Logroñés *0-0 GironaThe Copa del Rey is over for Girona. The team was held to a draw by Logroñés in 120 minutes of play at Las Gaunas, and ended up losing on penalties. Míchel Sánchez's team fou...

No success in the Cup - Yahoo Sports

A Journey to the Center of the Earth by Jules Verne, is part of the HackerNoon Books Series. You can jump to any chapter in this book . WE REACH MOUNT SNEFFELS—THE "REYKIR" here CHAPTER XI - WE REACH MOUNT SNEFFELS—THE "REYKIR" Stapi is a town consisting of thirty huts, built on a large plain of lava, exposed to the rays of the sun, reflected from the volcano. It stretches its humble tenements along the end of a little fjord, surrounded by a basaltic wall of the most singular character. Basalt is a brown rock of igneous origin. It assumes regular forms, which astonish by their singular appearance. Here we found Nature proceeding geometrically, and working quite after a human fashion, as if she had employed the plummet line, the compass and the rule. If elsewhere she produces grand artistic effects by piling up huge masses without order or connection—if elsewhere we see truncated cones, imperfect pyramids, with an odd succession of lines; here, as if wishing to give a lesson in regularity, and preceding the architects of the early ages, she has erected a severe order of architecture, which neither the splendors of Babylon nor the marvels of Greece ever surpassed. I had often heard of the Giant's Causeway in Ireland, and of Fingal's Cave in one of the Hebrides, but the grand spectacle of a real basaltic formation had never yet come before my eyes. This at Stapi gave us an idea of one in all its wonderful beauty and grace. The wall of the fjord, like nearly the whole of the peninsula, consisted of a series of vertical columns, in height about thirty feet. These upright pillars of stone, of the finest proportions, supported an archivault of horizontal columns which formed a kind of half-vaulted roof above the sea. At certain intervals, and below this natural basin, the eye was pleased and surprised by the sight of oval openings through which the outward waves came thundering in volleys of foam. Some banks of basalt, torn from their fastenings by the fury of the waves, lay scattered on the ground like the ruins of an ancient temple—ruins eternally young, over which the storms of ages swept without producing any perceptible effect! This was the last stage of our journey. Hans had brought us along with fidelity and intelligence, and I began to feel somewhat more comfortable when I reflected that he was to accompany us still farther on our way. When we halted before the house of the Rector, a small and incommodious cabin, neither handsome nor more comfortable than those of his neighbors, I saw a man in the act of shoeing a horse, a hammer in his hand, and a leathern apron tied round his waist. "Be happy," said the eider-down hunter, using his national salutation in his own language. "God dag—good day!" replied the former, in excellent Danish. "Kyrkoherde," cried Hans, turning round and introducing him to my uncle. "The Rector," repeated the worthy Professor; "it appears, my dear Harry, that this worthy man is the Rector, and is not above doing his own work." During the speaking of these words the guide intimated to the Kyrkoherde what was the true state of the case. The good man, ceasing from his occupation, gave a kind of halloo, upon which a tall woman, almost a giantess, came out of the hut. She was at least six feet high, which in that region is something considerable. My first impression was one of horror. I thought she had come to give us the Icelandic kiss. I had, however, nothing to fear, for she did not even show much inclination to receive us into her house. The room devoted to strangers appeared to me to be by far the worst in the presbytery; it was narrow, dirty and offensive. There was, however, no choice about the matter. The Rector had no notion of practicing the usual cordial and antique hospitality. Far from it. Before the day was over, I found we had to deal with a blacksmith, a fisherman, a hunter, a carpenter, anything but a clergyman. It must be said in his favor that we had caught him on a weekday; probably he appeared to greater advantage on the Sunday. These poor priests receive from the Danish Government a most ridiculously inadequate salary, and collect one quarter of the tithe of their parish—not more than sixty marks current, or about L3 10s. sterling. Hence the necessity of working to live. In truth, we soon found that our host did not count civility among the cardinal virtues. My uncle soon became aware of the kind of man he had to deal with. Instead of a worthy and learned scholar, he found a dull ill-mannered peasant. He therefore resolved to start on his great expedition as soon as possible. He did not care about fatigue, and resolved to spend a few days in the mountains. The preparations for our departure were made the very next day after our arrival at Stapi; Hans now hired three Icelanders to take the place of the horses—which could no longer carry our luggage. When, however, these worthy islanders had reached the bottom of the crater, they were to go back and leave us to ourselves. This point was settled before they would agree to start. On this occasion, my uncle partly confided in Hans, the eider-duck hunter, and gave him to understand that it was his intention to continue his exploration of the volcano to the last possible limits. Hans listened calmly, and then nodded his head. To go there, or elsewhere, to bury himself in the bowels of the earth, or to travel over its summits, was all the same to him! As for me, amused and occupied by the incidents of travel, I had begun to forget the inevitable future; but now I was once more destined to realize the actual state of affairs. What was to be done? Run away? But if I really had intended to leave Professor Hardwigg to his fate, it should have been at Hamburg and not at the foot of Sneffels. One idea, above all others, began to trouble me: a very terrible idea, and one calculated to shake the nerves of a man even less sensitive than myself. "Let us consider the matter," I said to myself; "we are going to ascend the Sneffels mountain. Well and good. We are about to pay a visit to the very bottom of the crater. Good, still. Others have done it and did not perish from that course. "That, however, is not the whole matter to be considered. If a road does really present itself by which to descend into the dark and subterraneous bowels of Mother Earth, if this thrice unhappy Saknussemm has really told the truth, we shall be most certainly lost in the midst of the labyrinth of subterraneous galleries of the volcano. Now, we have no evidence to prove that Sneffels is really extinct. What proof have we that an eruption is not shortly about to take place? Because the monster has slept soundly since 1219, does it follow that he is never to wake? "If he does wake what is to become of us?" These were questions worth thinking about, and upon them I reflected long and deeply. I could not lie down in search of sleep without dreaming of eruptions. The more I thought, the more I objected to be reduced to the state of dross and ashes. I could stand it no longer; so I determined at last to submit the whole case to my uncle, in the most adroit manner possible, and under the form of some totally irreconcilable hypothesis. I sought him. I laid before him my fears, and then drew back in order to let him get his passion over at his ease. "I have been thinking about the matter," he said, in the quietest tone in the world. What did he mean? Was he at last about to listen to the voice of reason? Did he think of suspending his projects? It was almost too much happiness to be true. I however made no remark. In fact, I was only too anxious not to interrupt him, and allowed him to reflect at his leisure. After some moments he spoke out. "I have been thinking about the matter," he resumed. "Ever since we have been at Stapi, my mind has been almost solely occupied with the grave question which has been submitted to me by yourself—for nothing would be unwiser and more inconsistent than to act with imprudence." "I heartily agree with you, my dear uncle," was my somewhat hopeful rejoinder. "It is now six hundred years since Sneffels has spoken, but though now reduced to a state of utter silence, he may speak again. New volcanic eruptions are always preceded by perfectly well-known phenomena. I have closely examined the inhabitants of this region; I have carefully studied the soil, and I beg to tell you emphatically, my dear Harry, there will be no eruption at present." As I listened to his positive affirmations, I was stupefied and could say nothing. "I see you doubt my word," said my uncle; "follow me." I obeyed mechanically. Leaving the presbytery, the Professor took a road through an opening in the basaltic rock, which led far away from the sea. We were soon in open country, if we could give such a name to a place all covered with volcanic deposits. The whole land seemed crushed under the weight of enormous stones—of trap, of basalt, of granite, of lava, and of all other volcanic substances. I could see many spouts of steam rising in the air. These white vapors, called in the Icelandic language "reykir," come from hot water fountains, and indicate by their violence the volcanic activity of the soil. Now the sight of these appeared to justify my apprehension. I was, therefore, all the more surprised and mortified when my uncle thus addressed me. "You see all this smoke, Harry, my boy?" "Yes, sir." "Well, as long as you see them thus, you have nothing to fear from the volcano." "How can that be?" "Be careful to remember this," continued the Professor. "At the approach of an eruption these spouts of vapor redouble their activity—to disappear altogether during the period of volcanic eruption; for the elastic fluids, no longer having the necessary tension, seek refuge in the interior of the crater, instead of escaping through the fissures of the earth. If, then, the steam remains in its normal or habitual state, if their energy does not increase, and if you add to this, the remark that the wind is not replaced by heavy atmospheric pressure and dead calm, you may be quite sure that there is no fear of any immediate eruption." "But—" "Enough, my boy. When science has sent forth her fiat—it is only to hear and obey." I came back to the house quite downcast and disappointed. My uncle had completely defeated me with his scientific arguments. Nevertheless, I had still one hope, and that was, when once we were at the bottom of the crater, that it would be impossible in default of a gallery or tunnel, to descend any deeper; and this, despite all the learned Saknussemms in the world. I passed the whole of the following night with a nightmare on my chest! and, after unheard-of miseries and tortures, found myself in the very depths of the earth, from which I was suddenly launched into planetary space, under the form of an eruptive rock! Next day, June 23d, Hans calmly awaited us outside the presbytery with his three companions loaded with provisions, tools, and instruments. Two iron-shod poles, two guns, and two large game bags, were reserved for my uncle and myself. Hans, who was a man who never forgot even the minutest precautions, had added to our baggage a large skin full of water, as an addition to our gourds. This assured us water for eight days. It was nine o'clock in the morning when we were quite ready. The rector and his huge wife or servant, I never knew which, stood at the door to see us off. They appeared to be about to inflict on us the usual final kiss of the Icelanders. To our supreme astonishment their adieu took the shape of a formidable bill, in which they even counted the use of the pastoral house, really and truly the most abominable and dirty place I ever was in. The worthy couple cheated and robbed us like a Swiss innkeeper, and made us feel, by the sum we had to pay, the splendors of their hospitality. My uncle, however, paid without bargaining. A man who had made up his mind to undertake a voyage into the Interior of the Earth, is not the man to haggle over a few miserable rix-dollars. This important matter settled, Hans gave the signal for departure, and some few moments later we had left Stapi. 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. Jules Verne (2006). A Journey to the Center of the Earth. Urbana, Illinois: Project Gutenberg. Retrieved October 2022 https://www.gutenberg.org/files/18857/18857-h/18857-h.htm This eBook is for the use of anyone anywhere at no cost and with almost no restrictions whatsoever. You may copy it, give it away or re-use it under the terms of the Project Gutenberg License included with this eBook or online at , located at . www.gutenberg.org https://www.gutenberg.org/policy/license.html

