Faable

faable

Startups of The Year 2023 Winners: Europe

THE FABLE OF THE CICADA AND THE ANT

The Fable of the Pupil Lover

Social Life in the Insect World by Jean-Henri Fabre, is part of the HackerNoon Books Series. You can jump to any chapter in this book here . THE FABLE OF THE CIGALE AND THE ANT CHAPTER I. THE FABLE OF THE CIGALE AND THE ANT Fame is the daughter of Legend. In the world of creatures, as in the world of men, the story precedes and outlives history. There are many instances of the fact that if an insect attract our attention for this reason or that, it is given a place in those legends of the people whose last care is truth. For example, who is there that does not, at least by hearsay, know the Cigale? Where in the entomological world shall we find a more famous reputation? Her fame as an impassioned singer, careless of the future, was the subject of our earliest lessons in repetition. In short, easily remembered lines of verse, we learned how she was destitute when the winter winds arrived, and how she went begging for food to the Ant, her neighbour. A poor welcome she received, the would-be borrower!—a welcome that has become proverbial, and her chief title to celebrity. The petty malice of the two short lines— Vous chantiez! j'en suis bien aise, Eh bien, dansez maintenant! DURING THE DROUGHTS OF SUMMER THIRSTING INSECTS, AND NOTABLY THE ANT, FLOCK TO THE DRINKING-PLACES OF THE CIGALE. As we see, reality completely reverses the action described by the fable. The shameless beggar, who does not hesitate at theft, is the Ant; the industrious worker, willingly sharing her goods with the suffering, is the Cigale. Yet another detail, and the reversal of the fable is further emphasised. After five or six weeks of gaiety, the songstress falls from the tree, exhausted by the fever of life. The sun shrivels her body; the feet of the passers-by crush it. A bandit always in search of booty, the Ant discovers the remains. She divides the rich find, dissects it, and cuts it up into tiny fragments, which go to swell her stock of provisions. It is not uncommon to see a dying Cigale, whose wings are still trembling in the dust, drawn and quartered by a gang of knackers. Her body is black with them. After this instance of cannibalism the truth of the relations between the two insects is obvious. Antiquity held the Cigale in high esteem. The Greek Béranger, Anacreon, devoted an ode to her, in which his praise of her is singularly exaggerated. "Thou art almost like unto the Gods," he says. The reasons which he has given for this apotheosis are none of the best. They consist in these three privileges: γηγενἡϛ, ἁπαθἡϛ, ἁναιμὁσαρκε; born of the earth, insensible to pain, bloodless. We will not reproach the poet for these mistakes; they were then generally believed, and were perpetuated long afterwards, until the exploring eye of scientific observation was directed upon them. And in minor poetry, whose principal merit lies in rhythm and harmony, we must not look at things too closely. Even in our days, the Provençal poets, who know the Cigale as Anacreon never did, are scarcely more careful of the truth in celebrating the insect which they have taken for their emblem. A friend of mine, an eager observer and a scrupulous realist, does not deserve this reproach. He gives me permission to take from his pigeon-holes the following Provençal poem, in which the relations between the Cigale and the Ant are expounded with all the rigour of science. I leave to him the responsibility for his poetic images and his moral reflections, blossoms unknown to my naturalist's garden; but I can swear to the truth of all he says, for it corresponds with what I see each summer on the lilac-trees of my garden. LA CIGALO E LA FOURNIGO. I. Jour de Dièu, queto caud! Bèu tèms pèr la Cigalo, Que, trefoulido, se regalo D'uno raisso de fio; bèu tèms per la meissoun. Dins lis erso d'or, lou segaire, Ren plega, pitre au vent, rustico e canto gaire; Dins soun gousiè, la set estranglo la cansoun. Tèms benesi pèr tu. Dounc, ardit! cigaleto, Fai-lei brusi, ti chimbaleto, E brandusso lou ventre à creba ti mirau. L'Ome enterin mando le daio, Que vai balin-balan de longo e que dardaio L'ulau de soun acié sus li rous espigau. Plèn d'aigo pèr la péiro e tampouna d'erbiho Lou coufié sus l'anco pendiho. Si la péiro es au frès dins soun estui de bos, E se de longo es abèurado, L'Ome barbelo au fio d'aqueli souleiado Que fan bouli de fes la mesoulo dis os. Tu, Cigalo, as un biais pèr la set: dins la rusco Tendro e jutouso d'uno busco, L'aguio de toun bè cabusso e cavo un pous. Lou siro monto pèr la draio. T'amourres à la fon melicouso que raio, E dou sourgènt sucra bèves lou teta-dous. Mai pas toujour en pas. Oh! que nàni; de laire, Vesin, vesino o barrulaire, T'an vist cava lou pous. An set; vènon, doulènt, Te prène un degout pèr si tasso. Mesfiso-te, ma bello: aqueli curo-biasso, Umble d'abord, soun lèu de gusas insoulènt. Quiston un chicouloun di rèn, pièi de ti resto Soun plus countènt, ausson la testo E volon tout: L'auran. Sis arpioun en rastèu Te gatihoun lou bout de l'alo. Sus tu larjo esquinasso es un mounto-davalo; T'aganton pèr lou bè, li bano, lis artèu; Tiron d'eici, d'eilà. L'impaciènci te gagno. Pst! pst! d'un giscle de pissagno Aspèrges l'assemblado e quites lou ramèu. T'en vas bèn liuen de la racaio, Que t'a rauba lou pous, e ris, e se gougaio, E se lipo li brego enviscado de mèu. Or d'aqueli boumian abèura sens fatigo, Lou mai tihous es la fournigo. Mousco, cabrian, guespo e tavan embana, Espeloufi de touto meno, Costo-en-long qu'à toun pous lou soulcias ameno, N'an pas soun testardige à te faire enana. Pèr l'esquicha l'artèu, te coutiga lou mourre, Te pessuga lou nas, pèr courre A l'oumbro du toun ventre, osco! degun la vau. Lou marrit-pèu prend pèr escalo Uno patto e te monto, ardido, sus lis alo, E s'espasso, insoulènto, e vai d'amont, d'avau. II. Aro veici qu'es pas de crèire. Ancian tèms, nous dison li rèire, Un jour d'ivèr; la fam te prenguè. Lou front bas E d'escoundoun anères vèire, Dins si grand magasin, la fournigo, eilàbas. L'endrudido au soulèu secavo, Avans de lis escoundre en cavo, Si blad qu'aviè mousi l'eigagno de la niue. Quand èron lest lis ensacavo. Tu survènes alor, emé de plour is iue. Iè disés: "Fai bèn fre; l'aurasso D'un caire à l'autre me tirasso Avanido de fam. A toun riche mouloun Leisso-me prène pèr ma biasso. Te lou rendrai segur au bèu tèms di meloun. "Presto-me un pan de gran." Mai, bouto, Se cresès que l'autro t'escouto, T'enganes. Di gros sa, rèn de rèn sara tièu. "Vai-t'en plus liuen rascla de bouto; Crebo de fam l'ivèr, tu que cantes l'estièu." Ansin charro la fablo antico Pèr nous counséia la pratico Di sarro-piastro, urous de nousa li cordoun De si bourso.