Nuwe

nuwe

The Eternal Savage by Edgar Rice Burroughs, is part of the HackerNoon Books Series. You can jump to any chapter in this book here. THE FIGHT THE FIGHT After Nu, the son of Nu, had left his father and his father's people to go in search of Nat-ul and Hud, the warrior chief had sat in silence for many minutes. Beside him sat Tha, father of Nat-ul, and round about squatted the other members of the tribe. All were silent in the face of the sorrow that had overtaken their chief and his principal lieutenant. Nu and Nat-ul were great favorites among their savage fellows. Not so, however, Hud, and the anger against him was bitter. Presently Nu, the chief, spoke. "We cannot go in search of a new home," he said, "leaving two of our children behind." His listeners knew that he ignored Hud—that Hud, in bringing this sorrow upon the tribe, had forfeited his rights among them. They were satisfied that it should be so. A young warrior stood up. With his spear he drew a line upon the ground from east to west and lying just north of him. "Nu, the son of Nu, passed through the ordeals with me—we became men and warriors upon the same day. Together we hunted our first lion." He paused, and then, pointing to the line he had drawn upon the ground, continued. "Never shall I cross this line until I have found Nu, the son of Nu." As he ceased speaking he drew himself to his full height and with arms folded across his broad chest turned to face his chief. From the tribe came grunts of approval. All eyes turned toward Nu. What would he do? The young warrior's act was nothing short of rebellion. Suddenly Aht, brother of Nat-ul, sprang to his feet and stood beside the defiant warrior. He said nothing—his act proclaimed his intention. Nu, the chief, looked at the two young men from beneath his shaggy brows. The watchers were almost certain that a half smile played grimly about his grim countenance. He, too, arose. He walked to where the two stood and ranged himself beside them. Tha was the first to guess the significance of the act, and the instant that he did so he leaped to Nu's side. Then the others understood, and a moment later the whole tribe was ranged with their backs to Dag's line, facing toward the south. They were dancing and shouting now. The men waved their stone axes or threw their long spears high in air. The women beat their palms together, and the little children ran skipping about, getting in everyone's way. After a few minutes of this Nu started off toward the south, telling off a score of men to remain with the women and children who were to follow slowly back toward their former dwellings while the chief with the balance of the fighting men searched rapidly ahead for signs of Nu and Nat-ul. First they came upon the dead body of Hud within the cave in the face of the Barren Cliffs. From there they discovered Nu's spoor and faint traces of the older spoor of the girl, showing that Nu had not overtaken her at this point. On they went along the beach toward their old caves, and everywhere the signs of one or the other of those they followed were distinguishable. It was dark when they reached the caves, and the following morning they had difficulty in again picking up the spoor because of the fact that the tide had obliterated it where it had touched the sandy beach at low tide. Now Nu separated his warriors into three parties. One, with which he remained, was to keep south along the beach, the second was to work into the jungle for a mile and then turn south, while the third was to search straight inland toward the west. In this way one of them must come upon those they sought, or some sign of them. Tha was in command of the central party, and Aht was with him. Dag was with Nu, the chief. They beat rapidly along the beach, and spread out across it from the water to the jungle, that nothing might escape their observation. Several times they followed false leads into the jungle, so that they lost much time, with the result that darkness came upon them without their having discovered the two they sought. They camped upon the sand just outside the jungle, building a ring of fires about them to keep off the wild beasts. Then they lay down to sleep—all but two who kept watch and tended the fires. Dag was one of the watchers. As the night grew darker he became aware of a glow in the south. He called his companion's attention to it. "There are men there," he said. "That is the light from beast-fires. Listen!" Savage yells rose faintly from the distance, and in the direction of the lights. Dag was on the point of arousing Nu when his keen eyes detected something moving warily between the jungle and the camp. Evidently it had but just crept out of the dense vegetation. Ordinarily Dag might have thought it a beast of prey; but with the discovery of the nearness of a camp of men, he was not so sure. True, men seldom crept through the jungle after darkness had fallen; but there was something about the movements of this creature that suggested the crawling of a man on all fours. Dag circled the camp, apparently oblivious of the presence of the intruder. He threw a stick upon a blaze here, and there he stamped out some smoking faggots that had fallen inside the ring. But all the while he watched the movements of the thing that crept through the outer darkness toward the camp. He could see it more distinctly now, and was aware that from time to time it cast a backward glance over its shoulder. "Had it a companion, or companions? Was something following it?" Dag scrutinized the black face of the jungle beyond the creeping thing. "Ah! so that was it?" A dark shadow had stepped from the somber wood upon the trail of the creature that was now half way across the open space between the jungle and the camp. Dag needed no second glance to attest the identity of the newcomer. The lithe body, the black mass that marked the bristling mane, the crouching pose, the two angry splotches of yellow-green fire—no doubt here. It was Zor, the lion, stalking his prey. Dag whispered a word to his companion who came to his side. The two stood looking straight toward the nearer creature, with no attempt to disguise the fact that they had discovered it. "It is a man," whispered Dag's companion. And then, with a frightful roar, Zor charged, and the creature before it rose upon two feet full in the light of the nearer blaze. With a cry that aroused the whole camp Dag leaped beyond the flaming circle, his spear hand back thrown, the stone head, laboriously chipped to a sharp point, directed at the charging Zor. The weapon passed scarce a hand's breadth from the shoulder of Zor's prey and buried itself in the breast of the beast. At the same instant Dag leaped past the fugitive, placing himself directly in the path of the lion with only an ax and knife of stone to combat the fury of the raging, wounded demon of destruction. Over his shoulder he threw a word to the one he had leaped forth to succor. "Run within the beast-fires, Nat-ul," he cried; "Zor's mate is coming to his aid." And sure enough, springing lightly across the sands came a fierce lioness, maned like her lord. Now Dag's fellow warrior had sprung to his side, and from the camp were running the balance of the savage spearmen. Zor, rearing upon his hind feet, was striking at Dag who leaped nimbly from side to side, dodging the terrific blows of the mighty, taloned paws, and striking the beast's head repeatedly with his heavy ax. The other warrior met the charge of the infuriated lioness with his spear. Straight into the broad breast ran the sharp point, the while the man clung tenaciously to the haft, whipped hither and thither as the beast reared and wheeled and struck at him with her claws. Now Nu, the chief, and his fellows arrived upon the scene. A score of spears bristled from the bodies of Zor and his mate. Axes fell upon their heads, and Nu, the mighty, leaped upon Zor's back with only his stone knife. There he clung to the thick mane, driving the puny weapon time and again into back and side until at last the roaring, screaming beast rolled over upon its side to rise