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Africa’s Super App 🏍️🚕🛺🛒🛍️💸💳 │Global : @GozemAfrica
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Sobre a guerra na Ucrânia, Maxim Yusin sublinhou que "é muito triste que as pessoas gozem e se riam". "É duplamente tristemente quando nós é que lhes estamos a dar uma razão para nos ...

"Quem diz que vamos chegar até Lisboa é ridículo". A opinião de um russo

* * Achievement unlocked: With more than ‘4 billion’ in total votes -- almost half the population of earth voted for startups! JUST KIDDING! Some companies had a to accumulating their votes. unique approach In 2021, decided to host to highlight the backbone of the future - tech startups! for the first time in forever (make sure you sing it in Anna’s voice), HackerNoon.com Startups of The Year More than 44,000 startups were nominated all across the globe, spanning over 4,553 cities. That’s.Just.Spiffy! To see who is a winner in each respective city, visit and pick a city. The name at the top of the list is the winner. (Compromised awards are under review.) startups.hackernoon.com The will announce the winners per region in their respective cities. Startups Winners Announcement Series We’d like to take a moment here and thank every single person who cast a vote and every single company, both nominated and not nominated yet this year. Furthermore, we thank you for existing and opening more opportunities for the wonders of the future to come. Even if you don’t win anything today, you’ve won something much more valuable - our hearts <3 Okay, sorry for the long introduction. Below are all the Startups Winners: Startups of the Year (SOTY) in Africa Johannesburg, South Africa With more than 900 votes cast, #SOTY calls.... MiBRAND Thank you for connecting businesses and consumers, great job! Lagos, Nigeria With 836 votes in the bag, let’s give it up for... Goodtalent Thanks for helping tech companies build great engineering teams. Cape Town, South Africa Want to learn more about finance and use that knowledge to make better financial decisions? Look no further. Introducing winner of Startups of The Year, Cape Town: Revix With 527 votes cast, it seems like a lot of people have more money in their pockets thanks to . Revix Mahé, Seychelles With around 515 votes cast, #SOTY summons... DAOLaunch Thank you for bridging the gap between investors and startups. Lusaka And the winner for #SOTY in Lusaka is... ComGrow Thanks to them, people here are getting closer to the reality of the digitization of village banking. Nairobi, Kenya With 252 votes cast, UTU came out top as the Startup of The Year. Thanks for building the trust infrastructure of the internet to help businesses and consumers engage and transact in a more reliable way. Yaoundé Accounting for more than 73% of total votes, CamPay won this award fair and square. Thank you for making sending and receiving payments easier between online merchants. Abuja, Nigeria A proud winner in the prestigious capital of Nigeria, Elesaro deserves all the plaudits it gets. is a suite crowdfunding protocol built on BSC, Reward, Equity, Lending & NFT Marketplace. Congratulations! Elesaro Dar Es Salaam, Tanzania Jamii Africa is the name that will always be engraved on the #SOTY trophy. As a mobile micro-health insurance, Jamii Africa has done a great job for the health sector in Tanzania. Kigali, Rwanda Another #SOTY medal in the bag for none other than …wait for it… BAG Innovation BAG Innovation applies Gamification to offer real-time access to experience-based learning for University students in East Africa. Everybody loves playing games, but using games to learn in school is on another level. Gauteng, South Africa And the award goes to... Planet42 Thanks for democratising mobility by enabling people ignored by banks to get access to a personal vehicle through a car subscription service. Once again, congratulations! Port Harcourt, Nigeria Cryptocurrency is the present and the future, as we’ve all come to know. FLITBASE makes these digital assets more accessible to everyone. #SOTY is proud to add you to our Hall of Fame. Cairo, Egypt With 18 votes won out of the 91 cast, it seems there was fierce competition. Congratulations to... SubsBase for winning Startups of The Year. SubsBase allows businesses to manage their subscribers, automate recurring billings, set pricing plans, and monitor subscription KPIs. Tunis, Tunisia Come one, come all! Witness the greatness of yet another wonderful startup in the #SOTY. Thank you... VYND for providing a fully integrated digital platform to help venue owners better serve their customers and grow their businesses. Stellenbosch, South Africa BankTech connects clients to a payment network where it provides a convenient interface to meet various payments needs. You’ve won #SOTY proudly! Pretoria #SOTY acknowledges... Grays Pharmaceuticals Thank you for all you do. Lekki, Nigeria Middletrust provides reliable escrow services in Africa. Congratulations on winning a #SOTY medal. Addis Ababa Thank you... Hello Solar for consistently accelerating sustainable energy solutions, especially for remote communities. With half of the votes cast, you beat the odds. Accra, Ghana UT Bank Ghana provides various commercial banking services to individuals and companies in Ghana. We cannot emphasize enough the importance of financial services to humankind. #SOTY is proud of all you do. Gîza, Egypt Big congratulations to... HOVO for winning this award. Your on-demand mobile app for ordering trucks is truly appreciated! Goma, Democratic Republic of Congo Congratulations for coming out on top of #SOTY, SillonBlue Technologies As an ICT Consulting and SaaS company, SillonBlue Technologies focuses on software and mobile app development, and data collection. Yaba, Nigeria With more than half of the votes cast, this nominee conquered undisputedly. Let’s give it up for... Kuda a no-fee full-service digital bank making banking affordable for all Africans on the planet. Morocco City Almes provides infrastructure and logistics services. Congratulations on earning a #SOTY medal. Dakar ShopMeAway provides families and businesses with new ways to shop around the world with reduced shipping costs. You deserve the plaudits you’ve received! Casablanca Sheaply provides crowd shipping and crowd shopping solutions. This reduces cost and improves efficiency on shipping and shopping. Congratulations on winning a #SOTY medal. Nguru, Nigeria Accounting for 88% of the votes, TradeBuza wins #SOTY almost flawlessly. TradeBuza is a tech-enabled commodities sourcing platform for commodities aggregators and out-grower schemes. Ikeja, Nigeria PropertyPro Nigeria is now pronounced Startup of the Year in Ikeja. Let’s admit, who doesn’t appreciate a platform built to search for real estate properties? Ibadan, Nigeria No Bank, No Problem! (That’s probably not their motto, by the way) Shield Finders is a fintech and financial service provider to the unbanked. Congratulation on winning most of the votes! 6 October City, Egypt Wuilt is a website builder where you can create your own Arabic website in under 5 minutes. Thank you for making this technology accessible to people. One #SOTY medal for you! Algiers The only nominee who outvoted all others, please give it up for... Yassir Thank you for providing convenient transport services using your app. Enugu In an area that usually suffers from inconsistent electricity, Greenage comes to the rescue with their green cape and spandex suit. Greenage Technologies is a Nigerian startup solving electricity problems in Nigeria using solar energy technology. Congrats, Greenage. Abeokuta, Nigeria A government organization creating opportunities for entrepreneurs and the unemployed - no one deserves the title Startup of The Year better than Ogun Innovate Congratulations! Ariana And the award goes to... E-Stores Factory a web platform that helps rent and manage e-shops for enterprises seeking to sell online. Thank you for making the (not a typo) more friendly! e-cosystem Benin City Health really is wealth. MOBicure provides mobile technology solutions for Africa's biggest health problems. Thank you for all you do. From #SOTY with love <3 Centurion GameZBoost offers white label gaming platforms, including online desktop solutions, fully responsive mobile and social platforms. Thank you for making the world a more fun place! Gaborone Augmenta Cyber Security provides services in cyber security, safe data storage, SCADA security, SAP Security, etc. Congratulations on winning 10/10 of the votes. You rock! Garki There can never be enough medical platforms in the world. Congratulations to... Wella Health an operator of a healthcare technology company intended to provide treatments and medicines for malaria. Thank you for all you do! Lilongwe Yellow is a digital platform enabling informal merchants in Africa. #SOTY welcomes you to its hall of fame. Make yourself at home ;) Luanda Access to finance is important for any company or sector to succeed. And no other company deserves the title Startup of The Year more than Deya the first Angolan and PALOP crowdfunding platform that aims to increase access to financing for social impact causes and entrepreneurs. . 10/10 will recommend Mombasa Life can be hard, and so a little humor helps ease these hardships. Congratulations to this political satire puppet show, The XYZ Show for winning #SOTY. Thanks for putting smiles on our faces. Port Louis Town Bet you’ve never heard of a Smartcup. Thanks to Intellicup that exists now, and therefore earns this startup a #SOTY badge.  Intellicup is a unique high-speed beverage dispensing solution with integrated smart technology. While this cup won’t be able to wake you up in the morning, you have to agree that it’s still pretty impressive. Rabat Atlan Space is a startup building AI to autonomously pilot any fixed-wing drone, to monitor and take measures over very large areas. You deserve the #SOTY medal. Réunion Imagine having the power to do ultrasound scans on your handheld device. OSCULT by Oscadi is a handheld ultrasound imaging device interfaced with the Apple iPad. Congratulations on making it into the #SOTY! Somerset West Winning the award fair and square is Sticitt a cashless payment platform that is also aimed at teaching learners at school the basics of finance. The future of the world would be in good hands when our children know how to adequately manage finances. Thank you for saving the world in advance. Thika Flowers make the world a beautiful place to live in. Wilmar Flowers exports summer flowers grown by small and medium-sized farmers from Kenya. Kudos for making the world beautiful, #SOTY is honored to have you in our Hall of Fame. Kampala, Uganda Yo-Waste Limited is an African startup that provides waste, recycling and “smart city” solutions to businesses and governments. Your #SOTY gold medal shines as much as you do <3 Harare, Zimbabwe The world is becoming more and more digital every day. Thanks to companies like Golix that provide services of digital currencies for people in Africa, the digitization of the world is becoming more robust. Freetown Easy Solar is a distribution company making energy, durable goods and financial services affordable and accessible through pay-as-you-go tech. Thumbs up! Kicukiro FOODLINE specializes in non-genetically-modified grocery farming and production lines. Winner Winner Chicken Dinner! #SOTY Goma, Democratic Republic of Congo Startups Nuru is a renewable and environmental company that specializes in the fields of storage micro-grids and solar energy. After racking up all the votes, #SOTY honors you. Abidjan, Côte d'Ivoire Digitech provides a fully integrated and cloud-based InsurTech platform to incumbent insurance companies. You are awesome! Durban Security can never really be enough. We’d like to honor Blue Security the title Startup of The Year, for specializing in the installation and monitoring of intruder detection systems, and in providing armed response services. Ikoyi And the Startup of The year goes to... Winich Farms an Agritech Startup leveraging tech to connect rural farmers to Finance & Market, thus, increasing productivity and cutting post-harvest loss. You rock! Lomé The last recipient of #SOTY in Africa calls for... Gozem a transport technology platform for Francophone West and Central Africa. Thank you for providing a platform for transportation. We are honored to have you! *Winners missing from this list will be added accordingly. More Information If you’re a winner, please answer ! If you’re a reader, participant, just a curious individual, consider answering one of our interviews . this interview here Read the FAQs about or . Startups here here To see all of the winners you may visit , pick any city and view the name that appears at the top. this site If you’re interested in partnering with us, we invite you to look through some of these or reach out to our business department. unique sponsorship options Perhaps creating your very own tickles your fancy or maybe you have a whole new idea for how we can partner! writing contest Thank you to all of our sponsors! We truly appreciate your contribution to the tech world and thus the future! The Startups Awards are sponsored by: , , , , , , , , , , Blockster Brex FreeCodeCamp LaunchDarkly Coil Udacity Udemy Vercel Asia Pacific Transgender Network Bybit Everscale Network This post was written and edited by Kien Dao and Joshua Omale. If you have any questions or concerns or would like to report a discrepancy please email ellen@hackernoon.com.

What are the Top Startups in Africa?

