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A Short History of the World, by H. G. Wells, is part of the HackerNoon Books Series. You can jump to any chapter in this book here . EGYPT, BABYLON AND ASSYRIA EGYPT, BABYLON AND ASSYRIA The Egyptians had never submitted very willingly to the rule of their Semitic shepherd kings and about 1600 A.D. a vigorous patriotic movement expelled these foreigners. Followed a new phase or revival for Egypt, a period known to Egyptologists as the New Empire. Egypt, which had not been closely consolidated before the Hyksos invasion, was now a united country; and the phase of subjugation and insurrection left her full of military spirit. The Pharaohs became aggressive conquerors. They had now acquired the war horse and the war chariot, which the Hyksos had brought to them. Under Thothmes III and Amenophis III Egypt had extended her rule into Asia as far as the Euphrates. We are entering now upon a thousand years of warfare between the once quite separated civilizations of Mesopotamia and the Nile. At first Egypt was ascendant. The great dynasties, the Seventeenth Dynasty, which included Thothmes III and Amenophis III and IV and a great queen Hatasu, and the Nineteenth, when Rameses II, supposed by some to have been the Pharaoh of Moses, reigned for sixty-seven years, raised Egypt to high levels of prosperity. In between there were phases of depression for Egypt, conquest by the Syrians and later conquest by the Ethiopians from the South. In Mesopotamia Babylon ruled, then the Hittites and the Syrians of Damascus rose to a transitory predominance; at one time the Syrians conquered Egypt; the fortunes of the Assyrians of Nineveh ebbed and flowed; sometimes the city was a conquered city; sometimes the Assyrians ruled in Babylon and assailed Egypt. Our space is too limited here to tell of the comings and goings of the armies of the Egyptians and of the various Semitic powers of Asia Minor, Syria and Mesopotamia. They were armies now provided with vast droves of war chariots, for the horse—still used only for war and glory—had spread by this time into the old civilizations from Central Asia. TEMPLE AT ABU SIMBEL Showing the statues of Rameses II at entrance Great conquerors appear in the dim light of that distant time and pass, Tushratta, King of Mitanni, who captured Nineveh, Tiglath Pileser I of Assyria who conquered Babylon. At last the Assyrians became the greatest military power of the time. Tiglath Pileser III conquered Babylon in 745 B.C. and founded what historians call the New Assyrian Empire. Iron had also come now into civilization out of the north; the Hittites, the precursors of the Armenians, had it first and communicated its use to the Assyrians, and an Assyrian usurper, Sargon II, armed his troops with it. Assyria became the first power to expound the doctrine of blood and iron. Sargon’s son Sennacherib led an army to the borders of Egypt, and was defeated not by military strength but by the plague. Sennacherib’s grandson Assurbanipal (who is also known in history by his Greek name of Sardanapalus) did actually conquer Egypt in 670 B.C. But Egypt was already a conquered country then under an Ethiopian dynasty. Sardanapalus simply replaced one conqueror by another. AVENUE OF SPHINXES Leading from the Nile to the great Temple of Karnak Photo: D. McLeish If one had a series of political maps of this long period of history, this interval of ten centuries, we should have Egypt expanding and contracting like an amœba under a microscope, and we should see these various Semitic states of the Babylonians, the Assyrians, the Hittites and the Syrians coming and going, eating each other up and disgorging each other again. To the west of Asia Minor there would be little Ægean states like Lydia, whose capital was Sardis, and Caria. But after about 1200 B.C. and perhaps earlier, a new set of names would come into the map of the ancient world from the north-east and from the north- west. These would be the names of certain barbaric tribes, armed with iron weapons and using horse-chariots, who were becoming a great affliction to the Ægean and Semitic civilizations on the northern borders. They all spoke variants of what once must have been the same language, Aryan. THE GREAT HYPOSTYLE HALL AT KARNAK Photo: D. McLeish Round the north-east of the Black and Caspian Seas were coming the Medes and Persians. Confused with these in the records of the time were Scythians and Samatians. From north-east or north-west came the Armenians, from the north- west of the sea-barrier through the Balkan peninsula came Cimmerians, Phrygians and the Hellenic tribes whom now we call the Greeks. They were raiders and robbers and plunderers of cities, these Ayrans, east and west alike. They were all kindred and similar peoples, hardy herdsmen who had taken to plunder. In the east they were still only borderers and raiders, but in the west they were taking cities and driving out the civilized Ægean populations. The Ægean peoples were so pressed that they were seeking new homes in lands beyond the Aryan range. Some were seeking a settlement in the delta of the Nile and being repulsed by the Egyptians; some, the Etruscans, seem to have sailed from Asia Minor to found a state in the forest wildernesses of middle Italy; some built themselves cities upon the south- east coasts of the Mediterranean and became later that people known in history as the Philistines. Of these Aryans who came thus rudely upon the scene of the ancient civilizations we will tell more fully in a later section. Here we note simply all this stir and emigration amidst the area of the ancient civilizations, that was set up by the swirl of the gradual and continuous advance of these Aryan barbarians out of the northern forests and wildernesses between 1600 and 600 B.C. And in a section to follow we must tell also of a little Semitic people, the Hebrews, in the hills behind the Phœnician and Philistine coasts, who began to be of significance in the world towards the end of this period. They produced a literature of very great importance in subsequent history, a collection of books, histories, poems, books of wisdom and prophetic works, the Hebrew Bible. In Mesopotamia and Egypt the coming of the Aryans did not cause fundamental changes until after 600 B.C. The flight of the Ægeans before the Greeks and even the destruction of Cnossos must have seemed a very remote disturbance to both the citizens of Egypt and of Babylon. Dynasties came and went in these cradle states of civilization, but the main tenor of human life went on, with a slow increase in refinement and complexity age by age. In Egypt the accumulated monuments of more ancient times—the pyramids were already in their third thousand of years and a show for visitors just as they are to- day—were supplemented by fresh and splendid buildings, more particularly in the time of the seventeenth and nineteenth dynasties. The great temples at Karnak and Luxor date from this time. All the chief monuments of Nineveh, the great temples, the winged bulls with human heads, the reliefs of kings and chariots and lion hunts, were done in these centuries between 1600 and 600 B.C., and this period also covers most of the splendours of Babylon. FRIEZE SHOWING EGYPTIAN FEMALE SLAVES CARRYING LUXURIOUS FOODS Photo: Jacques Boyer Both from Mesopotamia and Egypt we now have abundant public records, business accounts, stories, poetry and private correspondence. We know that life, for prosperous and influential people in such cities as Babylon and the Egyptian Thebes, was already almost as refined and as luxurious as that of comfortable and prosperous people to-day. Such people lived an orderly and ceremonious life in beautiful and beautifully furnished and decorated houses, wore richly decorated clothing and lovely jewels; they had feasts and festivals, entertained one another with music and dancing, were waited upon by highly trained servants, were cared for by doctors and dentists. They did not travel very much or very far, but boating excursions were a common summer pleasure both on the Nile and on the Euphrates. The beast of burthen was the ass; the horse was still used only in chariots for war and upon occasions of state. The mule was still novel and the camel, though it was known in Mesopotamia, had not been brought into Egypt. And there were few utensils of iron; copper and bronze remained the prevailing metals. Fine linen and cotton fabrics were known as well as wool. But there was no silk yet. Glass was known and beautifully coloured, but glass things were usually small. There was no clear glass and no optical use of glass. People had gold stoppings in their teeth but no spectacles on their noses. One odd contrast between the life of old Thebes or Babylon and modern life was the absence of coined money. Most trade was still done by barter. Babylon was financially far ahead of Egypt. Gold and silver were used for exchange and kept in ingots; and there were bankers, before coinage, who stamped their names and the weight on these lumps of precious metal. A merchant or traveller would carry precious stones to sell to pay for his necessities. Most servants and workers were slaves who were paid not money but in kind. As money came in slavery declined. A modern visitor to these crowning cities of the ancient world would have missed two very important articles of diet; there were no hens and no eggs. A French cook would have found small joy in Babylon. These things came from the East somewhere about the time of the last Assyrian empire. Religion like everything else had undergone great refinement. Human sacrifice for instance had long since disappeared; animals or bread dummies had been substituted for the victim. (But the Phœnicians and especially the citizens of Carthage, their greatest settlement in Africa, were accused, later of immolating human beings.) When a great chief had died in the ancient days it had been customary to sacrifice his wives and slaves and break spear and bow at his tomb so that he should not go unattended and unarmed in the spirit world. In Egypt there survived of this dark tradition the pleasant custom of burying small models of house and shop and servants and cattle with the dead, models that give us to-day the liveliest realization of the safe and cultivated life of these ancient people, three thousand years and more ago. THE TEMPLE OF HORUS AT EDFU Such was the ancient world before the coming of the Aryans out of the northern forests and plains. In India and China there were parallel developments. In the great valleys of both these regions agricultural city states of brownish peoples were growing up, but in India they do not seem to have advanced or coalesced so rapidly as the city states of Mesopotamia or Egypt. They were nearer the level of the ancient Sumerians or of the Maya civilization of America. Chinese history has still to be modernized by Chinese scholars and cleared of much legendary matter. Probably China at this time was in advance of India. Contemporary with the seventeenth dynasty in Egypt, there was a dynasty of emperors in China, the Shang dynasty, priest emperors over a loose-knit empire of subordinate kings. The chief duty of these early emperors was to perform the seasonal sacrifices. Beautiful bronze vessels from the time of the Shang dynasty still exist, and their beauty and workmanship compel us to recognize that many centuries of civilization must have preceded their manufacture. About HackerNoon Book Series: We bring you the most important technical, scientific, and insightful public domain books. This book is part of the public domain. H. G. Wells (2011). A Short History of the World. Urbana, Illinois: Project Gutenberg. Retrieved October 2022, from https://www.gutenberg.org/files/11870/11870-h/11870-h.htm This eBook is for the use of anyone anywhere at no cost and with almost no restrictions whatsoever. You may copy it, give it away or re-use it under the terms of the Project Gutenberg License included with this eBook or online at www.gutenberg.org , located at https://www.gutenberg.org/policy/license.html .

