nearby food processor is notified that a batch becomes available. A sales contract is drawn up between the processor and the producer, with the condition that if the location of the container is verified (IoT and logistics oracles, DePIN) on the buyer’s plant, the price will be paid and the buyer will be the owner of the NFT. The processor can merge it with other ingredients, machine capacities, and recipes, so an updated NFT is generated for wholesalers and retailers. On the packaging, a QR code with the NFT ID is placed. When a customer picks up the smoothie in the store, they can scan the QR code to download the metadata stored on Swarm, which can be authenticated based on the history stored on the blockchain. The product's CO2 and water footprint can be estimated most accurately from this metadata, supporting buyers to involve it in buying factors, thereby encouraging manufacturers to minimize CO2 emissions. This is how the trace.market project works in a nutshell. Currently, the project is in testing phase, welcoming API integrations to cope with the urging need for a transparent, Single Source of Truth in products’ sustainability, logistics, resiliency, quality, and custom but structured attributes. I plan to present the system's operation in depth, including the source code level, in a second article once the first version is complete. Kudos to Balint Drahota, the PM of trace.market for helping me clarify the concept, and inspiring me with excellent use cases. When most people think of NFTs, they think of Bored Ape monkeys and digital artworks, but in reality, it is a universal standard meant for much more. For example, NFTs perfectly represent real-world entities on the blockchain. An NFT can represent the ownership of a real piece of art (such as a painting), allowing it to be traded on the blockchain while the painting remains in a well-protected safe. Similarly, an NFT can represent raw materials, such as the steel needed for car manufacturing, or a container of berries that will eventually be made into smoothies. To these NFTs that represent real entities, we can also assign metadata that provides precise information about the entity associated with the NFT. For instance, in the case of a piece of art, this could include certificates proving authenticity and ownership, or in the case of raw materials, quality statements, and unique attributes like the place of origin. If the metadata is stored on immutable storage, then every change requires updating the metadata reference in the NFT, making the entity's history traceable throughout the blockchain. This history is unchangeable due to the nature of blockchain and immutable storage systems! This brings us to the trace.market project, which we are currently working on in close collaboration with the Swarm Foundation . The system's goal is to allow anyone to freely create NFTs representing physical entities on the Polygon blockchain and to assign metadata to them on Swarm. The blockchain ensures a reliable product history, which can be accessed on Swarm. trace.market Swarm Foundation From the end-users perspective, this looks like scanning a QR code (which contains the NFT ID) on the final product (such as a garment, a box of smoothies, or a car service book) using an app, where they can view the metadata and the complete history. For example, they can verify that the t-shirt was made by recycled textiles, or that the price of refurbished electronics is in balance with its defects. The project uses standard ERC721 NFTs, which are supplemented by two methods for storing and retrieving metadata: ERC721 function updateMetadata(uint256 tokenId, uint256 swarmHash)
function metadata(uint256 tokenId) function updateMetadata(uint256 tokenId, uint256 swarmHash)
function metadata(uint256 tokenId) The updateMetadata method allows us to modify the metadata assigned to the token. Since the address of content on Swarm is the keccak256 hash of that content, we use uint256 for storage. As the content stored on Swarm is immutable, any change requires us to modify the metadata hash. This ensures that the complete history can be traced on the blockchain. To facilitate traceability, we are also introducing a new event: updateMetadata event metadataUpdated(uint256 indexed _tokenId, uint256 _swarmHash); event metadataUpdated(uint256 indexed _tokenId, uint256 _swarmHash); The event is emitted with every change in metadata, allowing the complete history to be collected based on the tokenId. Since Swarm has its storage incentive system, the storage fee must be covered. This responsibility always falls on the current owner of the NFT. It is in their interest to do so because if the metadata is not accessible, significantly more trust will be needed to sell the product/ NFT. Once the NFT is transferred to a new owner, it then becomes the new owner's responsibility to pay for the storage. During the life cycle of a product, there may be events when the nature of the product changes. For example, locally grown berries are freeze-dried to serve them off-season, or a certified raw material is built into a premium product. Each activity output is detailed in a new NFT, so Industry 5.0’s smart manufacturing can be customized at scale. We refer to these in the system as transformations, and they can occur with the assistance of smart contracts. We distinguish three fundamental types of transformations: 1-1 transformation: In this case, one NFT transforms into another NFT. For example, a container of strawberries becomes a barrel of juice. 