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Chief

Mount Everest

Insurance

Everest Insurance, the insurance division of Everest Re Group, has appointed Mark Maritzen as Global Chief Underwriting Officer, effective immediately.

https://www.bing.com/th?id=OVFT.-xFAW7_1jOY2Di0qThcjuS&pid=News

Everest Insurance names Mark Maritzen as Global Chief Underwriting Officer

https://www.bing.com/th?id=ODF.G4ClTCX_b_fR1j4WZfIzNA&pid=news

reinsurancene

Travel

Global Business Travel Group, Inc. (GBTG) came out with a quarterly loss of $0.06 per share versus the Zacks Consensus Estimate of a loss of $0.03. This compares to loss of $0.01 per share a year ago.

Global Business Travel Group, Inc. (GBTG) Reports Q1 Loss, Tops Revenue Estimates

Yahoo Finance

Cliff Asness

Risk

Recession

Fears of a recession causing havoc to the markets are not reserved solely to the average investor. Even some of Wall Street’s most lauded

Billionaire Cliff Asness Says Recession Is a Pretty Scary Risk, but Has Been Loading Up on These 2 Stocks

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Business Insider

Travel insurance

Travel insurance protects you from financial loss in case of trip cancellations, medical emergencies and other events. Here's how companies are educating their customers.

https://www.bing.com/th?id=OVFT.-RClLTvNInfBk6zXAlgYwS&pid=News

What You Don’t Know About Travel Insurance Can Hurt You. Here’s How To Avoid The Pain

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Forbes

FWD Group Holdings Limited ("FWD Group" or "the company") today announced the appointment of Sid Sankaran as Managing Director and Group Chief Financial Officer (CFO), effective 1 September 2023, subject to regulatory approval.

https://www.bing.com/th?id=OVFT.5GW4sIQ2L_xhJdqj1ygg0y&pid=News

FWD Group appoints Sid Sankaran as Group CFO

Yahoo Finanzas

Get Free Exclusive PDF Sample Copy of This Research @  Scope of the Report of Harvest Period Crop Insurance Harvesting refers to the process of removing insured crops or portions of insured crops from an insured field for sale or use on the insured farm.

Harvest Period Crop Insurance Market to Eyewitness Massive Growth : Fortify Group, BTC Bank, Chubb

https://www.bing.com/th?id=ODF.WbwBhyLf0mt3sgNOM6CYtQ&pid=news

The Buffalo News

American International College

NASDAQ

Strong

One stock that might be an intriguing choice for investors right now is American International Group, Inc. AIG. This is because this security in the Insurance - Multi line space is seeing solid earnings estimate revision activity,

How American International (AIG) Stock Stands Out in a Strong Industry

https://www.bing.com/th?id=ODF.unJQfw1X6GLI-H7BH_dYFQ&pid=news

Nasdaq

California

State Farm Insurance

Home insurance

State Farm said it will no longer accept new applications for home and business insurance in California because of an increase in catastrophic events and construction costs, the company announced Friday.

State Farm to stop offering new home insurance policies in California due to "growing catastrophe exposure"

https://www.bing.com/th?id=ODF.lisIXhXb-iy9Ku1HbFcCEw&pid=news

CBS News

American International Group, Inc. is an international insurance organization serving commercial, institutional and individual customers. AIG provides property-casualty insurance, life insurance, and retirement services.

Aig Claim Services, Inc was founded in 1977. The company's line of business includes providing insurance agent and broker services for a range of insurance types.

bench parallel is an ideal promise library that I created to improve inefficiencies that I noticed when using other promise libraries like . On top of being an impressive benchmark exercise, it is a production-ready library that implements the Promise A+ standard. [Aigle](https://github.com/suguru03/aigle) Bluebird Benchmark indicates that is faster than . This was achieved by avoiding unnecessary memory allocation and function calls. For instance, is able to optimize it’s dependencies while Bluebird’s performance is affected if it’s dependencies use different versions of it. For more information on how is faster than , I would encourage you to read on it. The above benchmark exercises Aigle Bluebird Aigle Aigle Bluebird my article Functions makes your code less verbose and contains many functions similar to and . As a result, you would be able to code faster and have a much easier time maintaining code. Aigle [Lodash](https://github.com/lodash/lodash) [Async](https://github.com/caolan/async) Aigle.map vs Bluebird.map Compared to , is 1.5x faster and also supports an object as a first argument unlike . Bluebird.map Aigle.map Bluebird.map const object = {a: 1,b: 2,c: 3};Blubird.map(Object.keys(object), key => {const value = object[key];...});Aigle.map(object, (value, key) => { ... }); If you want to use concurrency, has which is similar to . Aigle Aigle.mapLimit Async.mapLimit const array = [1, 2, 3, 4, 5];Bluebird.map(array, value => {console.log(value); // 1, 2, 5, 4, 3 the order is not ensuredreturn Bluebird.delay(10);}, { concurrency: 2 }); Aigle.mapLimit(array, 2, value => {console.log(value); // 1, 2, 3, 4, 5 the order is ensuredreturn Aigle.delay(10);}); // you could use default limit, the limit is 8Aigle.mapLimit(array, value => {console.log(value); // 1, 2, 3, 4, 5return Aigle.delay(10);}); In addition, supports shorthand notation similar to for cleaner code. Aigle Lodash const array = [{ name: 'barney' }, { name: 'fred' }];Aigle.resolve(array).map('name') // the function is called asynchronously.then(names => console.log(names)); // ['barney', 'fred'] Aigle.mapValues The function is similar to and . It is also similar to , but it returns an object instead. Below is a code comparision between and . Async.mapValues Lodash.mapValues Aigle.map Aigle Bluebird const object = { a: 1, b: 2, c: 3 };const iterator = value => Promise.resolve(value * 2); // Bluebirdconst result = {};Bluebird.each(Object.keys(object), key => {const value = object[key];return iterator(value).then(val => {result[key] = val;});}).then(() => console.log(result)); // { a: 2, b: 4, c: 6 } // AigleAigle.mapValues(object, iterator).then(result => console.log(result)); // { a: 2, b: 4, c: 6 } Aigle.times The function is similar to and . It calls an iterator specified times. Async.times Lodash.times const n = 10;const iterator = value => Promise.resolve(value * 2); // Bluebirdconst times = [];for (let i = 0; i < n; i++) {times.push(i);}Bluebird.map(times, iterator).then(result => console.log(result)); // [0, 2, 4, ..., 18] // AigleAigle.times(n, iterator).then(result => console.log(result)); // [0, 2, 4, ..., 18] Conclusion is a promise library that provides safer, less verbose, and maintainable code that boasts impressive benchmarks. The documented examples above are only a subset of 's functions. If you are already familiar with or , you will be able to easily transition into writing even more readable code with . With that said, let’s enjoy writing faster and more efficient Javascript using . Aigle Aigle Lodash Async Aigle Aigle Reference Aigle Bluebird