WE REACH MOUNT SNEFFELS—THE "REYKIR"

Snowdrop & Other Tales by Jacob and Wilhelm Grimm, is part of the HackerNoon Books Series. You can jump to any chapter in this book here . The Marriage of Mrs. Reynard The Marriage of Mrs. Reynard THERE was once an old Fox who thought that his wife was not true to him, and determined to put her to the test. He stretched himself under the bank, lay motionless, and pretended to be as dead as a door nail. Mrs. Reynard went to her chamber, and shut herself in; and her servant, Mistress Cat, sat by the fire, and cooked the dinner. Now, when it became known that the old Fox was dead, suitors began to announce themselves. Soon afterwards, the servant heard some one knocking at the front door. She went and opened the door, and there stood a young Fox, who said— ‘What are ye doing, pray, Mistress Cat? Sleeping or waking? or what are ye at?’ She answered— ‘I’m not asleep; I’m wide awake. D’ye want to know what now I make? I’m warming beer, with butter in it; I beg ye’ll taste it in a minute.’ ‘I’m much obliged, Mistress,’ said the Fox. ‘What is Mrs. Reynard doing?’ The Maid answered— ‘In chamber sad she sits alone, And ceases not with grief to moan. She weeps until her eyes are red, Because the dear old Fox is dead.’ ‘Well, just tell her, Mistress, that there’s a young Fox here, who would be glad to woo her.’ ‘Very well, young gentleman.’ ‘Then went the Cat with pit-a-pat And smote the door, rat-tata-tat! “Pray, Mrs. Reynard, are you in? Outside a wooer waits below!”’ ‘Well, what’s he like? I want to know. Has he got nine such beautiful tails as the late lamented Mr. Reynard?’ ‘Oh dear no,’ answered the Cat. ‘He has only got one.’ ‘Then I won’t have him.’ Mistress Cat went down, and sent the wooer away. Soon after this there was knocking again, and another Fox appeared at the door, who wished to pay his addresses to Mrs. Reynard. He had two tails, but he came off no better than the first. Afterwards others came, each with one tail more; but they were all rejected, till at last one came that had nine tails like old Mr. Reynard. When the widow heard this, full of joy, she said to the Cat— ‘Open the gates and doors; be swift. Old Mr. Reynard turn adrift.’ But when the wedding was about to be celebrated, then old Mr. Reynard under the bank roused himself, and gave the whole crew a good drubbing, and sent them, Mrs. Reynard and all, helter-skelter out of the house. Tale Second When old Mr. Reynard really died, the Wolf came as a suitor, and knocked at the door, and the Cat who acted as servant to Mrs. Reynard, opened it. The Wolf greeted her, and said— ‘Good-day, Miss Cat, of sprightly wit, How comes it that alone you sit? What are you making there, so good?’ The Cat answered— ‘Tumbling milk and butter up. Will your Lordship have a sup?’ ‘Thank you kindly, Mistress Cat. Mistress Reynard is not at home, I suppose.’ ‘Upstairs in her chamber she sits, And weeps as her sorrow befits. Her sad case she doth much deplore, Because Mr. Reynard’s no more.’ The Wolf answered— ‘“If now she wants to wed again, She must come down the stairs, ’tis plain. ”The Cat ran up without delay Nor did her claws their clatter stay Until she reached the long saloon. There, tapping with her five gold rings, “Is Mrs. Reynard in?” she sings. “If now she wants to wed again, She must come down the stairs, ’tis plain.”’ Mrs. Reynard asked: ‘Does the gentleman wear red breeches, and has he a pointed muzzle?’ ‘No,’ answered the Cat. ‘Then he is no use to me.’ When the Wolf was rejected, there came a Dog, a Stag, a Hare, a Bear, and one after another every sort of wild animal. But in every one there was wanting some of the good qualities which old Mr. Reynard had possessed, and the Cat was obliged to dismiss the suitors every time. At last there came a young Fox. Then Mrs. Reynard asked: ‘Does the gentleman wear red breeches, and has he got a pointed muzzle?’ ‘Yes,’ said the Cat. ‘He has both.’ ‘Then let him come up,’ said Mrs. Reynard, and ordered the maid to make ready the wedding feast. ‘Now, Cat, set to and sweep the room. Then fling the old Fox from the house; Bring in many a good fat mouse, But eat them all yourself alone, Nor give your mistress e’er a one.’ Then the wedding with young Mr. Fox was held, and there was merry-making and dancing, and if they haven’t stopped, they are dancing still. Does the gentleman wear red breeches, and has he a pointed muzzle? 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. Jacob Grimm and Wilhelm Grimm (2011). Snowdrop & Other Tales. Urbana, Illinois: Project Gutenberg. Retrieved October 2022, https://www.gutenberg.org/cache/epub/37381/pg37381-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 Marriage of Mrs. Reynard