1-N transformation (split): In this case, one NFT splits into multiple NFTs. For example, a container of strawberries is divided into two containers, which are then shipped to different countries. N-1 transformation (merge): In this case, component  NFTs merge into one NFT. This is like when parts are shipped or assembled into a complete car. 1-1 transformation : In this case, one NFT transforms into another NFT. For example, a container of strawberries becomes a barrel of juice. 1-1 transformation 1-N transformation (split) : In this case, one NFT splits into multiple NFTs. For example, a container of strawberries is divided into two containers, which are then shipped to different countries. 1-N transformation (split) N-1 transformation (merge) : In this case, component  NFTs merge into one NFT. This is like when parts are shipped or assembled into a complete car. N-1 transformation (merge) With these three types, all transformations can be covered. The smart contract executing the transformation locks the source NFT (removes it from circulation) and mints new NFTs in its place. To execute the transformation, the smart contract can verify certain conditions. For example, in the case of a container of berries, a split operation is only permitted if the mass of the resulting new NFTs matches the mass of the source NFT. The mass measurement is performed by reliable external oracles. We store the NFT metadata in the Beeson format developed for Swarm. The advantage of this is that it is verifiable on the blockchain, enabling the transformation smart contract to perform basic Merkle proof checks on the metadata based solely on the Swarm hash (Merkle root). For example, the full price is paid only if the product is manufactured according to a recipe that is confidential but its hash is known. Of course, there are many factors in a transformation that may be difficult or impossible to verify with a smart contract. In such cases, permission from external oracles is required for the transformation (e.g. the location of a vessel). This could be a single oracle or a group of oracles. In the latter case, the transformation can be executed if the majority of the group approves it. Finally, for the sake of example, let's follow this short supply chain: First, the producer picks the bio berries and packs them into a container. Subsequently, an independent validator checks the field and its bio certificate. Photos of the Real-World Asset are uploaded to Swarm, which is linked to the NFT as metadata. Thus, the NFT representing the food is created. Tons of berries go bad or require huge energy during logistics, so a nearby food processor is notified that a batch becomes available. A sales contract is drawn up between the processor and the producer, with the condition that if the location of the container is verified (IoT and logistics oracles, DePIN) on the buyer’s plant, the price will be paid and the buyer will be the owner of the NFT. The processor can merge it with other ingredients, machine capacities, and recipes, so an updated NFT is generated for wholesalers and retailers. On the packaging, a QR code with the NFT ID is placed. When a customer picks up the smoothie in the store, they can scan the QR code to download the metadata stored on Swarm, which can be authenticated based on the history stored on the blockchain. The product's CO2 and water footprint can be estimated most accurately from this metadata, supporting buyers to involve it in buying factors, thereby encouraging manufacturers to minimize CO2 emissions. This is how the trace.market project works in a nutshell. Currently, the project is in testing phase, welcoming API integrations to cope with the urging need for a transparent, Single Source of Truth in products’ sustainability, logistics, resiliency, quality, and custom but structured attributes. I plan to present the system's operation in depth, including the source code level, in a second article once the first version is complete. Kudos to Balint Drahota, the PM of trace.market for helping me clarify the concept, and inspiring me with excellent use cases.