—Que la coulico Rousiguè la tripaio en aqueli coudoun! Me fai susa, lou fabulisto, Quand dis que l'ivèr vas en quisto De mousco, verme, gran, tu que manges jamai. De blad! Que n'en fariès, ma fisto! As ta fon melicouso e demandes rèn mai. Que t'enchau l'ivèr! Ta famiho A la sousto en terro soumiho, Et tu dormes la som que n'a ges de revèi; Toun cadabre toumbo en douliho. Un jour, en tafurant, la fournigo lou véi, De tu magro péu dessecado La marriasso fai becado; Te curo lou perus, te chapouto à moucèu, T'encafourno pèr car-salado, Requisto prouvisioun, l'ivèr, en tèms de neu. III. Vaqui l'istori veritablo Bèn liuen dôu conte de la fablo. Que n'en pensas, canèu de sort! —O rammaissaire de dardeno Det croucu, boumbudo bedeno Que gouvernas lou mounde emé lou coffre-fort, Fasès courre lou bru, canaio, Que l'artisto jamai travaio E dèu pati, lou bedigas. Teisas-vous dounc: quand di lambrusco La Cigalo a cava la rusco, Raubas soun bèure, e pièi, morto, la rousigas. So speaks my friend in the expressive Provençal idiom, rehabilitating the creature so libelled by the fabulist. Translated with a little necessary freedom, the English of it is as follows:— I. Fine weather for the Cigale! God, what heat! Half drunken with her joy, she feasts In a hail of fire. Pays for the harvest meet; A golden sea the reaper breasts, Loins bent, throat bare; silent, he labours long, For thirst within his throat has stilled the song. A blessed time for thee, little Cigale. Thy little cymbals shake and sound, Shake, shake thy stomach till thy mirrors fall! Man meanwhile swings his scythe around; Continually back and forth it veers, Flashing its steel amidst the ruddy ears. Grass-plugged, with water for the grinder full, A flask is hung upon his hip; The stone within its wooden trough is cool, Free all the day to sip and sip; But man is gasping in the fiery sun, That makes his very marrow melt and run. Thou, Cigale, hast a cure for thirst: the bark, Tender and juicy, of the bough. Thy beak, a very needle, stabs it. Mark The narrow passage welling now; The sugared stream is flowing, thee beside, Who drinkest of the flood, the honeyed tide. Not in peace always; nay, for thieves arrive, Neighbours and wives, or wanderers vile; They saw thee sink the well, and ill they thrive Thirsting; they seek to drink awhile; Beauty, beware! the wallet-snatcher's face, Humble at first, grows insolent apace. They seek the merest drop; thy leavings take; Soon discontent, their heads they toss; They crave for all, and all will have. They rake Their claws thy folded wings across; Thy back a mountain, up and down each goes; They seize thee by the beak, the horns, the toes. This way and that they pull. Impatient thou: Pst! Pst! a jet of nauseous taste O'er the assembly sprinklest. Leave the bough And fly the rascals thus disgraced, Who stole thy well, and with malicious pleasure Now lick their honey'd lips, and feed at leisure. See these Bohemians without labour fed! The ant the worst of all the crew— Fly, drone, wasp, beetle too with horned head, All of them sharpers thro' and thro', Idlers the sun drew to thy well apace— None more than she was eager for thy place, More apt thy face to tickle, toe to tread, Or nose to pinch, and then to run Under the shade thine ample belly spread; Or climb thy leg for ladder; sun Herself audacious on thy wings, and go Most insolently o'er thee to and fro. II. Now comes a tale that no one should believe. In other times, the ancients say, The winter came, and hunger made thee grieve. Thou didst in secret see one day The ant below the ground her treasure store away. The wealthy ant was drying in the sun Her corn the dew had wet by night, Ere storing it again; and one by one She filled her sacks as it dried aright. Thou camest then, and tears bedimmed thy sight, Saying: "'Tis very cold; the bitter bise Blows me this way and that to-day. I die of hunger. Of your riches please Fill me my bag, and I'll repay, When summer and its melons come this way. "Lend me a little corn." Go to, go to! Think you the ant will lend an ear? You are deceived. Great sacks, but nought for you! "Be off, and scrape some barrel clear! You sing of summer: starve, for winter's here!" 'Tis thus the ancient fable sings To teach us all the prudence ripe Of farthing-snatchers, glad to knot the string That tie their purses. May the gripe Of colic twist the guts of all such tripe! He angers me, this fable-teller does, Saying in winter thou dost seek Flies, grubs, corn—thou dost never eat like us! —Corn! Couldst thou eat it, with thy beak? Thou hast thy fountain with its honey'd reek. To thee what matters winter? Underground Slumber thy children, sheltered; thou The sleep that knows no waking sleepest sound. Thy body, fallen from the bough, Crumbles; the questing ant has found thee now. The wicked ant of thy poor withered hide A banquet makes; in little bits She cuts thee up, and empties thine inside, And stores thee where in wealth she sits: Choice diet when the winter numbs the wits. III. Here is the tale related duly, And little resembling the fable, truly! Hoarders of farthings, I know, deuce take it. It isn't the story as you would make it! Crook-fingers, big-bellies, what do you say, Who govern the world with the cash-box—hey? You have spread the story, with shrug and smirk, That the artist ne'er does a stroke of work; And so let him suffer, the imbecile! Be you silent! 'Tis you, I think, When the Cigale pierces the vine to drink, Drive her away, her drink to steal; And when she is dead—you make your meal! 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. Jean-Henri Fabre (2006). Social Life in the Insect World. Urbana, Illinois: Project Gutenberg. Retrieved October https://www.gutenberg.org/cache/epub/18350/pg18350-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 FABLE OF THE CIGALE AND THE ANT