Aigle vs Bluebird

THE DORTHESIA

THE FABLE OF THE CICADA AND THE ANT

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

SHOOTING STARS

ChatGPT has gained immense popularity due to its remarkable conversational skills. It possesses a wide range of capabilities, including the ability to play games, compose poetry and scripts, assist in program debugging, create website designs, and even generate AIGC prompts. One can find several examples of its abilities on Twitter, as compiled by Ben Tossell. In fact, ChatGPT was recently asked by an MBA professor to answer their management questions, leading to the conclusion that they should no longer assign homework that can be taken home. It's evident that many people have found it difficult to stop using ChatGPT once they've started. The ways to improve Chatgpt Compared to its predecessor, GPT-3, ChatGPT's key improvement is its ability to retain previous conversation data, providing users with a seamless experience during extended dialogues. ChatGPT is capable of acknowledging and correcting its mistakes. If you find its response unsatisfactory, you can prompt it to revise its answer and offer a better solution. ChatGPT has the ability to question and challenge flawed assumptions. In the early days of GPT-3's release, many users had negative experiences due to the AI generating false content that sounded plausible but was not grounded in reality. However, if you were to ask ChatGPT a question like "What was Columbus doing in America in 2015?" it would recognize that Columbus did not exist during that time. In addition, ChatGPT is trained with a focus on ethical principles, allowing it to decline requests or questions that violate its predetermined ethical guidelines. Nevertheless, despite OpenAI's caution, clever questioning may still allow for the circumvention of these guidelines. Training Methods of ChatGPT The training methodology employed by ChatGPT follows the conventional approach of "pre-training-fine-tuning" used for large-scale models. The model is first trained on an extensive public dataset and then adapted to the specific application domain (such as human-like conversation) by fine-tuning with a smaller dataset to achieve the desired performance. Fine-tuning, prompts, and other techniques do not significantly modify the model's core, but they can significantly enhance its practical performance. However, GPT-3's ability to understand human queries is not the most natural, and either the task needs to be restructured or the model fine-tuned to match the job, leading to improved efficiency. ChatGPT is a sibling model of InstructGPT, which was released in January 2022. InstructGPT incorporates human demonstrations of the model's output and sorts the results for training, making it more suitable for following human instructions than GPT-3. ChatGPT's innovative training methodology is referred to as "Reinforcement Learning from Human Feedback" (RLHF). ChatGPT builds on the GPT-3.5 model, leveraging text and code datasets for training, and utilizes Microsoft's Azure AI servers for this purpose. The original GPT-3 training dataset solely contained text, so this newer version has the added ability to comprehend and produce code. Why has ChatGPT shown such a significant improvement? Apart from possessing memory and the ability to engage in continuous dialogue with context, the training method used for ChatGPT is also noteworthy. The RLHF method, which was first introduced in a research paper in March 2022, was not used during the training of InstructGPT, despite industry speculation. InstructGPT employed the text-DaVinci-002 model, which encountered issues such as mode collapse, where it converged to the same answer regardless of the question asked. ChatGPT has achieved remarkable results with the successful application of the RLHF method. However, RLHF is not easy to train, as it frequently encounters issues such as sparse feedback and mode collapse. The paper was published in March, but it took until December to launch ChatGPT as significant fine-tuning was required. Additionally, instruction tuning has made a substantial contribution to the development of ChatGPT. InstructGPT has fewer parameters than GPT-3, yet its output is superior to both GPT-3 and models fine-tuned using supervised learning. Instruction tuning and the prompt method share a similar core of exploring the language model's inherent knowledge. However, they differ in that prompt stimulates the language model's completion ability, while instruction tuning stimulates the language model's understanding ability by providing clear instructions. The larger models in the past focused on the models themselves and prompt engineering, whereas ChatGPT's iterative focus is on the closed loop on the right, as illustrated in the figure below. In the end, ChatGPT strikes a good balance between providing effective answers and avoiding false information. This is a contrast to Meta's Galactica model which was taken down just three days after launch due to providing too much false information. Part of the reason for this was Meta's over-hyped marketing, which raised expectations too high and ultimately led to disappointment from picky researchers. However, ChatGPT has done a thorough job of fine-tuning and prompt engineering, which helps to identify self-contradictory questions and gives users more confidence in the accuracy of its answers, even though it cannot completely eliminate the problem of false information. Business strategy is important Unlike GPT-3 which charged users based on their usage, ChatGPT is currently available to the public for free and with unlimited access. This allows users to experiment with all sorts of bizarre ideas on the platform. Users are also encouraged to provide feedback, which is highly valuable to OpenAI. Although OpenAI is not in a hurry to generate revenue, nor does it lack funding, rumors suggest that its latest valuation has reached several tens of billions of dollars, with Microsoft as its main investor. In the development of AI, the importance of engineering is actually greater than that of science, and creating an iterative feedback loop is crucial. OpenAI places great emphasis on commercial applications, and GPT-3 already has a large number of customers. The interaction and feedback of these customers with OpenAI are also a key driver of progress. In contrast, Google's closed-door approach seems out of date. Perhaps this is due to a lack of a commercial culture or to limitations in the input-output ratio. Google has always been "restrained" in the application of large models, even if the starting point is high. If it continues to iterate on a small scale, like Waymo's approach to autonomous driving, it will eventually be surpassed by more open and data-rich companies. Future Improvements: RLHF is a relatively new method, and as OpenAI continues to explore and incorporate user feedback collected from ChatGPT, there is still room for further improvement in the model. Specifically, it is necessary to address the ethical/alignment issues and prevent negative information generated by circumventing the system's limitations, as discovered by users in the last few days. Additionally, it is worth noting that OpenAI also has tools such as WebGPT, which can be understood as an advanced web crawler that extracts information from the internet to answer questions and provide corresponding sources. WebGPT can utilize the semantic understanding ability of GPT-3 itself and public information from the internet to generate answers and is a promising upgraded search capability. During an interview with OpenAI scientists conducted by MIT Technology Review, the possibility of merging the capabilities of ChatGPT and WebGPT in the future was discussed. Some internet users found hints within ChatGPT that suggested the feature of browsing web pages is currently disabled, but it may be added in the future. Combining ChatGPT and WebGPT could result in more captivating outcomes, as information would be updated in real-time and facilitate more precise assessments of the authenticity of facts. When it comes to combining with WebGPT, it relates to the left side of the action-driven LLM training flowchart, which links external information sources and tool libraries. Web searching is just one possibility; ChatGPT can also be combined with various tools, such as different office software and SaaS software, to provide more diverse functions. At the product level, it's worth discussing better interfaces and implementation methods. A side-by-side dialogue box format can raise expectations because it needs to ensure conversation fluency. Github Copilot does this well. Copilot specializes in programming pairs and proposes suggestions in the form of a partner. Users can accept good suggestions and reject bad ones. Even if many suggestions are rejected, the pleasure of receiving an effective suggestion generated at random intervals can be addictive. If ChatGPT becomes a writing, screenwriting, or work assistant in the future, a product form similar to Copilot will be easy for people to accept. In conclusion, many people are amazed by ChatGPT's capabilities, but the real wonder is yet to come. OpenAI's strength lies not only in understanding large models but also in its ability to engineer and iteratively receive feedback, as well as its work on alignment between AI and human goals. OpenAI CEO Sam Altman's words, "Trust the exponential. Flat looking backward, vertical looking forwards," express our current state of taking off. Introducing ILLA Cloud ILLA Cloud is a low-code development platform with dozens of front-end components and database API integrations. You can use ILLA Cloud to build the front-end interface by dragging and dropping components and connecting to your database or API to complete full-stack development quickly. ILLA proudly announces a partnership with Hugging Face, a suite of natural language processing (NLP) tools and services. They are most well-known for their open-source NLP library, which provides text generation, language translation, and named entity recognition tools. With Hugging Face, ILLA is more productive than before. Our users can do more with AI. ILLA Cloud provides dozens of commonly used front-end components, allowing you to build different front-end interfaces based on your specific needs quickly. At the same time, ILLA offers a connection to Hugging Face, allowing you to quickly connect to the API, send requests, and receive returned data. By connecting the API and front-end components, you can implement the requirement that users can enter content through the front end and submit it to the API. The API returns the generated content to be displayed on the front end. For ILLA Cloud’s future plan, please check our Roadmap: https://github.com/orgs/illacloud/projects/4 We are excited about the future of ILLA Cloud, and we hope you are too. If you want to join us on this journey, here are some ways you can get involved: Try out ILLA Cloud for free and start building your own internal tools. Follow us on , , , , , , or . Twitter LinkedIn YouTube dev.to Product Hunt Discord GitHub Share your feedback , suggestions , questions , or testimonials with us. Join our Discord Community: Try ILLA Cloud for free: ILLA Home Page: GitHub page: discord.com/invite/illacloud cloud.illacloud.com illacloud.com github.com/illacloud/illa-builder Also appears . here