Supply Chain Tracking with Ethereum Swarm and NFTs: a brief introduction of trace.market 

Taking action has been a life-changing decision for me. It has brought a lot of clarity to my life and day-to-day activities. I get a lot of tasks done and improve myself consistently as well. It has put me on the path to slaying complacency, eliminating procrastination, and fulfilling my immense potential. These are four principles of how action breeds clarity, and how I implement them. 1. Learning by Doing Learning by doing stops us from overthinking things. We are only human. We will never be sure of every decision we make. We have an innate tendency to overthink and analyze situations before taking action. Overthinking leads to what is commonly known as "analysis paralysis." It blurs our perspective, creates confusion, and stagnates our progress. Overthinking creates multiple outcomes that lead to increased anxiety, dissatisfaction, and even paralysis. This concept was theorized by psychologist Barry Schwartz in his book “The Paradox of Choice: Why More Is Less.” Schwartz argues that having choices is generally considered a positive aspect of modern life. The abundance of options in various domains—such as career paths and life decisions—can overwhelm individuals and hinder their ability to make effective decisions. We should use our time efficiently and learn in the process. Do not overthink things. Just-In-Time Knowledge Over Just-In-Case Knowledge “Just-in-time knowledge” refers to acquiring skills and information when they are immediately needed for a specific task, problem, or situation. “Just-in-case knowledge” refers to learning skills and information in preparation for future needs. Both approaches have their advantages. Why is this knowledge important? A lot of people read, watch videos, and consume information that they are unlikely to put into practice. This knowledge is more of an entertainment source than it is a learned skill. We are more likely to change a light bulb than apply survival tactics when faced with a lion. There’s nothing wrong with knowing how to survive a lion attack, but it’s very unlikely that you’ll use that knowledge. People who prioritize this type of knowledge are also prone to procrastination. They focus solely on what could happen. This keeps them in an endless cycle of worry and anxiety that prevents them from accomplishing the goals they’ve set for themselves. We prioritize just-in-time knowledge and reference Tim Ferris, who said, “Prefer just-in-time knowledge over just-in-case knowledge.” 3. Do and Reflect Getting things done while learning relevant aspects is good for our growth. Reflection gives us new perspectives of ourselves that allow us to analyze and improve on areas we are weak. It is important to see your goals through. Doing so staves off doubt, depression, and anxiety. It builds your character in the process, putting you in a position to continually improve. Resist the temptation to quit or give up. Do not second-guess yourself by wondering if you should do something else instead. Guard your mind against self-doubt and stay focused on the task at hand. Take action over your patterns, routines, and nuances. Don’t allow complacency to creep in as it can drain you of all your potential, leaving you depressed and unfulfilled. Adapt to changing circumstances and build on routines that make you better. 4. Building Confidence & Embracing the Unknown The action involves stepping into the unknown. Embracing uncertainty allows us to confront fears and doubts that hinder progress. Navigating through uncharted territories is scary and unpleasant. However, doing so builds and grows confidence. This confidence stems from realizing we can overcome challenges and learn from them. Overthinking can create worst-case scenarios that fill us with fear. Fear can hold you back from achieving your potential. Take the leap of faith into the unknown, and overcome the challenges that await you. As David Goggins says, “Embrace the suck!” It might not be pleasurable in the beginning, but you will learn how to deal with it. You’ll master how to navigate uncharted waters and come out the other side stronger than before. Conclusion Action breeds clarity and encapsulates a fundamental truth that transcends various aspects of life. It allows us to break free from the constraints of overthinking. We gain insights, experiences, and wisdom that lead to a clearer understanding of ourselves and our environment. The courage to take action propels us toward our goals while fostering adaptability and self-confidence. Action holds the key to the clarity we seek. The world is your oyster. Stop overthinking and act starting today. For these and more thoughts, guides and