EGYPT, BABYLON AND ASSYRIA

SAN FRANCISCO, California, December 16th, 2024/Chainwire/--Forte has officially unveiled and launched the Forte Rules Engine, an open-source solution for developers to build safe, on-chain environments and manage digital asset economies for web3 apps. With the Rules Engine, developers can define and enforce rules, establish transaction guardrails, manage compliance obligations, and mitigate the risks of volatility and bad actors – all while supporting long-term digital asset utility and economic health. Developers can now utilize the Forte Rules Engine by visiting: forte.io/developers “The future of blockchain development is at a pivotal moment where the need to build strong foundations that foster safe, sustainable environments is paramount for blockchain projects and communities to thrive,” said Bela Pandya, CEO of Forte, “The Rules Engine was built to deliver these foundational technologies to developers that enable on-chain safeguards across a wide array of critical functions. From anti-dumping controls on airdrops to guardrails ensuring digital assets never interact with sanctioned wallets, and custom controls designed to mitigate volatility and market manipulation, the Rules Engine empowers developers to launch their projects confidently. This marks a new chapter for blockchain development, driven by compliance, economic stability, and a renewed sense of trust in blockchain development with much more on the horizon for the Forte Rules Engine.” Fully compatible with all EVM chains and web3 wallets, the Rules Engine provides developers the on-chain technology they need to build a safe, sustainable economy that their communities trust. This innovative suite of solutions aims to support: Safe Environments for Digital Assets The Forte Rules Engine employs on-chain guardrails to implement protective layers and safeguards that help mitigate risk and manage digital asset markets. The technology streamlines compliance navigation by leveraging Forte’s ecosystem of regulated partners to facilitate Know Your Customer (KYC) and Wallet protocols as well as sanctions enforcement, fostering responsible practices and building trust among users and communities. Through enhanced features such as Zero Knowledge (ZK) capabilities, developers can ensure privacy, verify identities, and assure transaction integrity. Economic Stability Developers will have access to a growing set of features designed to help launch, grow and scale a sustainable economy that their community can trust. This includes both templated and bespoke rulesets which can be designed to mitigate market volatility and manipulation, enforce token utility requirements, and effectively manage trading volume. The on-chain rulesets are designed for seamless integration and equipped with third-party integration options, ready to meet developer needs from day one. They offer the flexibility to adapt and evolve alongside the project, ensuring scalability and stability. Developers interested in leveraging the Forte Rules Engine for their next project can start building here. About Forte Forte provides open-source, on-chain solutions that foster safe environments and support healthy and stable digital asset economies. Our trust and privacy-preservation solutions empower developers to manage compliance risk, promote economic stability, and leverage instant liquidity. Developers can deploy flexible and adaptable blockchain solutions that evolve with their dynamic needs - fully compatible with all EVM chains and web3 wallets. Forte and its ecosystem partners are currently working with acclaimed developers to redefine the future of blockchain innovation. Contact Sibel Sunar 47 communications on behalf of Forte forte@fortyseven.com This story was distributed as a release by Chainwire under HackerNoon’s Business Blogging Program. Learn more about the program here. SAN FRANCISCO, California, December 16th, 2024/Chainwire/--Forte has officially unveiled and launched the Forte Rules Engine, an open-source solution for developers to build safe, on-chain environments and manage digital asset economies for web3 apps. With the Rules Engine, developers can define and enforce rules, establish transaction guardrails, manage compliance obligations, and mitigate the risks of volatility and bad actors – all while supporting long-term digital asset utility and economic health. Developers can now utilize the Forte Rules Engine by visiting: forte.io/developers forte.io/developers forte.io/developers “The future of blockchain development is at a pivotal moment where the need to build strong foundations that foster safe, sustainable environments is paramount for blockchain projects and communities to thrive,” said Bela Pandya, CEO of Forte, “The future of blockchain development is at a pivotal moment where the need to build strong foundations that foster safe, sustainable environments is paramount for blockchain projects and communities to thrive,” said Bela Pandya, CEO of Forte, “The Rules Engine was built to deliver these foundational technologies to developers that enable on-chain safeguards across a wide array of critical functions. From anti-dumping controls on airdrops to guardrails ensuring digital assets never interact with sanctioned wallets, and custom controls designed to mitigate volatility and market manipulation, the Rules Engine empowers developers to launch their projects confidently. This marks a new chapter for blockchain development, driven by compliance, economic stability, and a renewed sense of trust in blockchain development with much more on the horizon for the Forte Rules Engine.” “The Rules Engine was built to deliver these foundational technologies to developers that enable on-chain safeguards across a wide array of critical functions. From anti-dumping controls on airdrops to guardrails ensuring digital assets never interact with sanctioned wallets, and custom controls designed to mitigate volatility and market manipulation, the Rules Engine empowers developers to launch their projects confidently. This marks a new chapter for blockchain development, driven by compliance, economic stability, and a renewed sense of trust in blockchain development with much more on the horizon for the Forte Rules Engine.” Fully compatible with all EVM chains and web3 wallets, the Rules Engine provides developers the on-chain technology they need to build a safe, sustainable economy that their communities trust. This innovative suite of solutions aims to support: Safe Environments for Digital Assets The Forte Rules Engine employs on-chain guardrails to implement protective layers and safeguards that help mitigate risk and manage digital asset markets. The technology streamlines compliance navigation by leveraging Forte’s ecosystem of regulated partners to facilitate Know Your Customer (KYC) and Wallet protocols as well as sanctions enforcement, fostering responsible practices and building trust among users and communities. Through enhanced features such as Zero Knowledge (ZK) capabilities, developers can ensure privacy, verify identities, and assure transaction integrity. Economic Stability Developers will have access to a growing set of features designed to help launch, grow and scale a sustainable economy that their community can trust. This includes both templated and bespoke rulesets which can be designed to mitigate market volatility and manipulation, enforce token utility requirements, and effectively manage trading volume. The on-chain rulesets are designed for seamless integration and equipped with third-party integration options, ready to meet developer needs from day one. They offer the flexibility to adapt and evolve alongside the project, ensuring scalability and stability. Developers interested in leveraging the Forte Rules Engine for their next project can start building here . here here About Forte Forte provides open-source, on-chain solutions that foster safe environments and support healthy and stable digital asset economies. Our trust and privacy-preservation solutions empower developers to manage compliance risk, promote economic stability, and leverage instant liquidity. Forte Forte Developers can deploy flexible and adaptable blockchain solutions that evolve with their dynamic needs - fully compatible with all EVM chains and web3 wallets. Forte and its ecosystem partners are currently working with acclaimed developers to redefine the future of blockchain innovation. Contact Sibel Sunar 47 communications on behalf of Forte forte@fortyseven.com This story was distributed as a release by Chainwire under HackerNoon’s Business Blogging Program. Learn more about the program here. This story was distributed as a release by Chainwire under HackerNoon’s Business Blogging Program. Learn more about the program here . here here

Forte Unveils Open-Source Rules Engine For Safety And Economic Stability In Blockchain Development

Forte разкрива двигател с правила с отворен код за безопасност и икономическа стабилност при разработването на блокчейн

Forte presenta un motor de reglas de código abierto para la seguridad y la estabilidad económica en el desarrollo de blockchain

Forte-k iturburu irekiko arauen motorra aurkeztu du Segurtasunerako eta egonkortasun ekonomikorako Blockchain garapenean

Forte、ブロックチェーン開発の安全性と経済的安定性を実現するオープンソースのルールエンジンを発表

Forte Abimisi Moteur Ya Mibeko Ya source Ouverte Pona Sécurité Na Stabilité Economique Na Développement Blockchain

Forte dia namoaka maotera fitsipika misokatra ho an'ny fiarovana sy ny fahamarinan-toerana ara-toekarena amin'ny fampandrosoana Blockchain

Forte Memperkenalkan Enjin Peraturan Sumber Terbuka Untuk Keselamatan Dan Kestabilan Ekonomi Dalam Pembangunan Blockchain

Forte Yerekanye Gufungura-Inkomoko y'amategeko Moteri yumutekano no guhungabana mubukungu mugutezimbere

Forte ayaa daaha ka qaaday Matoorka Xeerarka Isha Furan ee Badbaadada iyo Deganaanshaha Dhaqaalaha ee Horumarinta Blockchain

Forte Inafichua Injini ya Sheria za Chanzo Huria kwa Usalama na Uthabiti wa Kiuchumi Katika Ukuzaji wa Blockchain

“Forte” “Blockchain” -iň ösüşinde howpsuzlyk we ykdysady durnuklylyk üçin açyk çeşme düzgünleriniň hereketlendirijisini açdy

Forte ra mắt công cụ mã nguồn mở để đảm bảo an toàn và ổn định kinh tế trong phát triển Blockchain