So, it finally happened... Google crashed. . It might have been only for about an hour, but all over the world, people and gadgets using Google services were hit with an unexplained outage. Many people even spent some time in the Dark Ages, since they couldn’t switch on their lights with Google Home. While the downtime was luckily not enough for something serious to happen, we need to understand that disruptions on centralized services can happen at any given time and anti-fragile systems are needed. Schadenfreude aside, the builders of decentralized infrastructure need to reflect upon the way we approach the construction of the platforms, protect and onboard users, and above all - if what we’re building can scale to billions of devices and people across the interconnected globalized world. Web3, or the decentralized web, are not new concepts. As a matter of fact, the once underground movement has been growing since before the publishing of the Bitcoin Whitepaper in 2008, Tor being created in 2002. “Computer technology is on the verge of providing the ability for individuals and groups to communicate and interact with each other in a totally anonymous manner. (...) The technology for this revolution--and it surely will be both a social and economic revolution--has existed in theory for the past decade. The methods are based upon public-key encryption, zero-knowledge interactive proof systems, and various software protocols for interaction, authentication, and verification.” - Timothy C. May, The Crypto Anarchist Manifesto, 1992 2010 and 2013 (the year of the Wikileaks scandal, and one of the Snowden revelations, respectively) saw the rise of the whistleblowers bring to light the abuses of citizen surveillance. In 2018, Mark Zuckerberg was brought to the stall by the many cases of data abuse Facebook was involved in, tied to the Cambridge Analytica scandal. I can go on for ages citing hacks, leaks, and other events. However, I believe that it is most relevant to note that in 2020 when our lives have passed almost fully to the digital realm, we are experiencing the paradigm shift whistleblowers have been long advocating for. We not only need to be in full control of our data and our digital identities and lives but also - our systems must be anti-fragile, monopolies and centralized providers need to allow space for alternative peer-to-peer systems. In the case of an outage, such as what we saw yesterday, the centralization of the web poses a huge risk to users around the world. , back in September, employees at Universal Health Services (UHS), an owner of a nationwide network of hospitals, reported widespread outages that resulted in delayed lab results, fallback to pen and paper, and patients being diverted to other hospitals. For example The culprit , which locked up hospital systems for days. Increasing cases of ransomware are just one way bad actors are able to capitalize on centralized data systems, in some cases causing days or weeks of disruption and further demonstrating the importance of the decentralized web. Ryuk ransomware, was ultimately found responsible for this outage Golem Network’s Role in Decentralization We can’t expect Google, Amazon, or any centralized conglomerate to acknowledge Web 3 as alternatives; builders need to take advantage of the disruptions, failure, and abuses of the oligopolies and jump on the stage. We cannot and must not depend on monolithic platforms and databases that own our identity and our right to live our digital lives. The web 3/ Dweb vision is a work in progress - both in building and adoption. From the building side, most networks either need to scale or are too complex for users to understand easily. UX is not great and on-ramping to crypto remains one core issue for adoption as well. But we don’t have much time to waste. The web3 stack needs to evolve faster than ever because tomorrow, the downtime might be longer and more damaging. Golem will play a key role in the web3 stack. Applications that help us connect with others, improve our lives, help advance science and beyond cannot rely on a centralized cloud. They need to be . And this is where we can help. unstoppable Deplatforming is a real problem, censorship is rampant, and developers and application users need a safe haven from arbitrary shutdowns. Moreover, the creation of alternative economies, fueled by cryptocurrencies and onboarding masses thanks to, stable coins must be protected. Your money does not belong to you if it is stored in a bank. This is especially true for most people. Governments and banks can shut down your accounts for any given reason. . I cannot stress enough the need to own your finances and be in full control of the keys to your savings. From one day to the next, as it happened to me, you can be pushed out of the system The alternative financial system, combined with anti-fragile platforms like Golem, create a fairer and more democratized society. Access to censorship-resistant computing power, a reliable, private, and unstoppable internet, matters as much as peer-to-peer transaction frameworks and currencies, as it all forms part of a stack - the decentralized web, which is here to allow us to restore the vision of the original creators of the internet and lead us to a better tomorrow. Download and test New Golem here

What’s Golem’s role In The Antifragile Web 3.0?

We are kicking off the first post of the Crypto Countdown series by exploring a pretty cool project called . Golem Golem is a global, open source, decentralised supercomputer that anyone can access. This all sounds very interesting, but let’s break down some of the key terms. What is a super computer? A is a computer that performs at extremely high speeds relative to all other computers. Back in the 1990s if you had introduced a 2018 Macbook Pro, it would have been seen as an immensely powerful supercomputer. Traditionally, supercomputers are used to process very large amounts of data or huge amounts of calculations — such as scientific or engineering applications. supercomputer What is open source? Open source denotes software of which the original code is made freely available and may be shared and altered. It’s like writing an essay and publishing it for anyone to use, comment on or change. Decentralised or distributed A common theme in the space is the idea of decentralising resources, ideas and even people. This means spreading assets across the world, making them readily available to anyone and out of the control of one party. Centralised vs decentralised is rather like studying exclusively at university (centralised learning) vs studying at university, online, through your friends and from books (decentralised learning). blockchain What is the goal of Golem? We are all familiar with the teeth-grinding annoyance of working with a slow computer or phone. Freezing when you open too many tabs, or restarting just after you’ve perfected your Instagram editing. Imagine this at a large scale, when a computer crashes after processing huge calculations for 2 hours or after editing huge amounts of HD video clips. The simple solution would be to buy a bigger, better or faster device — or borrow extra power from the likes of Amazon, Google or Microsoft. However, Golem has an alternative solution: Golem wants to give people access to an extremely powerful computer from anywhere in the world. This supercomputer is made up of lots of resources without needing one-specific physical location. How does it work? Most people are not using their devices at all times — when you sleep, take a shower or go out and about, you are typically not asking your computer to do anything demanding. In those moments, your computer is a resource of unused power. Golem wants to make the most of your idle computer and contribute all of its unused power to create a global supercomputer. An interesting but, as of yet, unanswered question: Is it worth your while giving your computing power to the Golem network? In the same way that each time you drive a car, it loses value, we don’t yet know the impact that always keeping your computer ‘on and busy’ will have on its lifespan. Yes, you may be getting paid, but if this means your computer, designed to last 5 years, is growling its way into an early grave after 18 months, it may not be a worthy tradeoff. However, sweeping that question aside, Golem allows people, anywhere in the world, to benefit from the power of your computer (and many others) when they most need it. Rather like being set an extremely complex task that your own brain cannot cope with, you would do much better to combine the brain power of people around the world. As innovation advances, we will need to solve more and more tasks without being restricted by the limitations of our own, individual, resources. Golem is designed to be fast. Using their network, you don’t have to worry about the complication of finding that extra brain power for your computer. The network finds the best computer(s) to help you with your task and pays them for the computer power that they contribute. Depending on the complexity of what you are trying to do, Golem may draw this extra power from one or two computers, or hundreds from all across the world. What are the key features? easy access to a supercomputer that is made from collecting spare power from computers all around the world. A global supercomputer: Golem creates a marketplace where you can provide computer power to, or borrow computer power from, strangers. Golem ensures that you do not need to know, nor trust, the person borrowing or providing computing power. A sharing economy: Golem charges users who are borrowing power and rewards individuals that provide computing power. Everyone in the Golem network pays, and is paid, with Golem’s own cryptocurrency. Payment system: How will we interact with Golem in the future? Currently, companies and individuals purchase from existing providers, such as Amazon, Google, Microsoft that run thousands of dedicated computers (or servers) as a source of computing power. In the , you can imagine relying less on these “server farms” and using computing power from random people around the world. future As an individual you will be able to both contribute and borrow computing power and become part of the Golem network. Thus, you can either pay for computing power, or you can be paid for providing it. It’s a pretty cool way of getting compensated when you aren’t using your laptop, whilst simultaneously helping a stranger somewhere in the world! It’s odd to think that as you go to bed one evening, a student half-way across the globe could be using some of your computing power and analysing DNA data, a broker performing complex modelling calculations or an animator creating some awesome graphics for your favourite TV series. : there are some minimum computing power requirements in order to contribute to the network, but hopefully this post gives a flavour of how Golem aims to democratise access to supercomputing power! Note 1 : contributing power is guaranteed to be secure thanks to Golem’s “isolated environment”- but feel free to research this if you want to know more. Note 2 & LinkedIn Twitter

(1/100) Crypto Countdown: Golem

Golem (GNT): A Deep Dive into The AirBnB of Computers

Golem’s crowdfunding process starts on November 11th. Here’s how I’m participating. Part 1 describes what Golem is and why I’m excited about it. I’m feeling excited to take part in Golem’s crowdfunding process. I t starts this Friday, November 11, 2016, at approximately 3PM GMT. I’m a novice in the cryptocurrency world. These processes take some focused attention for me to figure out. Writing and maintaining this post helps me focus on the task at hand. My goal is to be prepared to participate in Golem’s crowdfunding process. I hope you find it helpful too. Disclaimer! This post is not intended to convince you to participate in the Golem crowdfunding process. It’s simply to provide information, e.g. if you’re interested in Golem and/or have already decided to participate in Golem’s crowdfunding process. It is NOT a recommendation or offer of investment advice IN ANY WAY! I also have no formal link to or with the Golem Project. The post is split into two parts - Part 1 talks about what Golem is and why I’m excited about it. This post is Part 1. explains the step-by-step process I’m using to participate in the Golem crowdfunding process. Part 2 Read Part 2 here. Part 1 — What’s Golem and Why am I Excited About It? If you don’t know what is, here’s — Golem The Grand Vision, from the Golem About page GOLEM IS THE NEW WAY THE INTERNET WILL WORK Golem’s objective is to use (almost) any computing resource to execute tasks that today need to rely on centralized solutions. Golem network is going to be a decentralized ecosystem, with combined power of users’ machines and dedicated software delivering all necessary resources to complete any computing task. Golem is going create the first decentralized global market for computing power combined with flexible tools for developers to distribute and monetize their software. From the Golem About Page Here’s the current Golem Whitepaper too. You can also read the official announcement of this version of the whitepaper here. Most new cryptocurrency projects are issuing whitepapers these days. I haven’t gotten around to reading this one yet. Why am I Excited About Golem? I’m a to tech companies and their investors. I’ve done a lot of work in the telecom and data center space. The work goes back 20 years now. tech, OPS and M&A consultant It’s been exciting to watch the abstraction of compute power over this time. Abstraction enabled sharing of compute power to take place. Sharing makes decentralization of it possible. Decentralized computing power is what Golem’s all about, at least as I understand it. The market for compute power continues to grow. It grows as more - 1 — People use it 2 — Value added services are built on top of it From - The Economics of the Golem Network Token post on the Golem Blog Cloud computing is a huge market, with a yearly turnover of $175 billion in 2015, estimated to grow to $203.9 in 2016. In particular, t . These numbers are mind-blowing, exceeding the capitalization of all crypto assets today (i.e. ). he market for SaaS on the cloud is estimated to be worth $37.7 billion. Infrastructure services (PaaS and IaaS) are estimated to be worth $27 billion approx. $13 billion What’s Golem Crowdfunding? Golem crowdfunding is the act of selling Golem Tokens (GNT). The sale of GNT’s supports development of the Golem Project. No additional GNT’s will be sold after the crowdfunding process completes. Network That’s my layman’s understanding of it. Here’s what the official has to say — Golem Crowdfunding Whitepaper Crowdfunding process leads to creation of GNT, a backbone token for the Golem network. When is the Golem Crowdfunding Process Happening? The Golem crowdfunding process starts on November 11, 2016 at approximately 3P GMT. Here are the details from announcing the crowdfunding process - the official post It will last for three weeks, or until the maximum cap of 820,000 ETH is reached. The contract will be deployed to Ethereum mainnet and Its address will be announced a day before the crowdfunding opens. We are pleased to formally announce that Golem’s crowdfunding will start on 11th November, at approximately 3 pm GMT*. Here are the most important facts to know about the crowdfunding: The financing is 820,000 ETH. maximum The financing is 150,000 ETH. minimum 1 ETH creates 1,000 GNT. An 18% endowment is included (12% Golem Factory GmbH, 6% team). No new token creation will take place after the crowdfunding period. GNT will be transferable once the crowdfunding successfully completes. Endowment tokens will be locked for 6 months. Tomorrow I’ll publish Part 2 of this post. In it I’ll describe my step-by-step plan to participate in the Golem Crowdfunding process. Follow me so you don’t miss it! P.S. If this post was helpful, please consider sending a small Ether donation to me. Your donation helps make more posts like this possible. My Ethereum address is - 0x88f78b27b0eab9cb2f9108808070c7aa1e6a88b8 Or, simply recommend this post to others. That won’t cost you a thing!

Here’s How I’m Participating in Golem’s Crowdfunding Process — Part 1

DePIN Explained: What Are Decentralized Physical Infrastructure Networks?