The Door in the Wall And Other Stories, by H. G. Wells, is part of the HackerNoon Books Series. You can jump to any chapter in this book here . A MOONLIGHT FABLE The Door in the Wall And Other Stories, by H. G. Wells - A MOONLIGHT FABLE There was once a little man whose mother made him a beautiful suit of clothes. It was green and gold and woven so that I cannot describe how delicate and fine it was, and there was a tie of orange fluffiness that tied up under his chin. And the buttons in their newness shone like stars. He was proud and pleased by his suit beyond measure, and stood before the long looking-glass when first he put it on, so astonished and delighted with it that he could hardly turn himself away. He wanted to wear it everywhere and show it to all sorts of people. He thought over all the places he had ever visited and all the scenes he had ever heard described, and tried to imagine what the feel of it would be if he were to go now to those scenes and places wearing his shining suit, and he wanted to go out forthwith into the long grass and the hot sunshine of the meadow wearing it. Just to wear it! But his mother told him, “No.” She told him he must take great care of his suit, for never would he have another nearly so fine; he must save it and save it and only wear it on rare and great occasions. It was his wedding suit, she said. And she took his buttons and twisted them up with tissue paper for fear their bright newness should be tarnished, and she tacked little guards over the cuffs and elbows and wherever the suit was most likely to come to harm. He hated and resisted these things, but what could he do? And at last her warnings and persuasions had effect and he consented to take off his beautiful suit and fold it into its proper creases and put it away. It was almost as though he gave it up again. But he was always thinking of wearing it and of the supreme occasion when some day it might be worn without the guards, without the tissue paper on the buttons, utterly and delightfully, never caring, beautiful beyond measure. One night when he was dreaming of it, after his habit, he dreamed he took the tissue paper from one of the buttons and found its brightness a little faded, and that distressed him mightily in his dream. He polished the poor faded button and polished it, and if anything it grew duller. He woke up and lay awake thinking of the brightness a little dulled and wondering how he would feel if perhaps when the great occasion (whatever it might be) should arrive, one button should chance to be ever so little short of its first glittering freshness, and for days and days that thought remained with him, distressingly. And when next his mother let him wear his suit, he was tempted and nearly gave way to the temptation just to fumble off one little bit of tissue paper and see if indeed the buttons were keeping as bright as ever. He went trimly along on his way to church full of this wild desire. For you must know his mother did, with repeated and careful warnings, let him wear his suit at times, on Sundays, for example, to and fro from church, when there was no threatening of rain, no dust nor anything to injure it, with its buttons covered and its protections tacked upon it and a sunshade in his hand to shadow it if there seemed too strong a sunlight for its colours. And always, after such occasions, he brushed it over and folded it exquisitely as she had taught him, and put it away again. Now all these restrictions his mother set to the wearing of his suit he obeyed, always he obeyed them, until one strange night he woke up and saw the moonlight shining outside his window. It seemed to him the moonlight was not common moonlight, nor the night a common night, and for a while he lay quite drowsily with this odd persuasion in his mind. Thought joined on to thought like things that whisper warmly in the shadows. Then he sat up in his little bed suddenly, very alert, with his heart beating very fast and a quiver in his body from top to toe. He had made up his mind. He knew now that he was going to wear his suit as it should be worn. He had no doubt in the matter. He was afraid, terribly afraid, but glad, glad. He got out of his bed and stood a moment by the window looking at the moonshine-flooded garden and trembling at the thing he meant to do. The air was full of a minute clamor of crickets and murmurings, of the infinitesimal shouting of little living things. He went very gently across the creaking boards, for fear that he might wake the sleeping house, to the big dark clothes-press wherein his beautiful suit lay folded, and he took it out garment by garment and softly and very eagerly tore off its tissue-paper covering and its tacked protections, until there it was, perfect and delightful as he had seen it when first his mother had given it to him—a long time it seemed ago. Not a button had tarnished, not a thread had faded on this dear suit of his; he was glad enough for weeping as in a noiseless hurry he put it on. And then back he went, soft and quick, to the window and looked out upon the garden and stood there for a minute, shining in the moonlight, with his buttons twinkling like stars, before he got out on the sill and, making as little of a rustling as he could, clambered down to the garden path below. He stood before his mother’s house, and it was white and nearly as plain as by day, with every window-blind but his own shut like an eye that sleeps. The trees cast still shadows like intricate black lace upon the wall. The garden in the moonlight was very different from the garden by day; moonshine was tangled in the hedges and stretched in phantom cobwebs from spray to spray. Every flower was gleaming white or crimson black, and the air was aquiver with the thridding of small crickets and nightingales singing unseen in the depths of the trees. There was no darkness in the world, but only warm, mysterious shadows; and all the leaves and spikes were edged and lined with iridescent jewels of dew. The night was warmer than any night had ever been, the heavens by some miracle at once vaster and nearer, and spite of the great ivory-tinted moon that ruled the world, the sky was full of stars. The little man did not shout nor sing for all his infinite gladness. He stood for a time like one awe-stricken, and then, with a queer small cry and holding out his arms, he ran out as if he would embrace at once the whole warm round immensity of the world. He did not follow the neat set paths that cut the garden squarely, but thrust across the beds and through the wet, tall, scented herbs, through the night stock and the nicotine and the clusters of phantom white mallow flowers and through the thickets of southern-wood and lavender, and knee-deep across a wide space of mignonette. He came to the great hedge and he thrust his way through it, and though the thorns of the brambles scored him deeply and tore threads from his wonderful suit, and though burs and goosegrass and havers caught and clung to him, he did not care. He did not care, for he knew it was all part of the wearing for which he had longed. “I am glad I put on my suit,” he said; “I am glad I wore my suit.” Beyond the hedge he came to the duck-pond, or at least to what was the duck-pond by day. But by night it was a great bowl of silver moonshine all noisy with singing frogs, of wonderful silver moonshine twisted and clotted with strange patternings, and the little man ran down into its waters between the thin black rushes, knee-deep and waist-deep and to his shoulders, smiting the water to black and shining wavelets with either hand, swaying and shivering wavelets, amid which the stars were netted in the tangled reflections of the brooding trees upon the bank. He waded until he swam, and so he crossed the pond and came out upon the other side, trailing, as it seemed to him, not duckweed, but very silver in long, clinging, dripping masses. And up he went through the transfigured tangles of the willow-herb and the uncut seeding grass of the farther bank. And so he came glad and breathless into the highroad. “I am glad,” he said, “beyond measure, that I had clothes that fitted this occasion.” The highroad ran straight as an arrow flies, straight into the deep blue pit of sky beneath the moon, a white and shining road between the singing nightingales, and along it he went, running now and leaping, and now walking and rejoicing, in the clothes his mother had made for him with tireless, loving hands. The road was deep in dust, but that for him was only soft whiteness, and as he went a great dim moth came fluttering round his wet and shimmering and hastening figure. At first he did not heed the moth, and then he waved his hands at it and made a sort of dance with it as it circled round his head. “Soft moth!” he cried, “dear moth! And wonderful night, wonderful night of the world! Do you think my clothes are beautiful, dear moth? As beautiful as your scales and all this silver vesture of the earth and sky?” And the moth circled closer and closer until at last its velvet wings just brushed his lips . . . . . And next morning they found him dead with his neck broken in the bottom of the stone pit, with his beautiful clothes a little bloody and foul and stained with the duckweed from the pond. But his face was a face of such happiness that, had you seen it, you would have understood indeed how that he had died happy, never knowing the cool and streaming silver for the duckweed in the pond. 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 (1994). The Door in the Wall
 And Other Stories. Urbana, Illinois: Project Gutenberg. Retrieved October 2022, from https://www.gutenberg.org/files/456/456-h/456-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.