The Greedy Miner’s Dilemma: Flaws in DAG-Based Consensus Protocols

Table of Links Abstract and I. Introduction II. Background III. Problem Definition IV. DAG-Oriented Solutions V. Game Theoretical Analysis VI. Simulation Model VII. Evaluation VIII. Countermeasures IX. Discussion and Future Work X. Related Work XI. Conclusion and References VI. SIMULATION MODEL We created a simulation model to conduct various experiments investigating the behavior of DAG-PROTOCOL under incentive attacks related to the problems identified in Sec. III and thus Hypothesis 1. Some experiments were designed to provide empirical evidence for the conclusions from Sec. V. A. Abstraction of DAG-PROTOCOL For evaluation purposes, we simulated the DAGPROTOCOL (with RTS) by modeling the following aspects: • All blocks in DAG are deterministically ordered. • The mining rewards consist of transaction fees only. • A fee of a particular transaction is awarded only to a miner of the block that includes the transaction as the first one in the sequence of totally ordered blocks. Also, in terms of PHANTOM/GHOSTDAG terminology, we generalize and do not reduce transaction fees concerning the delay from “appearing” of the block until it is strongly connected to the DAG. Hence, we utilize γ = 1. In other words, for each block A, the discount function does not penalize a block according to its gap parameter c(A), i.e. γ(c(A)) = 1. Such a setting is optimistic for honest miners and maximizes their profits from transaction fees when following the RTS strategy. This abstraction enables us to model the concerned problems of considered DAG-PROTOCOLS (see Sec. IV). B. (Simple) Network Topology We created a simple network topology that is convenient for proof-of-concept simulations and encompasses some important aspects of the real-world blockchain network. In particular, we were interested in emulating the network propagation delay τ to be similar to in Bitcoin (i.e., ∼ 5s at most of the time in 2022), but using a small ring topology. To create such a topology, we assumed that the Bitcoin network contains 7592 nodes, according to the snapshot of reachable Bitcoin nodes found on May 24, 2022.[11] In Bitcoin core, the default value of the consensus node’s peers is set to 8 (i.e., the node degree).[12] Therefore, the maximum number of hops that a gossiped message requires to reach all consensus nodes in the network is ∼ 4.29 (i.e., log8(7592)). Moreover, if we were to assume 2 − 3x more independent blockchain clients (that are not consensus nodes), then this number would be increased to 4.83–4.96. To model this environment, we used the ring network topology with 10 consensus nodes (see Fig. 4), which sets the maximum value of hops required to propagate a message to 5. Next, we set the inter-node propagation delay ∂τ to 1s, which fits assumed τ (i.e., 5s / 5 hops = 1s). Later, we will create more complex network topology (see Sec. VII-E). C. Simulator There are many simulators [36] that model blockchain protocols, mainly focusing on network delays, different consensus protocols, and behaviors of specific attacks (e.g., SimBlock [4], Blocksim [1], Bitcoin-Simulator [17], etc.). However, none of these simulators was sufficient for our purposes due to missing support for multiple chains (or their abstraction) and incentive schemes assumed in DAG-PROTOCOLS. To verify Hypothesis 1, we built a simulator that focuses on the mentioned problems of DAG-PROTOCOLS. In detail, we started with the Bitcoin mining simulator [14], which is a discrete event simulator for the PoW mining on a single chain, enabling a simulation of network propagation delay within a specified network topology. We extended this simulator to support DAG-PROTOCOLs, enabling us to monitor transaction duplicity, throughput, and relative profits of miners with regard to their mining power. The simulator is written in C++. The implementation utilizes the Boost library [7] for better performance and the special structures for simulation, such as the multi-index mempool [22], enabling effective management of the mempool in the case of any transaction selection strategy.[13] In addition, we added more simulation complexity to simulate each block, including the particular transactions (as opposed to simulating only the number of transactions in a block [14]). Most importantly, we implemented two different transaction selection strategies – greedy and random. For demonstration purposes, we implemented the exponential distribution of transaction fees in mempool, based on several graph cuts of fee distributions in mempool of Bitcoin from [20].[14] Our simulator is available at https://www.dropbox. com/s/vqpgqqy01qh1pcv/. Authors:
(1) Martin Peresıni, Brno University of Technology, Faculty of Information Technology (iperesini@fit.vut.cz);
(2) Ivan Homoliak, Brno University of Technology, Faculty of Information Technology (ihomoliak@fit.vut);
(3) Federico Matteo Bencic, University of Zagreb, Faculty of Electrical Engineering and Computing (federico-matteo.bencic@fer.hr);
(4) Martin Hruby, Brno University of Technology, Faculty of Information Technology (hruby@fit.vut.cz);
(5) Kamil Malinka, Brno University of Technology, Faculty of Information Technology (malinka@fit.vut). This paper is available on arxiv under CC BY 4.0 DEED license. https://bitnodes.io/nodes/


Nevertheless, the node degree is often higher than 8 in reality [32].


Greedy transaction strategy requires a mempool with transactions ordered by fees, while RTS strategy requires the hash-map data structure. Therefore, it is challenging to efficiently utilize them at the same time.


Distribution of transaction fees in mempool might change over time; however, it mostly preserves the low number of high-fee transactions in contrast to the higher number of low-fee transactions, which is common with the exponential distribution. Table of Links Abstract and I. Introduction Abstract and I. Introduction II. Background II. Background III. Problem Definition III. Problem Definition IV. DAG-Oriented Solutions IV. DAG-Oriented Solutions V. Game Theoretical Analysis V. Game Theoretical Analysis VI. Simulation Model VI. Simulation Model VII. Evaluation VII. Evaluation VIII. Countermeasures VIII. Countermeasures IX. Discussion and Future Work IX. Discussion and Future Work X. Related Work X. Related Work XI. Conclusion and References XI. Conclusion and References VI. SIMULATION MODEL We created a simulation model to conduct various experiments investigating the behavior of DAG-PROTOCOL under incentive attacks related to the problems identified in Sec. III and thus Hypothesis 1. Some experiments were designed to provide empirical evidence for the conclusions from Sec. V. A. Abstraction of DAG-PROTOCOL A. Abstraction of DAG-PROTOCOL A. Abstraction of DAG-PROTOCOL For evaluation purposes, we simulated the DAGPROTOCOL (with RTS) by modeling the following aspects: • All blocks in DAG are deterministically ordered. • The mining rewards consist of transaction fees only. • A fee of a particular transaction is awarded only to a miner of the block that includes the transaction as the first one in the sequence of totally ordered blocks. Also, in terms of PHANTOM/GHOSTDAG terminology, we generalize and do not reduce transaction fees concerning the delay from “appearing” of the block until it is strongly connected to the DAG. Hence, we utilize γ = 1. In other words, for each block A, the discount function does not penalize a block according to its gap parameter c(A), i.e. γ(c(A)) = 1. Such a setting is optimistic for honest miners and maximizes their profits from transaction fees when following the RTS strategy. This abstraction enables us to model the concerned problems of considered DAG-PROTOCOLS (see Sec. IV). B. (Simple) Network Topology B. (Simple) Network Topology B. (Simple) Network Topology We created a simple network topology that is convenient for proof-of-concept simulations and encompasses some important aspects of the real-world blockchain network. In particular, we were interested in emulating the network propagation delay τ to be similar to in Bitcoin (i.e., ∼ 5s at most of the time in 2022), but using a small ring topology. To create such a topology, we assumed that the Bitcoin network contains 7592 nodes, according to the snapshot of reachable Bitcoin nodes found on May 24, 2022.[11] In Bitcoin core, the default value of the consensus node’s peers is set to 8 (i.e., the node degree).[12] Therefore, the maximum number of hops that a gossiped message requires to reach all consensus nodes in the network is ∼ 4.29 (i.e., log8(7592)). Moreover, if we were to assume 2 − 3x more independent blockchain clients (that are not consensus nodes), then this number would be increased to 4.83–4.96. To model this environment, we used the ring network topology with 10 consensus nodes (see Fig. 4), which sets the maximum value of hops required to propagate a message to 5. Next, we set the inter-node propagation delay ∂τ to 1s, which fits assumed τ (i.e., 5s / 5 hops = 1s). Later, we will create more complex network topology (see Sec. VII-E). C. Simulator C. Simulator C. Simulator There are many simulators [36] that model blockchain protocols, mainly focusing on network delays, different consensus protocols, and behaviors of specific attacks (e.g., SimBlock [4], Blocksim [1], Bitcoin-Simulator [17], etc.). However, none of these simulators was sufficient for our purposes due to missing support for multiple chains (or their abstraction) and incentive schemes assumed in DAG-PROTOCOLS. To verify Hypothesis 1, we built a simulator that focuses on the mentioned problems of DAG-PROTOCOLS. In detail, we started with the Bitcoin mining simulator [14], which is a discrete event simulator for the PoW mining on a single chain, enabling a simulation of network propagation delay within a specified network topology. We extended this simulator to support DAG-PROTOCOLs, enabling us to monitor transaction duplicity, throughput, and relative profits of miners with regard to their mining power. The simulator is written in C++. The implementation utilizes the Boost library [7] for better performance and the special structures for simulation, such as the multi-index mempool [22], enabling effective management of the mempool in the case of any transaction selection strategy.[13] In addition, we added more simulation complexity to simulate each block, including the particular transactions (as opposed to simulating only the number of transactions in a block [14]). Most importantly, we implemented two different transaction selection strategies – greedy and random. For demonstration purposes, we implemented the exponential distribution of transaction fees in mempool, based on several graph cuts of fee distributions in mempool of Bitcoin from [20].[14] Our simulator is available at https://www.dropbox. com/s/vqpgqqy01qh1pcv/. Authors: (1) Martin Peresıni, Brno University of Technology, Faculty of Information Technology (iperesini@fit.vut.cz); (2) Ivan Homoliak, Brno University of Technology, Faculty of Information Technology (ihomoliak@fit.vut); (3) Federico Matteo Bencic, University of Zagreb, Faculty of Electrical Engineering and Computing (federico-matteo.bencic@fer.hr); (4) Martin Hruby, Brno University of Technology, Faculty of Information Technology (hruby@fit.vut.cz); (5) Kamil Malinka, Brno University of Technology, Faculty of Information Technology (malinka@fit.vut). Authors: Authors: (1) Martin Peresıni, Brno University of Technology, Faculty of Information Technology (iperesini@fit.vut.cz); (2) Ivan Homoliak, Brno University of Technology, Faculty of Information Technology (ihomoliak@fit.vut); (3) Federico Matteo Bencic, University of Zagreb, Faculty of Electrical Engineering and Computing (federico-matteo.bencic@fer.hr); (4) Martin Hruby, Brno University of Technology, Faculty of Information Technology (hruby@fit.vut.cz); (5) Kamil Malinka, Brno University of Technology, Faculty of Information Technology (malinka@fit.vut). 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 available on arxiv https://bitnodes.io/nodes/ Nevertheless, the node degree is often higher than 8 in reality [32]. Greedy transaction strategy requires a mempool with transactions ordered by fees, while RTS strategy requires the hash-map data structure. Therefore, it is challenging to efficiently utilize them at the same time. Distribution of transaction fees in mempool might change over time; however, it mostly preserves the low number of high-fee transactions in contrast to the higher number of low-fee transactions, which is common with the exponential distribution. https://bitnodes.io/nodes/ https://bitnodes.io/nodes/ Nevertheless, the node degree is often higher than 8 in reality [32]. Nevertheless, the node degree is often higher than 8 in reality [32]. Greedy transaction strategy requires a mempool with transactions ordered by fees, while RTS strategy requires the hash-map data structure. Therefore, it is challenging to efficiently utilize them at the same time. Greedy transaction strategy requires a mempool with transactions ordered by fees, while RTS strategy requires the hash-map data structure. Therefore, it is challenging to efficiently utilize them at the same time. Distribution of transaction fees in mempool might change over time; however, it mostly preserves the low number of high-fee transactions in contrast to the higher number of low-fee transactions, which is common with the exponential distribution. Distribution of transaction fees in mempool might change over time; however, it mostly preserves the low number of high-fee transactions in contrast to the higher number of low-fee transactions, which is common with the exponential distribution.