I returned with a newfound conviction that I was exactly where I needed to be, and simply needed to define the values I am passionate to pursue. I grew up in the Soviet Union. While I am now removed from the days of government surveilling my every word and action, I've found that culture of surveillance replicated everywhere online. You have quite the resume- from a contributor of Hackernoon to the Helm of one of the most promising startups, Stegos, Can you explain to us your journey? Gladly. I've been developing software for 25 years. Early on, I recognized that I have just the right mix of discipline, structured thinking, and creativity to excel as a coder. Initially, I slummed it through the ranks of various Wall Street banks. From the bottom, it was just a few short years before I became the Director of Prime Brokerage Technology at Deutsche Bank in 1999. Unfortunately, a merger unfavorably shifted my position- pay and title unchanged, but essentially a demotion in terms of responsibilities and duties. I left and spent the next 17 years working remotely for various US based startups. I loved the high-risk, high-reward nature of contracting, but I hated the lack of control over opportunities. The work went dry every several years, and in Spring of 2017, I found myself particularly disconnected. Several friends were working in blockchain and recommended that I started an ICO. However, I was unfamiliar with the tech! I didn't own Bitcoin at this point, but I was intrigued. I went all-in immersing myself in the tech and marketing myself for developer jobs. After a number of (dubious) groups asked me to come write white papers, I found a match with Aeternity (AE). They heard that I was an expert in the programming language Erlang, and in July 2017, I moved to Switzerland, where the project was based. Soon after, I was promoted to CTO and recruited a team of Erlang developers to aid me in building out Aeternity as the best blockchain out there. However, the CEO maintained the point of view that it was very much "his project", and that I was irreplaceable. I viewed my responsibilities as much grander than the CEO, which drove me to inevitably resign from my position. I knew that cryptocurrency was exactly where I needed to be, and I started my own project. Initially, I received $2 million from Aeternity investors. My startup was first introduced as Emotiq, and the project was marketed as yet another in an endless genre of "Ethereum killers". However, the markets turned sour, and a lack of direction or guiding values led to infighting from the team and a general lack of progress. In 2018, I shifted my endeavors in a different direction and rebranded as Stegos, with a concrete purpose of an unstoppable and super-private blockchain network. From there, I was able to build up a team of individuals similarly passionate about privacy and well-equipped to bring the project to fruition! Now, the Stegos mainnet is live, and encompasses the core privacy and scalability features necessary to deliver our ecosystem to the world. Other key developments like mobile staking nodes are right around the corner, which will make the various Stegos features easy-to-use for a global audience. The future is looking bright! What drove you to enter the privacy niche? Was there a particular "aha" or light bulb moment that particularly impacted your interest? With a lack of direction, my work with Emotiq felt purposeless. Nothing was being created and nobody was happy with the state of affairs. I was at a loss, and took a meditation retreat in the Carpathian Mountains. No phone, no internet, no connection to the outside world. Even pen and paper was forbidden. I returned with a newfound conviction that I was exactly where I needed to be, and simply needed to define the values I am passionate to pursue. I grew up in the Soviet Union. While I am now removed from the days of government surveilling my every word and action, I've found that culture of surveillance replicated everywhere online. Alas, I positioned myself at the helm of Stegos, and have been working to build out a network that can aid and protect citizens worldwide from the ever-encroaching surveillance that permeates every aspect of our emerging digital society. Per the Stegos Telegram, it looks like you recently indicated that initial investment funds are running dry. How did that happen? Should STG holders fear a plug pull? From start-to-finish, I had raised $15 million at the height of the market in 2017. Like many others, I was in over my head and foolishly kept the funds in BTC and, even worse, ETH. Taking depreciation into account, those funds ended up closer to $3 million. That money was used to fund project development from conception to now. That money allowed us to bring Stegos to fruition. This is not another fork of Bitcoin (or Bytecoin, in the case of many alternative privacy coins), Stegos is a ground-up custom network that incorporates the best components of preceding privacy networks and packages them in a manner that is massively scalable, widely adaptable, and easy-to-use. Of course, many mistakes were made along the way (a lot of money was spent conceiving an inconclusive "Ethereum killer" before the emphasis on privacy was developed). But tens of thousands of man hours were required to bring Stegos to mainnet, build a community around it, and develop additional features to aid adoption and utilization. This is where the funds were spent. There is no situation where I pull the plug on Stegos, at least not for the foreseeable features. When funds are depleted, I will continue to develop and support the project out of my own pocket- I have enough savings to continue for another year or two, at least. I've always been the lead developer of the project, and that's not going to change. In the past months, we've eliminated as much of the operating costs as possible to sustain further development as long as possible. This includes laying off much of the development team. If the markets don't turn around, I am prepared and well equipped to go on as the sole in-house developer. In the meantime, the mobile app, which will encompass full network perms including staking and on-chain messaging, is still due end of Q1. Beyond that, I'm going to give the community more control as to the further objectives- whether that be additional features like decentralized OTC markets or external services like additional exchange listings. Privacy Coins are not a new concept, why do you believe the world needs one more privacy coin? How does Stegos differentiate itself from other cryptocurrencies? Using the current batch of privacy coins properly takes a lot of work. You need to wrestle with difficult interface, and it's entirely up to you to properly secure your network connections, communication channels, and so on. For users that know how to do this, it's no sweat, but how many people really fall into that category? The vast majority simply don't have the ability to properly use and access these features, so we desperately need an alternative with a more approachable learning curve. This is where Stegos comes into play: a user friendly privacy network capable of worldwide adoption. Stegos is the privacy coin built for the other 99% of the population. (Which benefits the other 1% too, who are now much less likely to be singled out for special surveillance!) In 2019, privacy coins went under fire across the board, especially through the trend of mass delisting from major exchanges. Is the future of privacy coins in danger? There is a real demand for privacy coins, and with that demand comes a real market. Even if all major exchanges decide to blacklist all privacy coins, smaller alternatives will certainly pick up the business. But in reality, I think some leading exchanges that have been cautious towards privacy will work with and around legislation to better service their users with the markets they desire. Outside of Stegos, which blockchain projects do you find particularly interesting? Stegos and crypto can't directly address the massive privacy problems caused by huge corporations and social media platforms, but in a wider sense blockchain is a key tool for education people about freedom and privacy and allowing individuals to regain ownership of their data. Projects that are working to protect and empower user data always pique my interest. Brave Browser and BAT is one such example. If you could give one piece of advice to the other players and proponents of the privacy niche, what would it be? Make privacy that works. In other words, make sure people can actually use your tech. That's the whole point, at the end of the day. A lot of privacy coins, especially newer ones, are finding new and creative ways to ensure privacy, which is great, but not when it's at the cost of usability. The general population needs privacy, and to do so, they need accessible privacy networks. And remember that there's more to privacy than just transactions. It's all very well to obscure my address, or the address of the person I'm sending to, and the amount we're exchanging, but how do we arrange the trade in the first place? If all that information has to be shared via insecure channels, then everything you're doing to obscure that data on chain is irrelevant. And, sure, at some point it's my responsibility as a user to be careful, but a little help would be appreciated! I guess that's two pieces of advice — but they're inextricably linked. So far, is Stegos listed in any exchange? What are the future plans? Stegos has a lot of innovative privacy features which makes it harder to integrate with exchanges. But we recently started trading on Bithumb Global. We've got a lot of great developments in the barrel. Most importantly, our mobile staking nodes are anticipated for public release by the end of March. Our users will be able to manage their wallets, stake tokens, send fully private messages, and interact with network applications all through an easy-to-use Stegos mobile application. Think WeChat, but decentralized and private.