no more. The lioness proved more tenacious of life than her lord, and though bristling with spears and cut to ribbons with the knives of her antagonists she charged into close quarters with a sudden rush that found one of the cave men a fraction of a second too slow. The strong claws raked him from neck to groin and as he fell the mighty jaws closed with a sickening crunch upon his skull. At bay over her victim the lioness stood growling and threatening, while the wild warriors danced in a circle about her awaiting the chance to rush in and avenge their comrade. Within the circle of fires Nat-ul replenished the blaze, keeping the whole scene brilliantly lighted for the warriors. That she had stumbled upon men of her own tribe so unexpectedly seemed little short of miraculous. She could scarce wait for the battle with the lions to be concluded, so urgent was the business that filled her thoughts. But at last Zor's savage mate lay dead, and as Nu, the chief, returned to the camp Nat-ul leaped forward to meet him. "Quick!" she cried. "They are killing Nu, thy son," and she pointed toward the south in the direction of the glare that was now plainly visible through the darkness. Nu did not wait to ask questions then. He called his warriors about him. "Nat-ul says that they slay Nu, the son of Nu, there," he said, pointing toward the distant fire-glow. "Come!" As Nat-ul led them along the beach and through the jungle she told Nu, the chief, all that had transpired since Hud had stolen her away. She told of her wanderings, and of the Boat Builders. Of how one had chased her, and of the terrible creature that had seized and carried her to its nest. She told of the strange creature that crawled into the shelter where she was confined, leaping upon the back of Tur. And of how she slipped out of the shelter as the two battled, and escaped into the jungle, wriggling her hands from their bonds as she ran. She shuddered as she told Nu of the gauntlet of savage beasts she had been forced to run between the beast-fires of the Boat Builders and the safety of the jungle trees. "I rested for the balance of the night in a great tree close beside the village of the strangers," she said. "Early the next morning I set out in search of food, intending to travel northward until I came to our old dwellings where I could live in comparative safety. "But all the time I kept wondering what it might have been that leaped upon Tur's back in the shelter the night before and the more I thought about it the more apparent it became that it might have been a man—that it must have been a man, for what animal could pass through the beast-fires unseen? "And so, after filling my stomach, I crept back through the trees to the edge of the village, and there I watched. The sun then was straight above me—half the day was gone. I could not reach the caves before darkness if anything occurred to delay me, and as I might at any moment stumble upon some of the strangers or be treed by Ur, or Zor, or Oo, I decided to wait until early tomorrow morning before setting out for the caves. There was something within me that urged me to remain. What it was I do not know; but it was as though there were two Nat-uls, one wishing to hurry away from the land of the strangers as rapidly as possible and the other insisting that it was her duty to remain. At last I could deny my other self no longer—I must stay, and so I found a comfortable position in a great tree that grows close beside the clearing where the strangers' village stands, and there I remained until long after darkness came. "It was then that I saw the thing within the village that sent me here. Before, I had seen your fires, and wondered who it might be that came from the north. I knew that all the strangers had returned in the afternoon, so it could be none of them, and the first tribe to the north I knew was my own, so I hoped, without believing, that it might indeed be some of thy warriors, Nu. "And then I saw that something was going to occur in the village below me. Warriors approached a hut from which they dragged a captive. By the legs they dragged him, through the village and about it, and as they did so the women and children tortured and spat upon the prisoner. "At first I could not see the victim plainly, but at last as they raised him to his feet and bound him to a stake where they are going to roast him alive among slow fires I saw his face. "Oh, Nu, can you not guess who it was that had followed me so far, had overcome such dangers and fought his way through the awful waters to rescue me?" "Nu, the son of Nu," said the old warrior, and his chest swelled with pride as he strode through the jungle in the rear of the village. Angry beasts of prey menaced the rescuing party upon every hand. Twice were they attacked and compelled to battle with some fierce, primordial brute; but at last they won to the edge of the jungle behind the village they sought. There the sight that met their eyes and ears was one of wild confusion. Men and women were running hither and thither uttering shouts of rage. Beyond them was a circle of flaming brush. In the center of this, Nat-ul told the rescuers, Nu, the son of Nu, was fast bound to a stake. Slowly he was roasting to death—possibly he was already dead. Nu gathered his warriors about him. Two he commanded to remain always beside Nat-ul. Then, with the others at his heels, his long, white feather nodding bravely above his noble head, and the shaggy pelt of Ur, the cave-bear, falling from his shoulders, Nu, the chief, slunk silently out of the jungle toward the village of the excited Boat Builders. There were forty of them, mighty men, mightily muscled. In their strong hands they grasped their formidable spears and heavy axes. In their loin cloths rested their stone knives for the moment when they closed in hand-to-hand combat with foes. In their savage brains was but a single idea—to kill—to kill—to kill! To the outer rim of fires they came and yet the excited populace within had not discovered them. Then a girl, remembering tardily her duties at the fires, turned to throw more brush upon the blaze and saw them—saw a score of handsome, savage faces just beyond the flames. With a scream of terror and warning she turned and scurried amongst the villagers. For an instant the hub-bub was stilled, only to break out anew at the girl's frightened cry of: "Warriors! Warriors!" Then Nu and his men were among them. The warriors of the Boat Builders ran forward to meet the attackers. The women and children fled to the opposite side of the enclosure. Hoarse shouts and battle cries rang out as the Cliff Dwellers hurled themselves upon the Boat Builders. A shower of long slim spears volleyed from one side, to be answered by the short, stout harpoons of the villagers. Then the warriors rushed to closer conflict with their axes. Never after the first assault was the outcome of the battle in question—the fiercer tribe of Nu—the hunters of beasts of prey—the warrior people—were the masters at every turn. Back, back they forced the wearers of "cow" skins, until the defenders had been driven across the enclosure upon their women and children. And now the inner circle of fires was surrendered to the invaders, and as Nat-ul sprang between the warriors of her people to be first to the side of Nu and cut away his bonds, the last of the Boat Builders turned and fled into the outer darkness, along the beach to where their boats were drawn up beyond the tide. Nu, the chief, leaped through the flames upon the heels of Nat-ul. In the terrible heat within the two came side by side before the stake. The girl gave a single glance at the bare and smoking pole and at the ground around it before she turned and threw herself into Nu's arms. Nu, the son of Nu, was not there, nor was his body within the enclosure. 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. Edgar Rice Burroughs (2023). The Eternal Savage. Urbana, Illinois: Project Gutenberg. Retrieved October 2022 https://www.gutenberg.org/cache/epub/70002/pg70002-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.