Simulation Model for DAG-PROTOCOL Under Incentive Attacks 

Authors:
(1) Renato P. dos Santos, CIAGE – Centre for Generative Artificial Intelligence in Cognition and Education. Table of Links Abstract and Introduction Materials And Methods Results and Analyses Prompts and generated texts Conceptualizing chemical reactions Deepening on understanding of chemical reactions Question about combustion Question about a graph of gases turning into water over time Question about the difference between atoms, molecules, and moles Deepening on the concept of mole Question about changing of state Question about an animated representation of water molecules undergoing phase changes Question about plasma, a state of matter Question about chemical bondings Question about illustration of chemical bonds Question about the essence of the type of chemical bonding Further analysis Conclusions Limitations of the study and possible future studies Author Contributions, Conflicts of interest, Acknowledgements, and References Abstract This research delves into the comparative advantages of Generative AI chatbots (GenAIbots) - ChatGPT, Bing Chat, Bard, and Claude - in the context of Chemistry education, framed within a constructivist perspective. Our primary objective was to identify which of these four AI tools is more effective for enhancing Chemistry learning. Employing a single-case study approach, we scrutinised interaction logs between the AI systems and a simulated student persona during Chemistry learning simulations, incorporating Content Analysis methodology to delve deeper into the discourse. Our findings underscore these tools' potential as "agents-to-think-with", enhancing critical thinking, problem-solving, comprehension, creativity, and tailored learning. Especially noteworthy is their ability to stimulate learners through Socratic-like questioning, aligning with constructionist principles. The research emphasises the pivotal role of prompt crafting to coax desired responses from GenAIbots, engendering iterative reflections. It also highlights the need for robust educator training to infuse these technologies into educational settings. Conclusively, while ChatGPT, Bing Chat, Bard, and Claude are poised to enrich Chemistry education by fostering dynamic, inclusive learning experiences, ChatGPT stood out, decisively surpassing Bing Chat's performance. Bard and Claude trailed closely, with all three showcasing a more in-depth, precise, and nuanced understanding, underscoring ChatGPT's adeptness at contextual comprehension. Keywords: Chemistry education, ChatGPT, Bing Chat, Bard, Claude, Artificial Intelligence in Education, agents-to-think-with Introduction Chemistry, a core Science Education subject, clarifies matter's properties and transformations, thus critically shaping our daily lives (Dunlop et al., 2020). However, Chemistry is also a complex and dynamic field that necessitates a profound grasp of fundamental concepts and principles, and it is sometimes difficult for students to associate real-life circumstances with abstract chemistry concepts (Dewi et al., 2021). Research in Chemistry Education seeks effective strategies to alleviate these learning difficulties (Permatasari et al., 2022; Timilsena et al., 2022; Tümay, 2016). Timilsena et al. (2022) identified difficulties in understanding the abstract nature of chemical reactions and factors such as inadequate teaching materials and curriculum complexity, emphasising the need for effective teaching strategies and tools. Tümay (2016) discussed students' struggles in understanding fundamental chemistry concepts and emphasised the importance of addressing misconceptions and learning difficulties. Dewi et al. (2021) underscored the need for critical thinking skills and the integration of digital technology to enhance the quality of Chemistry education for Generation Z students. Dunlop et al. (2020) proposed introducing philosophical dialogue in higher education to address undergraduate Chemistry students' challenges, suggesting that it can stimulate new ways of thinking. In 2023, Castro Nascimento and Pimentel undertook a study to evaluate the proficiency of the ChatGPT model by having it respond to five distinct tasks across various subfields of chemistry (Castro Nascimento & Pimentel, 2023). These tasks included converting compound names to their SMILES chemical representation and vice versa, procuring information on the octanol-water partition coefficient of chemical compounds, extracting structural information on coordination compounds, determining the water solubility of polymers, and identifying the molecular point groups of simple molecular compounds. The unsatisfactory outcomes highlighted potential limitations in the model's ability to adequately address these specific chemistry-related queries. Notably, the suboptimal performance observed might be attributed to utilising an antiquated version of ChatGPT, specifically the GPT-3 model introduced by OpenAI in 2020 (Brown et al., 2020). In a separate study conducted in the same year, Leon and Vidhani (2023) explored the reliability of ChatGPT responses within the framework of an introductory college-level Chemistry course. Their findings indicated a significant reliability concern, with ChatGPT failing to secure a score above 37%. Such a performance implies that learners relying on this tool for study support would predominantly receive incorrect responses, with the variability of the tool leading to different responses for individual learners. Although the authors didn't specify which version of ChatGPT they utilised in their study, their reference to "ChatGPT's free original version" and the citation of Floridi and Chiriatti's (2020) research on ChatGPT-3 hint at the likelihood that they employed this version. Additionally, Pimentel et al. (2023) delved into the efficacy of ChatGPT versions 3 and 4 in responding to intricate questions spanning six topics in Chemistry. While they concluded that both versions were currently inadequate in addressing the nuances of complex topics, they also observed noteworthy advancements from ChatGPT-3 to ChatGPT-4. Such progress signals promising potential for the tool to aid scientists in future literature reviews, surveys, and educational endeavours. Generative AI-powered chatbots (GenAIbots), including ChatGPT, Bing Chat, Bard, and Claude, have been introduced as innovative solutions to persistent challenges in Chemistry education (Baidoo-Anu & Owusu Ansah, 2023; Taylor et al., 2022). Their emergence offers a dynamic, inclusive educational environment, transforming how complex concepts are conveyed and understood. These GenAIbots simplify intricate topics, promote self-reflection, engage users in stimulating dialogues, facilitate personalised learning, and enhance critical thinking, collaboration, and cognitive development (Okonkwo & Ade-Ibijola, 2021). As a result, they have played a pivotal role in revolutionising Chemistry education. In the discourse on agency, traditional theories, such as the ones of Anscombe and Davidson, attribute actions to entities based on representational mental states encompassing desires, beliefs, and intentions. However, alternative perspectives, drawing from the works of Heider and Simmel, Dennett, Davidson, and Barandiaran et al., challenge this representation dependent view, suggesting potential agency without such mental representations (Schlosser, 2019). When considering GenAIbots within these frameworks, the entity's ability to respond based on training contrasts with its lack of self-awareness, intentions, and proactive behaviour— characteristics central to the agency. Consequently, while dominant philosophical and cognitive models would not grant ChatGPT agency, more expansive interpretations, as offered by these cited thinkers, could accept it. Building on this foundation and inspired by Melanie Swan's suggestion, we have expanded upon Papert's (1980) concept of "objects-to-think-with" to introduce “agents-to-thinkwith.” This positions GenAIbots as integral participants in the educational path, echoing Turkle's (1984) concept of "metacognitive machines," in the sense that they assist in enhancing one's awareness of their own cognitive processes, i.e., "thinking about thinking" as described by Flavell (1976). In Latour's (1991) perspective, we can perceive GenAIbots as "hybrids," which blur the boundaries between humans and non-human entities. Such a perspective further aligns with Swan's (2015) vision of a future where augmented humans and AI collaborate and thrive symbiotically. While GenAIbots posit themselves as powerful metacognitive educational tools, promoting critical thinking, problem-solving, and deep comprehension of concepts, it's imperative to acknowledge their limitations. For instance, there's the possibility of generating nonsensical or inaccurate content, as OpenAI (2023) highlighted. Nevertheless, the unique ability of GenAIbots to provide instant feedback, introduce diverse perspectives, and foster an interactive engagement with complex ideas solidifies their position as influential agents-to-think-with in the realm of Chemistry education. This paper is available on arxiv under CC BY-SA 4.0 DEED license. Authors: (1) Renato P. dos Santos, CIAGE – Centre for Generative Artificial Intelligence in Cognition and Education. Authors: Authors: (1) Renato P. dos Santos, CIAGE – Centre for Generative Artificial Intelligence in Cognition and Education. Table of Links Abstract and Introduction Abstract and Introduction Materials And Methods Materials And Methods Results and Analyses Results and Analyses Prompts and generated texts Prompts and generated texts Conceptualizing chemical reactions Conceptualizing chemical reactions Deepening on understanding of chemical reactions Deepening on understanding of chemical reactions Question about combustion Question about combustion Question about a graph of gases turning into water over time Question about a graph of gases turning into water over time Question about the difference between atoms, molecules, and moles Question about the difference between atoms, molecules, and moles Deepening on the concept of mole Deepening on the concept of mole Question about changing of state Question about changing of state Question about an animated representation of water molecules undergoing phase changes Question about an animated representation of water molecules undergoing phase changes Question about plasma, a state of matter Question about plasma, a state of matter Question about chemical bondings Question about chemical bondings Question about illustration of chemical bonds Question about illustration of chemical bonds Question about the essence of the type of chemical bonding Question about the essence of the type of chemical bonding Further analysis Further analysis Conclusions Conclusions Limitations of the study and possible future studies Limitations of the study and possible future studies Author Contributions, Conflicts of interest, Acknowledgements, and References Author Contributions, Conflicts of interest, Acknowledgements, and References Abstract This research delves into the comparative advantages of Generative AI chatbots (GenAIbots) - ChatGPT, Bing Chat, Bard, and Claude - in the context of Chemistry education, framed within a constructivist perspective. Our primary objective was to identify which of these four AI tools is more effective for enhancing Chemistry learning. Employing a single-case study approach, we scrutinised interaction logs between the AI systems and a simulated student persona during Chemistry learning simulations, incorporating Content Analysis methodology to delve deeper into the discourse. Our findings underscore these tools' potential as "agents-to-think-with", enhancing critical thinking, problem-solving, comprehension, creativity, and tailored learning. Especially noteworthy is their ability to stimulate learners through Socratic-like questioning, aligning with constructionist principles. The research emphasises the pivotal role of prompt crafting to coax desired responses from GenAIbots, engendering iterative reflections. It also highlights the need for robust educator training to infuse these technologies into educational settings. Conclusively, while ChatGPT, Bing Chat, Bard, and Claude are poised to enrich Chemistry education by fostering dynamic, inclusive learning experiences, ChatGPT stood out, decisively surpassing Bing Chat's performance. Bard and Claude trailed closely, with all three showcasing a more in-depth, precise, and nuanced understanding, underscoring ChatGPT's adeptness at contextual comprehension. Keywords: Chemistry education, ChatGPT, Bing Chat, Bard, Claude, Artificial Intelligence in Education, agents-to-think-with Keywords: Introduction Chemistry, a core Science Education subject, clarifies matter's properties and transformations, thus critically shaping our daily lives (Dunlop et al., 2020). However, Chemistry is also a complex and dynamic field that necessitates a profound grasp of fundamental concepts and principles, and it is sometimes difficult for students to associate real-life circumstances with abstract chemistry concepts (Dewi et al., 2021). Research in Chemistry Education seeks effective strategies to alleviate these learning difficulties (Permatasari et al., 2022; Timilsena et al., 2022; Tümay, 2016). Timilsena et al. (2022) identified difficulties in understanding the abstract nature of chemical reactions and factors such as inadequate teaching materials and curriculum complexity, emphasising the need for effective teaching strategies and tools. Tümay (2016) discussed students' struggles in understanding fundamental chemistry concepts and emphasised the importance of addressing misconceptions and learning difficulties. Dewi et al. (2021) underscored the need for critical thinking skills and the integration of digital technology to enhance the quality of Chemistry education for Generation Z students. Dunlop et al. (2020) proposed introducing philosophical dialogue in higher education to address undergraduate Chemistry students' challenges, suggesting that it can stimulate new ways of thinking. In 2023, Castro Nascimento and Pimentel undertook a study to evaluate the proficiency of the ChatGPT model by having it respond to five distinct tasks across various subfields of chemistry (Castro Nascimento & Pimentel, 2023). These tasks included converting compound names to their SMILES chemical representation and vice versa, procuring information on the octanol-water partition coefficient of chemical compounds, extracting structural information on coordination compounds, determining the water solubility of polymers, and identifying the molecular point groups of simple molecular compounds. The unsatisfactory outcomes highlighted potential limitations in the model's ability to adequately address these specific chemistry-related queries. Notably, the suboptimal performance observed might be attributed to utilising an antiquated version of ChatGPT, specifically the GPT-3 model introduced by OpenAI in 2020 (Brown et al., 2020). In a separate study conducted in the same year, Leon and Vidhani (2023) explored the reliability of ChatGPT responses within the framework of an introductory college-level Chemistry course. Their findings indicated a significant reliability concern, with ChatGPT failing to secure a score above 37%. Such a performance implies that learners relying on this tool for study support would predominantly receive incorrect responses, with the variability of the tool leading to different responses for individual learners. Although the authors didn't specify which version of ChatGPT they utilised in their study, their reference to "ChatGPT's free original version" and the citation of Floridi and Chiriatti's (2020) research on ChatGPT-3 hint at the likelihood that they employed this version. Additionally, Pimentel et al. (2023) delved into the efficacy of ChatGPT versions 3 and 4 in responding to intricate questions spanning six topics in Chemistry. While they concluded that both versions were currently inadequate in addressing the nuances of complex topics, they also observed noteworthy advancements from ChatGPT-3 to ChatGPT-4. Such progress signals promising potential for the tool to aid scientists in future literature reviews, surveys, and educational endeavours. Generative AI-powered chatbots (GenAIbots), including ChatGPT, Bing Chat, Bard, and Claude, have been introduced as innovative solutions to persistent challenges in Chemistry education (Baidoo-Anu & Owusu Ansah, 2023; Taylor et al., 2022). Their emergence offers a dynamic, inclusive educational environment, transforming how complex concepts are conveyed and understood. These GenAIbots simplify intricate topics, promote self-reflection, engage users in stimulating dialogues, facilitate personalised learning, and enhance critical thinking, collaboration, and cognitive development (Okonkwo & Ade-Ibijola, 2021). As a result, they have played a pivotal role in revolutionising Chemistry education. In the discourse on agency, traditional theories, such as the ones of Anscombe and Davidson, attribute actions to entities based on representational mental states encompassing desires, beliefs, and intentions. However, alternative perspectives, drawing from the works of Heider and Simmel, Dennett, Davidson, and Barandiaran et al., challenge this representation dependent view, suggesting potential agency without such mental representations (Schlosser, 2019). When considering GenAIbots within these frameworks, the entity's ability to respond based on training contrasts with its lack of self-awareness, intentions, and proactive behaviour— characteristics central to the agency. Consequently, while dominant philosophical and cognitive models would not grant ChatGPT agency, more expansive interpretations, as offered by these cited thinkers, could accept it. Building on this foundation and inspired by Melanie Swan's suggestion, we have expanded upon Papert's (1980) concept of "objects-to-think-with" to introduce “agents-to-thinkwith.” This positions GenAIbots as integral participants in the educational path, echoing Turkle's (1984) concept of "metacognitive machines," in the sense that they assist in enhancing one's awareness of their own cognitive processes, i.e., "thinking about thinking" as described by Flavell (1976). In Latour's (1991) perspective, we can perceive GenAIbots as "hybrids," which blur the boundaries between humans and non-human entities. Such a perspective further aligns with Swan's (2015) vision of a future where augmented humans and AI collaborate and thrive symbiotically. While GenAIbots posit themselves as powerful metacognitive educational tools, promoting critical thinking, problem-solving, and deep comprehension of concepts, it's imperative to acknowledge their limitations. For instance, there's the possibility of generating nonsensical or inaccurate content, as OpenAI (2023) highlighted. Nevertheless, the unique ability of GenAIbots to provide instant feedback, introduce diverse perspectives, and foster an interactive engagement with complex ideas solidifies their position as influential agents-to-think-with in the realm of Chemistry education. This paper is available on arxiv under CC BY-SA 4.0 DEED license. This paper is available on arxiv under CC BY-SA 4.0 DEED license. available on arxiv