insights visit my blog at martinbaun.com Taking action has been a life-changing decision for me. It has brought a lot of clarity to my life and day-to-day activities. I get a lot of tasks done and improve myself consistently as well. It has put me on the path to slaying complacency, eliminating procrastination, and fulfilling my immense potential. These are four principles of how action breeds clarity, and how I implement them. 1. Learning by Doing Learning by doing stops us from overthinking things. We are only human. We will never be sure of every decision we make. We have an innate tendency to overthink and analyze situations before taking action. Overthinking leads to what is commonly known as "analysis paralysis." It blurs our perspective, creates confusion, and stagnates our progress. Overthinking creates multiple outcomes that lead to increased anxiety, dissatisfaction, and even paralysis. This concept was theorized by psychologist Barry Schwartz in his book “The Paradox of Choice: Why More Is Less.” Schwartz argues that having choices is generally considered a positive aspect of modern life. The abundance of options in various domains—such as career paths and life decisions—can overwhelm individuals and hinder their ability to make effective decisions. We should use our time efficiently and learn in the process. Do not overthink things. Just-In-Time Knowledge Over Just-In-Case Knowledge “Just-in-time knowledge” refers to acquiring skills and information when they are immediately needed for a specific task, problem, or situation. “Just-in-case knowledge” refers to learning skills and information in preparation for future needs. Both approaches have their advantages. Why is this knowledge important? A lot of people read, watch videos, and consume information that they are unlikely to put into practice. This knowledge is more of an entertainment source than it is a learned skill. We are more likely to change a light bulb than apply survival tactics when faced with a lion. There’s nothing wrong with knowing how to survive a lion attack, but it’s very unlikely that you’ll use that knowledge. People who prioritize this type of knowledge are also prone to procrastination. They focus solely on what could happen. This keeps them in an endless cycle of worry and anxiety that prevents them from accomplishing the goals they’ve set for themselves. We prioritize just-in-time knowledge and reference Tim Ferris, who said, “Prefer just-in-time knowledge over just-in-case knowledge.” 3. Do and Reflect Getting things done while learning relevant aspects is good for our growth. Reflection gives us new perspectives of ourselves that allow us to analyze and improve on areas we are weak. It is important to see your goals through. Doing so staves off doubt, depression, and anxiety. It builds your character in the process, putting you in a position to continually improve. Resist the temptation to quit or give up. Do not second-guess yourself by wondering if you should do something else instead. Guard your mind against self-doubt and stay focused on the task at hand. Take action over your patterns, routines, and nuances. Don’t allow complacency to creep in as it can drain you of all your potential, leaving you depressed and unfulfilled. Adapt to changing circumstances and build on routines that make you better. 4. Building Confidence & Embracing the Unknown The action involves stepping into the unknown. Embracing uncertainty allows us to confront fears and doubts that hinder progress. Navigating through uncharted territories is scary and unpleasant. However, doing so builds and grows confidence. This confidence stems from realizing we can overcome challenges and learn from them. Overthinking can create worst-case scenarios that fill us with fear. Fear can hold you back from achieving your potential. Take the leap of faith into the unknown, and overcome the challenges that await you. As David Goggins says, “Embrace the suck!” It might not be pleasurable in the beginning, but you will learn how to deal with it. You’ll master how to navigate uncharted waters and come out the other side stronger than before. Conclusion Action breeds clarity and encapsulates a fundamental truth that transcends various aspects of life. It allows us to break free from the constraints of overthinking. We gain insights, experiences, and wisdom that lead to a clearer understanding of ourselves and our environment. The courage to take action propels us toward our goals while fostering adaptability and self-confidence. Action holds the key to the clarity we seek. The world is your oyster. Stop overthinking and act starting today. For these and more thoughts, guides and insights visit my blog at martinbaun.com martinbaun.com