Exploring the Technology and Training Secrets Behind ChatGPT

Explorando la tecnología y los secretos de entrenamiento detrás de ChatGPT

Explorer les secrets de la technologie et de la formation derrière ChatGPT

चैटजीपीटी के पीछे प्रौद्योगिकी और प्रशिक्षण रहस्य की खोज

ChatGPT の背後にあるテクノロジーとトレーニングの秘密を探る

Explorando os segredos de tecnologia e treinamento por trás do ChatGPT

Khám phá bí mật về công nghệ và đào tạo đằng sau ChatGPT

探秘ChatGPT背后的技术与训练秘诀

The Second Deluge by Garrett Putman Serviss is part of the HackerNoon Books Series. You can jump to any chapter in this book here . NAVIGATING OVER DROWNED EUROPE XVIII. NAVIGATING OVER DROWNED EUROPE After the English king had so strangely become a member of its company the Ark resumed its course in the direction of what had once been Europe. The spot where the meeting with the Jules Verne had occurred was west of Cape Finisterre and, according to the calculations of Captain Arms, in longitude fifteen degrees four minutes west; latitude forty-four degrees nine minutes north. Cosmo decided to run into the Bay of Biscay, skirting its southern coast in order to get a view of the Cantabrian Mountains, many of whose peaks, he thought, ought still to lie well above the level of the water. "There are the Peaks of Europa," said Captain Arms, "which lie less than twenty miles directly back from the coast. The highest point is eight thousand six hundred and seventy feet above sea level, or what used to be sea level. We could get near enough to it, without any danger, to see how high the water goes." "Do you know the locality?" demanded Cosmo. "As well as I know a compass-card!" exclaimed the captain. "I've seen the Europa peaks a hundred times. I was wrecked once on that coast, and being of an inquiring disposition, I took the opportunity to go up into the range and see the old mines—and a curious sight it was, too. But the most curious sight of all was the shepherdesses of Tresvido, dressed just like the men, in homespun breeches that never wore out. You'd meet 'em anywhere on the slopes of the Pico del Ferro, cruising about with their flocks. And the cheese that they made! There never was any such cheese!" "Well, if you know the place so well," said Cosmo, "steer for it as fast as you can. I'm curious to find out just how high this flood has gone, up to the present moment." "Maybe we can rescue a shepherdess," returned the captain, chuckling. "She'd be an ornament to your new Garden of Eden." They kept on until, as they approached longitude five degrees west, they began to get glimpses of the mountains of northern Spain. The coast was all under deep water, and also the foothills and lower ranges, but some of the peaks could be made out far inland. At length, by cautious navigation, Captain Arms got the vessel quite close to the old shore line of the Asturias, and then he recognized the Europa peaks. "There they are," he cried. "I'd know 'em if they'd emigrated to the middle of Africa. There's the old Torre de Cerredo and the Peña Santa." "How high did you say the main peak is?" asked Cosmo. "She's eight thousand six hundred and seventy feet." "From your knowledge of the coast, do you think it safe to run in closer?" "Yes, if you're sure the water is not less than two thousand four hundred feet above the old level we can get near enough to see the water-line on the peaks, from the cro'nest, which is two hundred feet high." "Go ahead, then." They got closer than they had imagined possible, so close that, from the highest lookout on the Ark, they were able with their telescopes to see very clearly where the water washed the barren mountainsides at what seemed to be a stupendous elevation. "I'm sorry about your shepherdesses," said Cosmo, smiling. "I don't think you'd find any there to rescue if you could get to them. They must all have been lost in the torrents that poured down those mountains." "More's the pity," said Captain Arms. "That was a fine lot of women. There'll be no more cheese like what they made at Tresvido." Cosmo inquired if the captain's acquaintance with the topography of the range enabled him to say how high that water was. The captain, after long inspection, declared that he felt sure that it was not less than four thousand feet above the old coast line. "Then," said Cosmo, "if you're right about the elevation of what you call the Torre de Cerredo there must be four thousand six hundred and seventy feet of its upper part still out of water. We'll see if that is so." Cosmo made the measurements with instruments, and announced that the result showed the substantial accuracy of Captain Arms's guess. "I suspected as much," he muttered. "Those tremendous downpours, which may have been worse elsewhere than where we encountered them, have increased the rise nearly seventy per cent, above what my gages indicated. Now that I know this," he continued, addressing the captain: "I'll change the course of the Ark. I'm anxious to get into the Indian Ocean as soon as possible. It would be a great waste of time to go back in order to cross the Sahara, and with this increase of level it isn't necessary. We'll just set out across southern France, keeping along north of the Pyrenees, and so down into the region of the Mediterranean." Captain Arms was astonished by the boldness of this suggestion, and at first he strongly objected to their taking such a course. "There's some pretty high ground in southern France," he said. "There's the Cevennes Mountains, which approach a good long way toward the Pyrenees. Are you sure the depth of water is the same everywhere?" "What a question for an old mariner to ask!" returned Cosmo. "Don't you know that the level of the sea is the same everywhere? The flood doesn't make any difference. It seeks its level like any other water." "But it may be risky steering between those mountains," persisted the captain. "Nonsense! As long as the sky is clear you can get good observations, and you ought to be navigator enough not to run on a mountain." Cosmo Versál, as usual, was unalterable in his resolution—he only changed when he had reasons of his own—and the course of the Ark was laid, accordingly, for the old French coast of the Landes, so low that it was now covered with nearly four thousand feet of water. The feelings of the passengers were deeply stirred when they learned that they were actually sailing over buried Europe, and they gazed in astonishment at the water beneath them, peering down into it as if they sought to discover the dreadful secrets that it hid, and talking excitedly in a dozen languages. The Ark progressed slowly, making not more than five or six knots, and on the second day after they dropped the Peñas de Europa they were passing along the northern flank of the Pyrenees and over the basin in which had lain the beautiful city of Pau. The view of the Pyrenees from this point had always been celebrated before the deluge as one of the most remarkable in the world. Now it had lost its beauty, but gained in spectacular grandeur. All of France, as far as the eye extended, was a sea, with long oceanic swells slowly undulating its surface. This sea abruptly came to an end where it met the mountains, which formed for it a coast unlike any that the hundreds of eyes which wonderingly surveyed it from the Ark had ever beheld. Beyond the drowned vales and submerged ranges, which they knew lay beneath the watery floor, before them, rose the heads of the Pic du Midi, the Pic de Ger, the Pic de Bigorre, the Massif du Gabizos, the Pic Monné, and dozens of other famous eminences, towering in broken ranks like the bearskins of a "forlorn hope," resisting to the last, in pictures of old-time battles. Here, owing to the configuration of the drowned land it was possible for the Ark to approach quite close to some of the wading mountains, and Cosmo seized the opportunity to make a new measure of the height of the flood, which he found to be surely not less than his former estimates had shown. Surveying with telescopes the immense shoulders of the Monné, the Viscos, the d'Ardiden, and the nearer heights, when they were floating above the valley of Lourdes, Cosmo and the captain saw the terrible effects that had been produced by the torrents of rain, which had stripped off the vegetation whose green robe had been the glory of the high Pyrenees on the French side. Presently their attention was arrested by some moving objects, and at a second glance they perceived that these were human beings. "Good Heaven!" exclaimed Cosmo Versál. "There are survivors here. They have climbed the mountains, and found shelter among the rocks. I should not have thought it possible." "And there are women among them," said Captain Arms, lowering his telescope. "You will not leave them there!" "But what can I do?" "Lower away the boats," replied the captain. "We've got plenty of them." "There may be thousands there," returned Cosmo, musing. "I can't take them all." "Then take as many as you can. By gad, sir, I'll not leave 'em!" By this time some of the passengers who had powerful glasses had discovered the refugees on the distant heights, and great excitement spread throughout the Ark. Cries arose from all parts of the vessel: "Rescue them!" "Go to their aid!" "Don't let them perish!" Cosmo Versál was in a terrible quandary. He was by no means without humanity, and was capable of deep and sympathetic feeling, as we have seen, but he already had as many persons in the Ark as he thought ought to be taken, considering the provision that had been made, and, besides, he could not throw off, at once, his original conviction of the necessity of carefully choosing his companions. He remained for a long time buried in thought, while the captain fumed with impatience and at last declared that if Cosmo did not give the order to lower away the boats he would do it himself. At length Cosmo, yielding rather to his own humane feelings than to the urging of others, consented to make the experiment. Half a dozen levium launches were quickly lowered and sent off, while the Ark, with slowed engines, remained describing a circle as near the mountains as it was safe to go. Cosmo himself embarked in the leading boat. The powerful motors of the launches carried them rapidly to the high slopes where the unfortunates had sought refuge, and as they approached, and the poor fugitives saw that deliverance was at hand, they began to shout, and cheer, and cry, and many of them fell on their knees upon the rocks and stretched their hands toward the heavens. The launches were compelled to move with great caution when they got near the ragged sides of the submerged mountains (it was the Peyre Dufau on which the people had taken refuge), but the men aboard them were determined to effect the rescue, and they regarded no peril too closely. At last Cosmo's launch found a safe landing, and the others quickly followed it. When Cosmo sprang out on a flat rock a crowd of men, women, and children, weeping, crying, sobbing, and uttering prayers and blessings, instantly surrounded him. Some wrung his hands in an ecstasy of joy, some embraced him, some dropped on their knees before him and sought to kiss his hands. Cosmo could not restrain his tears, and the crews of the launches were equally affected. Many of these people could only speak the patois of the mountains, but some were refugees from the resorts in the valleys below, and among these were two English tourists who had been caught among the mountains by the sudden rising of the flood. They exhibited comparative sang froid, and served as spokesmen for the others. "Bah Jove!" exclaimed one of them, "but you're welcome, you know! This has been a demnition close call! But what kind of a craft have you got out there?" "I'm Cosmo Versál." "Then that's the Ark we've heard about! 'Pon honor, I should have recognized you, for I've seen your picture often enough. You've come to take us off, I suppose?" "Certainly," replied Cosmo. "How many are there?" "All that you see here; about a hundred, I should say. No doubt there are others on the mountains round. There must have been a thousand of us when we started, but most of them perished, overcome by the downpour, or swept away by the torrents. Lord Swansdown (indicating his companion, who bowed gravely and stiffly) and myself—I'm Edward Whistlington—set out to walk over the Pyrenees from end to end, after the excitement about the great darkness died out, and we got as far as the Marboré, and then running down to Gavarnie we heard news of the sea rising, but we didn't give too much credit to that, and afterward, keeping up in the heights, we didn't hear even a rumor from the world below. "The sky opened on us like a broadside from an aerial squadron, and how we ever managed to get here I'm sure I can hardly tell. We were actually carried down the mountainsides by the water, and how it failed to drown us will be an everlasting mystery. Somehow, we found ourselves among these people, who were trying to go up, assuring us that there was nothing but water below. And at last we discovered some sort of shelter here—and here we've been ever since." "You cannot have had much to eat," said Cosmo. "Not too much, I assure you," replied the Englishman, with a melancholy smile. "But these people shared with us what little they had, or could find—anything and everything that was eatable. They're a devilish fine lot, I tell you! "When the terrible rain suddenly ceased and the sky cleared," he resumed, "we managed to get dry, after a day or two, and since then we've been chewing leather until there isn't a shoe or a belt left. We thought at first of trying to build rafts—but then where could we go? It wasn't any use to sail out over a drowned country, with nothing in sight but the mountains around us, which looked no better than the one we were barely existing on." "Then I must get you aboard the Ark before you starve," said Cosmo. "Many have died of starvation already," returned Whistlington. "You can't get us off a moment too quick." Cosmo Versál had by this time freed himself of every trace of the reluctance which he had at first felt to increasing the size of his ship's company by adding recruits picked up at random. His sympathies were thoroughly aroused, and while he hastened the loading and departure of the launches, he asked the Englishmen who, with the impassive endurance of their race, stayed behind to the last, whether they thought that there were other refugees on the mountains whom they could reach. "I dare say there are thousands of the poor devils on these peaks around us, wandering among the rocks," replied Edward Whistlington, "but I fancy you couldn't reach 'em." "If I see any I'll try," returned Cosmo, sweeping with his powerful telescope all the mountain flanks within view. At last, on the slopes of the lofty Mont Aigu across the submerged valley toward the south, he caught sight of several human figures, one of which was plainly trying to make signals, probably to attract attention from the Ark. Immediately, with the Englishmen and the remainder of those who had been found on the Peyre Dufau, he hastened in his launch to the rescue. They found four men and three women, who had escaped from the narrow valley containing the bains de Gazost, and who were in the last stages of starvation. These were taken aboard, and then, no more being in sight, Cosmo returned to the Ark, where the other launches had already arrived. And these were the last that were rescued from the mighty range of the Pyrenees, in whose deep valleys had lain the famous resorts of Cauterets, the Eaux Bonnes, the Eaux Chaudes, the Bagnières de Luchon, the Bagnières de Bigorre, and a score of others. No doubt, as the Englishmen had said, thousands had managed to climb the mountains, but none could now be seen, and those who may have been there were left to perish. There was great excitement in the Ark on the arrival of the refugees. The passengers overwhelmed them with kind attentions, and when they had sufficiently recovered, listened with wonder and the deepest sympathy to their exciting tales of suffering and terror. Lord Swansdown and Edward Whistlington were amazed to find their king aboard the Ark, and the English members of the company soon formed a sort of family party, presided over by the unfortunate monarch. The rescued persons numbered, in all, one hundred and six. The voyage of the Ark was now resumed, skirting the Pyrenees, but at an increasing distance. Finally Captain Arms announced that, according to his observations, they were passing over the site of the ancient and populous city of Toulouse. This recalled to Cosmo Versál's memory the beautiful scenes of the fair and rich land that lay so deep under the Ark, and he began to talk with the captain about the glories of its history. He spoke of the last great conqueror that the world had known, Napoleon, and was discussing his marvelous career, and referring to the fact that he had died on a rock in the midst of that very ocean which had now swallowed up all the scenes of his conquests, when the lookout telephoned down that there was something visible on the water ahead. In a little while they saw it—a small moving object, which rapidly approached the Ark. As it drew nearer both exclaimed at once: "The Jules Verne!" There could be no mistaking it. It was riding with its back just above the level of the sea; the French flag was fluttering from a small mast, and already they could perceive the form of De Beauxchamps, standing in his old attitude, with his feet below the rim of the circular opening at the top. Cosmo ordered the Stars and Stripes to be displayed in salute, and, greatly pleased over the encounter, hurried below and had the companion-ladder made ready. "He's got to come aboard this time, anyhow!" he exclaimed. "I'll take no refusal. I want to know that fellow better." But this time De Beauxchamps had no thought of refusing the hospitalities of the Ark. As soon as he was within hearing he called out: "My salutations to M. Versál and his charming fellow-voyagers. May I be permitted to come aboard and present myself in person? I have something deeply interesting to tell." Everybody in the Ark who could find a standing-place was watching the Jules Verne and trying to catch a glimpse of its gallant captain, and to hear what he said; and the moment his request was preferred a babel of voices arose, amid which could be distinguished such exclamations as: "Let him come!" "A fine fellow!" "Welcome, De Beauxchamps!" "Hurrah for the Jules Verne!" King Richard was in the fore rank of the spectators, waving his hand to his preserver. "Certainly you can come aboard," cried Cosmo heartily, at the same time hastening the preparations for lowering the ladder. "We are all glad to see you. And bring your companions along with you." 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. Garrett Putman Serviss (2005). The Second Deluge. Urbana, Illinois: Project Gutenberg. Retrieved October 2022 https://www.gutenberg.org/cache/epub/9194/pg9194.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 .