Enhancing Chemistry Learning with ChatGPT, Bing Chat, Bard, and Claude as Agents-to-Think-With

ChatGPT, Bing Chat, Bard, এবং Claude-এর সাথে এজেন্ট-টু-থিঙ্ক-এর মাধ্যমে রসায়ন শেখার উন্নতি করা

Verbesserung des Chemielernens mit ChatGPT, Bing Chat, Bard und Claude als Denkagenten

Mejora del aprendizaje de química con ChatGPT, Bing Chat, Bard y Claude como agentes con los que pensar

Améliorer l'apprentissage de la chimie avec ChatGPT, Bing Chat, Bard et Claude en tant qu'agents avec lesquels penser

चैटजीपीटी, बिंग चैट, बार्ड और क्लाउड के साथ रसायन विज्ञान सीखने को बेहतर बनाना, एजेंट-टू-थिंक-विथ

ChatGPT、Bing Chat、Bard、Claude をエージェントとして利用して化学学習を強化する

ChatGPT, Bing Chat, Bard 및 Claude를 생각하는 에이전트로 사용하여 화학 학습 강화

Aprimorando o aprendizado de química com ChatGPT, Bing Chat, Bard e Claude como agentes para pensar

Улучшение изучения химии с помощью ChatGPT, Bing Chat, Bard и Claude в качестве агентов для размышлений

Birlikte Düşünülecek Aracılar Olarak ChatGPT, Bing Chat, Bard ve Claude ile Kimya Öğrenimini Geliştirme

Nâng cao việc học hóa học với ChatGPT, Bing Chat, Bard và Claude với tư cách là Đại lý để suy nghĩ