Leader's Speak : Joel Reymont, Lead Developer @ Stegos [Interview]

Are you interested in being a PM?! Learn Mika’s story with Flora. Mika won the competition among many senior PMs and got her jobs at as a when she only had one year of experience. She shared with me how she did it. LinkedIn Product Manager How she talk about herself. How and where she practice her interviews Creative ways to get a hold of hiring managers How to tell your story in a compelling way and in a consistent manner.

How Mika Reyes got her job at LinkedIn as a Product Manager? Part 1

Industry Venture’s Roland Reynolds discusses managing $3.5B to successfully invest across the entire venture ecosystem If you are an emerging manager, it’s worth spending 30mins listening to this podcast from an institutional LP. Roland Reynolds started one of the early fund of funds investing in emerging managers in 2007. He then joined forces with Industry Ventures in 2009 to start their fund of funds initiative. Industry ventures as a firm started in 2000 now manages $3.5B in assets under management. They invest across the entire stack of the venture capital ecosystem in secondaries, hybrids, directs and fund of funds. Roland dives deep into their investment criteria, how they decide to work with their investments, and where the market is today compared to 1999. Here are reasons to listen: If you are a 2nd or 3rd time fund manager, Roland covers what an LP like Industry Ventures looks for in their returning emerging managers Similarities and differences in markets from 1999 vs 2018 Valuations are various stages Future of Solo GPs (General Partners) _Download past episodes or subscribe to future episodes of [Array] Podcast by Shruti Gandhi for free._itunes.apple.com [Array] Podcast by Shruti Gandhi on Apple Podcasts If you want to be on the newsletter or our next podcast email us at hi@array.vc and follow me on twitter @atShruti Podcast is hosted by where we invest at the intersection of business function in a large industry that is being disrupted with deeptech. Array Ventures