Action Breeds Clarity

Based on , is an iris biometric cryptocurrency project developed by San Francisco- and Berlin-based Tools for Humanity. Founded in 2019 by OpenAI chief executive Sam Altman, Max Novendstern, and Alex Blania. Wikipedia Worldcoin But if I try to define it with one sentence: Worldcoin is what Bitcoin should have been. Bitcoin was fundamentally conceived as a form of equitable currency not issued by governments but by the community itself. The system eliminates the need for central servers by operating on a peer-to-peer basis. Instead of relying on complex central banking policies, currency generation is governed by a straightforward algorithm. Roughly every ten minutes new bitcoins are created, and through a sort of lottery system, it's determined who receives them. Statistically, this lottery would result in an even distribution over time. The primary issue, however, is the system's complete anonymity, making it impossible to ensure that a participant only enters the lottery with a single ticket. Bitcoin's solution to this problem is the Proof of Work (PoW) mechanism, which requires a certain amount of computational effort for each lottery ticket. However, this leads to a scenario where those with greater computational power have a higher chance of winning, setting a clear path to the inevitable outcome. As usual market dynamics kicked in, nodes began to form groups, and individual nodes stood no chance of winning. Today, less than ten mining farms dominate the network, and thanks to ETFs, Bitcoin has merged into the traditional financial system, becoming just like any other financial instrument. Nothing remains of the anarchist hippie dream; due to its immense energy consumption, Bitcoin is more detrimental to the Earth than beneficial. Worldcoin solved a problem that Bitcoin could not, namely how to ensure the uniqueness of participants while keeping the system anonymous (Proof of Unique Humanness). The Orb To solve the problem, the Worldcoin team developed one of the world's best iris scanners, the rb. O Just like fingerprints, the iris is unique for every individual, making it perfect for proving the uniqueness of a member within a community. Similar to other biometric identification systems, the Orb generates a feature vector based on the iris. This feature vector is characteristic of the individual but does not allow for the recreation of the iris image, ensuring its security (akin to a hash). When we want to identify someone, we regenerate this feature vector. If it closely resembles the original vector within a certain margin (typically cosine distance), the identification is deemed successful. But how does the system remain anonymous? World ID: anonymous proof of personhood is a protocol to bring privacy-preserving global proof of personhood. To achieve this, it utilizes the library, which is based on zero-knowledge-proof technology. Since , I will only briefly describe the system's operation here. World ID Semaphore I have a full article on Semaphore The user generates an Identity Commitment, which Semaphore stores in a Merkle tree. Each Identity Commitment is unique and can have multiple Nullifiers associated with it. To generate a Nullifier, a unique public External Nullifier is required. For each External Nullifier, only one Nullifier can be generated. Thanks to zero-knowledge proof technology (on which ), it can be proven that a generated Nullifier corresponds to an Identity Commitment without revealing the Identity Commitment itself. I also have a full article Let's see how this works in practice using the example of an anonymous voting system. The goal is to ensure that every person with a World ID can only vote once. When a World ID is created in the World App, a unique Secret, and the corresponding Identity Commitment are generated. When someone is identified with the Orb, this Identity Commitment is recorded on the blockchain along with the iris hash, ensuring that a person can only register a single Identity Commitment. Essentially, the Identity Commitment acts like a public key, while the Secret is the corresponding private key used to authenticate Nullifiers. When voting begins, an External Nullifier is generated, serving as the unique identifier for the vote. Each time a vote is cast, the voter generates a Nullifier associated with this External Nullifier and submits their vote. Since only one Nullifier can be generated for each External Nullifier, an individual can only cast one vote. The Nullifier cannot be linked to the Identity Commitment in any way. If another vote is organized, it will have a different External Nullifier, for which the voter will generate a new, unique Nullifier that is completely independent of the one generated for the previous vote. Of course, this solution can be used in any case that requires unique anonymous registration. Use cases: defense against AI bots, Unconditional Basic Income, and Karma money Many might wonder why a blockchain project is interesting to Sam Altman, the CEO of OpenAI. With the advancement of AI, it's becoming increasingly difficult to distinguish bots from humans. There is a genuine concern that, with the help of advanced LLMs, targeted fake information spread on social networks could influence political elections or even the stock prices of companies (as envisioned by the computer in ). This is the primary reason why OpenAI does not release the source code of ChatGPT (at least that's what they claim). A defense against this is to somehow be able to distinguish bots from humans, for which World ID offers an excellent solution. West World Another typical AI-related use case is (UBI). Increasingly, serious consideration is being given to what might happen if, thanks to AGI and humanoid robots, nearly all human-performed work could be replaced by machines. The traditional work-based society would become inoperable, necessitating an alternative approach. Many believe that UBI, where everyone receives income on an unconditional basis instead of through employment, could be the solution. A version of this that operates beyond the control of governments could be a blockchain-based solution, functioning similarly to Bitcoin, but with the generated coins being distributed fairly among people. This wouldn't require massive computational power and significant amounts of energy; proving that we are real humans would be sufficient. The Worldcoin whitepaper specifically mentions the possibility of such a system, with a portion of Worldcoin's WLD tokens being distributed in this manner. However, a separate system dedicated solely to UBI, potentially with its own blockchain that anyone with a World ID could participate in operating, is also conceivable. Similar to Proof of Stake, each block would be authenticated by a node chosen at random, but here the stake is our own identity. This is the hypothetical network I mentioned in the introduction; this would be the "ideal Bitcoin." Universal Basic Income Finally, let me explain why Worldcoin and World ID started to interest me personally. I recently ( ), which operates similarly to the current fiat money system. Currently, banks create fiat money, which generates significant profits for them through interest. In 2008, we saw how poor decisions by banks could devastate the global economy, which also spurred the creation of Bitcoin. In the Karma Money system, individuals and companies lend to each other directly, bypassing banks (P2P lending). Transactions and members' debts are public, so everyone has a reputation. Based on this information, we can decide whether to lend to someone (accept their money) or not. In such a reputation-based monetary system, it's crucial that we can link individual participants' wallets to their identities. Without this, if someone were heavily indebted, they could simply switch wallets and start trading anew with a clean slate, never repaying their debts. However, if the wallet is linked to a World ID, then participants cannot hide their debts. This system could even complement the previously mentioned Universal Basic Income system, where we could spend not only from the income received as UBI but also from credit. published a concept called Karma Money GitHub repo To summarize the above, I believe that Worldcoin is a very exciting project, and if it fully achieves its set goal, namely to become 100% open-source and decentralized, it could become as significant as Bitcoin. Moreover, as I mentioned in the introduction, Worldcoin could be what Bitcoin should have been!