使用 ChatGPT、Bing Chat、Bard 和 Claude 作为思考代理来增强化学学习

VERSES closed out 2023 with an important video titled, The Year in AI 2023. If you missed it, take a look here: https://youtu.be/2NAO9qiuhww?si=zAH58k6NU00uL9qD&embedable=true What a mind-blowing year it’s been for Generative AI. We’ve all seen the multitude of 2023 wrap-up posts recapping the staggering growth of these amazing tools. I, for one, cannot imagine going back to working without them. The amount of time tools like ChatGPT, Claude, Fireflies, and others have saved me in tasks I can now perform in minutes that would have taken me days to complete beforehand. It really has been a great year for productivity! For last year’s words belong to last year’s language. And nest year’s words await another voice.” - T.S. Eliot 2023 is behind us now, and standing here on January 1st, 2024, a new year awaits with new technological developments that we can only imagine. These next years are primed to change the experience of living a human life on every conceivable level. One change we are definitely going to see this year is technology, which is altering everything we know about computing and the relationship between humans and machines. I’m referring to technology beyond the tools of Generative AI. The actual framework of computing is changing, and VERSES AI is leading that change. At the end of November 2023, at the , NVIDIA Founder Jensen Huang said: DealBook Summit “ ” You can’t solve this new way of doing computing by just designing a chip. Every aspect of the computer has fundamentally changed. And so everything from networking, to the switching, to the way computers are designed, to the chips themselves, all of the software that sits on top of it, and the methodology that pulls it all together — it’s a big deal because it’s a complete reinvention of the computer industry. And now we have a trillion dollar worth of data centers in the world, all of that’s going to get re-tooled. That’s the amazing thing. We are in the beginning of a brand new generation of computing. It hasn’t been reinvented in sixty years. This is why it’s such a big deal. It’s hard for people to wrap their head around it, but that was the great observation that we made. It includes a chip, but it’s not about that chip. As we begin this new year, it’s important to understand that Generative AI is not in competition with VERSES AI. They are entirely different kinds of AI, and they serve a different purpose. Generative AI is a tool within the framework of the next era of computing, and VERSES has built that framework. The next era of our internet — a common global network linking all legacy, current, and future technologies together with a common language for all, VERSES built the new protocol (HSTP and HSML) for it four years ago. Then they donated it to the public, because no one can own the internet. But they knew it was necessary for technology to be involved in this next era of computing. They knew it was necessary to establish a world model of real-time context, interoperability, interaction, and perception for building the AI technology of their vision for the future. At that time, the IEEE (the world’s largest core standards body) declared the Spatial Web Protocol “A Public Imperative” — their highest designation. The global core standards — socio-technical standards — for this new network protocol have been in development with the IEEE in the years since. In the meantime, VERSES has built the first platform to facilitate the world’s interaction within this new network — a network of shared intelligence powered by AI that perceives, learns, and acts on the world following the very principles of biological intelligence. It’s been a year since I published the first article of a Series titled “ ?” on December 30, 2022. In Part 1 of this series, I began to introduce Active Inference AI to my readers, explaining how it differs from Deep Learning. In this particular article, I touched on the VERSES’ AGI roadmap, ending the article with this: The Spatial Web and the Era of AI — What is Active Inference “Active Inference AI inside of Genius™ has access to the entire Spatial Web with IoT sensors and cameras and all context markers attached to all objects within all space and time. This AI becomes an ecosystem of Intelligent Agents that are all interoperable over HSTP. This Collective Intelligence is trained on realtime data and evolves making decisions and updating its interior model based on what is happening NOW, not on historical datasets. This is the missing piece of the data puzzle that gets us to Artificial General Intelligence. We now have the world model, and we now possess the ability for continuous and adaptive context markers enabling a cognitive model of this world and the ability to compute awareness. Dan Mapes, President, and Co-Founder, , and Director of ” “For the first time in history, starting now, we have the roadmap. AGI is now possible.” — VERSES AI The Spatial Web Foundation And here we are one year later… VERSES is coming in hot, announcing its AGI breakthrough just days before Christmas, along with its Genius platform and technology rollout timeline for 2024. After the past couple of years of private pilots and international programs, they’ve been teasing out announcements of their initial very strategic Beta platform partners every week during the last couple of months of 2023. So far, they’ve announced , , , , and , with more announcements to come on the first of this year. If you aren’t familiar with these companies, look them up. I think you’ll start to see the bigger picture unfolding. NASA Volvo Cortical Labs Nalantis SimWell This is HUGE people. Computing is changing on every basic and fundamental level. Generative AI is one piece of that computing puzzle. It’s a massive tool in this next era, but it’s not the framework for computing itself. What VERSES is building IS that framework, and the AI they are building will be a kind of overseeing AI for all the technology within this shared intelligent network of computing. A kind of AI that mimics biological intelligence. A safe AI that is programmable, explainable, trustworthy, and governable. One that safeguards our privacy on a computing network that solves the biggest problems and threats within the world wide web. This is AI that can be trusted to run mission-critical operations for smart city infrastructure, the supply chain, hospitals, airports, and so much more: A nervous system for the network, a nervous system for the planet. And one of the best parts: this is AI that is energy efficient in the same way as the human brain. The need for super-powerful processing chips will remain for Generative AI tools but will not be necessary for the distributed intelligence across this expanded global network. Active Inference AI offers a path to intelligence that minimizes complexity and energy, promising a profoundly scalable and cost-effective path to Artificial General Intelligence. By mimicking biology, we can nurture AI that thinks and learns while protecting the planet. 2024 is gearing up to be a defining year in history, and I can’t wait to see how it all unfolds. , and Explore the future of AI with Active Inference and gain insights into this revolutionary approach! at Visit my blog https://deniseholt.us/ Subscribe to my channels on , , , , and more. Subscribe to my newsletter on , for more ground breaking content. YouTube Spotify Apple Amazon LinkedIn Also published . here

Welcome to 2024: The Year Computing Changes Forever

LONDON, UK, December 22nd, 2023/Chainwire/--Revolutionary Web3 Platform Prepares to Award $1 Million USDC Prize. , the premier web3 competition platform leveraging blockchain technology, is counting down the last 6 hours in its record-breaking prize draw, where one lucky player will win a life-changing $1 Million USDC! MetaWin MetaWin Millionaire $1 Million Grand Prize Draws In Less than 6 Hours With the competition ending today, December 22nd at 21:00 GMT, there is still time to secure a free entry into the $1 Million USDC sweepstakes before registration closes. The $1 Million USDC prize is already staked in a smart contract on-chain, and will be instantly transferred to the randomly selected wallet, making one lucky participant an instant millionaire. Crypto Twitter Abuzz With Anticipation Interest in the competition is reaching fever pitch with multiple influential accounts entering the draw, with Pranksy, dannycrypt, sibel and noteezzy all showing off their entries. But even with the excitement mounting, there are currently just over 5000 registered entries, which still offers attractive odds for new participants. Just Released: More Than 50+ Eth In Free-to-Play NFT’s To celebrate the momentous countdown to the World’s biggest crypto prize, Metawin has just announced the release of in NFT’s in free-to-enter competitions. The competitions range in value from smaller, but high-quality projects, to more established blue chips such as Killabears, Miladys, Otherdeeds and more. 50+ ETH worth Entries are capped to ensure equal chances of winning, and just require a small gas fee to register the entry on the Ethereum blockchain. With new NFT prizes hitting the site every 30 minutes, participants can join in the excitement throughout the next 6 hours until the draw of the $1 Million USDC main prize. MetaWin's Exciting Journey: Anticipating the $1 Million USDC Winner With the countdown clock ticking down the final hours, MetaWin invites all potential participants to seize the moment and claim their chance at the $ 1 Million USDC prize. Users can claim their free entry via this link: - Opportunity for Users to Win $1 Million USDC Free Entry Available extends its best wishes to all entrants and looks forward to announcing the fortunate victor. MetaWin About MetaWin stands at the forefront of on-chain competition platforms, revolutionizing the landscape of digital contests. Through harnessing the power of blockchain technology, MetaWin delivers a competition experience characterized by transparency, fairness, and excitement. MetaWin With a track record of substantial giveaways and an unwavering commitment to innovation, MetaWin is shaping the future of online competitions. Contact MR MetaWin PR MetaWin press@metawin.inc This story was distributed as a release by Chainwire under HackerNoon’s Business Blogging Program. Learn more about the program . here

Clock Ticking: 6 Hours Left In MetaWin's Thrilling $1 Million USDC Prize Race

ঘড়ির টিক টিক বাজানো: মেটাউইনের রোমাঞ্চকর $1 মিলিয়ন ইউএসডিসি প্রাইজ রেসে 6 ঘন্টা বাকি

Die Uhr tickt: Noch 6 Stunden im spannenden USDC-Preisrennen von MetaWin im Wert von 1 Million US-Dollar

El reloj corre: quedan 6 horas en la emocionante carrera de premios de $ 1 millón de USDC de MetaWin

Le temps presse : il reste 6 heures dans la passionnante course aux prix USDC d'un million de dollars de MetaWin

घड़ी की टिक-टिक: मेटाविन की रोमांचक $1 मिलियन यूएसडीसी पुरस्कार दौड़ में 6 घंटे शेष हैं

時計の針が刻々と過ぎていく: MetaWin のスリル満点の 100 万ドル USDC 賞金レースは残り 6 時間

똑딱거리는 시간: MetaWin의 스릴 넘치는 100만 달러 USDC 상금 경주가 6시간 남았습니다.

O relógio está passando: faltam 6 horas para a emocionante corrida de prêmios de US$ 1 milhão em USDC da MetaWin

Часы тикают: осталось 6 часов до захватывающей призовой гонки MetaWin на 1 миллион долларов США

Saat İşliyor: MetaWin'in Heyecan Verici 1 Milyon Dolarlık USDC Ödül Yarışında 6 Saat Kaldı

Đồng hồ tích tắc: Còn 6 giờ trong cuộc đua giải thưởng hấp dẫn trị giá 1 triệu USDC của MetaWin