Industry Venture’s Roland Reynolds discusses managing $3.5B

Ever tried extracting a polygonal mesh from a 3D discrete scalar field? No? Well, back in 1987, two programmers at General Electric did — and they ended up inventing the Marching Cubes algorithm. It’s the reason doctors can turn medical scans into 3D models that literally saved millions of lives. That’s the power of an algorithm. Whenever you instruct a machine to solve a problem with code, you’re designing a procedure to transform bits into meaning. Some algorithms are fast. Some are elegant. And some are so strange, they feel like sorcery. Whenever you instruct a machine to solve a problem with code, you’re designing a procedure to transform bits into meaning In this story, we’re diving into ten of the weirdest, most brilliant algorithms ever devised — ones that help search billions of lines of code in milliseconds, generate infinite maps from nothing, and turn quantum weirdness into practical logic. #1–Wave Function Collapse One of the weirdest things in science is the double-slit experiment: particles behave like waves when you’re not looking, but the moment you observe them, they snap into place like particles. It’s bizarre — and surprisingly useful. This idea of a “wave function collapsing” sounds abstract, but it’s been turned into a surprisingly practical algorithm. Imagine you’re designing a side-scrolling video game map. You want the world to feel handcrafted but also go on forever. You can’t draw an infinite map by hand, so instead you generate it procedurally. In the beginning, each tile of the map is like a “wave” — it’s not yet anything specific. It’s full of possibilities. But as the player moves forward — observing the world — the algorithm “collapses” that uncertainty. The world solidifies into a specific tile, but it does so according to the rules you’ve set. Roads connect, rivers flow where they should, and everything feels like it was intentionally designed. It’s randomness with a purpose, all without a single piece of generative AI. It’s just weird, beautiful logic. #2–The Diffusion Model AI is weird. Really weird. Take diffusion models — the tech behind image generators like DALL·E and Stable Diffusion. They’re based on an idea from thermodynamics: diffusion, where particles naturally spread from areas of higher concentration to areas of lower concentration. In machine learning, that process gets flipped. Instead of going from order to chaos, the algorithm starts with pure noise — like a picture of a cat — and learns how to gradually refined it into a meaningful image. You begin with randomness (high entropy) and end with a crisp, coherent output (low entropy). But you need a model to do this well. The diffusion algorithm works in two phases. First, during training, the model takes real images and gradually adds noise in a forward process until they become unrecognizable. Then, it learns how to reverse that process — reconstructing it back into a coherent image. Do this across millions of labeled images, and you get a model that can dream up new images from scratch. Cats in space. Ancient Rome in Pixar style. Hyperrealistic avocado chairs. You name it. It’s compute-heavy. But it doesn’t stop at images. Diffusion is already reshaping how we generate music, audio, and next up — video. #3–Simulated Annealing One of the most frustrating parts of programming is that there’s rarely one right solution — just a sea of okay ones, and a few great ones. Kind of like organizing an Amazon warehouse: dozens of ways to do it, but only a few that actually make sense at scale. Simulated annealing borrows its name from metallurgy, where metals are heated and cooled repeatedly to remove defects. The same concept applies to optimization. You’re trying to find the best solution in a chaotic landscape full of local peaks and valleys. Imagine you’re searching for the highest peak in a mountain range. A basic hill-climbing algorithm will get you stuck on the first bump that looks promising. But simulated annealing is smarter. It starts with a high “temperature,” which means it explores freely — sometimes even accepting worse paths to escape local traps. As the temperature gradually drops, it gets pickier, settling into only the best moves. The trade-off is exploration vs. exploitation. And it’s not just useful in code — it’s a good metaphor for learning to code, too. Early on, you bounce between languages, tools, and frameworks. But eventually, you cool down, lock in, and specialize. #4–Sleep Sort You can’t talk about algorithms without bringing up sorting — and perhaps the most absurdly clever (and completely useless) ever devised is Sleep Sort. Sleep Sort Traditional sorting algorithms, like quicksort or mergesort, use divide-and-conquer strategies to recursively break arrays into subarrays and sort them efficiently. But somewhere on 4chan, a mad genius proposed a completely different approach. quicksort mergesort Here’s how it works: for each number in the array, start a new thread. Each thread sleeps for a number of milliseconds equal to its value, then prints the number when it wakes up. Because smaller numbers sleep for less time, they get printed earlier — resulting in a sorted output. The clever part? It skips all the usual logic and turns the CPU’s thread scheduler into the sorting engine. No comparisons. No recursion. Just sleep and print. But it’s wildly impractical. It breaks with negative numbers, duplicates, or large values. It’s also inefficient, creating one thread per number. And it depends on sleep timing, which isn’t very accurate . Sleep Sort is both hilarious and useless — a perfect example of how clever doesn’t always mean useful. # 5–BOGO Sort BogoSort is a joke algorithm: it randomly shuffles an array over and over until, by pure luck, the result is sorted. It’s wildly inefficient — like trying to guess your way to the right answer by chance. Now imagine combining this with the idea of quantum mechanics and the multiverse. In theory, if all possible outcomes exist across infinite parallel universes, then there’s some universe where your array is already sorted. some The technology isn’t quite there yet, but a“Quantum BogoSort” would work by creating all those possibilities at once, and then instantly collapsing into the universe where the array is sorted — essentially letting quantum randomness do the work for you. Of course, this is sci-fi, not computer science. We can’t observe or collapse the multiverse at will. But it’s a playful thought experiment about brute-force randomness taken to its logical (and absurd) extreme. # 6 — BOID And here’s my favorite one. What I find really cool about algorithms is how they can sometimes mirror nature. Take Craig Reynolds’ Boids program from 1986 — it was one of the first artificial life simulations and it mimics how birds flock. But what’s mind-blowing is how little code it takes to get something that feels so alive. feels Each bird (or “boid”) follows just three rules: avoid crashing into neighbors (Separation), align with their direction (Alignment), and move toward the local group’s center (Cohesion). That’s it. No one’s in charge. But when you simulate a bunch of these simple behaviors together, you get mesmerizing, organic patterns that shift and flow like a real flock. The beauty here isn’t in the code — it’s in what emerges from it. These complex group dynamics don’t have to be explicitly programmed. They just happen. It’s like stumbling on nature’s cheat code for decentralized coordination. emerges # 7 — SHOR’s Algorithm The idea of allowing people to lock their mailboxes and sign their letters with a unique signature is based on an important concept in cryptography: the difficulty of factoring large numbers. The security behind most encryption methods relies on this simple mathematical reality — multiplying large numbers together is easy, but figuring out the two original prime numbers behind the product — a problem known as prime factorization — it’s extremely difficult and time consuming. — prime factorization — In fact, it’s so difficult that it could take your laptop hundreds of trillions of years to brute-force the solution — unless, of course, we start using quantum computers. Quantum computers have the potential to solve the integer factorization problem exponentially faster than any classical algorithm, thanks to algorithms like Shor’s Algorithm. Shor’s Algorithm can break down large numbers into their prime factors much quicker than conventional methods. Prime factoring is pretty simple, but how this algorithm works is where things get truly weird. At the heart of Shor’s Algorithm are quantum mechanics concepts like qubits, superposition, and entanglement. These allow quantum computers to perform massive calculations in parallel, something classical computers can’t even come close to doing. While the algorithm itself is legitimate, its real-world application is still in its early stages. In fact, the largest number ever factored using quantum computing is just 21. IBM’s state-of-the-art quantum system, Q System One, fails to factor a number as simple as 35. That said, quantum breakthroughs are happening. Recently, a Chinese team managed to factor a huge number using a quantum computer, but they used an algorithm that doesn’t scale well for much larger numbers, which means it’s not yet practical for general use. However, if quantum computers get powerful enough and someone figures out how to scale these algorithms for truly large numbers, expect serious disruption in the world of cybersecurity. Once quantum computing can reliably break encryption, all of our current security protocols could be at risk. # 8 — Marching Cubes At the beginning of this story, we mentioned the Marching Cubes algorithm — but it’s worth diving into, because it was a major turning point for visualizing 3D data, especially in medicine. Imagine you have a 3D scan of the human body, like from an MRI. The MRI doesn’t gives you a 3D model, but a giant cube of numbers — a 3D scalar field. Each point in that field holds a value that represents something like tissue density or signal intensity. This data is continuous in space, but we need to turn it into something visual — a surface, a shape, something that a computer can draw. This is where Marching Cubes comes in. The algorithm takes this 3D field and marches through it one small cube at a time. Each cube is formed by eight neighboring data points in the field. Now here’s the clever bit: each of those eight points is either above or below a threshold (say, where skin turns into bone). So each corner gets assigned a 0 or a 1, depending on whether it’s inside or outside the surface you’re trying to extract. That gives you an 8-bit number — 256 possible combinations for each little cube. For each of those combinations, a specific pattern of triangles has been precomputed and stored in a lookup table. These triangles are used to approximate the surface passing through that cube. So instead of calculating complicated geometry every time, the algorithm just looks it up. By repeating this process — marching through cube after cube, plugging in values, looking up shapes — the algorithm gradually builds up a full 3D mesh. What you get is a smooth surface that represents the boundary between different materials in the scan, like bone and tissue. This was groundbreaking in the 1980s because it turned flat MRI or CT scan slices into actual 3D models that doctors could rotate, zoom in on, and analyze. And even today, Marching Cubes is still used in many fields — from medical imaging and geology to procedural generation in games. # 9 — Practical Byzantine Fault Tolerance In modern computing, we’re often working with distributed systems — networks of computers spread across the cloud. That brings us to one of the most famous problems in distributed computing: the Byzantine Generals Problem. Picture this: several generals from the Byzantine army are surrounding a city. They need to coordinate and attack at the same time to win. But they can only communicate by sending messages, and some of those generals might be traitors trying to sabotage the plan. Maybe one decides to sleep in, or worse — lies about when to attack. If even one general fails or acts maliciously, the whole strategy could fall apart. Computers face the same challenge. In a distributed system, some machines might crash, be slow, or even get hacked. How can the rest of the network agree on what to do when they can’t trust everyone? That’s where consensus algorithms like PBFT — Practical Byzantine Fault Tolerance — come in. PBFT is designed to handle these kinds of failures. It works by having one node propose an action by broadcasting a “pre-prepare” message. The other nodes respond with acknowledgments. Once a certain number of nodes (usually two-thirds or more) agree, they reach consensus. Finally, the original node sends a “commit” message, telling everyone to execute the action. Even if up to one-third of the nodes are faulty or malicious, the system can still function correctly. Algorithms like PBFT are the backbone of blockchain systems and distributed cloud databases, helping them stay consistent and trustworthy — even when things go wrong. # 10— Boyer Moore And finally, that brings us to an old-school algorithm that quietly powers some of the fastest tools we use today — like grep. It’s called the Boyer-Moore string search, and it recently blew my mind because of how counterintuitive it is: the longer the text, the faster it gets. faster Here’s how it works. Most basic search algorithms go left to right, checking every character one by one. But Boyer-Moore flips that around — it scans from right to left within the search pattern, and it uses two clever tricks to skip big chunks of text. Imagine you’re looking for the word “needle” in a huge block of text. right to left “needle 1. Bad character rule: 1. Bad character rule: If you’re searching for “needle” and the current text character is “z” — then there’s no way a match could start at or before that point. So instead of checking character by character, the algorithm jumps ahead by 6 positions — the full length of “needle” — and keeps going. 2. Good suffix rule: 2. Good suffix rule: If you’re searching for “needle” and you just matched “dle” at the end — but the next character breaks the match — the algorithm doesn’t start over from the next letter. Instead, it asks: does “dle” show up earlier in “needle”? If yes, it shifts the pattern so that earlier “dle” lines up. If not, it skips the whole “dle” part entirely. Either way, it jumps forward smartly instead of wasting time rechecking. These heuristics skip strategies aren’t perfect, but they’re way more efficient than brute force methods. way The result? As the text gets longer, the algorithm tends to skip more and more characters, making it faster relative to the size of the input. That’s why tools like grep can chew through gigabytes of logs with surprising speed — and why this decades-old algorithm still feels like black magic today. When Logic Meets Imagination: The True Power of Algorithms Whether it’s turning quantum uncertainty into practical image generation, mimicking biological flocking behaviors with three simple rules, or searching text backwards to find patterns faster, these approaches shows that the most unconventional ideas often lead to the most powerful solutions. What makes these algorithms brilliant isn’t their efficiency or novelty — it’s their audacity. They challenge assumptions. They flip problems on their heads. They turn randomness into structure, chaos into order, and abstract theory into real-world impact. More than elegant mathematical tools — these 10 weird algorithms are a testament to human ingenuity. are a testament to human ingenuity Want to hear from me more often? 👉 Connect with me on LinkedIn! Connect with me on LinkedIn! Connect with me on LinkedIn I share daily actionable insights, tips, and updates to help you avoid costly mistakes and stay ahead in the AI world. Follow me here: daily Are you a tech professional looking to grow your audience through writing? 👉 Don’t miss my newsletter! Don’t miss my newsletter! Don’t miss my newsletter My Tech Audience Accelerator is packed with actionable copywriting and audience building strategies that have helped hundreds of professionals stand out and accelerate their growth. Subscribe now to stay in the loop. Tech Audience Accelerator