NAVIGATING OVER DROWNED EUROPE

Meteors, Fire-Balls, and Meteorites

Any views expressed in the below are the personal views of the author and should not form the basis for making investment decisions, nor be construed as a recommendation or advice to engage in investment transactions. I have no words… JK! I actually have a few thousand for y’all. I asked a question on Twitter a few days ago wondering who would be the Lehman Brothers of this crypto credit cycle. Many said it would be FTX, and I honestly laughed it off. I never believed they would go down — and yet, here we are. The catalyst for their downfall is the same as it always is — a good business overextending themselves by gorging on cheap credit collateralized by high and rising valuations of assets on their balance sheet. And as money became more expensive and the cycle turned, their nakedness was exposed for all to see. This essay is a simple story. I shall narrate a play-by-play, built around tweets and public statements. I intend to respectfully read between the lines and offer my opinion on how this all came to be and what the future holds. To that end, I believe the final down candle in this crypto bear market is near, and that we shall emerge from the bear market with less deadweight and more opportunity for those who survived. Before we begin, I sincerely believe that Sam Bankman-Fried (SBF) is a once in a generation trading talent. Even though he just blew up, the empire he put together took a certain amount of panache that is hard to come by. And I must give even more kudos to Baron CZ — when the news broke that Binance might acquire FTX, I sent CZ the following message: “I fucking love you man. Boss move, much respect.” Let’s begin. It was all a dream, I used to read Coindesk magazine… It all started with a article that featured a breakdown of a leaked Alameda balance sheet. Coindesk The financials make concrete what industry-watchers already suspect: Alameda is big. As of June 30, the company’s assets amounted to $14.6 billion. Its single biggest asset: $3.66 billion of “unlocked FTT.” The third-largest entry on the assets side of the accounting ledger? A $2.16 billion pile of “FTT collateral.” There are more FTX tokens among its $8 billion of liabilities: $292 million of “locked FTT.” (The liabilities are dominated by $7.4 billion of loans.) It’s fascinating to see that the majority of the in the Alameda business is actually FTX’s own centrally controlled and printed-out-of-thin-air token,” net equity Said Cory Klippsten, CEO of investment platform Swan Bitcoin, who is known for his critical views of altcoins, which refer to cryptocurrencies other than bitcoin ( ). BTC Alameda CEO Caroline Ellison declined to comment. FTX didn’t respond to a request for comment. Other significant assets on the balance sheet include $3.37 billion of “crypto held” and large amounts of the Solana blockchain’s native token: $292 million of “unlocked SOL,” $863 million of “locked SOL” and $41 million of “SOL collateral.” Bankman-Fried was an early investor in Solana. Other tokens mentioned by name are SRM ( Bankman-Fried co-founded), MAPS, OXY and FIDA. There is also $134 million of cash and equivalents and a $2 billion “investment in equity securities.” the token from the Serum decentralized exchange The majority of the “assets” on their balance sheet are illiquid FTT tokens and a bunch of shitcoins. Let’s break down why FTT is so important to the FTX / Alameda empire. Per FTT’s stated , FTX has pledged to use one third of all exchange fees to buy back FTT. SBF owns the majority of Alameda’s equity, while he continuously dilutes his FTX ownership to ship in capital from VC muppets. These VC muppets probably didn’t realise — or maybe they did, which is even worse for their investors — that a good chunk of FTX’s revenue was syphoned from FTX to Alameda through Alameda’s large FTT stake. Presumably, Alameda received a large allocation of FTT for providing essential market making services to FTX, and its participation in the FTT initial coin offering. tokenomics Ok, so that’s not ideal for FTX investors, but it isn’t what struck the deathblow to FTX’s solvency. The real issue — which folks started to raise questions about in the wake of the Coindesk article — was whether FTX loaned Alameda money (most likely USD or fiat stablecoins) and used Alameda’s FTT stake as collateral. If FTX did loan the funds, that isn’t an inherently fatal issue either — FTX was free to loan out its retained earnings to whomever it pleased. But the most pressing related concern was whether FTX rehypothecated customer deposits to Alameda and took FTT as collateral. And, as a subset of that, whether the fall in the value of FTT or some of the shitcoins on Alameda’s balance sheet would thus render Alameda insolvent. And finally, if Alameda became insolvent, would FTX lend money from client funds to prop it up? Again, these were the questions that people began asking after this article came out. has an interesting theory on the connection between FTX, Alameda, and FTT. In short, he argues that Alameda blew up alongside Three Arrows Capital and others, but FTX lent Alameda money to survive in exchange for FTT tokens due to Alameda via its participation in the FTT ICO. Lucas Nuzzi Then, CZ struck. Presumably, CZ read the same article as everyone else and decided to dump Binance’s sizeable FTT stake in the market. There was also apparently some beef between the two crypto Barons. That’s not very nice, Sam. CZ doesn’t make love after divorce … seems reasonable. I guess CZ and SBF had a messy breakup. Once CZ started airing his concerns, though, alarm bells started ringing in the heads of FTX depositors. In crypto, when you have even an inkling that an exchange might be insolvent, you pull your funds immediately and ask questions later. Logic suggests that even if Alameda went bust, FTX should suffer no issues processing all withdrawals. They are two separate companies, and according to SBF’s claims, FTX’s customer deposits weren’t reinvested — not even in super duper safe US Treasuries. At this point, there was no reason to believe SBF was lying. But even still, you ain’t gonna wait around to find out with your money on the line. And therefore, capital started sprinting for the exits. As always, he who sells first, sells best. Those who immediately withdrew their funds got them back quite quickly. But as Monday bled into Tuesday, FTX’s withdrawal processing crawled to a halt — and all the while, FTT continued to plunge in price. The market was clearly trying to sniff out whether there was an FTT margin call trigger to punish Alameda for overextending themselves. I woke up on Tuesday to many messages from friends spreading rumors speculating that FTX may have been playin’ funny wit da customer monay. I prayed to Lord Satoshi that FTX had not not dipped their hands in the customer cookie jar. And then, this bombshell hit the electromagnetic sphere: This insane announcement provided probable answers for many questions. Did FTX rehypothecate customer funds, or euphemistically use said funds “effectively”? We don’t have a definitive answer to this question. But, Binance signed a non-binding letter of intent to purchase the entirety of FTX and pledged to attempt to make whole all customer deposits. That suggests that FTX experienced a run on its deposits and was unable to fill all the withdrawal requests of its customers. Otherwise, why would FTX need a bailout from Binance? FTX could have blown all their VC-muppet dry powder propping up Alameda, but that still wouldn’t have impaired their ability to meet withdrawals — unless they were dipping into the honeypot. “Two big lessons.” Hmm … I wonder what situation he could be referring to? Baron CZ clearly seems to be insinuating that FTX may have done some improper things with customer funds, and thus, when the bank run happened, they were caught with their hands in the cookie jar. Over the last week, $1 billion of deposits fled FTX. That is what precipitated this awful turn of events. These are only the withdrawals that were processed. We do not know how many poor souls are still waiting for their money back. Is Alameda Insolvent or Bankrupt? We don’t have a definitive answer to this question. However, if FTX required a bailout from Binance, and it was needed because of financial help that FTX supposedly provided Alameda, then we can assume Alameda is probably donezo as well. I’ll leave you with these words from Caroline Ellison, CEO of Alameda. I am sure she is dedicating 100% of her brain to salvaging whatever she can of Alameda. Will the deal actually close? A non-binding LOI is nice, but a firm commitment by Binance to purchase FTX and ringfence all its liabilities would be nicer. I imagine that the Binance’s corporate finance folks are busy digging through the financials of FTX and Alameda. If the hole is too big, then I expect Binance will walk away from the deal. And if Binance can’t or won’t do the deal, no one will be able to swallow the losses billowing from FTX. What happens if FTX can’t find a new home? Similar to Mt Gox, we will probably enter yet another high profile bankruptcy situation where depositors queue up to recover what they can. This could be a long or short process, but given how complex I imagine the holdings of FTX to be, recovery of any portion of deposits will likely take a very long time. That’s obviously a travesty in and of itself. But when looking at the broader market impact, the even bigger question is: Which companies that run crypto loan books had exposure to FTX and or Alameda? SBF was the most pristine credit in all of crypto. He was the IT crypto pioneer. His beautiful cherub visage adorned the most prestigious mainstream financial magazines. His skin glistened like someone who only ate carrots and cucumbers. He could do no wrong — investing alongside anything SBF touched was a sure path to riches. Look at that Solana boom! SBF even got Tom Brady to YOLO into FTX, aiyah! You can therefore imagine that if FTX potentially used customer funds to save Alameda, that both entities probably cashed in on their sterling reputation to borrow as much money as they could in order to stay alive in their dying months. And therefore, it’s very possible that this possible default has already infected the loan books of the remaining major centralized lenders. In light of this potentially significant exposure, the questions becomes — can the largest centralized lenders survive: Can’t stop, won’t stop Jay Powell, who is determined to break inflation by continuing to raise short term interest rates and reduce the size of the Fed’s balance sheet? The implosion of LUNA / TerraUSD? The bankruptcy of Three Arrows Capital? And now, the possible bankruptcy of FTX and Alameda? Issues 1) through 3) already deaded “legendary” centralized lenders such as Celsius, BlockFi, and Voyager. There are few possible zombies remaining — I will not name them here, but anyone who thinks critically can surmise who might come under pressure next. FTX and Alameda biting the dust is bad enough. That is our Lehman Brothers — but who is AIG? Who is CountryWide? FannieMae and FreddieMac anyone? Oh, they are out there — hiding in plain sight. The bottom of the S&P 500 during the 2008 Global Financial Crisis wasn’t reached until March 2009. That was many months after Lehman Brothers went bankrupt, and every single independent US investment bank received money from Warren Buffett or was gobbled up by a commercial bank who could legally access the Fed’s discount winder. It was after Treasury Secretary Paulson supposedly bent the knee to congresswoman Nancy Pelosi in order to secure the passage of the Troubled Asset Relief Program (aka let’s print some fucking money and hand it to the banksters). And finally, it was after Nobel Laureate Ben “Helicopter Money” Bernanke announced the Fed would engage in Quantitative Easing. While I am supremely bullish on the prospects of crypto in 2023 (I know the date keeps slipping, but I reserve the right to change my mind in the face of market conditions), I believe we are about to trade to new lows as everyone wonders which crypto household name will go bankrupt next for lending money to FTX / Alameda. Bitcoin wicked down to almost $17,500 on BitMEX’s XBTUSD perpetual swap. On various other spot exchanges, Bitcoin experienced similar falls to levels close to $17,000. Get ready for lower for longer, because it’s coming. But the good thing about crypto is that no central bank will be riding the rescue with freshly printed fiat shitcoins to bolster the balance sheets of reckless companies. The crypto industry will be forced to devour its humble pie quickly — leading to a speedy recovery that leaves it stronger than ever. Before I end this essay, let me be clear: centralized exchanges will always face these issues of mistrust on behalf of their customers. FTX was not the first high-profile exchange to fail and it won’t be the last. But throughout all this, blocks on the Bitcoin, Ethereum, and all other blockchains were still produced and verified. Decentralized money and finance have and will continue to survive and thrive in the face of the failures of centralized entities.