时钟滴答作响：MetaWin 激动人心的 100 万美元 USDC 奖金竞赛还剩 6 小时

In an unprecedented move by VERSES AI, today’s announcement of a breakthrough revealing a new path to AGI based on ‘natural’ rather than ‘artificial’ intelligence, appealing to their stated mission VERSES took out a full page ad in the NY Times with an open letter to the Board of Open AI “to build artificial general intelligence (AGI) that is safe and benefits all of humanity.” Specifically, the appeal addresses a clause in the that states in pursuit of their mission “to build artificial general intelligence (AGI) that is safe and benefits all of humanity,” and the concerns about late-stage AGI becoming a “competitive race without time for adequate safety precautions. Therefore, if a value-aligned, safety-conscious project comes close to building AGI before we do, we commit to stop competing with and start assisting this project.” Open AI Board’s charter What Happened? VERSES has achieved an AGI breakthrough within their alternative path to AGI, which is Active Inference. And they are appealing to Open AI “in the spirit of cooperation and in accordance with [their} charter.” According to their today, “VERSES recently achieved a significant internal breakthrough in Active Inference that we believe addresses the tractability problem of probabilistic AI. This advancement enables the design and deployment of adaptive, real-time Active Inference agents at scale, matching and often surpassing the performance of state-of-the-art deep learning. These agents achieve superior performance using orders of magnitude less input data and are optimized for energy efficiency, specifically designed for intelligent computing on the edge, not just in the cloud.” press release Why Is This Significant? In a video published as part of the announcement today titled, “ ” VERSES takes a look at the incredible journey of AI acceleration over this past year and what it suggests about the current path from Artificial Narrow Intelligence (where we are now) to Artificial General Intelligence — AGI (the holy grail of AI automation)… Noting that all of the major players of Deep Learning technology have publicly acknowledged throughout the course of 2023 that “another breakthrough” is needed to get to AGI. For many months now, there has been an overwhelming consensus that machine learning/deep learning cannot achieve AGI. Sam Altman, Bill Gates, Yann LeCunn, Gary Marcus, and many others have publicly stated so. The Year in AI 2023, Just last month, Sam Altman declared at the Hawking Fellowship Award event at Cambridge University that “another breakthrough is needed” in response to a question asking if LLMs are capable of achieving AGI. Even more concerning are the potential dangers of proceeding in the direction of machine intelligence, as evidenced by the “Godfather of AI,” Geoffrey Hinton, creator of backpropagation and the deep learning method, withdrawing from Google early this year over his own concerns of the potential harm to humanity by continuing down the path he had dedicated half a century of his life to. So, What Are The Potential Dangers of Deep Learning Neural Nets? The many problems that pose these potential dangers of continuing down the current path of , are compelling and quite serious. generative AI · Black box problem · Alignment problem · Generalizability problem · Halucination problem · Centralization problem — one corporation owning the AI · Clean data problem · Energy consumption problem · Data update problem · Financial viability problem · Guardrail problem · Copyright problem All Current AI Stems from This 'Artificial' DeepMind Path At University College London, two professors worked right next door to each other. One, Geoffrey Hinton, and the other, Karl Friston. Professor Hinton developed DeepMind, taking an engineering approach to artificial intelligence, resulting in the handful of monolithic foundation models that exist today from which all generative AI is built. Dr. Friston, Chief Science Officer at VERSES and one of the most renowned neuroscientists in history, had other ideas. He pioneered a method called Active Inference, based on a principle he discovered called The Free Energy Principle, opening up an entirely new path to AGI that is grounded in physics and based on the mathematics of natural, biological intelligence rather than mere machines and algorithms. In November 2018, WIRED published an article titled “The Genius Neuroscientist Who Might Hold the Key to True AI,” stating, “Karl Friston’s Free Energy Principle might be the most all-encompassing idea since the theory of natural selection.” In the video released today, Professor Friston says, “You look at machine learning, and you just look at the trajectory. It’s all a trajectory measured in terms of big data, or how many billions of parameters can your large language model handle? That’s exactly the wrong direction.” The AGI Breakthrough In today’s announcement, VERSES explains, “Building on this breakthrough, we developed a novel framework to facilitate the scalable generation of agents with radically improved generalization, adaptability, and computational efficiency. This framework also features superior alignability, interoperability, and governability in accordance with and complemented by the P2874 Spatial Web standards being developed by the Institute of Electrical and Electronics Engineers (IEEE).” VERSES AI is taking a First Principles approach to AI based on the understanding that the path to AGI must be based on natural intelligence. This direction follows the premise that intelligence is multidimensional, adaptive, efficient, and cooperative. The technology they have built demonstrates a systematic blueprint for achieving AGI and even super intelligence resulting from this path. This is the Breakthrough everyone is waiting for. The Research Roadmap Revealed Additionally, outlining the stages of development for Active Inference AI. VERSES unveiled its research roadmap and timeline publicly today, — This is contemporary state-of-the-art Al; namely, universal function approximation-mapping from input or sensory states to outputs or action states — that optimizes some well-defined value function or cost of (systemic) states. Examples include deep learning, Bayesian reinforcement learning, etc. S0: Systemic Intelligence Sentient behavior or active inference based on belief updating and propagation (i.e., optimizing beliefs about states as opposed to states ); where “sentient” means behavior that looks as if it is driven by beliefs about the sensory consequences of action. This entails planning as inference; namely, inferring courses of action that maximize expected information gain and expected value, where value is part of a generative (i.e., world) model; namely, prior preferences. This kind of intelligence is both information-seeking and preference-seeking. It is quintessentially curious, in virtue of being driven by uncertainty minimization, as opposed to reward maximization. S1: Sentient Intelligence — per se Sentient behavior — as defined under S1 — in which plans are predicated on the consequences of action for beliefs about states of the world, as opposed to states . i.e., a move from “what will happen if I do this?” to “what will I believe or know if I do this?”. This kind of inference generally uses generative models with discrete states that “carve nature at its joints”; namely, inference over coarse-grained representations and ensuing world models. This kind of intelligence is amenable to formulation in terms of modal logic, quantum computation, and category theory. This stage corresponds to “artificial general intelligence” in the popular narrative about the progress of AI. S2: Sophisticated Intelligence — per se The deployment of sophisticated AI to recognize the nature and dispositions of users and other AI and — in consequence — recognize (and instantiate) attentional and dispositional states of self; namely, a kind of minimal selfhood (which entails generative models equipped with the capacity for Theory of Mind). This kind of intelligence is able to take the perspective of its users and interaction partners — it is perspectival, in the robust sense of being able to engage in dyadic and shared perspective taking. S3: Sympathetic Intelligence — The kind of collective that emerges from the coordination of Sympathetic Intelligences (as defined in S3) and their interaction partners or users — which may include naturally occurring intelligences such as ourselves, but also other sapient artifacts. This stage corresponds, roughly speaking, to “artificial super-intelligence” in the popular narrative about the progress of AI — with the important distinction that we believe that such intelligence will emerge from dense interactions between agents networked into a hyper-spatial web. We believe that the approach that we have outlined here is the most likely route toward this kind of hypothetical, planetary-scale, distributed super-intelligence.” S4: Shared Intelligence — GENIUS™ Platform Timeline - What to Expect Today, for the first time, . The GENIUS™ Private Beta Program officially launches in January 2024, with previously announced Beta partners like NASA, SimWell, Cortical Labs, and NALANTIS, with more announcements soon. The GENIUS™ Public Beta is set for Summer 2024, with enhanced agent capabilities. Included in the timeline is a projection for Sentient Agents as “Personal Agents” planned for winter 2024 in a Private Beta. VERSES made public their ambitious timeline for the 2024 rollout of their platform Back to Today's Monumental Call for Cooperation and Collaboration Among the AI Community Not only are LLMs unlikely to achieve AGI, they are highly likely to bring about the worst potential for humanity. At the very least, they bring about confusion, misinformation and compound many of the things we already struggle with as a human race. The good news is Active Inference AI can benefit these machine models. They can inherit qualities from Active Inference that solve many of their issues. When faced with a new discovery for a potential path to AGI that is programmable, explainable, self-evolving, governable, and works to scale while preserving individual cultural differences and preferences, aligning with human values.; When it operates in a secure manner — overcoming the security issues and obstacles of using LLMs in an unsecured environment like the centralized World Wide Web; When it has the ability to specify and adhere to rules and regulations for various types, stages of intelligence, and governing needs of a wide gamut of Autonomous Intelligent Systems; We have the potential to do things right from the beginning. We have the potential to embark on this crucial next evolution of technology in cooperation between humans and machines for the ultimate good of the future of human civilization. OpenAI, from its inception, has ensured the public that its mission to develop “AGI that benefits all of humanity” would trump all else, including its own for-profit aspirations. Their charter states very clearly that they would set aside competition to join in support of any leading technology that adheres to that mission. VERSES AI is presenting technology and a timeline that fits the criteria they set in their charter. VERSES AI is demonstrating its own intention and integrity to the public welfare by reaching out to the Open AI board today to appeal to this spirit of cooperation within the AI community. In a space of technology that is rife with fear of a corporate race for domination, it’s hard to imagine a more meaningful gesture than that. At the end of the video, “The Year in AI 2023,” released today, narrated by VERSES CEO Gabriel Rene, states, “ ” We have achieved the breakthrough we believe leads to smarter, safer, and more sustainable general intelligence systems. This isn’t just about achieving AGI. It’s about what we can achieve with it. The natural path invites us to realign our relationship with technology, nature, and each other. And it calls on us to come together to imagine a smarter world and then build it. https://www.verses.ai/year-in-ai-2023-video “The Year in AI 2023,” Full video released today — by permission from VERSES AI , and Explore the future of AI with Active Inference and gain insights into this revolutionary approach! Visit my blog at https://deniseholt.us/ Subscribe to my channels on , YouTube , Spotify , Apple , and more. Subscribe to my newsletter on Amazon for more groundbreaking content. LinkedIn All content for Spatial Web AI is independently created by Denise Holt. Also published . here