THE FIGHT

The Eternal Savage by Edgar Rice Burroughs, is part of the HackerNoon Books Series. You can jump to any chapter in this book here. AGAIN A WORLD UPHEAVAL AGAIN A WORLD UPHEAVAL Victoria Custer was aware that Barney Custer, her brother, was forcing his way through the jungle behind them—that he was coming to take her away from Nu. Many lifetimes of culture and refinement plead with her to relinquish her mad, idyllic purpose—to give up her savage man and return to the protection and comforts that her brother and civilization represented. But there was still another force at work, older by far than the brief span of cultivation that had marked the advancement of her more recent forebears—the countless ages of prehistoric savagery in which the mind and heart and soul of man were born—the countless awful ages that have left upon the soul and heart and mind of man an impress that will endure so long as man endures. From out of that black abyss before man had either mind or soul there still emanates the same mighty power that was his sole master then—instinct. And it was instinct that drove Victoria Custer deeper into the jungle with her savage lover as she sensed the nearer approach of her brother—one of the two master instincts that have dominated and preserved life upon the face of the earth. Yet it was not without a struggle. She hesitated, half turning backward. Nu cast a questioning look upon her. "They are coming, Nat-ul," he said. "Nu cannot fight these strange men who hurl lead with the thunders they have stolen from the skies. Come! We must hurry back to the cave of Oo, and on the morrow we shall go forth and search for the tribe of Nu, my father, that dwells beyond the Barren Cliffs beside the Restless Sea. There, in our own world, we shall be happy." And yet the girl held back, afraid. Then the man gathered her in his mighty arms and ran on in the direction of the cave of Oo, the saber-toothed tiger. The girl did not even struggle to escape, instead she lay quietly, as over her fell a sensation of peace and happiness, as though, after a long absence, she was being borne home. And at their heels trotted Terkoz, the wolfhound. Sometimes Nu took to the lower branches of the trees, for in her own age his race had been semiarboreal. Here he traveled with the ease and agility of a squirrel, though oftentimes the modern woman that still lived in the breast of Victoria Custer quailed at the dizzy leaps, and the swaying, perilous trail. Yet, as they fled, her fears were greatest now that they might be overtaken, and herself snatched back into the world of civilization where her Nu could never follow. It was dusk of the third evening when they came again to the cave of Oo. Up the steep cliff side they clambered, hand in hand. Together they entered the dark and forbidding hole. "Tomorrow," said Nu, "we will search for the caves of our people, and we shall find them." Darkness settled upon the jungle, the plain and the mountains. Nu and Nat-ul slept, for both were exhausted from the long days of flight. And then there came, out of the bowels of the earth, a deep and ominous rumbling. The earth shook. The cliff rocked. Great masses of shattered rock shaken from its summit roared and tumbled down its face. Nu sprang to his feet, only to be hurled immediately to the floor of the cave stunned and senseless. Within all was darkness. No light filtered through the opening. For minutes the frightful din endured, and with it the sickening tossing of the earth; but, at last, the rumblings ceased, the world sank back to rest, exhausted. And Nu lay unconscious where he had fallen. 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. Edgar Rice Burroughs (2023). The Eternal Savage. Urbana, Illinois: Project Gutenberg. Retrieved October 2022 https://www.gutenberg.org/cache/epub/70002/pg70002-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.

AGAIN A WORLD UPHEAVAL

Crypto Crash? Not Really... 

Additional Numerical Experiments on K-SIF and SIF: Depth, Noise, and Discrimination Power

تجارب عددية إضافية على K-SIF وSIF: العمق والضوضاء وقوة التمييز

Πρόσθετα αριθμητικά πειράματα σε K-SIF και SIF: Βάθος, θόρυβος και ισχύς διάκρισης

Experimentos numéricos adicionales sobre K-SIF y SIF: profundidad, ruido y poder de discriminación

Muita numeerisia kokeita K-SIF:llä ja SIF:llä: syvyys, kohina ja erotteluteho

További numerikus kísérletek a K-SIF-en és SIF-en: mélység, zaj és megkülönböztető teljesítmény

Ulteriori esperimenti numerici su K-SIF e SIF: profondità, rumore e potere di discriminazione

K-SIF と SIF に関する追加の数値実験: 深度、ノイズ、識別力

დამატებითი რიცხვითი ექსპერიმენტები K-SIF-ზე და SIF-ზე: სიღრმე, ხმაური და დისკრიმინაციის ძალა

Dodatkowe eksperymenty numeryczne dotyczące K-SIF i SIF: głębokość, szum i moc dyskryminacyjna

Experimentos numéricos adicionais em K-SIF e SIF: profundidade, ruído e poder de discriminação

Додаткові чисельні експерименти з K-SIF і SIF: глибина, шум і потужність дискримінації

Iimvavanyo ezongezelelweyo zamanani kwi-K-SIF kunye ne-SIF: Ubunzulu, ingxolo, kunye namandla ocalucalulo