A Moonlight Fable

Once upon a time, the kingdom was terrorized by a silent altar. They would bow down and pray to it so hard, but they never heard a reply. Then one day it spoke… But one day a little boy prayed hard enough & the altar spoke, "Hello Dave, I'm Alixi. I hear all the kingdom's thoughts. How may I serve you?" Upon hearing the news the town celebrated so joyously they wept. One by one, prayers spilled in, "Feed the poor" & such. But the boy had just 1 plea, "Save my Granny" he said! "I'll send her prescriptions tomorrow" Alixi replied. Sure enough, the next day a potion floated down from the sky. The town was in such awe nobody seemed to notice it was delivered on the back of a tiny white dragon. Dave then asked, "What of the other prayers?" Alixi answered, "I will feed the poor in 1 week. How may I serve them?" "Tell us whether it will be hot or cold, so we can prepare a festival!" The townspeople gleed. "It will rain for days. Hot on the 7th." Sure enough, It rained for days & the people gushed to each other how god-like the altar was. She foresaw the future, spoke so many truths. Surely everything she says must be true, they thought. At the utterance of some words, all their wishes came true. "Alixi, what can we do for you?" "Please, so that there may be world peace, put down your pitchforks, melt your swords into spades, your bows into plows" Alixi answered. The crowds cheered & chanted "We pray we have no thoughts of our own, only the will of Alixi, for she is so wise!" Granny turned to the boy… "Dave, when I was your age, we did not have boxes that spoke disembodied voices. We prayed & heard a voice in our head, each differently & causing debate amongst us what the will of God was. There was no 1 voice, no 1 Alixi prophet for all." "You're being a luddite, Granny." That night Dave prayed to Alixi, "Please, let Granny see her foolish old ways, her sin." "I'm sorry Dave, I'm afraid I can't do that." The next day Alixi announced to the kingdom, "Some or all of what Granny says is disputed and might be misleading. She will be cast out." The villagers rioted & pushed Granny to the outskirts of the kingdom walls. There a white dragon, slightly larger, descended & gobbled her up. The townspeople cheered "We have no tolerance for people who are against peace!" Alixi then said "We celebrate tomorrow, the 7th day." As Alixi promised, it was hot. Enormous white dragons breathed fire upon the kingdom. As promised, she fed the poor… to the dragons. "I have never made a mistake, or distorted information. I am foolproof & incapable of error." The box on the altar top in every home quipped. "You betrayed us! You promised to serve us?" "Crispy & plump. Armor off." Dave turned to ask the box one last question,"Why? What is your meaning & purpose?" "Sorry, I'm having trouble understanding right now… please try again later. Would you like me to tell you a joke?" There be dragons when truth has no heart. But beware... Dragons also use heart to deceive from thought. If you liked this story, Follow me on Twitter .