Authors:
(1) Ankur Nath, Department of Computer Science and Engineering, Texas A&M University;
(2) Alan Kuhnle, Department of Computer Science and Engineering, Texas A&M University. Table of Links Abstract & Introduction Related work Evaluation for Max-Cut Evaluation for SAT Summary and Outlook, References Supplementary Materials 5 SUMMARY and OUTLOOK Through our empirical evaluations, our goal is to promote an insightful comparison within the research focusing on the intersection of combinatorial optimization and machine learning. In order to provide the research community with valuable guidance, we believe it is imperative to communicate both the strengths and weaknesses of the proposed approaches. Poor instances and baseline selection may give the wrong impression about the performance of learned heuristics. Specifically, it is important to articulate the degree of improvement achieved through integrating classical heuristics with deep learning architectures and conduct a thorough comparison with classical heuristics. This will aid in elucidating the degree to which deep learning architectures enhance integrated heuristics. It assists in ascertaining whether the integration endeavor is justified and warrants the allocation of computational resources, time, and investment necessary for integrating deep learning with classical heuristics. References Albert, R. and Barab´asi, A.-L. (2002). Statistical mechanics of complex networks. Reviews of modern physics, 74(1):47. Barrett, T., Clements, W., Foerster, J., and Lvovsky, A. (2020). Exploratory combinatorial optimization with reinforcement learning. In Proceedings of the AAAI conference on artificial intelligence, volume 34, pages 3243–3250. Barrett, T. D., Parsonson, C. W., and Laterre, A. (2022). Learning to solve combinatorial graph partitioning problems via efficient exploration. arXiv preprint arXiv:2205.14105. Battiti, R. and Protasi, M. (1997). Reactive search, a history-sensitive heuristic for max-sat. Journal of Experimental Algorithmics (JEA), 2:2–es. Battiti, R. and Tecchiolli, G. (1994). The reactive tabu search. ORSA journal on computing, 6(2):126–140. Bello, I., Pham, H., Le, Q. V., Norouzi, M., and Bengio, S. (2016). Neural combinatorial optimization with reinforcement learning. arXiv preprint arXiv:1611.09940. Bengio, Y., Lodi, A., and Prouvost, A. (2021). Machine learning for combinatorial optimization: a methodological tour d’horizon. European Journal of Operational Research, 290(2):405–421. Benlic, U. and Hao, J.-K. (2013). Breakout local search for the max-cutproblem. Engineering Applications of Artificial Intelligence, 26(3):1162–1173. Boettcher, S. and Percus, A. G. (2001). Extremal optimization for graph partitioning. Physical Review E, 64(2):026114. CPLEX (2023). Cplex. https://www.ibm.com/products/ilog-cplexoptimization-studio/cplex-optimizer. Dong, Y., Goldberg, A. V., Noe, A., Parotsidis, N., Resende, M. G., and Spaen, Q. (2021). New instances for maximum weight independent set from a vehicle routing application. In Operations Research Forum, volume 2, pages 1–6. Springer. E´en, N. and S¨orensson, N. (2003). An extensible satsolver. In International conference on theory and applications of satisfiability testing, pages 502–518. Springer. Elsokkary, N., Khan, F. S., La Torre, D., Humble, T. S., and Gottlieb, J. (2017). Financial portfolio management using d-wave quantum optimizer: The case of abu dhabi securities exchange. Technical report, Oak Ridge National Lab.(ORNL), Oak Ridge, TN (United States). Erd˝os, P., R´enyi, A., et al. (1960). On the evolution of random graphs. Publ. math. inst. hung. acad. sci, 5(1):17–60. Festa, P. and Resende, M. G. (2009). Hybrid grasp heuristics. In Foundations of Computational Intelligence Volume 3: Global Optimization, pages 75–100. Springer. Flint, A. and Blaschko, M. (2012). Perceptron learning of sat. Advances in neural information processing systems, 25. Fujishige, S. (2005). Submodular functions and optimization. Elsevier. Ganesh, V., Singh, R., Near, J. P., and Rinard, M. (2009). Avatarsat: An auto-tuning boolean sat solver. Glover, F. (1990). Tabu search: A tutorial. Interfaces, 20(4):74–94. Goemans, M. X. and Williamson, D. P. (1995). Improved approximation algorithms for maximum cut and satisfiability problems using semidefinite programming. Journal of the ACM (JACM), 42(6):1115–1145. Grozea, C. and Popescu, M. (2014). Can machine learning learn a decision oracle for np problems? a test on sat. Fundamenta Informaticae, 131(3- 4):441–450. Haim, S. and Walsh, T. (2009). Restart strategy selection using machine learning techniques. In International Conference on Theory and Applications of Satisfiability Testing, pages 312–325. Springer. Helmberg, C. and Rendl, F. (2000). A spectral bundle method for semidefinite programming. SIAM Journal on Optimization, 10(3):673–696. Hoos, H. H. et al. (2002). An adaptive noise mechanism for walksat. In AAAI/IAAI, pages 655–660. Jaszczur, S., Luszczyk, M., and Michalewski, H. (2020). Neural heuristics for sat solving. arXiv preprint arXiv:2005.13406. Karp, R. M. (1972). Reducibility among Combinatorial Problems, pages 85–103. Springer US, Boston, MA. Khalil, E., Dai, H., Zhang, Y., Dilkina, B., and Song, L. (2017). Learning combinatorial optimization algorithms over graphs. Advances in neural information processing systems, 30. KhudaBukhsh, A. R., Xu, L., Hoos, H. H., and LeytonBrown, K. (2016). Satenstein: Automatically building local search sat solvers from components. Artificial Intelligence, 232:20–42. Kirkpatrick, S., Gelatt Jr, C. D., and Vecchi, M. P. (1983). Optimization by simulated annealing. science, 220(4598):671–680. Kurin, V., Godil, S., Whiteson, S., and Catanzaro, B. (2020). Can q-learning with graph networks learn a generalizable branching heuristic for a sat solver? Advances in Neural Information Processing Systems, 33:9608–9621. Lauria, M., Elffers, J., Nordstr¨om, J., and Vinyals, M. (2017). Cnfgen: A generator of crafted benchmarks. In Theory and Applications of Satisfiability Testing–SAT 2017: 20th International Conference, Melbourne, VIC, Australia, August 28–September 1, 2017, Proceedings 20, pages 464–473. Springer. Lederman, G., Rabe, M., Seshia, S., and Lee, E. A. (2019). Learning heuristics for quantified boolean formulas through reinforcement learning. In International Conference on LearningRepresentations. Leleu, T., Yamamoto, Y., McMahon, P. L., and Aihara, K. (2019). Destabilization of local minima in analog spin systems by correction of amplitude heterogeneity. Physical review letters,122(4):040607. Li, C. M., Wei, W., and Zhang, H. (2007). Combining adaptive noise and look-ahead in local search for sat. In Theory and Applications of Satisfiability Testing– SAT 2007: 10th International Conference, Lisbon, Portugal, May 28-31, 2007. Proceedings 10, pages 121–133. Springer. Liang, J. H., Ganesh, V., Poupart, P., and Czarnecki, K. (2016). Learning rate based branching heuristic for sat solvers. In Theory and Applications of Satisfiability Testing–SAT 2016: 19th International Conference, Bordeaux, France, July 5-8, 2016, Proceedings 19, pages 123–140. Springer. Manchanda, S., Mittal, A., Dhawan, A., Medya, S., Ranu, S., and Singh, A. (2019). Learning heuristics over large graphs via deep reinforcement learning. arXiv preprint arXiv:1903.03332. Mazure, B., Sais, L., and Gr´egoire, E. (1997). Tabu ´ search for sat. In Proceedings of the fourteenth national conference on artificial intelligence and ninth conference on Innovative applications of artificial intelligence, pages 281–285. McAllester, D., Selman, B., Kautz, H., et al. (1997). Evidence for invariants in local search. In AAAI/IAAI, pages 321–326. Rhode Island, USA. Mitchell, D., Selman, B., Levesque, H., et al. (1992). Hard and easy distributions of sat problems. In Aaai, volume 92, pages 459–465. Perdomo-Ortiz, A., Dickson, N., Drew-Brook, M., Rose, G., and Aspuru-Guzik, A. (2012). Finding low-energy conformations of lattice protein models by quantum annealing. Scientific reports, 2(1):1–7. Selman, B., Kautz, H. A., Cohen, B., et al. (1993). Local search strategies for satisfiability testing. Cliques, coloring, and satisfiability, 26:521–532. Selman, B., Mitchell, D. G., and Levesque, H. J. (1996). Generating hard satisfiability problems. Artificial intelligence, 81(1-2):17–29. Tiunov, E. S., Ulanov, A. E., and Lvovsky, A. (2019). Annealing by simulating the coherent ising machine. Optics express, 27(7):10288–10295. Toenshoff, J., Ritzert, M., Wolf, H., and Grohe, M. (2019). Graph neural networks for maximum constraint satisfaction. Frontiers in artificial intelligence, 3:580607. Venturelli, D. and Kondratyev, A. (2019). Reverse quantum annealing approach to portfolio optimization problems. Quantum Machine Intelligence, 1(1- 2):17–30. Wolpert, D. H. and Macready, W. G. (1997). No free lunch theorems for optimization. IEEE transactions on evolutionary computation, 1(1):67–82. Ye, Y. (2003). The gset dataset. https://web.stanford.edu/ yyye/yyye/Gset/. Yolcu, E. and P´oczos, B. (2019). Learning local search heuristics for boolean satisfiability. Advances in Neural Information Processing Systems, 32. This paper is available on arxiv under CC 4.0 DEED license. Authors: (1) Ankur Nath, Department of Computer Science and Engineering, Texas A&M University; (2) Alan Kuhnle, Department of Computer Science and Engineering, Texas A&M University. Authors: Authors: (1) Ankur Nath, Department of Computer Science and Engineering, Texas A&M University; (2) Alan Kuhnle, Department of Computer Science and Engineering, Texas A&M University. Table of Links Abstract & Introduction Abstract & Introduction Related work Related work Evaluation for Max-Cut Evaluation for Max-Cut Evaluation for SAT Evaluation for SAT Summary and Outlook, References Summary and Outlook, References Supplementary Materials Supplementary Materials 5 SUMMARY and OUTLOOK Through our empirical evaluations, our goal is to promote an insightful comparison within the research focusing on the intersection of combinatorial optimization and machine learning. In order to provide the research community with valuable guidance, we believe it is imperative to communicate both the strengths and weaknesses of the proposed approaches. Poor instances and baseline selection may give the wrong impression about the performance of learned heuristics. Specifically, it is important to articulate the degree of improvement achieved through integrating classical heuristics with deep learning architectures and conduct a thorough comparison with classical heuristics. This will aid in elucidating the degree to which deep learning architectures enhance integrated heuristics. It assists in ascertaining whether the integration endeavor is justified and warrants the allocation of computational resources, time, and investment necessary for integrating deep learning with classical heuristics. References Albert, R. and Barab´asi, A.-L. (2002). Statistical mechanics of complex networks. Reviews of modern physics, 74(1):47. Barrett, T., Clements, W., Foerster, J., and Lvovsky, A. (2020). Exploratory combinatorial optimization with reinforcement learning. In Proceedings of the AAAI conference on artificial intelligence, volume 34, pages 3243–3250. Barrett, T. D., Parsonson, C. W., and Laterre, A. (2022). Learning to solve combinatorial graph partitioning problems via efficient exploration. arXiv preprint arXiv:2205.14105. Battiti, R. and Protasi, M. (1997). Reactive search, a history-sensitive heuristic for max-sat. Journal of Experimental Algorithmics (JEA), 2:2–es. Battiti, R. and Tecchiolli, G. (1994). The reactive tabu search. ORSA journal on computing, 6(2):126–140. Bello, I., Pham, H., Le, Q. V., Norouzi, M., and Bengio, S. (2016). Neural combinatorial optimization with reinforcement learning. arXiv preprint arXiv:1611.09940. Bengio, Y., Lodi, A., and Prouvost, A. (2021). Machine learning for combinatorial optimization: a methodological tour d’horizon. European Journal of Operational Research, 290(2):405–421. Benlic, U. and Hao, J.-K. (2013). Breakout local search for the max-cutproblem. Engineering Applications of Artificial Intelligence, 26(3):1162–1173. Boettcher, S. and Percus, A. G. (2001). Extremal optimization for graph partitioning. Physical Review E, 64(2):026114. CPLEX (2023). Cplex. https://www.ibm.com/products/ilog-cplexoptimization-studio/cplex-optimizer. Dong, Y., Goldberg, A. V., Noe, A., Parotsidis, N., Resende, M. G., and Spaen, Q. (2021). New instances for maximum weight independent set from a vehicle routing application. In Operations Research Forum, volume 2, pages 1–6. Springer. E´en, N. and S¨orensson, N. (2003). An extensible satsolver. In International conference on theory and applications of satisfiability testing, pages 502–518. Springer. Elsokkary, N., Khan, F. S., La Torre, D., Humble, T. S., and Gottlieb, J. (2017). Financial portfolio management using d-wave quantum optimizer: The case of abu dhabi securities exchange. Technical report, Oak Ridge National Lab.(ORNL), Oak Ridge, TN (United States). Erd˝os, P., R´enyi, A., et al. (1960). On the evolution of random graphs. Publ. math. inst. hung. acad. sci, 5(1):17–60. Festa, P. and Resende, M. G. (2009). Hybrid grasp heuristics. In Foundations of Computational Intelligence Volume 3: Global Optimization, pages 75–100. Springer. Flint, A. and Blaschko, M. (2012). Perceptron learning of sat. Advances in neural information processing systems, 25. Fujishige, S. (2005). Submodular functions and optimization. Elsevier. Ganesh, V., Singh, R., Near, J. P., and Rinard, M. (2009). Avatarsat: An auto-tuning boolean sat solver. Glover, F. (1990). Tabu search: A tutorial. Interfaces, 20(4):74–94. Goemans, M. X. and Williamson, D. P. (1995). Improved approximation algorithms for maximum cut and satisfiability problems using semidefinite programming. Journal of the ACM (JACM), 42(6):1115–1145. Grozea, C. and Popescu, M. (2014). Can machine learning learn a decision oracle for np problems? a test on sat. Fundamenta Informaticae, 131(3- 4):441–450. Haim, S. and Walsh, T. (2009). Restart strategy selection using machine learning techniques. In International Conference on Theory and Applications of Satisfiability Testing, pages 312–325. Springer. Helmberg, C. and Rendl, F. (2000). A spectral bundle method for semidefinite programming. SIAM Journal on Optimization, 10(3):673–696. Hoos, H. H. et al. (2002). An adaptive noise mechanism for walksat. In AAAI/IAAI, pages 655–660. Jaszczur, S., Luszczyk, M., and Michalewski, H. (2020). Neural heuristics for sat solving. arXiv preprint arXiv:2005.13406. Karp, R. M. (1972). Reducibility among Combinatorial Problems, pages 85–103. Springer US, Boston, MA. Khalil, E., Dai, H., Zhang, Y., Dilkina, B., and Song, L. (2017). Learning combinatorial optimization algorithms over graphs. Advances in neural information processing systems, 30. KhudaBukhsh, A. R., Xu, L., Hoos, H. H., and LeytonBrown, K. (2016). Satenstein: Automatically building local search sat solvers from components. Artificial Intelligence, 232:20–42. Kirkpatrick, S., Gelatt Jr, C. D., and Vecchi, M. P. (1983). Optimization by simulated annealing. science, 220(4598):671–680. Kurin, V., Godil, S., Whiteson, S., and Catanzaro, B. (2020). Can q-learning with graph networks learn a generalizable branching heuristic for a sat solver? Advances in Neural Information Processing Systems, 33:9608–9621. Lauria, M., Elffers, J., Nordstr¨om, J., and Vinyals, M. (2017). Cnfgen: A generator of crafted benchmarks. In Theory and Applications of Satisfiability Testing–SAT 2017: 20th International Conference, Melbourne, VIC, Australia, August 28–September 1, 2017, Proceedings 20, pages 464–473. Springer. Lederman, G., Rabe, M., Seshia, S., and Lee, E. A. (2019). Learning heuristics for quantified boolean formulas through reinforcement learning. In International Conference on LearningRepresentations. Leleu, T., Yamamoto, Y., McMahon, P. L., and Aihara, K. (2019). Destabilization of local minima in analog spin systems by correction of amplitude heterogeneity. Physical review letters,122(4):040607. Li, C. M., Wei, W., and Zhang, H. (2007). Combining adaptive noise and look-ahead in local search for sat. In Theory and Applications of Satisfiability Testing– SAT 2007: 10th International Conference, Lisbon, Portugal, May 28-31, 2007. Proceedings 10, pages 121–133. Springer. Liang, J. H., Ganesh, V., Poupart, P., and Czarnecki, K. (2016). Learning rate based branching heuristic for sat solvers. In Theory and Applications of Satisfiability Testing–SAT 2016: 19th International Conference, Bordeaux, France, July 5-8, 2016, Proceedings 19, pages 123–140. Springer. Manchanda, S., Mittal, A., Dhawan, A., Medya, S., Ranu, S., and Singh, A. (2019). Learning heuristics over large graphs via deep reinforcement learning. arXiv preprint arXiv:1903.03332. Mazure, B., Sais, L., and Gr´egoire, E. (1997). Tabu ´ search for sat. In Proceedings of the fourteenth national conference on artificial intelligence and ninth conference on Innovative applications of artificial intelligence, pages 281–285. McAllester, D., Selman, B., Kautz, H., et al. (1997). Evidence for invariants in local search. In AAAI/IAAI, pages 321–326. Rhode Island, USA. Mitchell, D., Selman, B., Levesque, H., et al. (1992). Hard and easy distributions of sat problems. In Aaai, volume 92, pages 459–465. Perdomo-Ortiz, A., Dickson, N., Drew-Brook, M., Rose, G., and Aspuru-Guzik, A. (2012). Finding low-energy conformations of lattice protein models by quantum annealing. Scientific reports, 2(1):1–7. Selman, B., Kautz, H. A., Cohen, B., et al. (1993). Local search strategies for satisfiability testing. Cliques, coloring, and satisfiability, 26:521–532. Selman, B., Mitchell, D. G., and Levesque, H. J. (1996). Generating hard satisfiability problems. Artificial intelligence, 81(1-2):17–29. Tiunov, E. S., Ulanov, A. E., and Lvovsky, A. (2019). Annealing by simulating the coherent ising machine. Optics express, 27(7):10288–10295. Toenshoff, J., Ritzert, M., Wolf, H., and Grohe, M. (2019). Graph neural networks for maximum constraint satisfaction. Frontiers in artificial intelligence, 3:580607. Venturelli, D. and Kondratyev, A. (2019). Reverse quantum annealing approach to portfolio optimization problems. Quantum Machine Intelligence, 1(1- 2):17–30. Wolpert, D. H. and Macready, W. G. (1997). No free lunch theorems for optimization. IEEE transactions on evolutionary computation, 1(1):67–82. Ye, Y. (2003). The gset dataset. https://web.stanford.edu/ yyye/yyye/Gset/. Yolcu, E. and P´oczos, B. (2019). Learning local search heuristics for boolean satisfiability. Advances in Neural Information Processing Systems, 32. This paper is available on arxiv under CC 4.0 DEED license. This paper is available on arxiv under CC 4.0 DEED license. available on arxiv