In recent years, meme-based and tap-to-earn games have surged in popularity, combining internet culture and rewarding gameplay for a novel experience. Meme-based games leverage humor and culturally relevant themes, giving players a playful, relatable environment where humor is as central as gameplay. Meanwhile, tap-to-earn games, as the name suggests, are highly accessible – users can simply tap to earn points or tokens. The concept taps into the appeal of casual gaming and adds an incentive layer through rewards, often in the form of cryptocurrencies or NFTs. These games stand out in an era where gamers are seeking both entertainment and engagement that goes beyond traditional gameplay. Among popular meme-based tap-to-earn games, Tap Warrior has emerged as a leading example on the Solana blockchain. It’s a tap-to-earn game that brings meme culture and crypto gaming together in a seamless, engaging package. Tap Warrior’s gameplay is straightforward yet engaging: players tap their way to higher scores, competing with others while earning rewards for their efforts. Designed for broad appeal, it caters to both experienced crypto enthusiasts and casual gamers who may just be venturing into blockchain gaming. The tap-to-earn model means that players can accumulate rewards by actively participating, making each tap a step toward ranking up, earning crypto, or collecting exclusive digital items. However it should be clear, Tap Warrior is a free-to-play game where users tap to accumulate points and unlock rewards without financial obligations. Players can enjoy the competitive, meme-driven environment while choosing whether or not to engage in cryptocurrency-related options like mining or staking. Tap Warrior makes it clear that it is not an investment platform, emphasizing that any crypto activity is optional and solely for entertainment. The game includes disclaimers about potential risks associated with cryptocurrency, such as market volatility, liquidity issues, and security vulnerabilities. These qualifications highlight that players should not expect financial returns and should only engage with crypto aspects if comfortable with the risks. This commitment to transparency and optional engagement with cryptocurrency makes Tap Warrior an accessible way to experience the crypto world. For those interested, Tap Warrior leverages Solana’s blockchain transparency and security, assuring users that their rewards and progress are protected. The game’s token-based economy allows players to earn cryptocurrency and collectible NFTs, adding tangible value to their gameplay. As they progress, players find themselves in a friendly competition to tap more, rank higher, and earn more rewards. This gamified experience keeps them engaged while drawing on the humor and accessibility that meme games are known for. As a free-to-play, meme-based tap-to-earn game, Tap Warrior offers an ideal balance for gamers looking for entertainment, community, and optional crypto engagement. With its emphasis on meme culture, transparency, and user choice, Tap Warrior redefines casual blockchain gaming, blending accessible fun with the potential to explore digital assets at one's discretion. Whether aiming to collect points, climb the ranks, or dabble in crypto, players can enjoy a flexible gaming experience that values both humor and security, creating a space where gaming meets decentralized innovation on their terms. Disclaimer and Disclosure The information provided in this article is for general informational purposes only and should not be construed as financial, investment, legal, or other advice. The views expressed are solely those of the author or contributor and do not necessarily reflect the official policy or position of Tap Warrior. This article does not constitute a solicitation or offer to buy or sell any assets, securities, or financial instruments, nor does it endorse or recommend any specific products, services, or investment strategies. Readers are encouraged to conduct their own research and seek the advice of a licensed professional regarding any financial decisions. Tap Warrior makes no representation as to the accuracy, completeness, or reliability of the information provided. Any reliance you place on such information is strictly at your own risk. Cryptocurrency and Web3 markets carry a high level of risk and may not be suitable for all investors. Please exercise caution, as there is a potential for significant financial loss. Tap Warrior and its affiliates, including marketing and PR agencies, disclaim any liability for any direct, indirect, or consequential losses or damages of any kind arising from reliance upon any information contained within this article or linked resources. In recent years, meme-based and tap-to-earn games have surged in popularity, combining internet culture and rewarding gameplay for a novel experience. Meme-based games leverage humor and culturally relevant themes, giving players a playful, relatable environment where humor is as central as gameplay. Meanwhile, tap-to-earn games, as the name suggests, are highly accessible – users can simply tap to earn points or tokens. The concept taps into the appeal of casual gaming and adds an incentive layer through rewards, often in the form of cryptocurrencies or NFTs. These games stand out in an era where gamers are seeking both entertainment and engagement that goes beyond traditional gameplay. Among popular meme-based tap-to-earn games, Tap Warrior has emerged as a leading example on the Solana blockchain. It’s a tap-to-earn game that brings meme culture and crypto gaming together in a seamless, engaging package. Tap Warrior’s gameplay is straightforward yet engaging: players tap their way to higher scores, competing with others while earning rewards for their efforts. Designed for broad appeal, it caters to both experienced crypto enthusiasts and casual gamers who may just be venturing into blockchain gaming. The tap-to-earn model means that players can accumulate rewards by actively participating, making each tap a step toward ranking up, earning crypto, or collecting exclusive digital items. However it should be clear, Tap Warrior is a free-to-play game where users tap to accumulate points and unlock rewards without financial obligations. Players can enjoy the competitive, meme-driven environment while choosing whether or not to engage in cryptocurrency-related options like mining or staking. Tap Warrior makes it clear that it is not an investment platform, emphasizing that any crypto activity is optional and solely for entertainment. The game includes disclaimers about potential risks associated with cryptocurrency, such as market volatility, liquidity issues, and security vulnerabilities. These qualifications highlight that players should not expect financial returns and should only engage with crypto aspects if comfortable with the risks. This commitment to transparency and optional engagement with cryptocurrency makes Tap Warrior an accessible way to experience the crypto world. For those interested, Tap Warrior leverages Solana’s blockchain transparency and security, assuring users that their rewards and progress are protected. The game’s token-based economy allows players to earn cryptocurrency and collectible NFTs, adding tangible value to their gameplay. As they progress, players find themselves in a friendly competition to tap more, rank higher, and earn more rewards. This gamified experience keeps them engaged while drawing on the humor and accessibility that meme games are known for. As a free-to-play, meme-based tap-to-earn game, Tap Warrior offers an ideal balance for gamers looking for entertainment, community, and optional crypto engagement. With its emphasis on meme culture, transparency, and user choice, Tap Warrior redefines casual blockchain gaming, blending accessible fun with the potential to explore digital assets at one's discretion. Whether aiming to collect points, climb the ranks, or dabble in crypto, players can enjoy a flexible gaming experience that values both humor and security, creating a space where gaming meets decentralized innovation on their terms. Disclaimer and Disclosure The information provided in this article is for general informational purposes only and should not be construed as financial, investment, legal, or other advice. The views expressed are solely those of the author or contributor and do not necessarily reflect the official policy or position of Tap Warrior. This article does not constitute a solicitation or offer to buy or sell any assets, securities, or financial instruments, nor does it endorse or recommend any specific products, services, or investment strategies. Readers are encouraged to conduct their own research and seek the advice of a licensed professional regarding any financial decisions. Tap Warrior makes no representation as to the accuracy, completeness, or reliability of the information provided. Any reliance you place on such information is strictly at your own risk. Cryptocurrency and Web3 markets carry a high level of risk and may not be suitable for all investors. Please exercise caution, as there is a potential for significant financial loss. Tap Warrior and its affiliates, including marketing and PR agencies, disclaim any liability for any direct, indirect, or consequential losses or damages of any kind arising from reliance upon any information contained within this article or linked resources.