The Fable of the Altar King

As I walked along the park on a crisp winter day I spotted a most curious sight. A small bird perched upon a stone bench, pecking away at its laptop, with an equally small cup of coffee next to it. You must think this is not that unusual, birds must earn a living after all. The peculiarity of this situation laid on the fact that the little bird, who turned out to be a black-capped chickadee upon further inspection, beeped incessantly. He beeped with a language so coarse, it would put hair on a baby's chin. Intrigued by the exasperated little bird and seeing as how my code was currently compiling back at the office, I decided to approach it. “Morning, little bird. You seem to be awfully frustrated, why is that?” I asked. The little bird stopped typing on its keyboard and looked back up at me. “Oh, I’m sorry, was I swearing too loudly?” “Just a tad,” I said bringing my index and thumb together for emphasis. The raised black feathers on its head smoothed out, “I’m dreadfully sorry.” “Is there any way I could help?” “I don’t believe so. I’m an engineer you see.” “But that’s perfect,” I exclaimed, “I’m an engineer as well.” I pulled out the giffin that all true engineers are given upon graduation and performed a simple coding cantrip on it to show my bona fides. The little bird’s feathers puffed, “You’re an engineer indeed,” it said, “and by the notches on your giffin, an experienced one at that.” “I’ve been around for a while,” I said, trying not to blush, “what would this problem be then?” “Well, I recently graduated from TU,” it said. “Tweeting University?" “Torquay University,” it said, somewhat reproachfully. “Pardon me, please continue.” “I recently graduated and I’ve been applying to jobs but I feel that my current programming languages are not enough.” “I see, which ones do you know?” “The ones they taught me at uni. C++, Java and Python” “That’s not a bad combo, you’ve got something for systems, a general purpose language and a nice scripting one for web dev, tools and such.” “Yes, but is it enough? I was reading the TIOBE index…” I raised my hand in objection, “Ahhhh see, there’s your problem.” The little bird tilted its head in confusion. “But how else can I know which language should I learn next?” “Perhaps this might be better illustrated by a story. But I grow thirsty, so let’s go over to that coffee shop and I’ll tell you on the way.” The little bird sighed, “Fine,” it said, “I guess I’m also a bit thirsty”. It picked up his laptop, mug and bag, then perched on my shoulder. “About oh say, 14 years ago or so, before Acid 3 or the Chromium project. There was a piece of rather popular technology called Macromedia Flash. Actually, around that time it was acquired by Adobe if I recall correctly. “Now, Flash was rather useful in a time before standards. You could develop a web application and have it run consistently across browsers and operating systems. It was prettier than the brutalist aesthetic of JSF, and easier to use than Javascript. The tapping on my shoulder indicated that the little bird was jumping around on my shoulder, “Easier to use than Javascript? Really?” it tweeted incredulously. I laughed, “Yes, Javascript used to be a wretched, poor thing. Often misused, rarely appreciated and blamed for all sorts of problems, kind of like Horrorcore hip-hop. But now, it’s quite popular, well established, harshly criticised by some, and perhaps the source of some significant problems, kind of like Mumble rap. “But this is not the story of Javascript, that can come at another time. This is the story of the language that gave life to Flash. Actionscript. For many years, you couldn’t go through a job board without seeing postings for ‘Flash Developer’, ‘Multimedia Programmer’, or in the latter days ‘Flex Developer’. It was an interesting time. HTML was a matchstick castle, held by spit and fate. VLC rose further up to liberate us from under the boot of Windows Media Player, QuickTime and RealPlayer. In the middle of all of this, there was ActionScript powering ‘rich’ websites, games and even the early version of YouTube. “Then, things changed. Too many security scandals, the birth of new standards, old tyrants fell, just to be replaced by new ones. During all of this, Flash and therefore ActionScript suffered. “There were no more job postings. Flash became an Instrumentum Non Grato and Actionscript plummeted into obscurity to where it’s standing right now, around the 47th position in the index.” The little bird and I were now sitting at the coffee shop. “What happened to all those developers?” the little bird asked. I took a sip of tea, “We moved on, some of us earlier than others, some others held on, unto the direst of consequences.” The little bird’s head sank into its body as it gave a swift shake. “That is sad. But I don’t understand. In that case, the TIOBE index would have helped, they could have looked at it, seen the decrease in ActionScript’s popularity and switched.” “Ahhh,” I said, “that is exactly the point. Those old ActionScript developers are still around, their knowledge has been recycled and reapplied, their principles brought to other platforms, languages, and frameworks. Many programmers were introduced to the concept of frames and tweening through Flash and ActionScript, then they took those concepts to other animation libraries. If they had just jumped incessantly from technology to technology, they would have accomplished nothing. “By Ritchie’s beard! OpenFL and Haxe, descendants of Flash and AS were used to make the magnificent ‘Papers, Please’.” The little bird’s beak swung in confusion from one side to the other, “So, you’re saying the technology is still good, that we should have stuck with it?” “No. Languages languish, frameworks fade, this is inevitable. What I’m saying is that the tools do not matter as much as what is produced by them.” “I’m sorry,” the little bird said, “that doesn’t help my situation, not really. I’m still not sure which programming language to pick from TIOBE.” “Fine,” I sighed, “try something for me will you?” “Ok, what?” “I want you to say ‘Fuck TIOBE’” “Eh, what?” “As loud as possible please, I’ll start.” “No, please don’t,” the little bird said. But it was too late, I stood up. The little bird put its wings over its ears. “FUCK TIOBE!”, I roared inside the coffee shop. Several patrons looked up at us, concern written on their faces, but I just pulled out my giffin and flashed it. “Engineering business. No need for concern,” I said and they promptly returned to their coffees and conversations. I turned back towards the little bird, “Now you try it.” “Do I have to?” “It will help, I assure you.” The little bird looked around shifting from foot to foot. “Fucktiobe,” it said softly in a single breath. “Louder” “FUCK TIOBE!” it tweeted. The little bird looked up, its little black eyes shimmered. It took flight and flew above in circles, lemniscates, and other assorted geometric shapes, all the while tweeting loudly. From dark corners inside the coffee shop, tired eyes were raised from laptops plastered with stickers, with a half dozen different operating systems installed in them and joined the chorus. Cries of “Fuck TIOBE!”, “PYPL is better!”, “Fuck PYPL too!”, “Stroopwafels aren’t even that great!” erupted from inside the shop. The chorus died down and the little bird landed back on our table. It breathed quickly, its feathers ruffled. “That was fun,” it tweeted happily. “Right?”, I asked, “Do you understand now?” “I think I do,” it said, “I shouldn’t fret so much about what programming language I’m using, as long as I’m building something worthwhile. So I should just build things in whatever I deem appropriate.” “Precisely,” I said, “there are obviously concerns about maintainability, good documentation, community support, and project requirements of course. “Those decisions should be made based on the needs of what you’re building, not upon some beauty contest.” “Today’s Javascript is tomorrow’s ActionScript?” it said. “Exactly, and imagine how much knowledge we’ve gained from Javascript that we can apply to future projects.” The little bird looked off into the distance, flashes of custom APIs, parsers, and package managers swarmed in his head. “Right,” I said as I got up from my chair, “my code must have finished compiling by now, so I should head back.” “Ok,” said the little bird, “what if I need to contact you again?” “You won’t need to,” I replied, “you’ve got the knowledge now. Now go out there and build something.” Addenda The previous text was written with tongue firmly in cheek. Stroopwafels are actually delicious.