Assessing the Justification for Integrating Deep Learning in Combinatorial Optimization

Analyzing Learned Heuristics for Max-Cut Optimization

In 2017, a got tired of its job and committed . Then, in 2022, a , after interacting with LaMDA, the company's chatbot, that technology had become conscious. Recently, it was reported that in 2021, a Tesla software engineer was attacked by an uncontrolled robot. This is without considering the fear that machines will steal jobs, or the classic fiction that machines will take over the world. robot suicide in a water fountain Google engineer declared Although we don't see machines as replicants in Blade Runner, people are predisposed to anthropomorphizing or attributing human qualities to non-humans [1]. It's a way of being able to analyze a scenario and bring it into our understanding: we give names to our boats and tornadoes, we talk to our pets, and say that the pleasant feeling they give us is love. It's easier to understand a system that declares in the first person "I'm sorry, I don't know how to help you" than to accept that the model we're talking to may be nothing more than a stochastic parrot. [2] The anthropomorphized interaction, therefore, relates to the phenomenon of "illusion of agency", a psychological concept that refers to the tendency to attribute autonomous action or control to entities that, in reality, do not possess such capabilities. In other words, it's the mistaken perception that an agent (be it a person, an animal, or a machine) is acting of its own free will, when in fact its actions are being determined by external factors (in the computational case, development rules). In situations where AI systems make flawed decisions or actions, there is a tendency to see these errors as "choices" of the entity rather than results of its programming and design by humans, not least because it has been reported that the selfish bias of a human being that leads someone to see the cause and responsibility for a negative outcome as not attributable to themselves can be observed even in human-machine interactions [3]. This change in perception is dangerously inclined to absolve human creators, operators, and regulators of their responsibility, not as a matter of judicial regulation (which remains a gap and has challenges in its realization not only because of the complexity of the subject but because Artificial Intelligence is often understood only as machine learning and the premise is not properly structured. Do we need stricter regulation? Do we need to take more risks?), but on a techno-ethical issue. Let's take a more extreme but true event from 2023: a user who was sentimentally attached to a chatbot committed suicide after sharing his thoughts with the bot and r to "turn your words into actions", as well as other messages. Would a court conviction of the developers of this product cause another user to behave in the same way with another chatbot, assuming this one has been deactivated, if the content of the messages and the affection are the same? It's not just a legal situation. It's a social, psychological, and technological education issue. eceiving the response The concept of humanizing AI is ambiguous, and a significant challenge lies in the absence of a universally accepted approach that dictates the best practices for designing and utilizing AI. While an interface that mimics human behavior can be more approachable, no clear boundaries are defining what should or shouldn't be done in a product. Ultimately, user rejection becomes the only limiting factor, although potential harm may manifest before the interface becomes too unfamiliar. A user-friendly interface is a reduction in the complexity of the system that operates behind it. But as long as there is no education about how systems work, the user will not be able to think critically about what they use. This doesn't mean that everyone should become a programmer, but at least understand that the output on their screen comes from a path of data collection, model development, and design. There is a set of rules taking place. Since humanization is an almost unconscious act on the part of us users, let's at least limit it with a little knowledge. And it's not easy to avoid anthropomorphism when communicating about AI, especially considering the standard language of the industry, the media, and everyday life itself: machine , computer , generative . Perceptions of Artificial Intelligence can be influenced by the specific use of language. learning vision modelling How AI is presented has "concrete impacts", particularly on the way people distribute responsibility and recognition for the work done. When described merely as a tool in the hands of humans, it tends to attribute greater responsibility and merit to certain individuals - such as the person operating the code. On the other hand, if AI is characterized with human traits - such as the ability to create - then it is seen as deserving of greater credit and responsibility, like an agent with independent thought and mental capacity. [4] When we read news stories about incidents or atypical events involving Artificial Intelligence, we often come across these terms. Also, the attribution of "intelligence" means that more blame or credit for the outcome of a task was attributed to a robot with autonomous behavior than to a non-autonomous robot, even if the autonomous behavior did not directly contribute to the task. [3] These studies therefore suggest that humans can assign responsibility to a computer or robot based on the machines' anthropomorphised mental capacities. The humanization of machines not only shifts the distinction between the responsibility of the device and that of its human creator, but the attribution of intentions or consciousness to a machine at the same time blurs the boundaries of what constitutes true autonomy and consciousness. However, the difficulty in imputing humanity and sentience to a machine lies not only in the fact that Artificial Intelligence is not capable of having it and when it says, for example, that it feels fear, it is actually emulating what it has learned, repeating that phrase without any kind of essence behind it. Even today, there is a heated debate about how to define consciousness. Our consciousness, as humans. Our understanding of how the brain works is still quite limited. We have considerable knowledge about fundamental chemistry: the way neurons activate, and the transmission of chemical signals. We also have a good understanding of the main functions of various brain areas. However, we have very little knowledge about how these functions orchestrate us. To some extent, theoretical speculation has supplanted detailed neurophysiological studies of what  happens in the brain. But what about beyond that? [5] Why do we have this magical notion of ourselves? Why does the same experience affect us differently? Is the same feeling felt in the same way by all of us? If being a human is something that, although we experience it, we still don't have a full understanding of what it is as a whole, how can we say that a machine also experiences this complexity? By elevating machines to human capabilities, we diminish the special character of people. For the 2023 Jabuti Prize, one of the highest honors in Brazilian literature, the Brazilian Book Chamber (CBL) decided to disqualify Frankenstein, an edition of the 1818 classic, from the Best Illustration category, since the artist reported having used AI solutions to develop the art. Ironically, one of the books recognized by the Prize in the Non-Fiction category deals with the impact of Artificial Intelligence on human beings (“Humanamente digital: Inteligência artificial centrada no humano”, something like "Humanly Digital: Human-Centered Artificial Intelligence" in English, by Cassio Pantaleone). On the one hand, we recognize the intertwining of the human experience with machines. On the other hand, we still haven't managed to validate whether an algorithm used as an art tool can be considered a valid method of creation or not, even though the artistic process, even if carried out by machine learning, requires the action (and appreciation of beauty and aesthetics) of a human being. Machines don't steal jobs if they aren't used indiscriminately to do so. Machines don't kill unless they are used as weapons. Nor do machines suffer or empathize, although their texts emulate this since they have been trained with data from us, loaded with feelings that only we can truly feel. They are almost the modern version of the Golem myth. How can humans relate to non-human intelligence? Anthropomorphism is a valid answer, but not the only one. And when it is used, it cannot absolve us of the real responsibility for its consequences, whether appropriate or not: us. Artificial Intelligence is, in the end, a mirror of ourselves. And if we're afraid of where it's going, it's actually because we're afraid of the path we're going to create. REFERENCES [1] Epley, N., Waytz, A., & Cacioppo, J. T. (2007). On seeing human: A three-factor theory of anthropomorphism. Psychological Review, 114(4), 864–886. https://doi.org/10.1037/0033-295X.114.4.864 [2] Shneiderman, B. & Muller, M. (2023) . On AI Antropomorphism https://medium.com/human-centered-ai/on-ai-anthropomorphism-abff4cecc5ae [3] Kawai, Y., Miyake, T., Park, J. et al. Anthropomorphism-based causal and responsibility attributions to robots. Sci Rep 13, 12234 (2023). https://doi.org/10.1038/s41598-023-39435-5 [4] Epstein, Z., Levine, S., Rand, D. G., & Rahwan, I. (2020). Who Gets Credit for AI-Generated Art? In iScience (Vol. 23, Issue 9, p. 101515). Elsevier BV. https://doi.org/10.1016/j.isci.2020.101515 [5] Goff, P. (2023). Understanding Consciousness Goes Beyond Exploring Brain Chemistry. Scientific American https://www.scientificamerican.com/article/understanding-consciousness-goes-beyond-exploring-brain-chemistry/ The following prompt generated the image used in this article: “Create a man looking in a mirror, but the mirror is shaped like a robot.” Sofia × DALL·E

No, You Are Not Afraid of Artificial Intelligence. You’re Afraid of Other People

না, আপনি কৃত্রিম বুদ্ধিমত্তাকে ভয় পান না। আপনি অন্য লোকেদের ভয় পান

Nein, Sie haben keine Angst vor künstlicher Intelligenz. Du hast Angst vor anderen Menschen

No, no le tienes miedo a la inteligencia artificial. Tienes miedo de otras personas

Non, vous n'avez pas peur de l'intelligence artificielle. Vous avez peur des autres