VERSES AI Announces AGI Breakthrough: Invokes Open AI's 'Assist' Clause

The Secret Adversary by Agatha Christie, is part of the HackerNoon Books Series. You can jump to any chapter in this book here . FURTHER ADVENTURES OF TOMMY CHAPTER XVI. FURTHER ADVENTURES OF TOMMY From a darkness punctuated with throbbing stabs of fire, Tommy dragged his senses slowly back to life. When he at last opened his eyes, he was conscious of nothing but an excruciating pain through his temples. He was vaguely aware of unfamiliar surroundings. Where was he? What had happened? He blinked feebly. This was not his bedroom at the Ritz. And what the devil was the matter with his head? “Damn!” said Tommy, and tried to sit up. He had remembered. He was in that sinister house in Soho. He uttered a groan and fell back. Through his almost-closed lids he reconnoitred carefully. “He is coming to,” remarked a voice very near Tommy’s ear. He recognized it at once for that of the bearded and efficient German, and lay artistically inert. He felt that it would be a pity to come round too soon; and until the pain in his head became a little less acute, he felt quite incapable of collecting his wits. Painfully he tried to puzzle out what had happened. Obviously somebody must have crept up behind him as he listened and struck him down with a blow on the head. They knew him now for a spy, and would in all probability give him short shrift. Undoubtedly he was in a tight place. Nobody knew where he was, therefore he need expect no outside assistance, and must depend solely on his own wits. “Well, here goes,” murmured Tommy to himself, and repeated his former remark. “Damn!” he observed, and this time succeeded in sitting up. In a minute the German stepped forward and placed a glass to his lips, with the brief command “Drink.” Tommy obeyed. The potency of the draught made him choke, but it cleared his brain in a marvellous manner. He was lying on a couch in the room in which the meeting had been held. On one side of him was the German, on the other the villainous-faced doorkeeper who had let him in. The others were grouped together at a little distance away. But Tommy missed one face. The man known as Number One was no longer of the company. “Feel better?” asked the German, as he removed the empty glass. “Yes, thanks,” returned Tommy cheerfully. “Ah, my young friend, it is lucky for you your skull is so thick. The good Conrad struck hard.” He indicated the evil-faced doorkeeper by a nod. The man grinned. Tommy twisted his head round with an effort. “Oh,” he said, “so you’re Conrad, are you? It strikes me the thickness of my skull was lucky for you too. When I look at you I feel it’s almost a pity I’ve enabled you to cheat the hangman.” The man snarled, and the bearded man said quietly: “He would have run no risk of that.” “Just as you like,” replied Tommy. “I know it’s the fashion to run down the police. I rather believe in them myself.” His manner was nonchalant to the last degree. Tommy Beresford was one of those young Englishmen not distinguished by any special intellectual ability, but who are emphatically at their best in what is known as a “tight place.” Their natural diffidence and caution fall from them like a glove. Tommy realized perfectly that in his own wits lay the only chance of escape, and behind his casual manner he was racking his brains furiously. The cold accents of the German took up the conversation: “Have you anything to say before you are put to death as a spy?” “Simply lots of things,” replied Tommy with the same urbanity as before. “Do you deny that you were listening at that door?” “I do not. I must really apologize—but your conversation was so interesting that it overcame my scruples.” “How did you get in?” “Dear old Conrad here.” Tommy smiled deprecatingly at him. “I hesitate to suggest pensioning off a faithful servant, but you really ought to have a better watchdog.” Conrad snarled impotently, and said sullenly, as the man with the beard swung round upon him: “He gave the word. How was I to know?” “Yes,” Tommy chimed in. “How was he to know? Don’t blame the poor fellow. His hasty action has given me the pleasure of seeing you all face to face.” He fancied that his words caused some discomposure among the group, but the watchful German stilled it with a wave of his hand. “Dead men tell no tales,” he said evenly. “Ah,” said Tommy, “but I’m not dead yet!” “You soon will be, my young friend,” said the German. An assenting murmur came from the others. Tommy’s heart beat faster, but his casual pleasantness did not waver. “I think not,” he said firmly. “I should have a great objection to dying.” He had got them puzzled, he saw that by the look on his captor’s face. “Can you give us any reason why we should not put you to death?” asked the German. “Several,” replied Tommy. “Look here, you’ve been asking me a lot of questions. Let me ask you one for a change. Why didn’t you kill me off at once before I regained consciousness?” The German hesitated, and Tommy seized his advantage. “Because you didn’t know how much I knew—and where I obtained that knowledge. If you kill me now, you never will know.” But here the emotions of Boris became too much for him. He stepped forward waving his arms. “You hell-hound of a spy,” he screamed. “We will give you short shrift. Kill him! Kill him!” There was a roar of applause. “You hear?” said the German, his eyes on Tommy. “What have you to say to that?” “Say?” Tommy shrugged his shoulders. “Pack of fools. Let them ask themselves a few questions. How did I get into this place? Remember what dear old Conrad said— with your own password, wasn’t it? How did I get hold of that? You don’t suppose I came up those steps haphazard and said the first thing that came into my head?” Tommy was pleased with the concluding words of this speech. His only regret was that Tuppence was not present to appreciate its full flavour. “That is true,” said the working man suddenly. “Comrades, we have been betrayed!” An ugly murmur arose. Tommy smiled at them encouragingly. “That’s better. How can you hope to make a success of any job if you don’t use your brains?” “You will tell us who has betrayed us,” said the German. “But that shall not save you—oh, no! You shall tell us all that you know. Boris, here, knows pretty ways of making people speak!” “Bah!” said Tommy scornfully, fighting down a singularly unpleasant feeling in the pit of his stomach. “You will neither torture me nor kill me.” “And why not?” asked Boris. “Because you’d kill the goose that lays the golden eggs,” replied Tommy quietly. There was a momentary pause. It seemed as though Tommy’s persistent assurance was at last conquering. They were no longer completely sure of themselves. The man in the shabby clothes stared at Tommy searchingly. “He’s bluffing you, Boris,” he said quietly. Tommy hated him. Had the man seen through him? The German, with an effort, turned roughly to Tommy. “What do you mean?” “What do you think I mean?” parried Tommy, searching desperately in his own mind. Suddenly Boris stepped forward, and shook his fist in Tommy’s face. “Speak, you swine of an Englishman—speak!” “Don’t get so excited, my good fellow,” said Tommy calmly. “That’s the worst of you foreigners. You can’t keep calm. Now, I ask you, do I look as though I thought there were the least chance of your killing me?” He looked confidently round, and was glad they could not hear the persistent beating of his heart which gave the lie to his words. “No,” admitted Boris at last sullenly, “you do not.” “Thank God, he’s not a mind reader,” thought Tommy. Aloud he pursued his advantage: “And why am I so confident? Because I know something that puts me in a position to propose a bargain.” “A bargain?” The bearded man took him up sharply. “Yes—a bargain. My life and liberty against——” He paused. “Against what?” The group pressed forward. You could have heard a pin drop. Slowly Tommy spoke. “The papers that Danvers brought over from America in the Lusitania.” The effect of his words was electrical. Every one was on his feet. The German waved them back. He leaned over Tommy, his face purple with excitement. “Himmel! You have got them, then?” With magnificent calm Tommy shook his head. “You know where they are?” persisted the German. Again Tommy shook his head. “Not in the least.” “Then—then——” angry and baffled, the words failed him. Tommy looked round. He saw anger and bewilderment on every face, but his calm assurance had done its work—no one doubted but that something lay behind his words. “I don’t know where the papers are—but I believe that I can find them. I have a theory——” “Pah!” Tommy raised his hand, and silenced the clamours of disgust. “I call it a theory—but I’m pretty sure of my facts—facts that are known to no one but myself. In any case what do you lose? If I can produce the papers—you give me my life and liberty in exchange. Is it a bargain?” “And if we refuse?” said the German quietly. Tommy lay back on the couch. “The 29th,” he said thoughtfully, “is less than a fortnight ahead——” For a moment the German hesitated. Then he made a sign to Conrad. “Take him into the other room.” For five minutes, Tommy sat on the bed in the dingy room next door. His heart was beating violently. He had risked all on this throw. How would they decide? And all the while that this agonized questioning went on within him, he talked flippantly to Conrad, enraging the cross-grained doorkeeper to the point of homicidal mania. At last the door opened, and the German called imperiously to Conrad to return. “Let’s hope the judge hasn’t put his black cap on,” remarked Tommy frivolously. “That’s right, Conrad, march me in. The prisoner is at the bar, gentlemen.” The German was seated once more behind the table. He motioned to Tommy to sit down opposite to him. “We accept,” he said harshly, “on terms. The papers must be delivered to us before you go free.” “Idiot!” said Tommy amiably. “How do you think I can look for them if you keep me tied by the leg here?” “What do you expect, then?” “I must have liberty to go about the business in my own way.” The German laughed. “Do you think we are little children to let you walk out of here leaving us a pretty story full of promises?” “No,” said Tommy thoughtfully. “Though infinitely simpler for me, I did not really think you would agree to that plan. Very well, we must arrange a compromise. How would it be if you attached little Conrad here to my person. He’s a faithful fellow, and very ready with the fist.” “We prefer,” said the German coldly, “that you should remain here. One of our number will carry out your instructions minutely. If the operations are complicated, he will return to you with a report and you can instruct him further.” “You’re tying my hands,” complained Tommy. “It’s a very delicate affair, and the other fellow will muff it up as likely as not, and then where shall I be? I don’t believe one of you has got an ounce of tact.” The German rapped the table. “Those are our terms. Otherwise, death!” Tommy leaned back wearily. “I like your style. Curt, but attractive. So be it, then. But one thing is essential, I must see the girl.” “What girl?” “Jane Finn, of course.” The other looked at him curiously for some minutes, then he said slowly, and as though choosing his words with care: “Do you not know that she can tell you nothing?” Tommy’s heart beat a little faster. Would he succeed in coming face to face with the girl he was seeking? “I shall not ask her to tell me anything,” he said quietly. “Not in so many words, that is.” “Then why see her?” Tommy paused. “To watch her face when I ask her one question,” he replied at last. Again there was a look in the German’s eyes that Tommy did not quite understand. “She will not be able to answer your question.” “That does not matter. I shall have seen her face when I ask it.” “And you think that will tell you anything?” He gave a short disagreeable laugh. More than ever, Tommy felt that there was a factor somewhere that he did not understand. The German looked at him searchingly. “I wonder whether, after all, you know as much as we think?” he said softly. Tommy felt his ascendancy less sure than a moment before. His hold had slipped a little. But he was puzzled. What had he said wrong? He spoke out on the impulse of the moment. “There may be things that you know which I do not. I have not pretended to be aware of all the details of your show. But equally I’ve got something up my sleeve that you don’t know about. And that’s where I mean to score. Danvers was a damned clever fellow——” He broke off as if he had said too much. But the German’s face had lightened a little. “Danvers,” he murmured. “I see——” He paused a minute, then waved to Conrad. “Take him away. Upstairs—you know.” “Wait a minute,” said Tommy. “What about the girl?” “That may perhaps be arranged.” “It must be.” “We will see about it. Only one person can decide that.” “Who?” asked Tommy. But he knew the answer. “Mr. Brown——” “Shall I see him?” “Perhaps.” “Come,” said Conrad harshly. Tommy rose obediently. Outside the door his gaoler motioned to him to mount the stairs. He himself followed close behind. On the floor above Conrad opened a door and Tommy passed into a small room. Conrad lit a hissing gas burner and went out. Tommy heard the sound of the key being turned in the lock. He set to work to examine his prison. It was a smaller room than the one downstairs, and there was something peculiarly airless about the atmosphere of it. Then he realized that there was no window. He walked round it. The walls were filthily dirty, as everywhere else. Four pictures hung crookedly on the wall representing scenes from Faust. Marguerite with her box of jewels, the church scene, Siebel and his flowers, and Faust and Mephistopheles. The latter brought Tommy’s mind back to Mr. Brown again. In this sealed and closed chamber, with its close-fitting heavy door, he felt cut off from the world, and the sinister power of the arch-criminal seemed more real. Shout as he would, no one could ever hear him. The place was a living tomb.... With an effort Tommy pulled himself together. He sank on to the bed and gave himself up to reflection. His head ached badly; also, he was hungry. The silence of the place was dispiriting. “Anyway,” said Tommy, trying to cheer himself, “I shall see the chief—the mysterious Mr. Brown and with a bit of luck in bluffing I shall see the mysterious Jane Finn also. After that——” After that Tommy was forced to admit the prospect looked dreary. About HackerNoon Book Series: We bring you the most important technical, scientific, and insightful public domain books. This book is part of the public domain. Agatha Christie (1998). The Secret Adversary. Urbana, Illinois: Project Gutenberg. Retrieved https://www.gutenberg.org/cache/epub/1155/pg1155-images.html This eBook is for the use of anyone anywhere at no cost and with almost no restrictions whatsoever. You may copy it, give it away or re-use it under the terms of the Project Gutenberg License included with this eBook or online at www.gutenberg.org , located at https://www.gutenberg.org/policy/license.html.

FURTHER ADVENTURES OF TOMMY

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