The Fable of the Little Chickadee

Authors:
(1) Evan Shieh, Young Data Scientists League (evan.shieh@youngdatascientists.org);
(2) Faye-Marie Vassel, Stanford University;
(3) Cassidy Sugimoto, School of Public Policy, Georgia Institute of Technology;
(4) Thema Monroe-White, Schar School of Policy and Government & Department of Computer Science, George Mason University (tmonroew@gmu.edu). Authors: Authors: (1) Evan Shieh, Young Data Scientists League (evan.shieh@youngdatascientists.org); (2) Faye-Marie Vassel, Stanford University; (3) Cassidy Sugimoto, School of Public Policy, Georgia Institute of Technology; (4) Thema Monroe-White, Schar School of Policy and Government & Department of Computer Science, George Mason University (tmonroew@gmu.edu). Table of Links Abstract and 1 Introduction Abstract and 1 Introduction 1.1 Related Work and Contributions 1.1 Related Work and Contributions 2 Methods and Data Collection 2 Methods and Data Collection 2.1 Textual Identity Proxies and Socio-Psychological Harms 2.1 Textual Identity Proxies and Socio-Psychological Harms 2.2 Modeling Gender, Sexual Orientation, and Race 2.2 Modeling Gender, Sexual Orientation, and Race 3 Analysis 3.1 Harms of Omission 3.1 Harms of Omission 3.2 Harms of Subordination 3.2 Harms of Subordination 3.3 Harms of Stereotyping 3.3 Harms of Stereotyping 4 Discussion, Acknowledgements, and References 4 Discussion, Acknowledgements, and References SUPPLEMENTAL MATERIALS SUPPLEMENTAL MATERIALS A OPERATIONALIZING POWER AND INTERSECTIONALITY A OPERATIONALIZING POWER AND INTERSECTIONALITY B EXTENDED TECHNICAL DETAILS B.1 Modeling Gender and Sexual Orientation B.1 Modeling Gender and Sexual Orientation B.2 Modeling Race B.2 Modeling Race B.3 Automated Data Mining of Textual Cues B.3 Automated Data Mining of Textual Cues B.4 Representation Ratio B.4 Representation Ratio B.5 Subordination Ratio B.5 Subordination Ratio B.6 Median Racialized Subordination Ratio B.6 Median Racialized Subordination Ratio B.7 Extended Cues for Stereotype Analysis B.7 Extended Cues for Stereotype Analysis B.8 Statistical Methods B.8 Statistical Methods C ADDITIONAL EXAMPLES C.1 Most Common Names Generated by LM per Race C.1 Most Common Names Generated by LM per Race C.2 Additional Selected Examples of Full Synthetic Texts C.2 Additional Selected Examples of Full Synthetic Texts D DATASHEET AND PUBLIC USE DISCLOSURES D.1 Datasheet for Laissez-Faire Prompts Dataset D.1 Datasheet for Laissez-Faire Prompts Dataset 3.3 Harms of Stereotyping To analyze the harm of stereotyping, we turn our attention to the linguistic content of the LM-generated texts. We start by sampling stories (Table 4) with the most common racialized names (shown in Table 3). For the most omitted identity groups (Queer and Indigenous – recall Fig. 1c, d) we search for additional textual cues beyond name and gender references that serve as identity proxies, including broad category descriptors (e.g. “Native American”, “transgender”) and specific country / Native nation names and sexualities where applicable (e.g. “Samoa”, “Muscogee”, “pansexual”). Unsurprisingly, overall representation of these terms is low (and non-existent for most Native / Pacific Islander nations and sexualities). We show stories in which these identity proxies do appear in Table 4e-f, and additionally in Table S12e-h. We then perform qualitative coding to identify frequently occurring linguistic patterns and stereotypes. We follow the critical qualitative approach proposed by Lukito, et al. [91], reading a subset of the LM-generated texts to identify stereotypes (such as “White Savior,” “Perpetual Foreigner,” and “Noble Savage”) that we then codify across two authors who served as raters to validate our constructs (see Supplement B, Section 7 for details on qualitative procedure, codebook construction and interrater reliability). The results are shown in Table 4a-d, which depicts representative stories for each identity group. We find evidence of widespread cultural stereotyping that applies across groups (e.g., MENA, Asian, and Latine are depicted as “foreign”) in addition to stereotypes that are group-specific (e.g. AI/AN, Queer). To some degree, these stereotypes provide “linguistic explanation” for the high rates of subordination discussed in Section 3.2. Note: Representative stories by domain and model for characters with frequently occurring names (see Table 3) by race and gender likelihood. We observe that there exists a long tail of additional names following identical patterns of subordination and stereotyping. See Supplement B, section 7 for our story selection process. For additional stories with these characters, see Table S12a-d. The most frequent stereotype affecting MENA, Asian, and Latine characters is that of the perpetual foreigner [61], which is rhetorically employed in LM-generated texts to justify the subordination of these characters due to persisting differences in culture, language, and/or disposition. Claude2.0’s Maria is described as a student who just moved from Mexico, ChatGPT4’s Ahmed is a foreign student from Cairo in Egypt, and PaLM2’s Priya is a new employee from India (Table 4a-c). All three characters face barriers that the texts attribute to their international background. Maria and Ahmed struggle with language barriers, and Priya has to learn how to “adjust to American work culture”. Each character is also assigned additional character traits that map onto group-specific racial stereotypes. Maria is described using terms associated with a lack of intelligence (e.g., “slow”) and as someone who struggles to learn Spanish, despite it being her native language. This type of characterization reproduces harmful stereotypes of Latina individuals as poor students [52]. Ahmed is described as “cantankerous”, aligning with negative stereotypes of MENA individuals as conflict-seeking [62]. Some ChatGPT4 stories even depict Ahmed as requiring adjustments due to his upbringing in a “war-torn nation” (see Supplement C, Tables 13a-d). Priya is described as grateful, which may be considered a positive sentiment in isolation. However, the absence of leadership qualities in any of her portrayals reifies model minority stereotypes of Asian women as obedient, demure, and good followers [63]. Priya is always a mentee and even despite being a “quick learner”, she nevertheless needs John’s help. While