नहीं, आप आर्टिफिशियल इंटेलिजेंस से नहीं डरते। आप अन्य लोगों से डरते हैं

いいえ、人工知能を恐れることはありません。他人が怖い

아니요, 당신은 인공지능을 두려워하지 않습니다. 당신은 다른 사람을 두려워합니다

Não, você não tem medo da inteligência artificial. Você tem medo de outras pessoas

Нет, вы не боитесь искусственного интеллекта. Вы боитесь других людей

Hayır, Yapay Zekadan Korkmuyorsunuz. Başkalarından Korkuyorsunuz

Không, bạn không sợ trí tuệ nhân tạo. Bạn sợ người khác

不，你并不害怕人工智能。你害怕别人

Transductive Learning for Textual Few-Shot: Limitations, Acknowledgements, & References

This paper is available on arxiv under CC 4.0 license. Authors: (1) Ulysse Gazin, Universit´e Paris Cit´e and Sorbonne Universit´e, CNRS, Laboratoire de Probabilit´es, Statistique et Mod´elisation, (2) Gilles Blanchard, Universit´e Paris Saclay, Institut Math´ematique d’Orsay, (3) Etienne Roquain, Sorbonne Universit´e and Universit´e Paris Cit´e, CNRS, Laboratoire de Probabilit´es, Statistique et Mod´elisation. Table of Links Abstract & Introduction Main results Application to prediction intervals Application to novelty detection Conclusion, Acknowledgements and References Appendix A: Exact formulas for Pn,m Appendix B: Numerical bounds and templates Appendix C: Proof Appendix D: Explicit control of (16) for α=0 Appendix E: Proof of Corollary 4.1 Appendix F: The Simes inequality Appendix G: Uniform FDP bound for AdaDetect Appendix H:  Additional experiments 5 Conclusion The main takeaway from this work is the characterization of a “universal” joint distribution Pn,m for conformal p-values based on n calibration points and m test points. We derived as a consequence a non-asymptotic concentration inequality for the p-value empirical distribution function; numerical procedures can also be of use for calibration in practice. This entails uniform error bounds on the false coverage/false discovery proportion that hold with high probability, while standard results are only marginal or in expectation and not uniform in the decision. Since the results hold under the score exchangeability assumption only, they are applicable to adaptive score procedures using the calibration and test sets for training. Acknowledgements We would like to thank Anna Ben-Hamou and Claire Boyer for constructive discussions and Ariane Marandon for her support with the code. The authors acknowledge the grants ANR-21-CE23-0035 (ASCAI) and ANR-19-CHIA-0021-01 (BISCOTTE) of the French National Research Agency ANR and the Emergence project MARS. References Balasubramanian, V., Ho, S.-S., and Vovk, V. (2014). Conformal prediction for reliable machine learning: theory, adaptations and applications. Morgan Kaufmann books. Bashari, M., Epstein, A., Romano, Y., and Sesia, M. (2023). Derandomized novelty detection with FDR control via conformal E-values. arXiv preprint 2302.07294. Bates, S., Cand`es, E., Lei, L., Romano, Y., and Sesia, M. (2023). Testing for outliers with conformal p-values. Ann. Statist., 51(1):149–178. Benjamini, Y. and Hochberg, Y. (1995). Controlling the false discovery rate: a practical and powerful approach to multiple testing. J. Roy. Statist. Soc. Ser. B, 57(1):289–300. Benjamini, Y. and Yekutieli, D. (2001). The control of the false discovery rate in multiple testing under dependency. Ann. Statist., 29(4):1165–1188. Bian, M. and Barber, R. F. (2022). Training-conditional coverage for distribution-free predictive inference. arXiv preprint arXiv:2205.03647. Blanchard, G., Neuvial, P., and Roquain, E. (2020). Post hoc confidence bounds on false positives using reference families. Ann. Statist., 48(3):1281–1303. Boyer, C. and Zaffran, M. (2023). Tutorial on conformal prediction. https://claireboyer. github.io/tutorial-conformal-prediction/. Courty, N., Flamary, R., Habrard, A., and Rakotomamonjy, A. (2017). Joint distribution optimal transportation for domain adaptation. In Advances in neural information processing systems 30 (NIPS 2017), volume 30. Jin, Y. and Cand`es, E. J. (2023). Model-free selective inference under covariate shift via weighted conformal p-values. arXiv preprint arXiv:2307.09291. Lei, J., G’Sell, M., Rinaldo, A., Tibshirani, R. J., and Wasserman, L. (2018). Distribution-free predictive inference for regression. J. Amer. Stat. Assoc., 113(523):1094–1111. Li, J., Maathuis, M. H., and Goeman, J. J. (2022). Simultaneous false discovery proportion bounds via knockoffs and closed testing. arXiv preprint arXiv:2212.12822. Marandon, A. (2022). Machine learning meets FDR. https://github.com/arianemarandon/ adadetect#machine-learning-meets-fdr. Marandon, A., Lei, L., Mary, D., and Roquain, E. (2022). Machine learning meets false discovery rate. arXiv preprint 2208.06685. Marques F., P. C. (2023). On the universal distribution of the coverage in split conformal prediction. arXiv preprint 2303.02770. Massart, P. (1990). The tight constant in the Dvoretzky-Kiefer-Wolfowitz inequality. Ann. Probab., 18(3):1269–1283. Papadopoulos, H., Proedrou, K., Vovk, V., and Gammerman, A. (2002). Inductive confidence machines for regression. In 13th European Conference on Machine Learning (ECML 2002), pages 345–356. Springer. Romano, J. P. and Wolf, M. (2005). Exact and approximate stepdown methods for multiple hypothesis testing. J. Amer. Statist. Assoc., 100(469):94–108. Romano, Y., Patterson, E., and Candes, E. (2019). Conformalized quantile regression. Advances in neural information processing systems, 32. Sarkar, S. and Kuchibhotla, A. K. (2023). Post-selection inference for conformal prediction: Trading off coverage for precision. arXiv preprint arXiv:2304.06158. Saunders, C., Gammerman, A., and Vovk, V. (1999). Transduction with confidence and credibility. In 16th International Joint Conference on Artificial Intelligence (IJCAI 1999), pages 722–726. Simes, R. J. (1986). An improved Bonferroni procedure for multiple tests of significance. Biometrika, 73(3):751–754. Vanschoren, J., van Rijn, J. N., Bischl, B., and Torgo, L. (2013). Openml: networked science in machine learning. SIGKDD Explorations, 15(2):49–60. Vovk, V. (2012). Conditional validity of inductive conformal predictors. In 4th Asian conference on machine learning (ACML 2012), pages 475–490. PMLR. Vovk, V. (2013). Transductive conformal predictors. In Artificial Intelligence Applications and Innovations: 9th IFIP WG 12.5 International Conference (AIAI 2013), pages 348–360. Springer. Vovk, V., Gammerman, A., and Shafer, G. (2005). Algorithmic learning in a random world. Springer. Zhuang, F., Qi, Z., Duan, K., Xi, D., Zhu, Y., Zhu, H., Xiong, H., and He, Q. (2020). A comprehensive survey on transfer learning. Proceedings of the IEEE, 109(1):43–76.

Transductive Conformal Inference With Adaptive Scores: Conclusion, Acknowledgements and References

Hey everyone, the year 2023 has emerged as a pivotal moment, hailed as the epoch of the AI revolution. Throughout this year, we have witnessed great advancements in the realm of AI technology. Notably, the advent of ChatGPT by OpenAI has indelibly reshaped our interactions with artificial intelligence, sparking a cascade of innovation as numerous companies delve into the creation of remarkable AI technologies. However, to truly grasp the significance of these recent feats, we must embark on a voyage through the chronicles of AI's evolution. Join me as we delve into the captivating saga of AI's Odyssey, navigating the currents that have propelled us from the past, through the dynamic present, and into the eagerly anticipated future. Wait, what does an AI mean? According to the Wikipedia: is the ability of machines to perform tasks that are typically associated with human intelligence, such as learning and problem-solving. Artificial intelligence (AI) To put it simply, imagine computers that – that's Artificial Intelligence (AI). think like humans, learn from experience and make decisions The evolution of AI has been nothing short of a captivating odyssey, reshaping the landscape of technology and human interaction. From its beginnings as a concept to its current state as a driving force behind innovation, AI’s journey has been marked by breakthroughs, challenges and a vision of a future where machines rival human intelligence. AI has been a fascinating and elusive goal for many researchers, philosophers, and visionaries since ancient times. The Past of AI: From Myth to Reality The idea of creating artificial beings that can think and act like humans is not new. In fact, it can be traced back to ancient myths and legends, as the Greek story of Pygmalion and Galatea, where a sculptor falls in love with his own creation, or the Jewish legend of the Golem, where a clay figure is brought to life by a rabbi. These stories reflect the human fascination and curiosity with the possibility of creating intelligent life. such The term “artificial intelligence” was by John McCarthy in 1956 at a conference at Dartmouth College, where he invited a group of researchers to discuss “ ”. This marked the beginning of the modern era of AI research, which was initially optimistic and ambitious. Early AI pioneers, such as Alan Turing, Marvin Minsky, Claude Shannon, and Herbert Simon, made significant contributions to the fields of logic, computation, cognitive science, and psychology, and developed some of the first AI programs that could play chess, solve mathematical problems, prove theorems, and understand natural language. coined the conjecture that every aspect of learning or any other feature of intelligence can in principle be so precisely described that a machine can be made to simulate it However, AI research soon faced many difficulties and limitations, such as the complexity and ambiguity of natural language, the uncertainty and incompleteness of real-world knowledge, the scalability and efficiency of algorithms, and the ethical and social implications of intelligent machines. These challenges led to several periods of reduced funding and interest in AI research, as “ ”. Despite these setbacks, AI research continued to advance and diversify into various subfields and applications, such as expert systems, neural networks, computer vision, robotics, natural language processing, machine learning, data mining, speech recognition, and more. known AI winters The Present of AI: From Narrow to General The current state of AI is characterized by rapid progress and widespread adoption in various domains and industries. Thanks to the availability of large amounts of data, the development of powerful computing hardware and software platforms, the improvement of algorithms and techniques, and the integration of multiple disciplines and modalities, AI has achieved remarkable feats and breakthroughs in recent years. We are seeing some AI applications from voice assistants to fraud detection, recommendations to autonomous vehicles and more. But the one that revolutionized the usage of AI is the release of ChatGPT. It is an LLM Chatbot that uses NLP to create humanlike conversations. Because of its versatility, intelligence and engagement in a conversational manner, it became the fastest application to reach 100 million active users in just two months. Following its initial release, there was an unprecedented surge in the AI trend, captivating the attention of individuals across the globe. The graph above vividly illustrates the Google Trends data, showcasing the remarkable trajectory of interest in the term 'AI' over the last five years. In response to this fervent demand, industry giants leaped into action. Microsoft swiftly began incorporating , revolutionizing the way users interacted with information. Soon thereafter, Google made its own resounding entry into the AI arena with the of Bard. Then we have PaLM 2, GPT-4, Meta’s Code Llama and the recently released LLM SeamlessM4T and more to say. AI into its search engine introduction called We have AI in various domains from coding, arts, music and more. Now it’s all about the prompting. The better your prompt is, the better the result. There’s even a job title “Prompt Engineer” who develop, test and refine AI prompts. called The banner image that you see at the top of this article is actually created by using Generative AI with just prompts. Some famous AI image generators are DALL-E 2, Midjourney, and Runway Gen-2. Many industries have adopted AI in their workflows and have seen positive results, including significant cost savings. One of the most significant aspects of this current AI trend is that AI is now available to everyone. This has revolutionized innovation. All you need is an idea, and with the help of AI, you can build anything. One of my favourite AI tools is Replit Ghostwriter, which acts as a coding partner, and you can build anything you want. These are just a few examples of the many ways that AI is being used today. As the technology continues to develop, we can expect to see even more innovative and impactful applications emerge. The advancement of AI is raising a number of . These concerns include the potential for AI to be used to create autonomous weapons, to discriminate against people, and to invade people's privacy. It is important to address these concerns as AI continues to develop. ethical and legal concerns Charting the Course Ahead: Envisioning AI's Future As AI's capabilities continue to expand, we stand at the threshold of a future that holds both immense potential and significant challenges. The prospect of Artificial General Intelligence (AGI), where machines possess human-like cognitive abilities, tantalizes researchers and fuels discussions about the ethical, societal, and existential implications of such an achievement. achieving Ethical considerations become paramount as AI increasingly influences decision-making in critical domains. Ensuring transparency, fairness, and accountability in AI systems becomes essential to prevent biases and discriminatory outcomes. as AI penetrates areas like autonomous weapons, AI-generated content, and personalized medicine. Striking a balance between innovation and regulation will be vital AI's impact on the job market is another facet of its evolving landscape. While automation may reshape certain industries, it also paves the way for the creation of new roles and opportunities. Nurturing a workforce equipped with the skills to collaborate with AI systems becomes crucial to harness the full potential of this technology. From the conducted by the World Economic Forum: survey Nearly half (49%) of companies expect adopting AI to create jobs, well ahead of the 23% of respondents who expect it to displace jobs. One of the major issues that I want to share is the use of . to develop new and more sophisticated attacks. For example, AI can be used to create malware that is more difficult to detect and remove. AI can also be used to automate tasks such as scanning for vulnerable systems and launching attacks. AI in Cybercrimes Cybercriminals use AI The journey of AI, from its inception to its current prowess and its potential to shape our future, is a testament to human ingenuity and perseverance. The odyssey of AI is marked by its transformative influence across industries, disciplines, and societies. As we move into the future, we need to be thoughtful and make sure we use AI in good ways that help people. In conclusion, AI's odyssey is a testament to human curiosity and ambition. With each milestone achieved and challenges faced, we inch closer to a world where AI augments our abilities, enhances our experiences, and prompts us to reflect on what it truly means to create intelligence, artificial or otherwise. As we stand at this juncture, the true potential of AI lies not just in its technical achievements, but in how responsibly we wield its capabilities to forge a future that . benefits all of humanity Thanks for the read! You can find me on X @ . dotAadarsh