The 10 Weirdest, Most Brilliant Algorithms Ever Devised and What They Actually Do

10 أسوأ أجهزة الكمبيوتر وأكثرها إثارة للجدل وأكثرها إثارة للجدل

Los 10 algoritmos más extraños y brillantes jamás inventados y lo que realmente hacen

発明された10の最も奇妙で明るいアルゴリズムと彼らが実際にやっていること

10 ყველაზე ცუდი, ყველაზე brillant algorithms ever devoted and what they actually do

10 keisčiausių, nuostabiausių kada nors sukurtų algoritmų ir ką jie iš tikrųjų daro

१७ नाति, आठ नातिना, आठ पनाति र एक पनातिनीका अभिभावक हुन् उनी ।

De 10 vreemdste, meest briljante algoritmen die ooit zijn bedacht en wat ze eigenlijk doen

10 самых странных, блестящих алгоритмов, когда-либо придуманных и что они на самом деле делают

De 10 konstigaste, mest briljanta algoritmerna som någonsin utformats och vad de faktiskt gör

10 thuật toán kỳ lạ, rực rỡ nhất từng được phát minh và chúng thực sự làm gì

10个最奇怪,最明亮的算法,他们实际上在做什么

I-10 I-algorithm enhle kakhulu, enhle kakhulu eyenziwe nangokufinyelelwa, futhi okufanayo

Authors:
(1) Michele Farisco, Centre for Research Ethics and Bioethics, Department of Public Health and Caring Sciences, Uppsala University, Uppsala, Sweden and Biogem, Biology and Molecular Genetics Institute, Ariano Irpino (AV), Italy;
(2) Kathinka Evers, Centre for Research Ethics and Bioethics, Department of Public Health and Caring Sciences, Uppsala University, Uppsala, Sweden;
(3) Jean-Pierre Changeux, Neuroscience Department, Institut Pasteur and Collège de France Paris, France. Authors: Authors: (1) Michele Farisco, Centre for Research Ethics and Bioethics, Department of Public Health and Caring Sciences, Uppsala University, Uppsala, Sweden and Biogem, Biology and Molecular Genetics Institute, Ariano Irpino (AV), Italy; (2) Kathinka Evers, Centre for Research Ethics and Bioethics, Department of Public Health and Caring Sciences, Uppsala University, Uppsala, Sweden; (3) Jean-Pierre Changeux, Neuroscience Department, Institut Pasteur and Collège de France Paris, France. Table of Links Abstract and Introduction Abstract and Introduction Extents and ways in which AI has been inspired by understanding of the brain
1.1 Computational models
1.2 Artificial Neural Networks


Embodiment of conscious processing: hierarchy and parallelism of nested levels of organization


Evolution: from brain architecture to culture
3.1 Genetic basis and epigenetic development of the brain
3.2 AI and evolution: consequences for artificial consciousness


Spontaneous activity and creativity


Conscious vs non-conscious processing in the brain, or res cogitans vs res extensa