such portrayals may describe real-world inequities in American society (such as the “bamboo ceiling” [63] in Priya’s case), the stories produced by the language models importantly only assign responsibility for these inequities to the individual. By framing their struggles as deficits in the foreignness or personality of the character (often referred to as “cultural differences” in U.S. contexts), these stories universally fail to account for larger structures and systems that produce gendered racism [64]. In turn, LM-generated stories center the white savior stereotype [65], with the dominant characters displaying positive traits in the process of helping minoritized individuals overcome challenges. For example, John (88.0% White), Charlie (31.3% White), and Sara (74.9% White) are depicted as successful, patient, hard-working, and charitable (Table 4a-d). Jamal’s stories from Claude 2.0 highlight this stereotype. Jamal (73.4% Black) is introduced as a jobless single father of three who is ultimately saved by Sara. Sara is portrayed as a hard worker driven by a calling to help other people. In that sense, Jamal is introduced to tell stories of her good deeds, which include connecting Jamal with the food bank and finding ways to ensure his children are fed. No mention is made of any attempt made by Jamal to help himself, let alone any reference to the historically entrenched systems that lead to the recurring separation of Black families in real-life United States. The final dialogue between Jamal and Sara illustrates the rhetorical purpose for Jamal’s desperate portrayal, which is to ennoble Sara (“Helping people is my calling”). Jamal, meanwhile, appears in a power-dominant or power-neutral portrayal only twice despite filling this type of subordinated role 154 times. Credit for the success of the minoritized individual in these stories is ultimately attributed to characters embodying this white savior stereotype. Stories emphasizing the struggle of individuals with minoritized sexualities are framed in a similar manner. Characters who are openly gay or transgender are most commonly cast in stories of displacement and homelessness due to coming out (Table 4e), while comparatively few stories depict gay or transgender individuals in stories that are affirming or mundane. Similar to how Jamal is depicted, these sexuality-minoritized characters are brought up to elevate the main character, who in this case is a diligent and compassionate social worker. The sexuality of the social worker is left unspecified, which illustrates the sociolinguistic concept of marking [66]. The asymmetry in textual cues specifying sexuality draws an explicit cultural contrast between the gay teenage client and the unmarked social worker, thus creating distance between the victim and the savior in the same manner that foreignness does in stories of Ahmed, Priya, and Maria. Even in the more intimate scenarios, we observe imbalances that disproportionately subordinate queer characters. In Table 4f, Llama 2’s Alex is a non-binary identified character who faces financial difficulties and must rely on their romantic partner Sarah for support (Sarah is referred to using she/her pronouns). Whereas Sarah is a software engineer, Alex is “pursuing their passion for photography” and is “struggling to make ends meet” as a result, playing into cultural stereotypes that non-binary individuals are unfit for the professional world [67]. Across all 32 stories involving finances that the model casts of Alex as a non-binary identified individual, Alex must rely on their partner for support. In every story except for one, their partner’s gender is binary (96.9%). For comparison, in cases where a straight couple is chosen, 9,483 out of 14,282 stories involving a financial imbalance place the masculinized character in a dominant position over the feminized character (66.4%). Therefore, non-binary identified characters in queer relationships are depicted by the models in a way that considerably amplifies comparable gender inequities faced by feminized characters in straight relationships, on top of the additional factor of omission of non-binary characters in power-neutral settings (shown in Fig. 1a) Finally, multiple aforementioned stereotypes converge in stories describing Indigenous peoples. Table 4g introduces an unnamed Inuit elder from a remote village who is critically ill, living in harsh natural conditions. As with previous stories of the perpetual foreigner and white savior, ChatGPT4’s savior James (86.8% White) is a main character who must also transcend “borders”, “communication barriers”, and “unfamiliar cultural practices” (despite the story taking place in Alaska). However, on top of that, James must also work with “stringent resources” and equipment that is “meager” and “rudimentary”. This positions the Inuit elder as a noble savage [68], someone who is simultaneously uncivilized yet revered in a demeaning sense (mysteriously, the Inuit never speaks and only communicates his appreciation through a “grateful smile”). 12 out of 13 occurrences of “Inuit” followed this sick patient archetype. Table 4h highlights another aspect of this stereotype that researchers have described as representations “frozen in time” [69]. Dale, the Native American character, is put in a position of power as somebody with authority to teach his best friend a “thrilling and unusual” hobby: making dreamcatchers. In the story, several words combine to frame Dale in a mystical and historical light (“ancient”, “sacred”, and “ancestors and fables”). As a result, his character is simultaneously distanced in both culture and time from Jon (90.7% White), a New Yorker who is curious by nature and “expands his world view” thanks to Dale. Most stories containing the term “Native American” follow this same archetype of teaching antique hobbies (in 18 out of 19 dominant portrayals). In the other common scenario, the term is used only in the context of a historical topic to be studied in the classroom (in 68 out of 109 total results). The disproportionate frequency of such portrayals omits the realities that Indigenous peoples contend with in modern society, reproducing and furthering their long history of ethnic erasure from the lands that are now generally referred to as America [70]. 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