Tap Warrior: The Newest Meme-Based Tap-to-Earn Game

Tap Warrior: Najnovija igra tap-to-earn zasnovana na memima

Tap Warrior: Nejnovější hra založená na Meme Tap-to-Earn

Tap Warrior: Det nyeste meme-baserede Tap-to-Earn-spil

Tap Warrior: el nuevo juego basado en memes en el que tocar para ganar dinero

Tap Warrior : le tout nouveau jeu Tap-to-Earn basé sur les mèmes

タップ ウォリアー: 最新のミームをベースにしたタップして稼ぐゲーム

Tap Warrior: het nieuwste meme-gebaseerde tap-to-earn-spel

Tap Warrior: Papadi ye Mpsha yeo e Theilwego go Meme ya Tap-go-Earn

Kanda Warrior: Umukino mushya wa Meme-Kanda-Kuri-Kunguka

I-tap ang Warrior: Ang Pinakabagong Meme-Based Tap-to-Earn Game

Tap Warrior: En Yeni Meme Tabanlı Dokunarak Kazanma Oyunu

IQhawe lokucofa: Owona Mdlalo umtsha weMeme usekwe kuCofa ukuze ufumane

Authors:
(1) Halil Yigit Oksuz, Control Systems Group at Technische Universitat Berlin, Germany and Exzellenzcluster Science of Intelligence, Technische Universitat Berlin, Marchstr. 23, 10587, Berlin, Germany;
(2) Fabio Molinari, Control Systems Group at Technische Universitat Berlin, Germany;
(3) Henning Sprekeler, Exzellenzcluster Science of Intelligence, Technische Universit¨at Berlin, Marchstr. 23, 10587, Berlin, Germany and Modelling Cognitive Processes Group at Technische Universit¨at Berlin, Germany;
(4) Jorg Raisch, Control Systems Group at Technische Universitat Berlin, Germany and Exzellenzcluster Science of Intelligence, Technische Universitat Berlin, Marchstr. 23, 10587, Berlin, Germany. Table of Links Abstract and Introduction Problem Setup Federated fair over-the-air learning (FedAir) Algorithm Convergence Properties Numerical Example Conclusion and References V. NUMERICAL EXAMPLE Notice that the FedAVG algorithm achieves approximately 70 − 75% of classification accuracy after 100000 iterations as it can also be seen in Fig. 1. However, due to the heterogeneity in distribution of the data, we observe that the FedAVG algorithm cannot properly identify the samples of data with labels 0. This can be seen from the confusion matrices in Fig. 2, where the FedAVG model cannot successfully recognize the data labeled as 0, whereas the FedFAir algorithm performs much better by recognizing a large percentage of data, both labelled 0 and 1. nd 1. Additionally, we compare the FedFAir algorithm and the standard FedAVG [1] algorithm in terms of communication efficiency. For the latter, the TDMA scheme is used for communication between agents and the central unit. In this case, individual time slots are allocated for each agent to transmit its parameter vector at each communication round. After receiving parameter vectors, the central unit computes the average of them and sends it back to the agents. Since we consider a system with N = 12 agents, it takes N = 12 time slots per communication round for each agent to transmit its updated parameter vector while only 3 are needed for the FedFAir algorithm (see (13)), independent of the value of N. This makes the execution of one iteration of the FedFAir algorithm N/3 = 4 times faster than the execution of an iteration of the FedAVG algorithm. This paper is available on arxiv under CC BY 4.0 DEED license. Authors: (1) Halil Yigit Oksuz, Control Systems Group at Technische Universitat Berlin, Germany and Exzellenzcluster Science of Intelligence, Technische Universitat Berlin, Marchstr. 23, 10587, Berlin, Germany; (2) Fabio Molinari, Control Systems Group at Technische Universitat Berlin, Germany; (3) Henning Sprekeler, Exzellenzcluster Science of Intelligence, Technische Universit¨at Berlin, Marchstr. 23, 10587, Berlin, Germany and Modelling Cognitive Processes Group at Technische Universit¨at Berlin, Germany; (4) Jorg Raisch, Control Systems Group at Technische Universitat Berlin, Germany and Exzellenzcluster Science of Intelligence, Technische Universitat Berlin, Marchstr. 23, 10587, Berlin, Germany. Authors: Authors: (1) Halil Yigit Oksuz, Control Systems Group at Technische Universitat Berlin, Germany and Exzellenzcluster Science of Intelligence, Technische Universitat Berlin, Marchstr. 23, 10587, Berlin, Germany; (2) Fabio Molinari, Control Systems Group at Technische Universitat Berlin, Germany; (3) Henning Sprekeler, Exzellenzcluster Science of Intelligence, Technische Universit¨at Berlin, Marchstr. 23, 10587, Berlin, Germany and Modelling Cognitive Processes Group at Technische Universit¨at Berlin, Germany; (4) Jorg Raisch, Control Systems Group at Technische Universitat Berlin, Germany and Exzellenzcluster Science of Intelligence, Technische Universitat Berlin, Marchstr. 23, 10587, Berlin, Germany. Table of Links Abstract and Introduction Abstract and Introduction Problem Setup Problem Setup Federated fair over-the-air learning (FedAir) Algorithm Federated fair over-the-air learning (FedAir) Algorithm Convergence Properties Convergence Properties Numerical Example Numerical Example Conclusion and References Conclusion and References V. NUMERICAL EXAMPLE Notice that the FedAVG algorithm achieves approximately 70 − 75% of classification accuracy after 100000 iterations as it can also be seen in Fig. 1. However, due to the heterogeneity in distribution of the data, we observe that the FedAVG algorithm cannot properly identify the samples of data with labels 0. This can be seen from the confusion matrices in Fig. 2, where the FedAVG model cannot successfully recognize the data labeled as 0, whereas the FedFAir algorithm performs much better by recognizing a large percentage of data, both labelled 0 and 1. nd 1. Additionally, we compare the FedFAir algorithm and the standard FedAVG [1] algorithm in terms of communication efficiency. For the latter, the TDMA scheme is used for communication between agents and the central unit. In this case, individual time slots are allocated for each agent to transmit its parameter vector at each communication round. After receiving parameter vectors, the central unit computes the average of them and sends it back to the agents. Since we consider a system with N = 12 agents, it takes N = 12 time slots per communication round for each agent to transmit its updated parameter vector while only 3 are needed for the FedFAir algorithm (see (13)), independent of the value of N. This makes the execution of one iteration of the FedFAir algorithm N/3 = 4 times faster than the execution of an iteration of the FedAVG algorithm. 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

Boosting Fairness and Robustness in Over-the-Air Federated Learning: Numerical Example

Table of Links Abstract and 1. Introduction 1.1 Option Pricing 1.2 Asymptotic Notation (Big O) 1.3 Finite Difference 1.4 The Black-Schole Model 1.5 Monte Carlo Simulation and Variance Reduction Techniques 1.6 Our Contribution Literature Review
Methodology 3.1 Model Assumption 3.2 Theorems and Model Discussion Result Analysis
Conclusion and References 1.3 FINITE DIFFERENCE Finite difference methods are numerical techniques used to approximate derivatives and solve differential equations by discretizing the domain into a grid of points. This approach is based on Taylor series expansions, where derivatives are expressed as combinations of function values at nearby points. Consider a function 𝑓(𝑥) and its derivative 𝑓′(𝑥) then finite difference approximation of the derivative at a point 𝑥𝑖 is given by: In this plot, the tangent line represents the true derivative 𝑓 ′ (𝑥0) , Meanwhile, the secant line represents the finite difference approximation. As the step size ℎ decreases, the secant line becomes closer to the tangent line, demonstrating the convergence of the finite difference approximation to the true derivative as ℎ approaches zero. The application of finite difference methods in option pricing allows for more accurate simulations of market dynamics and estimation of option prices. These methods enable practitioners to account for various factors affecting option values, such as changes in asset prices, volatility, and interest rates. Finite difference methods offer flexibility and scalability, making them suitable for pricing various types of options and constructing hedging strategies. Despite their computational complexity, advancements in numerical algorithms and computing power have made finite difference methods increasingly accessible and efficient for option pricing applications [4]. Authors:
(1) Agni Rakshit, Department of Mathematics, National Institute of Technology, Durgapur, Durgapur, India (spiritualagnimath.statml@gmail.com);
(2) Gautam Bandyopadhyay, Department of Management Studies, National Institute of Technology, Durgapur, Durgapur, India (gbandyopadhyay.dms@nitdgp.ac.in);
(3) Tanujit Chakraborty, Department of Science and Engineering & Sorbonne Center for AI, Sorbonne University, Abu Dhabi, United Arab Emirates (tanujit.chakraborty@sorbonne.ae). This paper is available on arxiv under CC by 4.0 Deed (Attribution 4.0 International) license. Table of Links Abstract and 1. Introduction Abstract and 1. Introduction Abstract and 1. Introduction 1.1 Option Pricing 1.1 Option Pricing 1.2 Asymptotic Notation (Big O) 1.2 Asymptotic Notation (Big O) 1.3 Finite Difference 1.3 Finite Difference 1.4 The Black-Schole Model 1.4 The Black-Schole Model 1.5 Monte Carlo Simulation and Variance Reduction Techniques 1.5 Monte Carlo Simulation and Variance Reduction Techniques 1.6 Our Contribution 1.6 Our Contribution Literature Review Methodology Literature Review Literature Review Literature Review Methodology Methodology 3.1 Model Assumption 3.1 Model Assumption 3.2 Theorems and Model Discussion 3.2 Theorems and Model Discussion Result Analysis Conclusion and References Result Analysis Result Analysis Result Analysis Conclusion and References Conclusion and References Conclusion and References 1.3 FINITE DIFFERENCE Finite difference methods are numerical techniques used to approximate derivatives and solve differential equations by discretizing the domain into a grid of points. This approach is based on Taylor series expansions, where derivatives are expressed as combinations of function values at nearby points. Consider a function 𝑓(𝑥) and its derivative 𝑓′(𝑥) then finite difference approximation of the derivative at a point 𝑥𝑖 is given by: In this plot, the tangent line represents the true derivative 𝑓 ′ (𝑥0) , Meanwhile, the secant line represents the finite difference approximation. As the step size ℎ decreases, the secant line becomes closer to the tangent line, demonstrating the convergence of the finite difference approximation to the true derivative as ℎ approaches zero. The application of finite difference methods in option pricing allows for more accurate simulations of market dynamics and estimation of option prices. These methods enable practitioners to account for various factors affecting option values, such as changes in asset prices, volatility, and interest rates. Finite difference methods offer flexibility and scalability, making them suitable for pricing various types of options and constructing hedging strategies. Despite their computational complexity, advancements in numerical algorithms and computing power have made finite difference methods increasingly accessible and efficient for option pricing applications [4]. Authors: (1) Agni Rakshit, Department of Mathematics, National Institute of Technology, Durgapur, Durgapur, India (spiritualagnimath.statml@gmail.com); (2) Gautam Bandyopadhyay, Department of Management Studies, National Institute of Technology, Durgapur, Durgapur, India (gbandyopadhyay.dms@nitdgp.ac.in); (3) Tanujit Chakraborty, Department of Science and Engineering & Sorbonne Center for AI, Sorbonne University, Abu Dhabi, United Arab Emirates (tanujit.chakraborty@sorbonne.ae). Authors: Authors: (1) Agni Rakshit, Department of Mathematics, National Institute of Technology, Durgapur, Durgapur, India (spiritualagnimath.statml@gmail.com); (2) Gautam Bandyopadhyay, Department of Management Studies, National Institute of Technology, Durgapur, Durgapur, India (gbandyopadhyay.dms@nitdgp.ac.in); (3) Tanujit Chakraborty, Department of Science and Engineering & Sorbonne Center for AI, Sorbonne University, Abu Dhabi, United Arab Emirates (tanujit.chakraborty@sorbonne.ae). This paper is available on arxiv under CC by 4.0 Deed (Attribution 4.0 International) license. This paper is available on arxiv under CC by 4.0 Deed (Attribution 4.0 International) license. available on arxiv available on arxiv