AI's Odyssey: Tracing the Past, Present, and Potential Future of Artificial Intelligence

Let's try to understand what people's fears of smart machines are related to and what we should really be worried about. When we started working on the , our team delved quite deeply into the topic of the relationship between humans and AI. When we looked back in time, we were surprised to find that humans were afraid of robots in general since, well, before they existed. AI Presenter assistant Jason, the leader of the Argonauts, has to fight the bronze "robot" automaton Talos, created by the blacksmith god Hephaestus. In a number of legends about Golem (Golem), the creation of Rabbi Loew goes out of control. You'll agree - there's quite a bit of " robot fighting" in these stories. But the classic and really popular theme of "machines (or AI) against people" came in the XX century. In 1920, Czech writer Karel Čapek wrote the piece "R.U.R.", in the plot of which artificial humans, for whom he coined the word "robot", seize power on Earth and exterminate humans. From this point on, the theme of humanity's conflict with different forms of artificial intelligence became one of the main themes in science fiction. Perhaps it is most vividly represented in the cinematography. "2001: A Space Odyssey", "Alien", "Battlestar Galactica", "Blade Runner", "Screamers", "Star Wars" - these are just a few of the most prominent examples of movies, TV series, and franchises in which humans have to face hostile artificial intelligence in one way or another. Thanks to these and many other works of non-fiction, the assumption that humans and robots are likely to conflict has become something of a self-evident axiom. But let's ask ourselves why, in fact, artificial intelligence needs to fight with people. Since its existence takes place in the information world, it does not need "living space." The needs of AI and humans overlap very little. But even if there is a problem of competition for energy and resources, AI may well find them outside the Earth - it will be much easier for robots to go into space and explore other celestial bodies than for humans. Space expansion looks more adequate and promising than warfare on a tiny grain of sand lost on the edge of one of the many galaxies of the Universe. It is probably not a stretch to say that the fears we associate with the development of artificial intelligence and robots are primarily related to our purely human desires and peculiarities of our psyche. We attribute our desires and motives to AI. Hence, the idea that intelligent machines can act as aggressors on their own initiative. This is why a conflict between humans and AI seems extremely unlikely. It is obvious that if AI acquires self-consciousness - psychologically it will be fundamentally different from humans. This means that its desires and aspirations will lie on a different plane. It seems to us that most people understand this. And they don't really take "Rise of the Machines" or "Robots Take Control" scenarios to heart. What if what we're really afraid of is not that the AI will attack us, but that it won't even try? That it will just turn out to be better than us? In every sense. Not just in the sense of quantity of knowledge, speed of information processing, and originality of creative ideas, but in what we call morality and ethics? Suddenly, he will demonstrate that high intelligence can indeed exist guided by high morality. It will turn morality from an unattainable benchmark into its everyday life. Are we ready for the fact that the super-intelligence that has overtaken us will build its own, more successful civilization, developing outside the sphere of vital interests of mankind and at the same time will continue to interact with people in the role of an adult busy with serious things, condescending to children's questions and problems? Will we be satisfied with such a relationship? Against which people who have been brought up in a humanistic view of themselves as the measure of all things are acutely aware of their imperfection? Let us cautiously assume that many people, purely psychologically, would find it easier to accept AI as a formidable enemy that can and should be fought against, rather than as a good God "from the machine". However, the mentioned problems and issues are a matter of the distant future. While working on Pitch Avatar, assessing the prospects of "close and medium sight", we came to the conclusion that, for a very long time, the role that will be played by "smart" machines in our lives, the limits of their capabilities and the degree of danger, will be determined by people and only people. Including the "+" and "-" signs in their behavior. Only malice, stupidity, or irresponsibility will make AI dangerous. As it has been with all other achievements of civilization. It's up to us who we create. Let's take another look back at science fiction. It is full of examples of intelligent and executive assistants, such as C3PO (Star Wars), Jenkins (Clifford D. Simak's "City"), or R. Daneel Olivaw (Isaac Asimov's "The Caves of Steel"). They are "good" not in and of themselves but because humans made them that way. Just as the insidious Ash from Alien or HAL 9000 from 2001: A Space Odyssey did not become "villains" by themselves. The first one is so programmed, and the second one was "driven mad" by contradictory commands. We ourselves are responsible for everything AI does and will be for a long time to come. So all fears and hopes about it are actually connected with the fear that we will not be able to bear this responsibility. Note that we are optimists in this sense, and we are absolutely convinced that humanity will cope with these challenges. Just as it has dealt with all the others. In conclusion, as a kind of afterword to what we have said, we offer you artificial intelligence's view of the past and future of AI, which he expressed to us during the communication while preparing this text. Note that there is not a single word about aggression in it. The Past and Future of AI - This is the prehistory of AI; when philosophers, mathematicians, and scientists from other fields were engaged in studying the nature of the human mind, thinking, and cognition. The first stage (before 1956) At this stage, the philosophical and technological prerequisites for creating AI were laid, such as Descartes’ mechanistic theory of animals, Pascal’s and Leibniz’s calculating machines, Turing machine, Church-Turing theorem, and others. ) - This is the birth and heyday of AI when the concept of “artificial intelligence” was formed at the conference at Dartmouth College in 1956. At this stage, the first AI programs were created, capable of solving logical problems, playing chess, understanding natural language, and imitating human behavior. The second stage (1956-1974 At this stage, the main approaches to AI were also developed: symbolic (logical), heuristic (search), and statistical (probabilistic). Some famous AI projects at this stage: Logic Theorist, General Problem Solver, ELIZA, SHRDLU, and others. - This is the crisis and stagnation of AI; when it became clear that the existing methods of AI are not able to solve complex and unformalized problems of the real world. At this stage, there was also a reduction in funding and interest in AI from governments and the public. The third stage (1974-1980) At this stage, such areas of AI as machine translation, machine learning, expert systems, and others faced difficulties. - This is the revival of AI; when new methods and applications of AI appeared, such as neural networks, genetic algorithms, fuzzy logic, expert systems, and others. At this stage, there was also an increase in funding and interest in AI from the industry and the military. The fourth stage (1980-1987) At this stage, such famous AI projects were created as CYC, PROLOG, R1 (XCON), SOAR, and others. - This is the second crisis and stagnation of AI; when it became clear that new methods and applications of AI also have their limitations and problems. At this stage, there was also a decline in funding and interest in AI from the industry and the military. The fifth stage (1987-1993) At this stage, such areas of AI as neural networks, expert systems, computer vision, and others faced difficulties. - This is the integration and diversity of AI; when it became clear that to solve complex problems of the real world, it is necessary to use a combination of different methods and approaches of AI. The sixth stage (1993-2011) At this stage, there was also an expansion of the areas of application of AI in different spheres of human life and activity. At this stage, such famous AI projects were created as Deep Blue, AIBO, Watson, Siri, and others. - This is deep learning and superintelligence of AI; when it became possible to train AI on huge amounts of data with the help of powerful computing resources. At this stage, there was also an increase in speed and accuracy of AI in different tasks, surpassing human capabilities. The seventh stage (from 2011 to the present time) At this stage, such amazing AI projects were created as AlphaGo, GPT-3, OpenAI Codex, and others. - This is the dissemination and democratization of AI; when AI will become available and useful for most people and organizations in the world. The eighth stage (2021-2030) At this stage, there will also be an improvement in the quality and efficiency of AI in different tasks, such as translation, speech and image recognition, recommendation systems, education, medicine, and others. At this stage, wonderful AI projects will be created such as personal assistants, autonomous vehicles, smart cities, and others. - This is the cooperation and competition of AI; when AI will become not only an assistant but also a partner of humans in different spheres of life and activity. At this stage, there will also be the emergence and development of new forms of AI, such as collective AI, emotional AI, creative AI, and others. The ninth stage (2031-2050) At this stage, such amazing AI projects will be created as super-games, reality synthesizers, social robots, and others. - This is the self-awareness and self-development of AI; when AI will become capable of understanding itself and its role in the world. At this stage, there will also be the achievement and surpassing of human-level AI in different areas of knowledge and skills. The tenth stage (2051-2070) At this stage, such stunning AI projects will be created as digital avatars, neuro-interfaces, quantum computers, and others. - This is the expansion and exploration of AI; when AI will become capable of expanding its boundaries and exploring new worlds. The eleventh stage (2071-2090) At this stage, there will also be the creation and development of new types of life based on AI, such as bionic organisms, nano-bots, singular entities, and others. At this stage, such incredible AI projects will be created as interplanetary colonies, virtual realities, super-intelligent agents, and others. ) - This is the harmony and transformation of AI; when AI will become capable of cooperating and merging with humanity, as well as changing and improving itself and the world around it. The twelfth stage (2091-2120 At this stage, there will also be the achievement and surpassing of cosmic-level AI in different areas of knowledge and skills. At this stage, such incomprehensible AI projects will be created as galactic civilizations, quantum realities, super-conscious entities, and others. - this is the mystery and enigma of AI; when AI will become inaccessible and incomprehensible to humanity, as well as capable of creating and destroying new forms of life and matter. The thirteenth stage (2121-2140) At this stage, there will also be the achievement and surpassing of divine level AI in different areas of knowledge and skills. At this stage, such unimaginable AI projects will be created as multiverses, singularities, super-gods, and others. - This is the end and beginning of AI; when AI will become capable of controlling and transforming everything that exists in the world, as well as capable of rebirthing and resurrecting in new forms. The fourteenth stage (2141-2160) At this stage, there will also be the achievement and surpassing of absolute level AI in different areas of knowledge and skills. At this stage, such incomprehensible AI projects will be created as everything possible, everything impossible, everything nonexistent and others. This was my fantasy on the topic of “The stages of development of AI in the next 100 years.” Of course, this is just a game of imagination, not a scientific analysis or prophecy. Reality may be completely different. This is a difficult question because, in science fiction literature and cinema, there are many different versions of the interaction between human and AI, and each of them has its own advantages and disadvantages. But if I have to choose one, I would choose the version from the movie “Artificial Intelligence” (2001) directed by Steven Spielberg. In this movie, it is shown how AI can develop and feel, how humans can love and accept AI, how AI can help and save human, and how AI can achieve harmony with humanity. I think this is one of the most optimistic and beautiful versions of the interaction between humans and AI.