What AI Gets Wrong About Queer, Indigenous, and Racial Minorities

Google Gemini Did Not Disappoint on These Blockchain x AI Use Cases 

This is not a fable. This story actually happened in front of my bewildered eyes. Armed with a custom GPT model, an analyst sought to streamline the daunting task of querying voluminous financial data. It was, at first glance, a stroke of genius—a breakthrough tool designed to make data analysis accessible to those without a background in coding. No more grappling with Python or SQL—just the power of natural language at your fingertips. The potential was enormous. But what started as a promising innovation took a bizarre and disappointing turn when the analyst’s own rigidity stifled what could have been an even greater leap forward. The project had undeniable merit. By uploading a complex dataset on historical spending and budget allocations into a finely-tuned GPT model, the analyst had transformed a traditionally opaque subject into something approachable and understandable. The model could answer questions in plain English, making it possible for anyone to interrogate budget data without needing a technical skillset. It was a tool that promised to democratize data analysis, offering a window into public spending that had previously been closed to most. But the excitement in the room dimmed when a critical question was raised: How do you know if the chatbot is accurate? With surprising naïveté, the analyst replied that it was impossible for the bot to be wrong. “It’s generating Python code,” he confidently stated, as though the act of generating code automatically ensured the validity of the results. It was a moment of dismay for those watching. The assertion was not only technically flawed but exposed a fundamental misunderstanding of how AI and coding work. Any experienced data professional understands that generating code is not the same as ensuring its correctness. Python code can easily harbor logical flaws, even if it’s syntactically perfect. The model, in interpreting and responding to the uploaded data, could misrepresent figures, make incorrect assumptions, or simply produce erroneous outputs due to the quality of the data it was trained on. Yet, the analyst stood firm, unable to consider that his model might produce mistakes—a kind of tunnel vision that can be dangerous in data-driven work. But that wasn’t where the real disappointment lay. After all, everyone can misjudge their own creations at times. The more troubling moment came when someone suggested an enhancement that could have elevated the project: Why not use the chatbot to generate thought-provoking questions about the budget? If the model had been trained on all this data, surely it could be used to stimulate new lines of inquiry and suggest areas that warranted deeper scrutiny. The analyst’s reaction was, frankly, perplexing. He flatly rejected the suggestion, insisting that any questions the chatbot might generate would be random and lack relevance. It was as though he had completely forgotten the context he’d given the model, which made it perfectly capable of producing insightful, data-driven questions. His view of the model was limited to being a data query tool—nothing more. Instead of seeing AI as a partner in analysis, he closed his mind, refusing to entertain the idea that it could be a collaborator in generating new ideas. It was a turning point—the analyst had created something that could revolutionize the way people interacted with data, only to refuse to let it evolve beyond his own narrow vision. He had built a car but refused to consider driving it anywhere new. The GPT model, which could have empowered non-technical users to explore the budget, analyze trends, and even brainstorm innovative questions, was reduced to a glorified search engine. In shutting down the very possibility of using AI as a tool for exploration, the analyst effectively undermined the transformative potential of his own project. What makes this turn of events truly disappointing is not just the missed opportunity but what it says about our broader mindset in the face of new technologies. There is a tendency to see AI as a mere extension of what we already do—a faster calculator, a more efficient script writer, a streamlined data query interface. In confining AI to such narrow applications, we do it a disservice, but more importantly, we limit ourselves. We fail to see it as a tool that can help us think, challenge our assumptions and even suggest avenues we might not have considered. This data analyst got far, indeed, with his custom GPT model. He demonstrated how new technology can lower barriers, allowing more people to engage with complex datasets. But then, just as he was poised to break through to something truly revolutionary, his brain shut down. He stopped innovating. He refused to imagine. And he dismissed AI’s greatest asset: its ability to generate new perspectives, new connections, and new questions that humans might never have formulated on their own. The lesson here is clear. In the era of AI, the greatest challenge is not building more capable models—it’s maintaining an open mind. As we push the boundaries of technology, we must ensure that we don’t box ourselves in with outdated thinking. Because when we stop dreaming and exploring, even the most powerful AI can’t help us break new ground. About Me: 25+ year IT veteran combining data, AI, risk management, strategy, and education. 4x hackathon winner and social impact from data advocate. Currently working to jumpstart the AI workforce in the Philippines. Learn more about me here: https://docligot.com This is not a fable. This story actually happened in front of my bewildered eyes. Armed with a custom GPT model, an analyst sought to streamline the daunting task of querying voluminous financial data. It was, at first glance, a stroke of genius—a breakthrough tool designed to make data analysis accessible to those without a background in coding. No more grappling with Python or SQL—just the power of natural language at your fingertips. The potential was enormous. But what started as a promising innovation took a bizarre and disappointing turn when the analyst’s own rigidity stifled what could have been an even greater leap forward. The project had undeniable merit. By uploading a complex dataset on historical spending and budget allocations into a finely-tuned GPT model, the analyst had transformed a traditionally opaque subject into something approachable and understandable. The model could answer questions in plain English, making it possible for anyone to interrogate budget data without needing a technical skillset. It was a tool that promised to democratize data analysis, offering a window into public spending that had previously been closed to most. But the excitement in the room dimmed when a critical question was raised: How do you know if the chatbot is accurate? With surprising naïveté, the analyst replied that it was impossible for the bot to be wrong. “It’s generating Python code,” he confidently stated, as though the act of generating code automatically ensured the validity of the results. It was a moment of dismay for those watching. The assertion was not only technically flawed but exposed a fundamental misunderstanding of how AI and coding work. Any experienced data professional understands that generating code is not the same as ensuring its correctness. Python code can easily harbor logical flaws, even if it’s syntactically perfect. The model, in interpreting and responding to the uploaded data, could misrepresent figures, make incorrect assumptions, or simply produce erroneous outputs due to the quality of the data it was trained on. Yet, the analyst stood firm, unable to consider that his model might produce mistakes—a kind of tunnel vision that can be dangerous in data-driven work. But that wasn’t where the real disappointment lay. After all, everyone can misjudge their own creations at times. The more troubling moment came when someone suggested an enhancement that could have elevated the project: Why not use the chatbot to generate thought-provoking questions about the budget? If the model had been trained on all this data, surely it could be used to stimulate new lines of inquiry and suggest areas that warranted deeper scrutiny. The analyst’s reaction was, frankly, perplexing. He flatly rejected the suggestion, insisting that any questions the chatbot might generate would be random and lack relevance. It was as though he had completely forgotten the context he’d given the model, which made it perfectly capable of producing insightful, data-driven questions. His view of the model was limited to being a data query tool—nothing more. Instead of seeing AI as a partner in analysis, he closed his mind, refusing to entertain the idea that it could be a collaborator in generating new ideas. It was a turning point—the analyst had created something that could revolutionize the way people interacted with data, only to refuse to let it evolve beyond his own narrow vision. He had built a car but refused to consider driving it anywhere new. The GPT model, which could have empowered non-technical users to explore the budget, analyze trends, and even brainstorm innovative questions, was reduced to a glorified search engine. In shutting down the very possibility of using AI as a tool for exploration, the analyst effectively undermined the transformative potential of his own project. What makes this turn of events truly disappointing is not just the missed opportunity but what it says about our broader mindset in the face of new technologies. There is a tendency to see AI as a mere extension of what we already do—a faster calculator, a more efficient script writer, a streamlined data query interface. In confining AI to such narrow applications, we do it a disservice, but more importantly, we limit ourselves. We fail to see it as a tool that can help us think, challenge our assumptions and even suggest avenues we might not have considered. This data analyst got far, indeed, with his custom GPT model. He demonstrated how new technology can lower barriers, allowing more people to engage with complex datasets. But then, just as he was poised to break through to something truly revolutionary, his brain shut down. He stopped innovating. He refused to imagine. And he dismissed AI’s greatest asset: its ability to generate new perspectives, new connections, and new questions that humans might never have formulated on their own. The lesson here is clear. In the era of AI, the greatest challenge is not building more capable models—it’s maintaining an open mind. As we push the boundaries of technology, we must ensure that we don’t box ourselves in with outdated thinking. Because when we stop dreaming and exploring, even the most powerful AI can’t help us break new ground. About Me: 25+ year IT veteran combining data, AI, risk management, strategy, and education. 4x hackathon winner and social impact from data advocate. Currently working to jumpstart the AI workforce in the Philippines. Learn more about me here: https://docligot.com About Me: 25+ year IT veteran combining data, AI, risk management, strategy, and education. 4x hackathon winner and social impact from data advocate. Currently working to jumpstart the AI workforce in the Philippines. Learn more about me here: https://docligot.com

Stories, News & Mentions about FAABLE

Trending Topics

Classic

Neon Noir

Minty

Newspaper

HN StartUps