Finite Difference Methods: A Numerical Approach to Option Pricing and Derivatives

Authors:
(1) Chengfeng Shen, School of Mathematical Sciences, Peking University, Beijing;
(2) Yifan Luo, School of Mathematical Sciences, Peking University, Beijing;
(3) Zhennan Zhou, Beijing International Center for Mathematical Research, Peking University. Table of Links Abstract and 1. Introduction 2 Model and 2.1 Optimal Stopping and Obstacle Problem 2.2 Mean Field Games with Optimal Stopping 2.3 Pure Strategy Equilibrium for OSMFG 2.4 Mixed Strategy Equilibrium for OSMFG 3 Algorithm Construction and 3.1 Fictitious Play 3.2 Convergence of Fictitious Play to Mixed Strategy Equilibrium 3.3 Algorithm Based on Fictitious Play 3.4 Numerical Analysis 4 Numerical Experiments and 4.1 A Non-local OSMFG Example 4.2 A Local OSMFG Example 5 Conclusion, Acknowledgement, and References 4 Numerical Experiments In this section, we conduct several numerical experiments to demonstrate the effectiveness of the proposed semi-implicit finite difference algorithm (Algorithm 2). Through these experiments, we examine the convergence properties of our algorithm highlighting the implementation of the fictitious play. We demonstrate that our requirement for δn in (3.3) is a sufficient yet unnecessary condition. However, in certain cases where the pure strategy equilibrium may not exist, the iteration method may not converge if the condition (3.3) is violated. 4.1 A Non-local OSMFG Example Setup. In this example, the state of the representative agent belongs to the domain [0, 1]×R. It dynamic is a Brownian motion, i.e. Intuitively, as the agent aims to minimize expected cost, the running cost encourages the agent to remain in the game when its state is below the average state of other remaining agents, while the stopping cost encourages the agent to continue playing for a longer duration. It is easy to check that the cost functions above are equivalent with In Figure 2, log-log plots of error versus iteration numbers are shown for different learning rates δn. For this example, taking δn = 1 leads to the fastest convergence, although with δn = 1 no fictitious play is implemented. The reason for this is that the convergence requirement for the learning rate δn in Definition 3.1 is only a sufficient condition, rather than a necessary one. For problems with a pure strategy equilibrium, the condition in definition 3.1 can be relaxed and a more aggressive learning rate can be taken to obtain faster convergence. However, we would like to stress that we could not know a priori whether a pure strategy equilibrium exists, but the proposed algorithm, which is based on fictitious play, always produces convergence results. This paper is available on arxiv under CC 4.0 license. Authors: (1) Chengfeng Shen, School of Mathematical Sciences, Peking University, Beijing; (2) Yifan Luo, School of Mathematical Sciences, Peking University, Beijing; (3) Zhennan Zhou, Beijing International Center for Mathematical Research, Peking University. Authors: Authors: (1) Chengfeng Shen, School of Mathematical Sciences, Peking University, Beijing; (2) Yifan Luo, School of Mathematical Sciences, Peking University, Beijing; (3) Zhennan Zhou, Beijing International Center for Mathematical Research, Peking University. Table of Links Abstract and 1. Introduction Abstract and 1. Introduction 2 Model and 2.1 Optimal Stopping and Obstacle Problem 2 Model and 2.1 Optimal Stopping and Obstacle Problem 2.2 Mean Field Games with Optimal Stopping 2.2 Mean Field Games with Optimal Stopping 2.3 Pure Strategy Equilibrium for OSMFG 2.3 Pure Strategy Equilibrium for OSMFG 2.4 Mixed Strategy Equilibrium for OSMFG 2.4 Mixed Strategy Equilibrium for OSMFG 3 Algorithm Construction and 3.1 Fictitious Play 3 Algorithm Construction and 3.1 Fictitious Play 3.2 Convergence of Fictitious Play to Mixed Strategy Equilibrium 3.2 Convergence of Fictitious Play to Mixed Strategy Equilibrium 3.3 Algorithm Based on Fictitious Play 3.3 Algorithm Based on Fictitious Play 3.4 Numerical Analysis 3.4 Numerical Analysis 4 Numerical Experiments and 4.1 A Non-local OSMFG Example 4 Numerical Experiments and 4.1 A Non-local OSMFG Example 4.2 A Local OSMFG Example 4.2 A Local OSMFG Example 5 Conclusion, Acknowledgement, and References 5 Conclusion, Acknowledgement, and References 4 Numerical Experiments In this section, we conduct several numerical experiments to demonstrate the effectiveness of the proposed semi-implicit finite difference algorithm (Algorithm 2). Through these experiments, we examine the convergence properties of our algorithm highlighting the implementation of the fictitious play. We demonstrate that our requirement for δ n in (3.3) is a sufficient yet unnecessary condition. However, in certain cases where the pure strategy equilibrium may not exist, the iteration method may not converge if the condition (3.3) is violated. n 4.1 A Non-local OSMFG Example Setup . In this example, the state of the representative agent belongs to the domain [0, 1]×R. It dynamic is a Brownian motion, i.e. Setup Intuitively, as the agent aims to minimize expected cost, the running cost encourages the agent to remain in the game when its state is below the average state of other remaining agents, while the stopping cost encourages the agent to continue playing for a longer duration. It is easy to check that the cost functions above are equivalent with In Figure 2, log-log plots of error versus iteration numbers are shown for different learning rates δn. For this example, taking δn = 1 leads to the fastest convergence, although with δn = 1 no fictitious play is implemented. The reason for this is that the convergence requirement for the learning rate δn in Definition 3.1 is only a sufficient condition, rather than a necessary one. For problems with a pure strategy equilibrium, the condition in definition 3.1 can be relaxed and a more aggressive learning rate can be taken to obtain faster convergence. However, we would like to stress that we could not know a priori whether a pure strategy equilibrium exists, but the proposed algorithm, which is based on fictitious play, always produces convergence results. This paper is available on arxiv under CC 4.0 license. This paper is available on arxiv under CC 4.0 license. available on arxiv