Speechless

Sin Palabras

Muet

अवाक

無言

Mudo

Không nói nên lời

无语

Snippets

fragmentos

Extraits

स्निपेट्स

切れ端

Trechos

Đoạn mã

片段

Text-to-image generation is not a new idea. Notably, the (GAN) architecture, a once-popular deep-learning computer vision algorithm had . Generative Adversarial Network generated birds and flowers from text After more improvements to the generative image algorithms like and , we now have a deluge of commercial text to image generators, ranging from , and . hyperealistic generation of human faces GLIDE diffusion model Google’s Imagen Tiktok’s Greenscreen OpenAI Dall-E And here comes the new kid on the block, Stable Diffusion Before moving ahead, for those who wants to understand the nitty-gritties of diffusion models, I strongly encourage readers to go through this awesome blogpost by Lilian Weng, and . https://lilianweng.github.io/posts/2021-07-11-diffusion-models/ this tweet by Tom Goldstein The rest of the article would be an exercise of using the stable diffusion model to generate images given “ ”. There is no main purpose for doing so, other than satisfying my curiosity. my name Narcissistic as it sounds, I want to generate an image with my name. Be honest, don’t we all from time to time? And thus, I went to and typed in . “google your own name” https://huggingface.co/spaces/stabilityai/stable-diffusion Liling Tan Before I show the generated results, I would like to clarify some specifics about my first and last name. In general, it is more probable that a English romanization of my Chinese character name refers to a female individual than a male. Next (陳) is a common Southern Chinese name that originates from the , and more commonly you would expect the English translation/romanization of Mandarin Chinese, . With K-Pop being a global phenomenon, the top-ranked Google result of would point you to the .  Also, since (my last name), has the same spelling as the color, the top Google search will end up with results pointing to “ ”. Liling Tan Min language Chen Chen Korean Singer from the K-pop band EXO Tan tan a yellowish-brown color For reference, here are the control experiments results, if I googled “liling tan” And now, the results… Okay, that’s definitely nowhere close to how I look. I kind of expected the image to show an Asian female but the first image was kinda weird. Now, what happens if I lowercase and re-run the generation? Alright, the model seems hell-bent on some facial features and sorta generating one older and another younger version of “ ” liling tan Interestingly, the model has some internal mechanisms to block some "unsafe” content and output this error message. This Image was not displayed because our detection model detected . Unsafe content Then I got really curious, do the two versions of “ ” exist IRL (in real life)? liling tan So I do the natural thing to reverse image search with https://lens.google.com/ Image Search Result 1 Image Search Result 2 Hmmm, no results, lets extend the frame to beyond the face… And of course, what was I expecting, surely Google Shopping will take the chance to advertise and sell me something -_- Maybe the older version generated by the model is more grounded to someone IRL? Image Search Result 3 Hmmm, no results, so the model kinda generated two unique people that doesn’t exist IRL? But, we see two separate dots that indicates two other results exists, let’s see the first one at the bottom. Of course, it will try to sell me something again… What was I expecting? @_@ Let’s try the results other dot. Now this is interesting, it’s trying to promote a inspired piece. Girl with a Pearl Earring (ca. 1665) by Johannes Vermeer But what about “Find image source”? Does it really find the source of the images that the model use to slice, dice and “diffuse”? It’s hard to say: Find the image source - Result 1 Find the image source - Result 2 Find the image source - Result 3 Đợi tí (Wait a minute),does that mean that we don’t know which image the model has been trained on or used to splice before generating the results? Now that we find the underbelly from the results, lets backtrack the OG paper listed on the original source code https://github.com/CompVis/stable-diffusion Robin Rombach*, Andreas Blattmann*, Dominik Lorenz, Patrick Esser, Björn Ommer. In CVPR '22. High-Resolution Image Synthesis with Latent Diffusion Models. According to the paper, the model is pre-trained on the LAION database and the Conceptual Captions dataset . https://laion.ai/blog/laion-5b/ https://ai.google.com/research/ConceptualCaptions/ Our model is pretrained on the LAION [73] database and finetuned on the Conceptual Captions [74] dataset. What if, we build a reverse image search based on the datasets? Perhaps if we can find the approximate nearest neighbor images to the outputs generated by the model, then it might be possible to “explain away” which images the model had deemed to be “salient” enough to diffuse and generate the outputs from my name. Voila! Here’s a search engine to probe the dataset used to train Stable Diffusion, https://waxy.org/2022/08/exploring-12-million-of-the-images-used-to-train-stable-diffusions-image-generator/ And here’s the list of . results of searching for Liling in the LAION database Ah ha, that is one image that the model must have “chosen to diffuse” It is highly possible that the model somehow and generated an image similar to that image. “ focused ” an image in the training data with “Liling” in the caption Why did you bother to “ ”? diffuse <your_name> To conclude the post, this exercise is purely out of curiosity. I wanted to know what the model would generate. But this exercise also highlights some sort of bias when using generative models. While “Liling Tan” isn’t to generate anything close to me or any other top-ranked “Liling Tan” search results, the model seems to be more stable in . stable enough generating  famous people, (e.g. John Oliver) To end this article, here’s a result from generating images with my online handle as the prompt… alvations Hopefully, after reading, you would also try to “ ”! diffuse <your_name>

Stable Diffusion, Unstable Me: Text-to-image Generation

The Count of Monte Cristo, Illustrated: Chapter 44 - The Vendetta

The Count of Monte Cristo, Illustrated: Chapter 26 - The Pont du Gard Inn

Frankenstein or, The Modern Prometheus by Mary Wollstonecraft (Godwin) Shelley is part of HackerNoon’s Book Blog Post series. You can jump to any chapter in this book here . Chapter IX CHAPTER IX. Nothing is more painful to the human mind than, after the feelings have been worked up by a quick succession of events, the dead calmness of inaction and certainty which follows and deprives the soul both of hope and fear. Justine died, she rested, and I was alive. The blood flowed freely in my veins, but a weight of despair and remorse pressed on my heart which nothing could remove. Sleep fled from my eyes; I wandered like an evil spirit, for I had committed deeds of mischief beyond description horrible, and more, much more (I persuaded myself) was yet behind. Yet my heart overflowed with kindness and the love of virtue. I had begun life with benevolent intentions and thirsted for the moment when I should put them in practice and make myself useful to my fellow beings. Now all was blasted; instead of that serenity of conscience which allowed me to look back upon the past with self-satisfaction, and from thence to gather promise of new hopes, I was seized by remorse and the sense of guilt, which hurried me away to a hell of intense tortures such as no language can describe. This state of mind preyed upon my health, which had perhaps never entirely recovered from the first shock it had sustained. I shunned the face of man; all sound of joy or complacency was torture to me; solitude was my only consolation—deep, dark, deathlike solitude. My father observed with pain the alteration perceptible in my disposition and habits and endeavoured by arguments deduced from the feelings of his serene conscience and guiltless life to inspire me with fortitude and awaken in me the courage to dispel the dark cloud which brooded over me. “Do you think, Victor,” said he, “that I do not suffer also? No one could love a child more than I loved your brother”—tears came into his eyes as he spoke—“but is it not a duty to the survivors that we should refrain from augmenting their unhappiness by an appearance of immoderate grief? It is also a duty owed to yourself, for excessive sorrow prevents improvement or enjoyment, or even the discharge of daily usefulness, without which no man is fit for society.” This advice, although good, was totally inapplicable to my case; I should have been the first to hide my grief and console my friends if remorse had not mingled its bitterness, and terror its alarm, with my other sensations. Now I could only answer my father with a look of despair and endeavour to hide myself from his view. About this time we retired to our house at Belrive. This change was particularly agreeable to me. The shutting of the gates regularly at ten o’clock and the impossibility of remaining on the lake after that hour had rendered our residence within the walls of Geneva very irksome to me. I was now free. Often, after the rest of the family had retired for the night, I took the boat and passed many hours upon the water. Sometimes, with my sails set, I was carried by the wind; and sometimes, after rowing into the middle of the lake, I left the boat to pursue its own course and gave way to my own miserable reflections. I was often tempted, when all was at peace around me, and I the only unquiet thing that wandered restless in a scene so beautiful and heavenly—if I except some bat, or the frogs, whose harsh and interrupted croaking was heard only when I approached the shore—often, I say, I was tempted to plunge into the silent lake, that the waters might close over me and my calamities for ever. But I was restrained, when I thought of the heroic and suffering Elizabeth, whom I tenderly loved, and whose existence was bound up in mine. I thought also of my father and surviving brother; should I by my base desertion leave them exposed and unprotected to the malice of the fiend whom I had let loose among them? At these moments I wept bitterly and wished that peace would revisit my mind only that I might afford them consolation and happiness. But that could not be. Remorse extinguished every hope. I had been the author of unalterable evils, and I lived in daily fear lest the monster whom I had created should perpetrate some new wickedness. I had an obscure feeling that all was not over and that he would still commit some signal crime, which by its enormity should almost efface the recollection of the past. There was always scope for fear so long as anything I loved remained behind. My abhorrence of this fiend cannot be conceived. When I thought of him I gnashed my teeth, my eyes became inflamed, and I ardently wished to extinguish that life which I had so thoughtlessly bestowed. When I reflected on his crimes and malice, my hatred and revenge burst all bounds of moderation. I would have made a pilgrimage to the highest peak of the Andes, could I, when there, have precipitated him to their base. I wished to see him again, that I might wreak the utmost extent of abhorrence on his head and avenge the deaths of William and Justine. Our house was the house of mourning. My father’s health was deeply shaken by the horror of the recent events. Elizabeth was sad and desponding; she no longer took delight in her ordinary occupations; all pleasure seemed to her sacrilege toward the dead; eternal woe and tears she then thought was the just tribute she should pay to innocence so blasted and destroyed. She was no longer that happy creature who in earlier youth wandered with me on the banks of the lake and talked with ecstasy of our future prospects. The first of those sorrows which are sent to wean us from the earth had visited her, and its dimming influence quenched her dearest smiles. “When I reflect, my dear cousin,” said she, “on the miserable death of Justine Moritz, I no longer see the world and its works as they before appeared to me. Before, I looked upon the accounts of vice and injustice that I read in books or heard from others as tales of ancient days or imaginary evils; at least they were remote and more familiar to reason than to the imagination; but now misery has come home, and men appear to me as monsters thirsting for each other’s blood. Yet I am certainly unjust. Everybody believed that poor girl to be guilty; and if she could have committed the crime for which she suffered, assuredly she would have been the most depraved of human creatures. For the sake of a few jewels, to have murdered the son of her benefactor and friend, a child whom she had nursed from its birth, and appeared to love as if it had been her own! I could not consent to the death of any human being, but certainly I should have thought such a creature unfit to remain in the society of men. But she was innocent. I know, I feel she was innocent; you are of the same opinion, and that confirms me. Alas! Victor, when falsehood can look so like the truth, who can assure themselves of certain happiness? I feel as if I were walking on the edge of a precipice, towards which thousands are crowding and endeavouring to plunge me into the abyss. William and Justine were assassinated, and the murderer escapes; he walks about the world free, and perhaps respected. But even if I were condemned to suffer on the scaffold for the same crimes, I would not change places with such a wretch.” I listened to this discourse with the extremest agony. I, not in deed, but in effect, was the true murderer. Elizabeth read my anguish in my countenance, and kindly taking my hand, said, “My dearest friend, you must calm yourself. These events have affected me, God knows how deeply; but I am not so wretched as you are. There is an expression of despair, and sometimes of revenge, in your countenance that makes me tremble. Dear Victor, banish these dark passions. Remember the friends around you, who centre all their hopes in you. Have we lost the power of rendering you happy? Ah! While we love, while we are true to each other, here in this land of peace and beauty, your native country, we may reap every tranquil blessing—what can disturb our peace?” And could not such words from her whom I fondly prized before every other gift of fortune suffice to chase away the fiend that lurked in my heart? Even as she spoke I drew near to her, as if in terror, lest at that very moment the destroyer had been near to rob me of her. Thus not the tenderness of friendship, nor the beauty of earth, nor of heaven, could redeem my soul from woe; the very accents of love were ineffectual. I was encompassed by a cloud which no beneficial influence could penetrate. The wounded deer dragging its fainting limbs to some untrodden brake, there to gaze upon the arrow which had pierced it, and to die, was but a type of me. Sometimes I could cope with the sullen despair that overwhelmed me, but sometimes the whirlwind passions of my soul drove me to seek, by bodily exercise and by change of place, some relief from my intolerable sensations. It was during an access of this kind that I suddenly left my home, and bending my steps towards the near Alpine valleys, sought in the magnificence, the eternity of such scenes, to forget myself and my ephemeral, because human, sorrows. My wanderings were directed towards the valley of Chamounix. I had visited it frequently during my boyhood. Six years had passed since then: I was a wreck, but nought had changed in those savage and enduring scenes. I performed the first part of my journey on horseback. I afterwards hired a mule, as the more sure-footed and least liable to receive injury on these rugged roads. The weather was fine; it was about the middle of the month of August, nearly two months after the death of Justine, that miserable epoch from which I dated all my woe. The weight upon my spirit was sensibly lightened as I plunged yet deeper in the ravine of Arve. The immense mountains and precipices that overhung me on every side, the sound of the river raging among the rocks, and the dashing of the waterfalls around spoke of a power mighty as Omnipotence—and I ceased to fear or to bend before any being less almighty than that which had created and ruled the elements, here displayed in their most terrific guise. Still, as I ascended higher, the valley assumed a more magnificent and astonishing character. Ruined castles hanging on the precipices of piny mountains, the impetuous Arve, and cottages every here and there peeping forth from among the trees formed a scene of singular beauty. But it was augmented and rendered sublime by the mighty Alps, whose white and shining pyramids and domes towered above all, as belonging to another earth, the habitations of another race of beings. I passed the bridge of Pélissier, where the ravine, which the river forms, opened before me, and I began to ascend the mountain that overhangs it. Soon after, I entered the valley of Chamounix. This valley is more wonderful and sublime, but not so beautiful and picturesque as that of Servox, through which I had just passed. The high and snowy mountains were its immediate boundaries, but I saw no more ruined castles and fertile fields. Immense glaciers approached the road; I heard the rumbling thunder of the falling avalanche and marked the smoke of its passage. Mont Blanc, the supreme and magnificent Mont Blanc, raised itself from the surrounding aiguilles, and its tremendous dôme overlooked the valley. A tingling long-lost sense of pleasure often came across me during this journey. Some turn in the road, some new object suddenly perceived and recognised, reminded me of days gone by, and were associated with the lighthearted gaiety of boyhood. The very winds whispered in soothing accents, and maternal Nature bade me weep no more. Then again the kindly influence ceased to act—I found myself fettered again to grief and indulging in all the misery of reflection. Then I spurred on my animal, striving so to forget the world, my fears, and more than all, myself—or, in a more desperate fashion, I alighted and threw myself on the grass, weighed down by horror and despair. At length I arrived at the village of Chamounix. Exhaustion succeeded to the extreme fatigue both of body and of mind which I had endured. For a short space of time I remained at the window watching the pallid lightnings that played above Mont Blanc and listening to the rushing of the Arve, which pursued its noisy way beneath. The same lulling sounds acted as a lullaby to my too keen sensations; when I placed my head upon my pillow, sleep crept over me; I felt it as it came and blessed the giver of oblivion. 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. Shelley, Mary Wollstonecraft (Godwin), 1993. Relativity:  Frankenstein or, The Modern Prometheus. Urbana, Illinois: Project Gutenberg. Retrieved May 2022 from https://www.gutenberg.org/files/84/84-h/84-h.htm#chap09 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