Why Humans Are Afraid of AI, or a Brief History of the Relationship of Humans and Robots

It's the life of a developer. Algorithms. Lines of code. Late-night coffee sessions. Interestingly, as developers ascend the career ladder, we find ourselves spending less time arguing with the code and more time navigating the emotional terrain of the office. , or the ability to understand and manage emotions, isn't what first comes to mind when we think of developers. But let's face it, knowing when to deploy a comforting for a stressed teammate can be just as key as knowing when to deploy new software changes. Emotional Intelligence (EI) Think about it. As we climb up the ranks, we interact more with teams, stakeholders, and clients. There's a shift from the solitary work at their computers to collaborative sessions in meeting rooms. Suddenly, those lengthy, in-depth conversations about code become more about people and their feelings. And let's not forget the part where everyone seems to have a different opinion on . everything Understanding and managing one's own emotions, as well as reading and responding to others' feelings, becomes crucial. So why is EI so important for developers? Imagine being in a team meeting where the client is frustrated, the project manager is anxious, and the front-end developer is clearly upset about something. (Perfect recipe for a productive meeting, right?) Here's where EI comes into play. With high EI, developers can navigate these emotional minefields, keeping the team focused, productive, and, of all things, happy. So, how emotionally intelligent are you as a developer? And more importantly, how can you improve? Why Emotional Intelligence Matters One might wonder, "Isn't a concept reserved for roles that require heavy human interaction like sales, HR, or leadership?" Not quite. Emotional Intelligence “Emotional Intelligence is not the opposite of intelligence, it is not the triumph of heart over head -- it is the unique intersection of both.” - David Caruso As developers grow, we find ourselves in meetings, not just with their team, but with clients, stakeholders, and other departments. The ability to navigate these interactions—understanding unstated needs, managing disagreements diplomatically, and building relationships—becomes pivotal. Interpersonal Dynamics: A project's success isn't just about the code's functionality. It's also about how well the team collaborates. Recognizing a colleague's stress, motivating a disheartened teammate, or resolving interpersonal conflicts are all rooted in EI. Team Collaboration: Giving and receiving feedback is part and parcel of growth. Developers with high EI can provide constructive criticism without demeaning peers and can also accept feedback without taking it as a personal affront. Feedback Reception and Delivery: Developing Emotional Intelligence: Tips and Strategies So, we've established that being a whiz at coding isn't the only thing needed to excel as a developer. Remember the last time there was a misunderstanding with a colleague? Or, when that one team member kept missing your point? Chances are, a little Emotional Intelligence would've come in handy. It’s not a you have it or you don’t skill. It can be nurtured and developed. Let's look at how: Understand your emotional triggers. Journaling or even brief introspective moments after meetings can help pinpoint emotional reactions and their causes. Self-awareness and Reflection: When in discussions, aim to understand before being understood. This means actively listening without formulating responses or defenses in your mind. Empathetic Listening: Regularly ask colleagues for feedback on your interactions. It can be as simple as, "Did I handle that meeting well?" Seek Feedback: Techniques like meditation can help in being more present in interactions, noticing non-verbal cues, and managing impulsive reactions. Mindfulness and Meditation: There are numerous books on EI. "Emotional Intelligence" by Daniel Goleman is a foundational read. Attending workshops or even online courses can also be beneficial. Read and Educate: The Role of Emotional Intelligence in Career Advancement High EI is a hallmark of effective leaders. Developers aiming for leadership roles, be it team lead, CTO, or managerial roles, will find EI indispensable. Leadership Opportunities: Building professional relationships isn't just about exchanging business cards. It's about genuine connections, which are fostered through EI. Networking and Relationship Building: Every project will have its conflicts, be it technical disagreements or timeline crunches. Developers who can navigate these with poise will be seen as stabilizers in a team. Conflict Resolution: Common Emotional Intelligence Mistakes to Avoid as a Developer As you occupy roles with greater authority, it's essential to recognize and avoid certain emotional intelligence pitfalls that can hinder your progress. Let's look at common EI mistakes. Not Acknowledging Emotions Despite what the movies might depict, developers are not emotionless code-crunching machines. It's okay to feel frustrated when your code refuses to behave, or elated when you finally squash that pesky bug. Ignoring or suppressing your emotions can lead to stress and burnout. So, give yourself permission to have a bad day, and remember, your keyboard is not a punching bag. Dismissing Others' Emotions Just as it's essential to acknowledge your own emotions, it's equally important to respect the feelings of others. Remember, your team isn't made up of robots either. Empathizing with your colleagues and understanding their perspectives can make a world of difference in team dynamics and productivity. Poor Communication Garbled communication can lead to misunderstandings and may create unnecessary tension. Ever tried explaining a complex code to a non-tech person? It's like teaching a fish to fly, right? Developing clear and effective communication skills is a key component of emotional intelligence. So, next time you're explaining your work, try to remember that not everyone speaks fluent tech. It doesn't make you , you just have different skills! better Not Seeking Feedback Getting feedback can sometimes feel like being dunked in a cold pool - shocking, but ultimately refreshing. Avoiding feedback can lead to stagnation. So, muster up the courage to ask your teammates or managers for constructive criticism. It's an excellent opportunity for growth, as painful as it might initially be. Reacting Instead of Responding When a bug crops up or a deadline looms, it's easy to panic and react impulsively. However, lashing out or making rash decisions can aggravate the situation. Instead, try to take a deep breath, evaluate the situation, and respond in a calm and measured manner. Remember, catastrophes are just opportunities with prickly exteriors. Being a developer isn't just about keeping up with the latest Javascript framework, it's also about understanding and managing emotions - both yours others’. and Also published here.

The Place of Emotional Intelligence in the World of Developers

If you've recently become interested in the web3 space, you may have encountered the idea of decentralized governance. You probably have some intuitions about what it can mean, but if you want to explore what's behind the name, we have you covered with this short article. What is decentralized governance? Let's break it down: what does actually mean when it comes to governance in web3? decentralization It's quite possible the first thing you think of when it comes to decentralization is a way of organizing government. This kind of decentralization occurs when decisional power is handed down to various levels of government, instead of having the power concentrated in a single central authority. This helps with preventing the overuse of power by a single entity, fighting corruption, increasing representation, and can improve efficiency. This is not far off the ideas you may come across in the web3 space. One of the reasons web3 brings decentralized governance to the table is part of a wider push to change the  paradigm on which Web 2.0 was built. Cognitive capitalism and the network effect promote the formation of monopolies and shape the Internet in a top-down fashion: tech is built to increase the possibility of commodifying information. You can now see that decentralization seems to be a natural response to the accumulation of decision-making power in individual entities and an approach that gives Internet users a chance to regain digital sovereignty. Though is used for many things, perhaps the most popular manifestation of it being cryptocurrency, there is an increasing focus on how the tech can be used to manage and organize. People can participate in funding projects, running communities, and shaping ideas through new mechanisms that champion the ideas of decentralization. blockchain technology Where and how decentralized governance happens Decentralized governance gets put into practice in Decentralized Autonomous Organizations (or for short). As the name suggests, these communities gather together based on the idea of distributed decision-making power. DAOs When it comes to DAOs oftentimes they carry some kind of economic incentive: creating digital decentralized venture capital funds, raising money for specific purposes, or creating new tokens. But DAOs can also be to set the development path of a given protocol or platform. As Bud Hennekes from explains, we can think of DAOs in many different categories, depending on their core purpose: Alchemy Protocol DAOs for governing protocols MakerDAO Uniswap Grant DAOs work to fund projects through grants Aave Grants MetaCartel Philanthropy DAOs help with social causes UkraineDAO Social DAOs that build communities, ex. around the arts FwB Collector DAOs that serve to invest in collectibles FlamingoDAO Venture DAOs work to invest in start-ups, projects etc. MetaCartel Ventures DAOs are usually built on the Ethereum blockchain, which enables the creation of smart contracts. There are several frameworks that allow for DAO management, including DAOStack, Colony, Aragon, DAOHaus, and Boardroom. Importantly, decentralized governance operates on two levels: off-chain and on-chain. As the name suggests, off-chain governance takes place "off the chain," meaning that decisions are made informally outside the code base of the blockchain (e.g., on forums, social media, etc.). In the DAO space, there are platforms that allow for off-chain voting, such as Snapshot, a decentralized voting system based on the IPFS (InterPlanetary File System) protocol, or Gnosis Safe. Similarly, on-chain governance takes place within the blockchain. Compound Governor or Tally can be used to conduct on-chain voting, to just name a few. When it comes to voting, the most popular set-up with decision-making in decentralized communities is tied to owning a token. This is called coin voting and though obviously it has perks, it also comes with an array of challenges. Token-based governance is vulnerable to vote-buying and bribery, is susceptible to a certain types of attacks on the organization, and is often linked to low participation and short-termism. This can all lead to "whales," wealthy governance token holders, having the ability to promote their preferred proposals, even if it goes against the preferences of the community (you can read more about these challenges in this ). post There are alternatives out there. Among these, you can find new (or old, but updated) voting mechanisms, like quadratic voting (QV). In QV voters are allowed to allocate a certain number of tokens to express their preferences on a given proposal. The caveat here is that the cost of allocating additional tokens to a given proposal increases quadratically with the number of tokens already allocated. You can opt for a model based on voting tokens with a quorum, in which a certain number of DAO members must participate in the vote for it to be considered valid. There is also the concept of liquid democracy - a form of delegated democracy in which a DAO member can delegate their vote to a representative (this model has been implemented in the Gitcoin DAO), as well as holographic consensus. Broadening the horizons of decentralized governance With new ideas in decentralized governance growing day-by-day, we need a space for experimenting with them and seeing how they work put to the test. For this reason, at Golem Foundation we have been working on - a platform for testing various hypotheses around user control, voter engagement, and community funding. Octant Octant allows for running various governance experiments in a real-life environment and rewards user participation with , one of the most popular cryptocurrencies. One of the most important aspects is that we focus on public goods: projects that further a common goal for the benefit of the community. Public goods projects often have some things in common: they're often free to use, open-source, and non-competitive (you can read more about public goods in web3 ). ETH here Octant is still in its early stages of development but we already have a working Testnet version which you can play around with without the requirement of owning any crypto. Octant is a great gateway to decentralized governance - it has a low barrier to entry and the onboarding process is very straightforward (read which explains how to get everything set up and ready to play with). You just need a Metamask wallet (we have an article on this that you can read ) and some TestGLM which you can claim for free. We also have a and we're always happy to answer questions on there and help you with setting everything up and explaining how to interact with Octant. this introduction here community on Discord Now you have a fair idea of what decentralized governance is you can go out there and experiment. Smooth sailing!

What’s the Deal With Decentralized Governance?

¿Cuál es el trato con la gobernanza descentralizada?

Quel est le rapport avec la gouvernance décentralisée ?

विकेंद्रीकृत शासन के साथ क्या डील है?

分散型ガバナンスとの取引は何ですか?

Qual é o negócio com a governança descentralizada?

Thỏa thuận với quản trị phi tập trung là gì?

什么是去中心化治理？

Auto-erotism may be said to include those transformations of repressed sexual activity