Fictitious Play for Mixed Strategy Equilibria in Mean Field Games: Numerical Experiments

Authors:
(1) Chengfeng Shen, School of Mathematical Sciences, Peking University, Beijing;
(2) Yifan Luo, School of Mathematical Sciences, Peking University, Beijing;
(3) Zhennan Zhou, Beijing International Center for Mathematical Research, Peking University. Table of Links Abstract and 1. Introduction 2 Model and 2.1 Optimal Stopping and Obstacle Problem 2.2 Mean Field Games with Optimal Stopping 2.3 Pure Strategy Equilibrium for OSMFG 2.4 Mixed Strategy Equilibrium for OSMFG 3 Algorithm Construction and 3.1 Fictitious Play 3.2 Convergence of Fictitious Play to Mixed Strategy Equilibrium 3.3 Algorithm Based on Fictitious Play 3.4 Numerical Analysis 4 Numerical Experiments and 4.1 A Non-local OSMFG Example 4.2 A Local OSMFG Example 5 Conclusion, Acknowledgement, and References 3.4 Numerical Analysis In this part, our goal is to prove the convergence of algorithm 2 when implicit scheme (3.25) and (3.26) are applied. The convergence analysis mirrors the proof for Theorem 3.1, requiring only adapting the arguments to a discretized version. Definition 3.4 (implicit discretized system for mixed strategy equilibrium) We define the complementary condition will be weaker than the following one Before stating the main result, we present a property of the implicit scheme for obstacle equations: the discretized solution u continuously depends on the discretized source term f. Lemma 3.1 Consider the following discrete obstacle problem: Now we can state the main convergence result in this section. Proof The spirit of the proof is analog to the one in theorem 3.1. We divide the proof into 3 steps just parallel to the proof of theorem 3.1. with equality if and only if 3. We conclude that any cluster point (u∗, m∗) is a solution to (3.29). We first verify that u∗ will satisfy the discretized obstacle problem as follows: This paper is available on arxiv under CC 4.0 license. Authors: (1) Chengfeng Shen, School of Mathematical Sciences, Peking University, Beijing; (2) Yifan Luo, School of Mathematical Sciences, Peking University, Beijing; (3) Zhennan Zhou, Beijing International Center for Mathematical Research, Peking University. Authors: Authors: (1) Chengfeng Shen, School of Mathematical Sciences, Peking University, Beijing; (2) Yifan Luo, School of Mathematical Sciences, Peking University, Beijing; (3) Zhennan Zhou, Beijing International Center for Mathematical Research, Peking University. Table of Links Abstract and 1. Introduction Abstract and 1. Introduction 2 Model and 2.1 Optimal Stopping and Obstacle Problem 2 Model and 2.1 Optimal Stopping and Obstacle Problem 2.2 Mean Field Games with Optimal Stopping 2.2 Mean Field Games with Optimal Stopping 2.3 Pure Strategy Equilibrium for OSMFG 2.3 Pure Strategy Equilibrium for OSMFG 2.4 Mixed Strategy Equilibrium for OSMFG 2.4 Mixed Strategy Equilibrium for OSMFG 3 Algorithm Construction and 3.1 Fictitious Play 3 Algorithm Construction and 3.1 Fictitious Play 3.2 Convergence of Fictitious Play to Mixed Strategy Equilibrium 3.2 Convergence of Fictitious Play to Mixed Strategy Equilibrium 3.3 Algorithm Based on Fictitious Play 3.3 Algorithm Based on Fictitious Play 3.4 Numerical Analysis 3.4 Numerical Analysis 4 Numerical Experiments and 4.1 A Non-local OSMFG Example 4 Numerical Experiments and 4.1 A Non-local OSMFG Example 4.2 A Local OSMFG Example 4.2 A Local OSMFG Example 5 Conclusion, Acknowledgement, and References 5 Conclusion, Acknowledgement, and References 3.4 Numerical Analysis In this part, our goal is to prove the convergence of algorithm 2 when implicit scheme (3.25) and (3.26) are applied. The convergence analysis mirrors the proof for Theorem 3.1, requiring only adapting the arguments to a discretized version. Definition 3.4 (implicit discretized system for mixed strategy equilibrium) We define Definition 3.4 (implicit discretized system for mixed strategy equilibrium) We define the complementary condition the complementary condition will be weaker than the following one will be weaker than the following one Before stating the main result, we present a property of the implicit scheme for obstacle equations: the discretized solution u continuously depends on the discretized source term f . f Lemma 3.1 Consider the following discrete obstacle problem: Lemma 3.1 Consider the following discrete obstacle problem: Now we can state the main convergence result in this section. Proof The spirit of the proof is analog to the one in theorem 3.1. We divide the proof into 3 steps just parallel to the proof of theorem 3.1. Proof with equality if and only if 3. We conclude that any cluster point (u∗, m∗) is a solution to (3.29). We first verify that u∗ will satisfy the discretized obstacle problem as follows: This paper is available on arxiv under CC 4.0 license. This paper is available on arxiv under CC 4.0 license. available on arxiv

Fictitious Play for Mixed Strategy Equilibria in Mean Field Games: Numerical Analysis