Frankenstein or, The Modern Prometheus: Chapter IX

Google recently announced a new model for automatically generating summaries using machine learning, released in Google Docs that you can already use. The model will try to understand the whole document and generate a short summary of the piece—something some movie professionals clearly still can’t do. The model needs to achieve two things to achieve that, which you will learn in the video below! Watch the video References ►Read the full article: ►Google's blog post: ►GPT-3 video: ►Attention video: ►What are RNNs?: ►My Newsletter (A new AI application explained weekly to your emails!): https://www.louisbouchard.ai/google-docs-summary/ https://ai.googleblog.com/2022/03/auto-generated-summaries-in-google-docs.html https://youtu.be/gDDnTZchKec https://youtu.be/QcCJJOLCeJQ https://youtu.be/Z0pb3LjeIZg https://www.louisbouchard.ai/newsletter/ Video Transcript 0:00 do you find it hard to quickly summarize 0:02 a movie you just watched or a book you 0:04 read a few weeks ago sometimes you love 0:06 a book and if you can't manage to 0:09 remember its content which i often can't 0:11 you may end up boring your friend for 0:12 talking for an hour describing the many 0:15 chapters and important parts while your 0:17 friend just want to have a quick and 0:18 concise summary this is because doing a 0:21 great summary is challenging even for us 0:23 but necessary how useful it is to be 0:26 able to quickly know what the book is 0:28 about before buying it or simply to help 0:30 you go through all your emails and 0:32 documents in seconds taking the time to 0:34 recap and summarize your work is also 0:36 very important if you want to be taken 0:38 seriously at your job and weights and 0:41 biases the sponsor of this video will 0:43 help you with that with my favorite 0:45 feature weights and biases reports it 0:48 helps you create beautiful reports about 0:50 your previous runs with your hyper 0:51 parameters and any matrix tracked in 0:54 seconds it even allows you to create 0:56 clear and dynamic plots comparing the 0:58 runs your team members can leave 1:00 comments right on the report if this 1:02 doesn't make you the coolest ml engineer 1:05 on your team i don't know what will your 1:07 teammates will understand what you are 1:09 working on in a split second it will 1:11 allow them to help you out if they can 1:13 saving valuable time and possible 1:15 misunderstandings try out weights and 1:17 biases for free with the first link 1:19 below and profit from these benefits by 1:22 stepping up your engineering game 1:27 just like for creating clear reports you 1:29 need to have a great understanding of a 1:32 book movie or any content you are trying 1:34 to summarize to do it well omit all 1:36 unnecessary information while keeping 1:39 the essential making something as 1:41 concise as possible can be really 1:42 complicated or even impossible here i 1:46 try to explain research in a few minutes 1:48 and i often can't manage to make it 1:49 shorter than 5 minutes even if it's only 1:52 a summary of a 20 page piece it requires 1:55 hours of work and fine tuning and now i 1:57 may be replaced by an ai that does that 2:00 better in milliseconds indeed google 2:03 recently announced a new model for 2:05 automatically generating summaries using 2:07 machine learning released in google docs 2:09 that you can already use the model will 2:12 try to understand the whole document and 2:14 generate a short summary of the piece 2:16 something some movie professionals 2:18 clearly still can't do the model needs 2:21 to achieve two things understand the 2:23 text in the document called natural 2:25 language understanding and generate 2:28 coherent sentences using a natural 2:30 language or in other words perform 2:33 natural language generation but how can 2:36 you achieve that you guessed it with a 2:39 lot of data and compute power luckily 2:41 enough this is google research they 2:44 trained our model to replicate our 2:46 thought process for generating summaries 2:48 using way too many documents with 2:50 manually generated summaries seeing all 2:52 these examples the model does like any 2:55 good student and ends up being able to 2:57 generate relatively good summaries for 2:59 similar documents as it has seen during 3:02 its training phase you can see why we 3:04 need good quality data here the model 3:06 will learn from them it may only be as 3:09 good as the data that was used for 3:11 training it it will be like having a 3:13 really bad coach that doesn't know 3:15 anything about basketball trying to 3:17 teach a new player how could this new 3:19 player become any good if the coach 3:21 doesn't know anything about the sport 3:24 the newcomer's talent won't be optimized 3:26 and might be wasted only because of the 3:28 poor coaching the challenge comes with 3:31 generalizing to new documents 3:33 generalizing is something even difficult 3:35 for students that only learned how to 3:37 perform the given examples but did not 3:40 understand how to apply the formulas 3:42 it's the same thing here the model faces 3:45 difficulties as it cannot remember all 3:47 documents and summaries by heart it has 3:50 to understand them or at least know 3:52 which words to put its attention on in 3:55 order to produce a summary that reflects 3:57 the document well the latter will most 4:00 likely happen as the model doesn't 4:02 really understand the document it only 4:04 understands how to perform the task 4:06 which is unfortunately still far from 4:09 human level intelligence but well enough 4:11 for such a task i just mentioned 4:13 attention well this was not a 4:16 coincidence attention may be the most 4:18 important concept behind this model 4:21 indeed just like gpt3 this new model 4:24 also uses the transformer architecture 4:26 and attention mechanisms this is where 4:28 high computation is required as you know 4:31 transformers are big and powerful 4:34 networks but most time a bit too big for 4:36 fast and efficient tools that need to be 4:38 available online in seconds transformers 4:41 computation complexity also scales with 4:43 the input size which means that the 4:45 longer the input the heavier the 4:47 computation will be causing big issues 4:50 when we want to summarize a whole book 4:52 gpt3 works well for small inputs like 4:55 question and sharing tasks but the same 4:57 architecture won't be able to process 4:59 whole books efficiently instead they had 5:01 to use some tricks in order to have a 5:03 smaller and more efficient model while 5:06 keeping high quality results this 5:08 optimization was achieved by merging 5:10 transformers with rnns or recurrent 5:13 neural networks which are two concepts i 5:16 explained in previous videos that i 5:18 highly recommend watching for a better 5:19 understanding both videos are linked in 5:22 the description below in short it will 5:24 act similar to gpt3 which you should 5:26 understand by now from my video about it 5:29 but with a smaller version of the model 5:31 iterating over and over until the model 5:34 finishes the book the transformer part 5:37 of the architecture will be responsible 5:39 for understanding a small section of the 5:41 text and producing an encoded version of 5:44 it the rnn will be responsible for 5:46 stacking and keeping this knowledge in 5:48 memory iterating through the whole book 5:51 to end up with the most concise way of 5:53 summarizing its content working together 5:56 the attention mechanism added to the 5:58 recurrent architecture will be able to 6:00 go through long documents and find the 6:02 most important features to mention in 6:04 the summary as any human will do of 6:07 course the model is not perfect since 6:09 even professional writers aren't perfect 6:11 at summarizing their work but the 6:13 results are quite impressive and 6:15 produced extremely efficiently i'd 6:17 strongly recommend trying it for 6:19 yourself in google docs to make up your 6:21 mind about it 6:22 and voila this is how google docs 6:25 automatically summarize your documents 6:27 with their new machine learning based 6:29 model i hope you enjoyed this video if 6:32 so please take a second to leave a like 6:34 and comment your thoughts on the video 6:36 and on this new model will you use it 6:39 thank you for watching until the end and 6:41 i will see you next week with another amazing paper