A Brief Introduction of Worldcoin: Proof of Unique Humanness on Blockchain

Authors: (1) Gopal Yadav, Department of Physics, Indian Institute of Technology & Chennai Mathematical Institute. Table of Links Abstract & Introduction Brief Review of Wedge Holography Emerging Multiverse from Wedge Holography Application to Information Paradox Application to Grandfather Paradox Conclusion Acknowledgements and References 2 Brief Review of Wedge Holography • CF Td−1 living on the wedge of common boundaries of two AdSd’s. Boundary description: • Two Karch-Randall branes of geometry AdSd (Q1 and Q2) glued to each other at the interface point by a transparent boundary condition. Intermediate description: • Einstein gravity in (d + 1)-dimensional bulk, AdSd+1. Bulk description: Precisely, correspondence can be interpreted as: “Classical gravity in (d + 1)-dimensions has a holographic dual theory on the defect which is CFT in (d − 1)-dimensions”. Wedge holography is useful in the computation of the Page curve of black holes. Let us understand this connection. In the intermediate description, we consider a black hole on Q2 whose Hawking radiation will be collected by weakly gravitating bath Q1 (i.e., T1 < T2). To calculate the entanglement entropy in the intermediate description, one is required to use the semiclassical formula: where γ is the minimal surface in bulk. In wedge holography, there is one more extremal surface, Hartman-Maldacena surface [30], which starts at the defect, crosses the horizons, and meets its thermofield double. By plotting the entanglement entropies contributions of these surfaces, we can get the Page curve [2]. This paper is under CC 4.0 license. available on arxiv