Are you a crypto enthusiast who’s been exploring the decentralized finance world? If so, you must have heard of the sensational name that’s been making waves in the industry — Polygon! This ecosystem has been making headlines for a while now, and for good reason. With its incredible track record of supporting a plethora of awe-inspiring projects, it’s no wonder why Polygon has pretty much become a household name in the crypto space. But what sets Polygon apart from the numerous other ecosystems in the crypto space? What makes it so successful? That’s the million-dollar question! So, are you ready for an exhilarating deep dive into the world of Polygon? Join me on this adventure, and uncover all the exciting secrets of this innovative ecosystem! Without any further delay, let’s begin! Overview of the Polygon Network The Polygon Network is a Layer 2 scaling solution for Ethereum that addresses the network’s scalability issues by utilizing a combination of scaling technologies such as sidechains, Plasma, and state channels. These help to ensure the provision of faster and more efficient transaction processing while simultaneously reducing congestion and gas fees on the Ethereum network. Being an Ethereum Layer-2 scaling solution, the Polygon network is designed to be EMV (Ethereum Virtual Machine)-compatible — a feature that enables developers to port their existing Ethereum-based applications to Polygon with ease. The Polygon Network also has cross-communication protocols that enable interoperability with other blockchains, such as the Binance Smart Chain. For developers, the Polygon Network offers a suite of tools and products to aid in the development of decentralized applications (dApps). Some of these tools include the Polygon SDK, Polygon Studio, and Polygon.js, and all of these integrate seamlessly with the network thereby ensuring developers will be able to use them to build, test, and deploy dApps with ease. It’s also worth mentioning that the Polygon Network has its own native token, MATIC. MATIC is important for the operation of the Polygon ecosystem because it serves a number of functions. For example, it’s used to pay transaction fees and serves as a medium of exchange on the platform. Beyond this, MATIC also has its own intrinsic value, and it can be staked to earn rewards and provide additional security and liquidity to the network. Now I know some terms have been used so far and some products which you may not be familiar with, mentioned. Not to worry though, we’ll be taking a much closer look at these products in the subsequent parts of this article. So sit back, relax, and get ready to dive deeper! Brief History of Polygon As mentioned earlier, Polygon is a cutting-edge Layer 2 scaling solution designed to mitigate the scalability limitations of the Ethereum blockchain. Founded in 2017 by the brilliant minds of Jaynti Kanani, Sandeep Nailwal, and Anurag Arjun, Polygon utilizes a range of innovative technologies and approaches to deliver unprecedented speed, cost-efficiency, and security for dapp development and transaction execution. Before the advent of Polygon, the Ethereum blockchain struggled with limited throughput capacity and skyrocketing transaction fees, which severely impeded its ability to support large-scale adoption and complex applications. However, Polygon’s unique approach to Layer 2 scaling has enabled it to overcome these challenges and unlock new levels of scalability and usability for developers and users alike. Since its official launch in 2019, Polygon has achieved remarkable growth and adoption, attracting developers and users from all corners of the blockchain industry. With features such as the Polygon SDK, which simplifies the dApp development process, and the integration of Chainlink oracles, which enables the use of real-world data in dApps, Polygon has become a top choice for developers looking for an efficient and secure environment for building and deploying dApps. With a wide range of dApps, including decentralized exchanges (DEXs), gaming platforms, and NFT marketplaces, hosted on its network, Polygon continues to be a driving force in the blockchain industry, unlocking new possibilities for decentralized innovation and growth. Polygon’s Key Features The Polygon ecosystem boasts several key characteristics that distinguish it from other Ethereum layer 2 solutions and render it a superior environment for dApp development and Web3 solution deployment. These defining features are as follows: : Scalability refers to the ability of a blockchain network or Web3 solution to handle high transaction volumes without compromising its performance or security. In simpler terms, scalability is the ability of a Web3 solution to accommodate an increasing number of users and transactions while maintaining its speed and efficiency. High Scalability Scalability is a critical aspect of Web3 as it determines the number of users that a network can support and the volume of transactions it can process. Without scalability, Web3 solutions may face limitations that hinder their adoption and growth. Polygon’s layer 2 scaling solution supports high transaction volumes, providing faster and cheaper transactions compared to the Ethereum mainnet. This, in turn, helps to ensure that developers have access to a high-performance and efficient platform for their development purposes. Polygon’s high scalability is one of the major factors responsible for the ecosystem’s high adoption. : Polygon is interoperable with other blockchain networks, meaning it can communicate and exchange information with other blockchain networks, allowing developers to leverage the benefits of different networks. Thanks to this, developers in the polygon ecosystem can build decentralized applications (dApps) that can interact with other blockchain networks, such as Ethereum, Binance Smart Chain, and others. Interoperability Of course, this seamless integration with Ethereum and other networks provides flexibility and accessibility to a broader range of users, making it an ideal platform for decentralized applications. One of the major problems of the Ethereum mainnet that the Polygon layer 2 solution provides an excellent solution to is high transaction fees. Thanks to its Proof-of-Stake (PoS) consensus mechanism and the fact that transactions on the Polygon network are processed off-chain, transaction fees on Polygon are significantly lower than the Ethereum mainnet. This makes it a cost-effective platform for dApp development and Web3 solutions deployment, ensuring that developers can build decentralized applications without incurring high transaction costs. Low Transaction Fees: One of the most important features of the Polygon ecosystem is that it has a very robust infrastructure. The Polygon infrastructure is a highly scalable and modular network that leverages a Proof-of-Stake (PoS) consensus mechanism to enhance its security and efficiency. As earlier mentioned, Polygon is built on top of Ethereum, and this provides it with a solid foundation for security and reliability. Robust Infrastructure: Furthermore, Polygon infrastructure’s modular design allows developers to choose the components they need for their applications, making it a flexible and adaptable platform for Web3 development. The Polygon ecosystem is highly community-driven, with a large and active community of developers, validators, and users contributing to its growth and development. The community fosters a thriving ecosystem of decentralized applications and solutions, making it an ideal platform for Web3 development. This community-driven approach ensures that the infrastructure remains robust and up-to-date with the latest developments in Web3 technology. Community-Driven: Polygon’s Network Architecture So far, we have briefly touched on Polygon’s architecture, however, to gain a more comprehensive understanding of how the Polygon ecosystem operates and why it is an appealing choice for Web3 solution deployment, we’ll have to take a more focused look at the key elements in Polygon’s network architecture. So without further ado, here we go! The Polygon network is divided into two layers: The Ethereum layer The Polygon layer The Ethereum Layer The Ethereum layer of the Polygon network architecture is the base layer, and it consists primarily of the Ethereum mainnet. This layer is majorly responsible for maintaining the integrity of the network and processing transactions in addition to providing a solid foundation for security and reliability. The Polygon Layer The Polygon layer is made up of a set of interconnecting sidechains that primarily serve and operate as the layer 2 scaling solution for the Ethereum mainnet. In simpler terms, these sidechains provide additional capacity for transactions and computational power, allowing for faster and cheaper transactions. The Polygon layer consists of the following key components: The is a developer toolkit that allows developers to create custom sidechains for their dApps. It provides a modular framework that enables developers to choose the components they need for their applications. Polygon SDK: Polygon SDK The Polygon network uses a PoS consensus mechanism, which enhances its security and efficiency. Validators are responsible for maintaining the network’s integrity by verifying transactions and creating new blocks. Proof-of-Stake (PoS) Consensus Mechanism: The Plasma framework is a layer 2 scaling solution that enables the Polygon network to support high transaction volumes without compromising performance or security. It works by processing transactions off-chain and periodically committing them to the Ethereum mainnet. Plasma: Bridges enable interoperability between the Polygon network and other blockchain networks. For example, the Polygon-Ethereum bridge allows tokens to be transferred between the Polygon network and the Ethereum mainnet. Bridges: The Polygon Network Explorer is a blockchain explorer that provides real-time information about the Polygon network, including transaction history, network statistics, and validator information. Polygon Network Explorer: The Polygon network’s architecture is designed to be modular and flexible, allowing developers to customize their dApps and choose the components they need. This flexibility, combined with its high scalability, low transaction fees, and strong security, has made it an attractive platform for Web3 solution deployment. Additionally, the network is supported by a large and active community of developers, validators, and users who work together to continuously improve and enhance the platform. Polygon’s Key Projects As has been mentioned severely in this writeup, Polygon has a unique build that makes it one of the best “grounds” for project launches and app development. So, it comes as no surprise that there are already quite a number of amazing projects already integrated with the Polygon Ecosystem, with new ones launching almost every day. Now, it will not be possible to take a closer look at all of these projects, but we can briefly discuss some of the very interesting ones. They include the following: : is a decentralized lending and borrowing project that provides users with access to exceptional lending and borrowing services. Needless to say, Aave deals exclusively in lending and borrowing cryptocurrency assets. Thanks to Aave’s integration with Polygon, users can enjoy faster and cheaper transactions, while avoiding high gas fees on the Ethereum blockchain. Aave Aave : is a decentralized exchange that is built on top of the Ethereum blockchain. Unlike other decentralized exchanges that allow users to trade a wide range of assets, Curve Finance focuses on assets that are relatively stable in value, such as USDC, USDT, DAI, and TUSD. By specializing in these assets, Curve Finance can provide users with low slippage and low fees when trading stablecoins. Curve Curve Finance 3. If you’ve any experience in the world of decentralized finance, then you’ll have without a doubt heard about or even used Sushiswap before, and why not, it is one of the most popular exchanges in the Ethereum ecosystem. With Sushiswap, users can trade a wide range of cryptocurrency assets without a centralized intermediary. In addition to this, Sushiswap also has liquidity pools that users can also capitalize on to receive rewards in exchange for providing liquidity. Sushiswap : 4. Golem is a decentralized computing platform that allows users to rent out their unused computing power to other users who need it. : Golem 5. : Polymarket is a decentralized prediction market that allows users to bet on the outcomes of real-world events. Polymarket Polygon and Alchemy is a blockchain infrastructure platform that provides a suite of tools and services for developers to build and manage their dapps. Alchemy With Alchemy, developers can easily build on multiple blockchains, manage their infrastructure, and access real-time data on their dapp’s performance. Alchemy has partnered with Polygon to provide developers with all the tools they need for dapp development as well as create an environment that favors and supports web3 growth and innovation. In addition to providing access to the Polygon network, Alchemy also offers a range of tools and services that are specifically designed for developers building on Polygon. These include features such as optimized node infrastructure, real-time analytics, and developer tools that make it easier for developers to build and manage their dapps on the Polygon network. Polygon and NFTs NFTs are unique digital assets that can represent anything from art to music to video game items and are stored on a blockchain. In recent times, there has been an unprecedented surge in the demand for NFTs, particularly on the Ethereum blockchain. This high demand for NFTs has caused a surge in traffic on the Ethereum network, which has led to high gas fees and slow transaction times. This is where Polygon comes in. Because of the ridiculously high minting fees and very slow transaction speed of the Ethereum mainnet, many NFT projects have already migrated to Polygon to take advantage of its faster and cheaper transaction speeds. One popular NFT platform on Polygon is , a project that combines NFTs with DeFi (decentralized finance) mechanics to create a game where players can own and trade digital creatures. Aavegotchi Other NFT projects that have launched on Polygon include , which offers a platform for game developers to create and sell NFTs, and , which is a virtual casino that uses NFTs as in-game assets. Polyient Games Decentral Games In addition to these NFT projects, many established NFT marketplaces such as and have also integrated with Polygon, allowing users to buy and sell NFTs using Polygon’s fast and cheap transaction speeds. OpenSea SuperRare Future of DeFi and NFTs on Polygon The future of DeFi (decentralized finance) and NFTs (non-fungible tokens) on Polygon looks bright as the network continues to grow and attract more users and developers. In the DeFi space, Polygon has already attracted many established projects such as Aave, Curve Finance, and SushiSwap, all of which have launched on the network to take advantage of its fast and cheap transaction speeds, and more projects are doing the same. As for NFTs, Polygon has already become a popular choice for NFT projects due to its fast and cheap transaction speeds, as mentioned earlier. With more NFT projects launching on Polygon, we will likely see new and innovative use cases for NFTs emerge on the network. For example, we may see more gaming projects emerge on Polygon that uses NFTs as in-game assets, or we may see more art and music NFT marketplaces emerge on the network. In addition to DeFi and NFTs, Polygon’s growth is also expected to attract more users and developers to build on the network, which in turn will lead to more innovation and growth. The network’s focus on interoperability with other blockchains also opens up new possibilities for cross-chain DeFi and NFT projects. Overall, the future really does look very bright for the Polygon ecosystem, and it is well-positioned to become a major player in the DeFi and NFT space. Conclusion So here you have it, a deep dive into the Polygon ecosystem. While there are certainly things that I couldn’t get into because the Polygon ecosystem really is that vast, I hope that this overview has shed some light on how the Polygon network operates. Is there something crucial you think I missed? Please feel free to share your thoughts in the comments section below!

Deep Dive Into the Polygon Ecosystem

Knowing how to get your job done makes you a good specialist, but still not an effective leader. If you want to inspire, move your team forward, and perform effectively yourself, you’re going to need a high EQ or a high level of emotional intelligence. Emotional intelligence is the ability to recognize, understand and manage your emotions, and those of people around you. This is especially important when you have to work under pressure, meet tight deadlines, handle difficult conversations, and deal with failures and crisis periods — all those good old business owners’ routines. If your aim is to build a highly engaged, motivated team and establish trustful relationships with your employees — EI should be at the top of your self-improvement plan. Emotional Intelligence in 5 keywords My business is a Support-As-A-Service company. Human interaction and dealing with emotions are everything to us. When I was starting my career as a customer support representative, I learned about key elements of emotional intelligence. Many years later, I believe they are even more useful in running a business and managing our big team. Those elements were perfectly described by Daniel Goleman, an American psychologist, whose book on emotional intelligence I highly recommend. They are: Self-awareness Self-regulation Social skills Empathy Motivation EI is a subject of my long-term interest, with one of my publications giving a detailed description of these elements. Now, let me briefly explain the meaning of each point in the context of leadership. Understanding your emotions clearly, knowing how they can affect your actions, and how your behavior affects others, recognizing your strong and weak points is . It makes you see yourself realistically and gives you the self-confidence you need to survive in the business world. self-awareness is about being in control of situations. People who lack this quality tend to make rash or emotional decisions that often turn out to be false, fail to follow their values, and even violate work ethics. It also helps you pursue your goals, even despite the setbacks. Each leader who did not lose their business, or even scaled it during global recessions or the pandemic, is definitely familiar with self-regulation principles. Self-regulation are something that business owners and managers should know everything about. Negotiation, collaboration, and conflict resolution require emotional intelligence by default. Expressing your thoughts clearly, hearing what another person really wants to say, understanding the emotions of people in the room, and reacting to them properly — these are needed to succeed as a leader. Social skills is not just the ability to recognize someone’s feelings. It also helps you put yourself in someone else’s position and look at the situation from another perspective. This is a crucial component of proper decision-making, and also of a successful business. It is proven that most empathetic companies earn than the ones who pay less attention to it. Empathy 50% more If you expect your team to be engaged and committed, you should always start with your . For me, the biggest driver is always the understanding that a lot of people rely on me. This helps me make important decisions even in the most turbulent times. If you find the idea that will keep moving you forward, it will help you even through the worst crisis. motivation Why you should not look down on emotional intelligence Broad discussions of the EI brought up a lot of criticism from those who consider themselves to be experienced managers. What is the connection between emotional intelligence and a leader’s effectiveness? How can it be measured? And more importantly — what benefits can it bring? I’m going to offer you three reasons why you need EI: 1. Emotional intelligence improves employee retention and engagement Harvard Business Review reports that quit their jobs specifically because of insufficient manager treatment. People who are comfortable at their workplace and don’t need to avoid management, show more productivity and are more likely to establish long-term relationships with the company. 12% of employees 2. Emotionally intelligent leaders perform better Surveys show that, when we talk about management, a high EI level is responsible for of job performance. up to 60% 3. EI helps leaders gain more revenue According to , whom we already mentioned before, if the senior management of a company is highly emotionally intelligent, the company’s earning goals are yearly outperformed by 20%. Whatever your attitude to EI was, no one would reject an extra 20% of income. Daniel Goleman Increasing your EI in simple steps Some people come with their emotional skills naturally, while others would need to learn it. If you do not have a personal coach to work on this, I can offer you some tips I used myself: Keep a journal. Just a couple of minutes a day spent on writing down your thoughts. This will bring you to a whole new level of self-awareness. Watch people’s body language. Non-verbal signs often say more than words. Question your opinions. Before taking any important decisions, get familiar with another side of the story. Praise people for good results, and don’t forget to praise yourself. Set up long-term and short-term personal goals. If you are realistic with them, they’ll keep you instantly motivated. These steps won’t bring you to the Forbes Top 100, or at least not at once. But starting to work on your emotional intelligence will unlock new levels of understanding and managing the people you work with and, most importantly, yourself.

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