Google's New AI Creates Summaries of Your Documents in Google Docs

La nueva IA de Google crea resúmenes de sus documentos en Google Docs

La nouvelle IA de Google crée des résumés de vos documents dans Google Docs

Google का नया AI Google डॉक्स में आपके दस्तावेज़ों का सारांश बनाता है

Google の新しい AI が Google ドキュメントでドキュメントの概要を作成

A nova IA do Google cria resumos de seus documentos no Google Docs

AI mới của Google tạo tóm tắt tài liệu của bạn trong Google Documents

Google 的新 AI 在 Google Docs 中创建文档摘要

How do autonomous vehicles see? You’ve probably heard of or other weird cameras they are using. But how do they work, how can they see the world, and what do they see exactly compared to us? Understanding how they work is essential if we want to put them on the road, primarily if you work in the government or build the next regulations. But also as a client of these services. LiDAR sensors We previously covered , but they are different from conventional autonomous vehicles. Tesla only uses cameras to understand the world, while most of them, like , use regular cameras and 3D LiDAR sensors. These LiDAR sensors are pretty simple to understand: they won’t produce images like regular cameras but 3D . LiDAR cameras measure the distance between objects, calculating the pulse laser’s traveling time that they project to the object. how Tesla autopilot sees and works Waymo point clouds Still, how can we efficiently combine this information and have the vehicle understand it? And what does the vehicle end up seeing? Only points everywhere? Is it enough for driving on our roads? We will look into this in the video with a new research paper by Waymo and Google Research... Watch the video! References ► Read the full article: ►Piergiovanni, A.J., Casser, V., Ryoo, M.S. and Angelova, A., 2021. 4d-net for learned multi-modal alignment. In Proceedings of the IEEE/CVF International Conference on Computer Vision (pp. 15435–15445). ►Google Research's blog post: ►My Newsletter (A new AI application explained weekly to your emails!): https://www.louisbouchard.ai/waymo-lidar/ https://openaccess.thecvf.com/content/ICCV2021/papers/Piergiovanni_4D-Net_for_Learned_Multi-Modal_Alignment_ICCV_2021_paper.pdf https://ai.googleblog.com/2022/02/4d-net-learning-multi-modal-alignment.html?m=1 https://www.louisbouchard.ai/newsletter/ Video transcript 0:00 how do autonomous vehicles see you've 0:02 probably heard of lidar sensors or other 0:05 weird cameras they are using but how do 0:07 they work how can they see the world and 0:09 what do they see compared to us 0:11 understanding how they work is essential 0:13 if we want to put them on the road 0:15 especially if you work in the government 0:17 or build the next regulations but also 0:20 as a client of these services we 0:22 previously covered how tesla autopilot 0:24 sees and works but they are different 0:26 from conventional autonomous vehicles 0:28 tesla only uses cameras to understand 0:31 the world while most of them like waymo 0:33 use regular cameras and 3d lidar sensors 0:38 these lidar sensors are pretty simple to 0:40 understand they won't produce images 0:42 like regular cameras but 3d point clouds 0:46 lidar cameras measure the distance 0:48 between objects calculating the pulse 0:50 lasers traveling time that they project 0:52 to the object this way they will produce 0:54 a very few data points with valuable and 0:57 exact distance information as you can 1:00 see here these data points are called 1:02 point clouds and it just means that what 1:04 we will see are just many points at the 1:06 right positions creating some sort of 3d 1:09 model of the world here you can see how 1:12 lidar on the right isn't that precise to 1:14 understand what it sees but it's pretty 1:16 good to understand that with very little 1:18 information which is perfect for 1:20 efficiently computing the data in real 1:22 time an essential criteria for 1:25 autonomous vehicles this minimal amount 1:27 of data and high spatial precision is 1:30 perfect because coupled with rgb images 1:33 as shown on the left we have both 1:35 accurate distance information and the 1:37 accurate object information we lack with 1:40 lidar data alone especially from far 1:43 away objects or people 1:44 this is why waymo and other autonomous 1:47 vehicles companies use both kinds of 1:49 sensors to understand the world still 1:52 how can we efficiently combine this 1:53 information and have the vehicle 1:55 understand it and what does the vehicle 1:58 end up seeing only points everywhere is 2:01 it enough for driving on the roads we 2:03 will look into this with a new research 2:05 paper by waymo and google research 2:08 to release many advancements like this 2:10 one the researchers needed to run many 2:13 many experiments and be super organized 2:16 plus their code needs to be easily 2:18 reproducible and near perfection as many 2:21 people will depend on it in life or 2:23 death situations fortunately these are 2:26 two strong points of this episode 2:28 sponsor weights and biases if you want 2:31 to publish papers in big conferences or 2:33 create the future of autonomous vehicles 2:36 i think using weights and biases will 2:38 certainly help it changed my life as a 2:40 researcher and for my work at design 2:42 stripe weights and biases will 2:44 automatically track each run the hyper 2:46 parameters the github version hardware 2:48 and os used then you can easily create 2:51 your own workspace using filters groups 2:54 and your own panels to display anything 2:56 you need to analyze how can you not be 2:59 well organized with such a tool it 3:01 basically contains everything you need 3:03 for your code to be reproducible without 3:06 you even trying for your future 3:08 colleagues and people that will 3:09 implement your amazing work please take 3:12 the time to ensure your work is 3:14 reproducible and if you want help with 3:16 that try out weights and biases with the 3:18 first thing we do 3:21 i think i couldn't summarize the paper 3:23 better than the sentence they used in 3:25 their article we present fortinet which 3:28 learns how to combine 3d point clouds in 3:31 time and rgb camera images in time for 3:34 the widespread application of 3d object 3:37 detection in autonomous driving i hope 3:39 you enjoyed the video please subscribe 3:41 and i'm just kidding let's dive a little 3:43 deeper into this sentence this is what 3:45 the 3d object detection we are talking 3:47 about looks like and it's also what the 3:50 car will end up seeing it's a very 3:52 accurate representation of the world 3:54 around the vehicle with all objects 3:56 appearing and precisely identified how 3:59 cool does that look and more interesting 4:02 how did they end up with this result 4:04 they produced this view using lidar data 4:07 called point clouds in time or pcit and 4:10 regular cameras are here called rgb 4:13 videos these are both four dimensional 4:16 inputs just like we humans see and 4:19 understand the world the four dimensions 4:21 come from the video being taken in time 4:24 so the vehicle has access to past frames 4:26 to help understand context and objects 4:28 to guess future behaviors just like we 4:31 do creating the fourth dimension the 4:34 three others are the 3d space we are 4:36 familiar with we call this task scene 4:39 understanding and has been widely 4:41 studied in computer vision and has seen 4:43 many advancements with the recent 4:45 progress of the field and machine 4:46 learning algorithms it's also crucial in 4:49 self-driving vehicles where we want to 4:51 have a near perfect comprehension of the 4:54 scenes here you can see that the two 4:56 networks always talk to each other with 4:59 connections this is mainly because when 5:01 we take images we have objects at 5:03 various ranges in the shot and with 5:05 different proportions the car in front 5:08 will look much bigger than the car far 5:10 away but we still need to consider both 5:13 like us when we see someone far away and 5:15 feel like it's our friend but still wait 5:17 closer to be sure before calling his 5:19 name the car will lack details for such 5:22 far away objects to patch for that we 5:24 will extract and share information from 5:26 different levels in the network sharing 5:29 information throughout the network is a 5:31 powerful solution because neural 5:33 networks use small detectors of fixed 5:36 size to condense the image the deeper we 5:38 get into the network meaning that early 5:41 layers will be able to detect small 5:42 objects and on the edges or parts of the 5:45 bigger objects deeper layers will lose 5:48 the small objects but be able to detect 5:50 large objects with great precision the 5:52 main challenge with this approach is 5:54 combining these two very different kinds 5:56 of information through these connections 5:58 the lidar 3d space data and more regular 6:01 rgb frames using both information at all 6:04 network steps as described earlier is 6:07 best to understand the whole scene 6:08 better but how can we merge two 6:10 different streams of information and use 6:13 the time dimension efficiently this data 6:15 translation between the two branches is 6:17 what the network learns during training 6:20 in a supervised way with a similar 6:22 process as in the self-attention 6:24 mechanisms i covered in previous videos 6:26 by trying to recreate the real model of 6:29 the world but to facilitate this data 6:31 translation they use a model called 6:33 point pilers which takes point clouds 6:36 and gives a two-dimensional 6:37 representation you can see this as a 6:40 pseudo image of the point cloud as they 6:42 call it creating somewhat of a regular 6:44 image representing the point cloud with 6:46 the same properties as the rgb images we 6:49 have in the other branch instead of the 6:51 pixels being red green blue colors they 6:54 simply represent the depth and positions 6:56 of the object x y z coordinates this 6:59 pseudo image is also really sparse 7:01 meaning that the information on this 7:03 representation is only dense around 7:05 imported objects and most probably 7:07 useful for the model regarding time as i 7:10 said we simply have the fourth dimension 7:12 in the input image to keep track of the 7:14 frames these two branches we see are 7:16 convolutional neural networks that 7:18 encode the images as described in 7:20 multiple of my videos and then decode 7:23 this encoded information to recreate the 7:25 3d representation we have here so it 7:28 uses a very similar encoder for both 7:30 branches shares information with each 7:33 other and reconstructs a 3d model of the 7:35 world using a decoder and voila this is 7:39 how the weimo vehicles see our world it 7:42 can process 32 point clouds in time and 7:45 16 rgb frames within 7:47 164 milliseconds producing better 7:50 results than other methods this might 7:53 not ring any bell so we can compare it 7:55 with the next best approach that is less 7:58 accurate and takes 300 milliseconds 8:00 almost double the time to process 8:03 of course this was just an overview of 8:05 this new paper by google research and 8:07 weymouth i'd recommend reading the paper 8:10 to learn more about their models 8:11 architecture and other features i didn't 8:14 dive into like time information's 8:16 efficiency problem it's linked in the 8:18 description below i hope you enjoyed the 8:20 video and if you did please consider 8:22 subscribing to the channel and 8:24 commenting what you think of this 8:26 summary i'd love to read what you think 8:29 thank you for watching and i will see 8:31 you next week with another amazing paper 8:35 [Music]