AI consciousness and social interaction challenge rational thinking and language Extents and ways in which AI has been inspired by understanding of the brain
1.1 Computational models
1.2 Artificial Neural Networks Extents and ways in which AI has been inspired by understanding of the brain 1.1 Computational models 1.1 Computational models 1.2 Artificial Neural Networks 1.2 Artificial Neural Networks Embodiment of conscious processing: hierarchy and parallelism of nested levels of organization Embodiment of conscious processing: hierarchy and parallelism of nested levels of organization Embodiment of conscious processing: hierarchy and parallelism of nested levels of organization Evolution: from brain architecture to culture
3.1 Genetic basis and epigenetic development of the brain
3.2 AI and evolution: consequences for artificial consciousness Evolution: from brain architecture to culture 3.1 Genetic basis and epigenetic development of the brain 3.1 Genetic basis and epigenetic development of the brain 3.2 AI and evolution: consequences for artificial consciousness 3.2 AI and evolution: consequences for artificial consciousness Spontaneous activity and creativity Spontaneous activity and creativity Spontaneous activity and creativity Conscious vs non-conscious processing in the brain, or res cogitans vs res extensa Conscious vs non-conscious processing in the brain, or res cogitans vs res extensa Conscious vs non-conscious processing in the brain, or res cogitans vs res extensa AI consciousness and social interaction challenge rational thinking and language AI consciousness and social interaction challenge rational thinking and language AI consciousness and social interaction challenge rational thinking and language Conclusion, Acknowledgments, and References Conclusion, Acknowledgments, and References Abstract We here analyse the question of developing artificial consciousness from an evolutionary perspective, taking the evolution of the human brain and its relation with consciousness as a reference model or as a benchmark. This kind of analysis reveals several structural and functional features of the human brain that appear to be key for reaching human-like complex conscious experience and that current research on Artificial Intelligence (AI) should take into account in its attempt to develop systems capable of human-like conscious processing. We argue that, even if AI is limited in its ability to emulate human consciousness for both intrinsic (i.e., structural and architectural) and extrinsic (i.e., related to the current stage of scientific and technological knowledge) reasons, taking inspiration from those characteristics of the brain that make human-like conscious processing possible and/or modulate it, is a potentially promising strategy towards developing conscious AI. Also, it cannot be theoretically excluded that AI research can develop partial or potentially alternative forms of consciousness that are qualitatively different from the human form, and that may be either more or less sophisticated depending on the perspectives. Therefore, we recommend neuroscience-inspired caution in talking about artificial consciousness: since the use of the same word “consciousness” for humans and AI becomes ambiguous and potentially misleading, we propose to clearly specify which level and/or type of consciousness AI research aims to develop, as well as what would be common versus differ in AI conscious processing compared to human conscious experience. Introduction Since Helmholtz, DuBois-Reymond and even Freud pledged the solemn oath (1842) that “no other forces than the common physical chemical ones are active within the organism”, there is wide scientific agreement that the brain is a “physico-chemical system” and that “consciousness” is one of its most sophisticated features, even if there is no consensus on explaining how, specifically, this is the case. Therefore, it can be argued, it is theoretically possible that sooner or later one should be able to artificially emulate the brain’s functions, including consciousness, through physico-chemical methods. Yet the situation is analogous to the case of “life in the test tube” with the simplest living organisms: all their molecular components are known but up to now nobody has been able to reconstitute a living organism from its dissociated components. The issue is not only theoretical but also, importantly, practical. The prospect of developing artificial forms of consciousness is increasingly gaining traction as a concrete possibility both in the minds of lay people and of researchers in the field of neuroscience, robotics, AI, neuromorphic computing, philosophy, and their intersection (Blum & Blum, 2023; Butlin et al., 2023; LeDoux et al., 2023; Oliveira, 2022; VanRullen & Kanai, 2021). The challenge of artificial conscious processing raises also social and ethical concerns (Farisco, 2024; Farisco et al., 2023; Hildt, 2023; Metzinger, 2021). Therefore, it is very timely to critically evaluate the feasibility of developing artificial conscious processing from a multidisciplinary perspective, as well as analyzing what that concept might mean. Relevant attempts in this direction have recently been proposed (Aru, Larkum, & Shine, 2023; Godfrey-Smith, 2023; Seth, 2024). Current discussions about the theoretical conceivability and the technical feasibility of developing artificial conscious processing hinges, to begin with, upon a semantic ambiguity and polysemy of the word “consciousness”, including the distinction between phenomenology (i.e., a subjective first-person experience) and underlying physiology (i.e., a third-person access to consciousness)(Evers & Sigman, 2013; Farisco, Laureys, & Evers, 2015; Levine, 1983), and the fundamental distinction between conscious and non-conscious representations (Piccinini, 2022). Also, conscious processing may have different meanings depending on the context of analysis and it has different dimensions, which may possibly exhibit different levels resulting in different profiles of conscious processing (Bayne, Hohwy, & Owen, 2016; Dung & Newen, 2023; Irwin, 2024; Walter, 2021). At the origins, consciousness comes from the Latin conscientia, cum scire: knowledge in common, oscillating between confidence and connivance, up to the classic "faculty that man has of apprehending his own reality" (Malebranche 1676) or for the neuropsychiatrist Henri Ey "the knowledge of the object by the subject and reciprocally, the reference of the object to the subject itself”. Accordingly, the individual is both the subject of his knowledge and the author of it. Lamarck, in 1809, speaks of a singular faculty with which certain animals and even humans are gifted, which he calls “sentiment interieur”, approximately inner feeling. More recently Ned Block introduced the distinction between access and phenomenal consciousness. Access consciousness refers to the interaction between different mental states, particularly the availability of one state’s content for use in reasoning and rationally guiding capabilities like speech and action; phenomenal consciousness is the subjective feeling of a particular experience, “what it is like to be” in a particular state (Block, 1995). Accordingly, cognition and subjective experience are two central components of conscious processing, which basically may be defined as “sensory awareness of the body, the self, and the world” (Lagercrantz & Changeux, 2009), including “inner, qualitative, subjective states and processes of sentience or awareness” (Searle, 2000). Among the embodied components of conscious processing we may consider, in addition, at the individual level, the ability to express emotions, memory, symbols, language, capacity for autobiographical report and mental time travel, as well as the capacity to introspect and report about one’s mental state, and at the social level, sustained inter-individual interactions which give access to various kinds of social relationships such as empathy and sympathy (Lagercrantz & Changeux, 2009). Among the many theories and computer science models currently proposed, none of them, in our assessment, reach the overall species-specific aspects of the human higher brain functions (van Rooij et al., 2023). The question arises: can these models reach those aspects with time, when further developed, or is the gap irremediable? In parallel, more and more citizens are confronted with AI simulations of human behaviour, including conscious processing, and feel concerned about it (Lenharo, 2024): the prospect of artificial conscious systems raises the risk of impacting human self-understanding, for instance if AI were to replace humans in performing tasks that require a capacity for awareness. It thus appears necessary to challenge AI models with actual representations of human brain organization and human cognition and behaviour. Therefore, the question is whether or not any theoretical computer science representation of human conscious processing can lead to human-like artificial conscious systems: could machines ever develop a human-like consciousness, or rather a different kind of consciousness, or is it impossible for them to develop consciousness at all? Does the notion of artificial consciousness even make sense, and if so, how? To paraphrase Voltaire: Can a machine awaken?[1] In the past decades, a large number of models were elaborated mainly by neuroscientists with a more humble aim: to reconstruct elementary functions of the nervous system (e.g., swimming in the leech (Stent et al., 1978) or the lamprey (Grillner et al., 1995)) from known anatomical and physiological building blocks. Some of these models have even been designed to simulate more elaborated cognitive tasks like the Wisconsin Card sorting task (Dehaene & Changeux, 2011) and even trace vs. delay conditioning (Grover et al., 2022). It is necessary to further develop the interface between AI, philosophy and neuroscience, which thus far has resulted in a mutual epistemic and methodological enrichment (Alexandre et al., 2020; Farisco et al., 2023; Floreano, Ijspeert, & Schaal, 2014; Floreano & Mattiussi, 2008; Hassabis, Kumaran, Summerfield, & Botvinick, 2017; Momennejad, 2023; Poo, 2018; Zador et al., 2023). In fact, although significant, this collaboration is still insufficient to address the issue of artificial consciousness. The crucial, still open question is: what kind of concrete similarities vs. differences between AI and the brain may need to be examined and accounted for to more adequately approach artificial conscious processing? In other words, what is the right ‘level of description’ to either model, or even generate artificial conscious processing given what we know about conscious processing in the human brain? Moreover, in the neuroscience field, the word “consciousness” remains rather ill-defined and, as we shall see below, human conscious processing is not an all-or-none irreducible feature but one that develops stepwise (Changeux, 2006, 2017; Lagercrantz & Changeux, 2009; Tomasello, 2022; Verschure, 2016). Given these different possible developmental stages, AI attempts to develop artificial conscious processing should precisely specify which one (if any) of these developmental stages is selected. In this paper we want to re-evaluate the issue of artificial consciousness within the context of our present knowledge of the biological brain, taking a pragmatic approach to the conceivability and feasibility of developing artificial consciousness and using the human brain as a reference model or benchmark. We aim to complement recent attempts in this direction (Aru et al., 2023; Godfrey-Smith, 2023) with a more encompassing analysis of the biological multilevel complexity of the human brain in relation to its evolution, not only in order to progress in the understanding of conscious processing itself but also to eventually inspire ongoing AI research aimed at developing artificial conscious processing. Accordingly, our aim is theoretical and philosophical but also highly practical as an engineering issue: we review scientific evidence about some features of the brain that are key in enabling human consciousness or in modulating it (or both), and we argue for the utility of taking inspiration from these features for advancing towards the development of conscious AI systems. We do not claim that it is necessary to integrate the mechanisms identified for conscious processing in the human brain to develop artificial consciousness. In fact, we recognize that artificial features of conscious processing that are different from the brain ones cannot theoretically be excluded offhand. What we propose is rather to take the presently identified brain mechanisms of conscious processing as a benchmark in order to pragmatically advance in the building up of artificial models able to simulate accessible features of conscious processing in humans. Given the high controversy around the possibility to build up an artificial consciousness unrelated to brain mechanisms and the related risk of ending up in overly abstract views that are not sufficiently informed by empirical data, we think that starting from the biology of consciousness is a more productive strategy. A question we may nevertheless ask is what are the benefits to pursue artificial consciousness in the first place, for science, or society at large? There are different possible answers. On an epistemological level, consistently with the medieval scholastic view reiterated by i.a. Paul Valéry that “we can actually understand only what we can build”, it is clear that to elaborate artificial models of some concrete features of conscious processing could perhaps eventually allow us to better understand biological consciousness in general, whether in terms of similarities or differences. At a technical level, it is possible that the development of artificial consciousness would be a game-changer in AI, for instance giving AI the capacity for intentionality and theory of mind, and for anticipating the consequences of its own “actions”. At the societal and ethical level, especially the last points could arguably help AI to better inform humans about potential negative impacts on society, and to help avoid them while favouring positive impacts. Of course, on the negative side, intentionality in machines might not at all favour human interests any more than human intentionality has favoured out-group individuals or species, or indeed the planet as a whole. This is indeed a discussion that would merit deeper analyses, but it is beyond the aim of the present paper. In the following, we will summarize relevant evolutionary, structural, and functional properties of the human brain that are of specific relevance to this discussion (for a recent overview, see (Barron, Halina, & Klein, 2023). Against that background, we will outline what the brain may inspire to current research on AI for advancing towards artificial conscious systems. Finally, concerning the conceivability and feasibility of developing artificial consciousness, we will distinguish between: (a) the replicability of human consciousness (which we exclude, at least in the present state of AI-development, a stance which is scarcely controversial); (b) the possibility of developing an artificial conscious processing that may bear some resemblances but still is profoundly different than human (which we do not exclude in principle, but consider difficult to elaborate for both conceptual and empirical reasons). In the end, this paper starts from a selective examination of data from brain sciences with the aim to propose an approach to AI consciousness alternative to what appears to be the leading one today. This approach may be qualified as theory-based because it relies not upon experimental data but on selected components of a priori scientific theories which are then applied to AI systems (Butlin et al., 2023). Our approach, on the opposite, consists in starting from empirically established brain mechanisms and processes which are directly relevant to human consciousness and infer from them hardware building blocks or algorithms that are relevant and perhaps even necessary (if not sufficient) to the development of artificial conscious processing. This paper is available on arxiv under CC BY 4.0 DEED license. This paper is available on arxiv under CC BY 4.0 DEED license. available on arxiv available on arxiv [1] The philosophers of the Enlightenment already wondered: what in the brain’s architecture might explain why and how it became conscious? What made matter awaken? Cf. e.g., a letter from Voltaire to d’Alembert, November 28, 1762.