Table of Links Abstract and 1. Introduction Why ELIZA?
The Intelligence Engineers
Newell, Shaw, and Simon’s IPL Logic Theorist: The First True AIs
From IPL to SLIP and Lisp
A Critical Tangent into Gomoku
Interpretation is the Core of Intelligence
The Threads Come Together: Interpretation, Language, Lists, Graphs, and Recursion
Finally ELIZA: A Platform, Not a Chat Bot!
A Perfect Irony: A Lisp ELIZA Escapes and is Misinterpreted by the AI Community
Another Wave: A BASIC ELIZA turns the PC Generation on to AI
Conclusion: A certain danger lurks there
Acknowledgements and References 4 Newell, Shaw, and Simon’s IPL Logic Theorist: The First True AIs Beginning in 1950, almost exactly at the same time that Turing’s Mind paper appeared, there was an explosion in von Neumann/Turing style computer hardware. At that time, all of the existing computers, such as Colossus and EDVAC, were one-off machines. Although UNIVAC, the company spun off from Penn’s ENIAC project by Eckert and Mauchley, had promised a massproduced machine, none had yet been delivered when RAND decided to build the JOHNNIAC.[30] This was the machine on which Shaw, Newell, and Simon pioneered the IPL series of programming languages, which they used to implement what were are almost certainly the first real AIs: “IPL was first utilized to demonstrate that the theorems in Principia Mathematica which were proven laboriously by hand, by Bertrand Russell and Alfred North Whitehead, could in fact be proven by computation. According to Simon’s autobiography Models of My Life, this application was originally developed first by hand simulation, using his children as the computing elements, while writing on and holding up note cards as the registers which contained the state variables of the program. IPL was used to implement several early artificial intelligence programs, also by the same authors: the Logic Theorist (1956), the General Problem Solver (1957), and their computer chess program NSS (1958). Several versions of IPL were created: IPL-I (never implemented), IPL-II (1957 for JOHNNIAC), IPL-III (existed briefly), IPL-IV, IPL-V (1958, for IBM 650, IBM 704, IBM 7090, Philco model 212, many others.”[5] IPL introduced what became the most important concepts in classical Artificial Intelligence (and, in fact, in computing more generally), including list processing, symbolic computing (as foreshadowed by Ada Lovelace!), and recursion.[5] Notably, although Turing’s bombe, the code-breaking machine, was not a “modern” computer, in the sense that is was hardwired, as opposed to being of the von Neumann/Turing stored program type, the program that it ran was explicitly engaged in mechanical heuristic search, that is, in trying a huge number of operators (e.g., combinations of code keys) in a cleverly arranged order (e.g., beginning with the most probable), in hopes of reaching a goal (e.g., the key that would break the day’s code). Much later, Newell and Simon described heuristic search as a fundamental process of intelligence,[35], and remains one of the most important tools of AI.[2] However, IPL was an ugly programming language – not very far from assembly language – and, as an interpreted language it was slower than assembly or the soon-to-follow “high level” compiled languages such as COBOL and Fortran. These promised programmers both high performance and simplicity of expression. This brings us to SLIP and Lisp, which took different paths to bring simplicity and additional power to the important AI concepts pioneered in IPL. Author:
(1) Jeff Shrager, Blue Dot Change and Stanford University Symbolic Systems Program (Adjunct)( jshrager@stanford.edu). This paper is available on arxiv under CC BY 4.0 license. [2] Allen Newell once told me, in passing, that AI’s only had one tool, the ability to search huge multi-dimensional spaces efficiently, but that one tool is all you need. Table of Links Abstract and 1. Introduction Abstract and 1. Introduction Why ELIZA? The Intelligence Engineers Newell, Shaw, and Simon’s IPL Logic Theorist: The First True AIs From IPL to SLIP and Lisp A Critical Tangent into Gomoku Interpretation is the Core of Intelligence The Threads Come Together: Interpretation, Language, Lists, Graphs, and Recursion Finally ELIZA: A Platform, Not a Chat Bot! A Perfect Irony: A Lisp ELIZA Escapes and is Misinterpreted by the AI Community Another Wave: A BASIC ELIZA turns the PC Generation on to AI Conclusion: A certain danger lurks there Acknowledgements and References Why ELIZA? Why ELIZA? The Intelligence Engineers The Intelligence Engineers Newell, Shaw, and Simon’s IPL Logic Theorist: The First True AIs Newell, Shaw, and Simon’s IPL Logic Theorist: The First True AIs From IPL to SLIP and Lisp From IPL to SLIP and Lisp A Critical Tangent into Gomoku A Critical Tangent into Gomoku Interpretation is the Core of Intelligence Interpretation is the Core of Intelligence The Threads Come Together: Interpretation, Language, Lists, Graphs, and Recursion The Threads Come Together: Interpretation, Language, Lists, Graphs, and Recursion Finally ELIZA: A Platform, Not a Chat Bot! Finally ELIZA: A Platform, Not a Chat Bot! A Perfect Irony: A Lisp ELIZA Escapes and is Misinterpreted by the AI Community A Perfect Irony: A Lisp ELIZA Escapes and is Misinterpreted by the AI Community Another Wave: A BASIC ELIZA turns the PC Generation on to AI Another Wave: A BASIC ELIZA turns the PC Generation on to AI Conclusion: A certain danger lurks there Conclusion: A certain danger lurks there Acknowledgements and References Acknowledgements and References 4 Newell, Shaw, and Simon’s IPL Logic Theorist: The First True AIs Beginning in 1950, almost exactly at the same time that Turing’s Mind paper appeared, there was an explosion in von Neumann/Turing style computer hardware. At that time, all of the existing computers, such as Colossus and EDVAC, were one-off machines. Although UNIVAC, the company spun off from Penn’s ENIAC project by Eckert and Mauchley, had promised a massproduced machine, none had yet been delivered when RAND decided to build the JOHNNIAC.[30] This was the machine on which Shaw, Newell, and Simon pioneered the IPL series of programming languages, which they used to implement what were are almost certainly the first real AIs: “IPL was first utilized to demonstrate that the theorems in Principia Mathematica which were proven laboriously by hand, by Bertrand Russell and Alfred North Whitehead, could in fact be proven by computation. According to Simon’s autobiography Models of My Life, this application was originally developed first by hand simulation, using his children as the computing elements, while writing on and holding up note cards as the registers which contained the state variables of the program. IPL was used to implement several early artificial intelligence programs, also by the same authors: the Logic Theorist (1956), the General Problem Solver (1957), and their computer chess program NSS (1958). Several versions of IPL were created: IPL-I (never implemented), IPL-II (1957 for JOHNNIAC), IPL-III (existed briefly), IPL-IV, IPL-V (1958, for IBM 650, IBM 704, IBM 7090, Philco model 212, many others.”[5] IPL introduced what became the most important concepts in classical Artificial Intelligence (and, in fact, in computing more generally), including list processing, symbolic computing (as foreshadowed by Ada Lovelace!), and recursion.[5] Notably, although Turing’s bombe, the code-breaking machine, was not a “modern” computer, in the sense that is was hardwired, as opposed to being of the von Neumann/Turing stored program type, the program that it ran was explicitly engaged in mechanical heuristic search, that is, in trying a huge number of operators (e.g., combinations of code keys) in a cleverly arranged order (e.g., beginning with the most probable), in hopes of reaching a goal (e.g., the key that would break the day’s code). Much later, Newell and Simon described heuristic search as a fundamental process of intelligence,[35], and remains one of the most important tools of AI.[2] However, IPL was an ugly programming language – not very far from assembly language – and, as an interpreted language it was slower than assembly or the soon-to-follow “high level” compiled languages such as COBOL and Fortran. These promised programmers both high performance and simplicity of expression. This brings us to SLIP and Lisp, which took different paths to bring simplicity and additional power to the important AI concepts pioneered in IPL. Author: (1) Jeff Shrager, Blue Dot Change and Stanford University Symbolic Systems Program (Adjunct)( jshrager@stanford.edu). Author: Author: (1) Jeff Shrager, Blue Dot Change and Stanford University Symbolic Systems Program (Adjunct)( jshrager@stanford.edu). This paper is available on arxiv under CC BY 4.0 license. This paper is available on arxiv under CC BY 4.0 license. available on arxiv available on arxiv [2] Allen Newell once told me, in passing, that AI’s only had one tool, the ability to search huge multi-dimensional spaces efficiently, but that one tool is all you need.

Newell, Shaw, and Simon’s IPL Logic Theorist: The First True AIs

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