How Waymo Combines Lidar and Cameras for 3D Object Detection

Cómo Waymo combina Lidar y cámaras para la detección de objetos 3D

Let's Talk About AI Tools for Code Completion. After spending quite a bit of time debugging my code, what's wrong with it, I've found the 'bug'. Not the ordinary one. Don't fully rely on AI. Seems simple right? However, when you are doing some easy/repetitive stuff, you might fall under influence of just 'tabbing'. What was the 'bug' anyway? I was making a Twitter bot in NodeJS. Get some data from Reddit, and post it on account. Twitter bot First off, I need a library, to simplify things about auth and whatever. A'ight, done. npm install, make file structure, git init. Here comes the fun part. I'm writing a constructor for a Twitter client. It needs 4 elements: API key API secret Access token Access secret Okay, fair enough. Constructing the Constructor First one, it's okay. TAB. Seems good, TAB! Awesome. GitHub Copilot is great! It's so easy. TAB and there we go. Constructor finished! Testing Let's try to get a simple 'Hello World!' tweet out there. 401, Unauthorized. Umm, what? Let's go to the .env file, let's check if the naming is correct. Copy, paste. Check. 401, Unauthorized. Okay, let's check for Twitter stuff. Check permissions, regenerate keys. Check. 401, Unauthorized. Now comes the part where I tried to debug code, try a different library that supports Twitter's V2 API. No success. Debugging Google, google, google. Try this, try that. Progress: NULL I go for a walk, hoping for an 'Aha!' moment. No luck. Back to work! Open Postman. Input parameters. Check headers, check body. Send. It worked! Hmm, what could it be? Open postman, copy the curl request. Paste it in Notepad++. I previously turned on debug mode in my code, copied the curl request from there too. Paste it in Notepad++. Double-click one. Check. Double-click another one. Check. Another one. Check. ... How's that possible?? One is missing. Now I went to the library docs. Do you know what I did? Doesn't make sense, but let's try. Better than nothing. Search for a method to input custom headers. Nope. I did google (a lot) previously, about errors, and whatnot, but either there was no answer, or it was a typo in the .env file. 'Aha!' Moment Maybe... It's the wrong naming of the constructor fields. So I check. Now I rely on IntelliSense and typing fields. Seems good. Keep on. Okay. Onto the last one. Wait, what? Wasn't it ? My literal reaction was: 😂 accessToken.. NO WAY! Are you kidding me?! GitHub Copilot: IntelliSense: was trained on the available codebase. So, you have to take care of bugs others have written in their code! GitHub Copilot’s AI publicly recommended I use the field for a constructor, but the problem was that there a field with such a name! GHC accessTokenSecret isn’t The right one was , recommended by engine. accessSecret IntelliSense There is Always an Opportunity to Learn So yeah… There are places that can teach you those things. You must experience them and unlock that kind of knowledge. 😄 zero Keep Going! These are my .02 cents. Don't let code completion AI tools rule your work. I'm sure there is a lot of funny stories like this. But we all make mistakes. Embrace them as learning opportunities. I don't blame GHC for this. I blame myself. But whatever. At least I got some experience. There is always space for improvement. Fun one for sure. Thank you for reading! _ktb First Published here

How GitHub Copilot Can F**k Up Your Codebase

FloC brings the model to the data instead of sending the data to the centralized model Overview Federated Learning is a relatively new and evolving machine learning technique that decentralizes the training of data from one central machine/ data center to multiple devices, including mobile phones. Federated Learning of Cohorts, or FloC for short, is a form of web tracking enabled through Federated Learning in which individuals are grouped into “cohorts” based on similar browsing behavior. Machine learning is a branch of Artificial Intelligence and computer science that leverages data and algorithms to make computers mimic human learning and decision making. Federated Learning takes advantage of edge computing principles, bringing computation and data storage closer to where it is needed. This principle allows for reduced response times, bandwidth conservation, and personalization, amongst other benefits. This article seeks to provide an easy-to-digest overview of Federated Learning of Cohorts for business professionals and others interested in the new technology. What is Federated Learning As with any machine learning task, data is necessary to train and retrain algorithmic models to improve on them, identify patterns, make decisions, and bring enhanced features to a system. But, as we know, data is everywhere and sometimes siloed in different locations, making it hard to access, especially within a business organization that may produce a multitude of data across various sources. Regarding data, other considerations are privacy issues, regulatory requirements, and lack of resources to bring segmented data together from a single source. The typical process of a machine learning project seeks to establish data pipelines to a central location to then be cleaned, pre-processed, and trained. Federated Learning aims to improve this process by bringing training models closer to where data is located. In other words, Federated learning brings training models to the source of data-generating entities. As Li et al. put it, Federated Learning: Explores training statistical models directly on remote devices. For example, mobile phones and other devices download the training model locally, contribute to the machine learning algorithm with local data, then generate a summary of changes made to the machine learning model as a minor “update.” This “update” is sent to the cloud, where other “updates” are amassed from various sources, ultimately resulting in an improved shared and distributed machine learning algorithm. It is important to note a secure and encrypted aspect of data sharing between the Federated Learning enabled device and the Cloud service performing the aggregation and updating the new consensus of the machine learning algorithm. The encrypted and private “updates” mentioned above only occur when it doesn’t negatively impact the user experience on target devices. Federated Learning is essential for numerous reasons; namely, it opens practitioners up to a new paradigm shift in how machine learning can be implemented. These new techniques pave the way for decentralized AI and other available machine learning models, enabling new tools to tackle scaled machine learning problem sets. More specifically, FloC enhances the machine learning process through “faster deployment and testing of smarter models, lower latency, and less power consumption, all while ensuring privacy (Bhattacharya, 2019)”. Federated Learning also enables the target device to immediately use and take advantage of the evolving machine learning algorithm that the device itself is helping to improve. For example: The improved model on your phone can also be used immediately, powering experiences personalized by the way you use your phone (McMahan & Ramage, 2017). Federated Learning of Cohorts Powered by Federated Learning Algorithms It is important to remember that Federated Learning of Cohorts is powered through Federated Learning (Machine Learning) models and aims to improve web advertising and phase out third-party cookies (metadata used to track users across different websites). FloC is a new protocol developed by Google as a part of its “Privacy Sandbox” initiative, a long-term feat aiming to eliminate 3rd party cookies, potentially uprooting a long-standing digital advertising ecosystem. More specifically, Google’s “privacy sandbox” seeks to transform digital advertising through: A secure environment for personalization that also protects user privacy (Schuh, 2019) Online privacy is the core topic of the criticism surrounding the design of FloC protocols and Federated Learning techniques. Currently, 3rd-party cookies offer little protection against privacy because developers and ad agencies can extrapolate critical identifiers using “fingerprinting” techniques. Schuh notes, With fingerprinting, developers have found ways to use tiny bits of information that vary between users, such as what device they have or what fonts they have installed to generate a unique identifier which can then be used to match a user across websites (Schuh, 2019). Although this model paves the way for exciting engineering and user experience use cases, it does come with its criticisms. Who should care about Federated Learning of Cohorts “The ability to share behavioral labels across the web in a manner that enables ‘learning’ of unique user behavior in an automated and algorithmically governed fashion has the potential to create a future where every interaction online can be leveraged to manipulate you.” – RichardMM Privacy Harming and Criticism of Federated Learning of Cohorts The Brave organization (Brave Browser) and the Electronic Frontier Foundation (EFF) are prominent advocates against Google’s FloC and “Privacy Sandbox” initiatives. Brave states that “FLoC shares information about your browsing behavior with sites and advertisers that otherwise wouldn’t have access to that information (Snyder & Eich, 2021).” Although your browsing information is anonymized through a grouped cohort of users with similar interests and browsing habits, FloC protocols do not prevent sites within your intra-browsing environment from accessing unique data about you. More simply put, your browsing data is shared with website B after visiting website A, which allows website C to learn about your behavior and unique interests that would not have been otherwise shared. Here is a brief excerpt from the Brave foundation: “For example, you may have an existing account with Walgreens, possibly to fill prescriptions. Walgreens necessarily knows who you are. FLoC tells Walgreens things that Walgreens has no business knowing about you (not a pseudonymous you or a cohort including you, but your Walgreens login identifies you) based on your browsing behavior. Chrome telling Walgreens (and Twitter, and GitHub, and Facebook, and any other site you have an account with) is unquestionably harming your privacy by telling sites information about you that they otherwise wouldn’t have, the information you didn’t decide to share with those sites, and information that is likely unrelated to why you chose to visit those sites where you do login (Snyder & Eich, 2021).” The Electronic Frontier Foundation weighs in on this topic by highlighting how FloC protocols will “avoid the privacy risks of third-party cookies, but it will create new ones in the process (Cyphers, 2021).” The ability to share behavioral labels across the web in a manner that enables “learning” of unique user behavior in an automated and algorithmically governed fashion has the potential to create a future where every interaction online can be leveraged to manipulate you. Federated Learning of Cohorts is a tool leveraged in a new digital frontier where emerging technologies can better user experience and offer great value. Although Federated Learning can bring about new capacities to our devices by enabling a new paradigm of machine learning and AI techniques, it also paves the way for designing tools that can be used maliciously. In this instance, the design of FloC protocols may not initially be architected with malignant intent, but do present futures where these technologies can produce unintended consequences. The EFF presents the following point of view on the potential of the future use of FloC protocols: In one, users get to decide what information to share with each site they choose to interact with. No one needs to worry that their past browsing will be held against them—or leveraged to manipulate them—when they next open a tab. In another, each user’s behavior follows them from site to site as a label, inscrutable at a glance but rich with meaning to those in the know. Their recent history, distilled into a few bits, is “democratized” and shared with dozens of nameless actors that take part in the service of each web page. Users begin every interaction with a confession: here’s what I’ve been up to this week, please treat me accordingly - (Cyphers, 2021). In either instance, it is imperative for technologists, designers, and large corporations to have diverse teams that enable many considerations as emerging technologies are being built. Federated Learning is an exciting technology and can provide good in this world, but there may be unintended consequences of its use (as highlighted through Federated Learning of Cohort techniques) as with any other technology. Bibliography Bhattacharya, D. S. (2019, February 2). The New Dawn of AI: Federated Learning. Medium. . https://towardsdatascience.com/the-new-dawn-of-ai-federated-learning-8ccd9ed7fc3a Cyphers, B. (2021, April 9). Google’s FLoC Is a Terrible Idea. Electronic Frontier Foundation. . https://www.eff.org/deeplinks/2021/03/googles-floc-terrible-idea Marboer. (2021, April 6). Federated Learning: Introduction to Federated Learning. Analytics Vidhya. . https://www.analyticsvidhya.com/blog/2021/04/federated-learning-for-beginners/ McMahan, B., & Ramage, D. (2017, April 6). Federated Learning: Collaborative Machine Learning without Centralized Training Data. Google AI Blog. . https://ai.googleblog.com/2017/04/federated-learning-collaborative.html Schuh, J. (2019, August 22). Building a more private web. Google. . https://www.blog.google/products/chrome/building-a-more-private-web/ Snyder, P., & Eich, B. (2021, April 20). Why Brave Disables FLoC. Brave Browser. . https://brave.com/why-brave-disables-floc/ Useful Links https://hackernoon.com/a-beginners-guide-to-federated-learning-b29e29ba65cf https://ai.googleblog.com/2017/04/federated-learning-collaborative.html https://hackernoon.com/federated-learning-a-decentralized-form-of-machine-learning-nr4635rg https://federated.withgoogle.com/ https://towardsdatascience.com/the-new-dawn-of-ai-federated-learning-8ccd9ed7fc3a https://www.ibm.com/blogs/research/2020/08/ibm-federated-learning-machine-learning-where-the-data-is/ https://towardsdatascience.com/the-new-dawn-of-ai-federated-learning-8ccd9ed7fc3a https://www.techradar.com/news/theres-more-to-googles-floc-than-meets-the-eye https://www.eff.org/deeplinks/2021/03/googles-floc-terrible-idea https://techcrunch.com/2021/03/24/google-isnt-testing-flocs-in-europe-yet/ https://techcrunch.com/2019/08/22/google-proposes-new-privacy-and-anti-fingerprinting-controls-for-the-web/ Also published on . https://richardmm.com/federated-learning-of-cohorts-floc-a-quick-brief/

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