A Brief Review of Wedge Holography

A journey from the origins of computing and data analytics to what we now call the "Modern Data Stack". What comes next? The Origins of Computing and Data Analytics The origins of computing and began in the mid-1950s and started taking shape with the introduction of SQL in 1970: data analytics 1954: - “ ”, machine translation of Russian to English. Natural Language Processing (NLP) Georgetown-IBM experiment 1960: Punch Cards 1970: Structured Query Language (SQL) 1970s: - Create a language to “allow executives to build models without intermediaries” Interactive Financial Planning Systems 1972: - One of the earliest applications of modern computing, a natural language information retrieval system, helped geologists access, compare and evaluate chemical-analysis data on moon rock and soil composition C, LUNAR 1975: - The first Online Analytical Processing (OLAP) system, intended to analyse business data from different points of view Express 1979: - The first spreadsheet computer program VisiCalc 1980s: - “Computerized Collaborative Work System” Group Decision Support Systems The “Modern Data Stack” The "Modern Data Stack" is a set of technologies and tools used to collect, store, process, analyze, and visualize data in a well-integrated cloud-based platform. Although QlikView was pre-cloud, it is the earliest example of what most would recognize as an analytics dashboard used by modern platforms like Tableau and PowerBI: 1994: - “Dashboard-driven Analytics” QlikView 2003: Tableau 2009: - “Computational Search Engine” Wolfram Alpha 2015: PowerBI 2017: - “Search-driven Analytics” ThoughtSpot Paper, Query Languages, Spreadsheets, Dashboards, Search, what next? Some of the most innovative analytics applications, at least in terms of user experience, convert human language to some computational output: A tale as old as time, LUNAR was first developed in the 70s to help geologists access, compare, and evaluate chemical analysis data using natural language. Salesforce WikiSQL introduced the first extensive compendium of data built for the text-to-SQL use case but only contained simple SQL queries. The Yale Spider dataset introduced a benchmark for more complex queries, and most recently, BIRD introduced real-world “dirty” queries and efficiency scores to create a proper benchmark for text-to-SQL applications. Text-to-SQL: Wolfram Alpha, ThoughtSpot Text-to-Computational-Language: ChatGPT Advanced Data Analysis Text-to-Code: Is "Conversation-Driven Data Analytics" a natural evolution? UX of modern analytics interfaces like , becoming more intuitive, enabled by NLP and LLMs search and chat are evolving Analytics interfaces have origins in but decision-makers are still largely enabling decision-makers, reliant on data analysts Many decision-maker , best suited to “throwaway analytics” queries are ad-hoc Insight generation is a where many insights are gained in conversations about data, possibly with peers creative process The data analytics , from the imagination of analysis to the presentation of results workflow is disjointed Acknowledgments Dates for the section "The Origins of Computing and Data Analytics" thanks to and . https://web.paristech.com/hs-fs/file-2487731396.pdf http://dssresources.com/history/dsshistoryv28.html Also published here.

A Brief History of Computing and Data Analytics - From Punch Cards to the "Modern Data Stack"

কম্পিউটিং এবং ডেটা বিশ্লেষণের সংক্ষিপ্ত ইতিহাস - পাঞ্চ কার্ড থেকে "আধুনিক ডেটা স্ট্যাক" পর্যন্ত

Eine kurze Geschichte der Informatik und Datenanalyse – von Lochkarten bis zum „modernen Datenstapel“

Una breve historia de la informática y el análisis de datos: desde las tarjetas perforadas hasta la "pila de datos moderna"

Une brève histoire de l'informatique et de l'analyse de données - Des cartes perforées à la « pile de données moderne »

कंप्यूटिंग और डेटा एनालिटिक्स का संक्षिप्त इतिहास - पंच कार्ड से "आधुनिक डेटा स्टैक" तक

コンピューティングとデータ分析の歴史 - パンチカードから「最新のデータスタック」まで

컴퓨팅 및 데이터 분석의 간략한 역사 - 펀치 카드에서 "현대 데이터 스택"까지

Uma Breve História da Computação e Análise de Dados – Dos Cartões Perfurados à “Pilha de Dados Moderna”

Краткая история вычислений и анализа данных - от перфокарт до «современного стека данных»

Bilgi İşlem ve Veri Analitiğinin Kısa Tarihi - Delikli Kartlardan "Modern Veri Yığınına"

Sơ lược về lịch sử tính toán và phân tích dữ liệu - Từ thẻ đục lỗ đến "Ngăn xếp dữ liệu hiện đại"

计算和数据分析简史 - 从打孔卡到“现代数据堆栈”

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