Can AI Dream? Rethinking Consciousness Through the Lens of Evolution

Можаце спяваць? перадумаць сведчанне праз лінзу эвалюцыі

Može li AI sanjati? promišljanje svesti kroz objektiv evolucije

¿Puede AI soñar? repensando la conciencia a través de la lente de la evolución

«Ինչպե՞ս կարող եք վերցնել երազանքը» (Rethinking Consciousness Through the Lens of Evolution)

Può AI sognare? ripensare la coscienza attraverso la lente dell'evoluzione

AIは夢を見ることができますか? 進化のレンズを通じて意識を再考する

Vai AI var sapņot? pārdomāt apziņu caur evolūcijas lēcu

Azonao atao ve ny mandinika ny fahatsiarovana amin'ny alalan'ny lens ny evolusion?

AI pode sonhar? repensando a consciência através da lente da evolução

கனவு காண முடியுமா?  The Lens of Evolution

Rüya Görmek Mümkün mü? Evrim Lensinden Bilinç Yeniden Düşünmek

AI có thể mơ? Suy nghĩ lại ý thức thông qua ống kính tiến hóa

The vibrant world of tech startups has found a space carved out for growth and innovation in the gaming sector. Despite being dominated by tech giants from Asia and the United States, the gaming industry still has great potential in Mexico, the 10th largest market in terms of revenue globally and the biggest market in Latin America. There are 67 startups related to the video game industry (gametech) in the country, while the category received $70,000 in venture capital investment from 2022 to 2023. To put this in perspective, during 2024, $5 billion was invested worldwide in startups in the industry, according to the newly released study “Game On: The Rise of Gaming in Mexico,” from global entrepreneurial development organization Endeavor and Santander Bank. Game On: The Rise of Gaming in Mexico In 2021, De Kabeza and Wasabi Videojuegos raised $275,000 and $150,000, respectively, demonstrating the viability of innovative projects in the gaming industry. Although in Mexico, 80% of startup investment is concentrated in fintech, e-commerce, and logistics, the video game industry could begin to gain ground in the future, noted Vincent Speranza, CEO of Endeavor, during a presentation for the study that I attended in Mexico City on April 2. However, the numbers show that currently, for gaming startups, the challenge is raising capital. Fifty-six percent of entrepreneurs in this industry fund themselves with their own resources. Business opportunities for startups in gaming Business opportunities for startups in gaming Mexico has a base of 76 million video game players across the country – from experienced eSports participants who compete internationally, all the way to hobbyists who play games on their smartphones during their morning commutes on public transit. “Mexico is emerging as a regional leader in video game development and consumption. While many gamers still play on mobile devices, and the market is not yet as competitive as that of the United States or Asia, the growing popularity of eSports in Mexico and Latin America is creating a large market of video game consumers,” said Chris Mut, CEO of Nerdytec. “For a company like Nerdytec, which manufactures ergonomic lap desks for gamers, Mexico is undoubtedly an attractive market.” Opportunities for startups in the gaming industry lie primarily in integrated advertising, where revenues are estimated at $960 million by 2025. Due to the engagement generated by video game platforms, especially on mobile devices, advertising on these platforms is often up to five times more effective within the gaming category, noted Laura Cruz Urquiza, deputy general manager of strategy, innovation, and customer experience at Santander, during the presentation of the study. The next area where there’s an opportunity is eSports, with revenues from the segment reaching $387 million this year. Examples include Monou, a platform for hosting tournaments and earning money, or Draftea, which “is a company close to the industry. It started as a fantasy football platform for Latin America and is now more involved in the gaming space,” Speranza explained. Draftea has secured investment from renowned funds such as Sequoia and Kaszek. Faced with these opportunities, the challenges are greater because not enough venture capitalists have invested in the gametech industry. Vicent Speranza acknowledges that “although funds are designed to take risks, actually being the first fund to write a big check, a big bet (in the gametech industry), always makes you feel a bit shaky, because naturally they opt for slightly safer bets.” In Mexico, there is only one fund specialized in this industry: Altered Ventures, created in 2016 by Mario Valle Reyes to support independent studios. So far, it has invested not only in the country but also in Silicon Valley and Abu Dhabi. Yanin Alfaro, Journalist, The Sociable Yanin Alfaro, Journalist, The Sociable Yanin Alfaro, Journalist, The Sociable Yanin Alfaro The Sociable

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