In a significant development, the National Stock Exchange (NSE) has introduced 45 new stocks to the Futures & Options (F&O) segment, effective November 29, 2024. This list includes prominent names such as Jio Financial Services, Zomato, and Avenue Supermarts.

Nifty 50 Rejig: Jio Financial Services, Zomato shares among top contenders for inclusion in March 2025 — JM Financial | Stock Market News

Lewis and Clark County is getting a museum dedicated to its history. It opens next week, but MTN visited 618 Helena Avenue for a sneak peek.

Lewis and Clark County Historical Society is opening a new museum

New York City's iconic Barnes & Noble on 5th Avenue recently featured two of KnowBe4's leading cybersecurity experts...

KnowBe4's Cybersecurity Experts Shine at Barnes & Noble in New York City

Each entity believes that driving economic growth into the area will also help alleviate some of the ongoing issues.

Prospect Avenue business leaders focusing on economic growth

General view of the Microsoft store on Fifth Avenue on July 19, 2024 in New York City. Businesses and transport worldwide were affected by a global technology outage that was attributed to a software update issued by CrowdStrike, a cybersecurity firm whose software is used by many industries ...

Microsoft’s security culture reboot includes cyber governance council, all-staff trainings | CyberScoop

Avenue Stores LLC was a specialty retailer in the United States offering plus-size clothing to women who wear larger-size clothing. The company serves a target audience of women aged between 25 and 55 years of age, wearing apparel of size 14 or larger, and also sells shoes and accessories.

Avenue (store) - Wikipedia

Hudson’s Bay Company, the owner of the Saks Fifth Avenue department store chain, said it would bid with the state to convert the top three floors of its flagship store in Midtown Manhattan into a Monte Carlo-style casino.

Saks Fifth Avenue Owner Unveils Plan for Casino at NYC Store - ...

Avenue

avenue

For a long time, smaller businesses and entrepreneurs have been held back by the rigid constraints of the traditional banking sector, which imposes burdensome requirements on those seeking capital. With DeFi, small businesses can benefit from a more efficient and streamlined approach to financial transactions, with seamless payments, simpler lending and borrowing processes and much easier access to all-important capital. The innovations found in DeFi boost access while eliminating cumbersome approval processes and high transaction fees, and can potentially become a catalyst for much faster business growth. Let’s take a look at how some of the most innovative DeFi protocols can shake up the small business world. Aave: Generate yield on your crypto holdings Aave is one of the best known DeFi protocols of all, and in many ways it can be thought of as a decentralized bank, as it provides an array of services that can be utilized by small businesses to streamline their financial operations. Its core functions include interest generation, where businesses can deposit cryptocurrency to earn interest. It also acts as a source of funding, giving users the option to borrow a range of crypto assets through collateralized loans. The platform is notable for its flexibility, with features like collateral swapping ensuring businesses can utilize a variety of crypto assets. For small businesses that already accept crypto payments, Aave provides a way for them to unlock the value of those holdings without having to swap them all for fiat. Businesses can deposit a range of crypto assets into Aave’s lending pools, with the potential interest adjusting automatically based on demand. If more people want to borrow a certain kind of asset, the rate increases to encourage more lenders to deposit, creating opportunities for businesses to generate substantial yield on previously idle crypto assets. Centrifuge: Obtain credit fast using physical assets as collateral Finding funding can be a major challenge for small businesses, as there are very few options available in traditional finance, besides wealth investors and conventional banks. Centrifuge gives businesses another option – the ability to tokenize real-world assets and use them as collateral to obtain credit in an instant at very competitive rates. To borrow money from Centrifuge, businesses can use its associated decentralized application Tinlake, which is a marketplace for tokenized RWAs. It provides businesses with a streamline way to tokenize assets, such as invoices that have not yet been settled, or real estate owned by the business, so these can be used as collateral to obtain funding. With Tinlake, investors can also trade these RWAs over its decentralized marketplace, and businesses can even participate as small investors, depositing crypto into its liquidity pools for other businesses to borrow against. The great thing about Centrifuge is that its tokenization process is fast and efficient, meaning businesses can obtain capital in a fraction of the time it would take to secure a loan from a bank. It’s all about fast access to liquid funds, enabling entrepreneurs to obtain the capital they need to stock up on their inventories or invest in key parts of their business at just the right time. Fuse.io: Grow your business globally with scalable crypto payments The Fuse Network can be thought of as a business-to-business payments infrastructure network that supports the development of blockchain applications that can provide more streamlined and transparent services to users. The platform achieves blazing-fast transaction speeds thanks to its delegated proof-of-stake architecture, which enhances decentralization and security. Payment transactions are offloaded to a layer-2 network to ensure rapid execution. The business model is similar in many ways to Visa and Mastercard, only its focused on Web3 payments, enabling businesses to create new applications that support crypto payments, or add this support to existing apps. There are two key elements of Fuse that businesses need to know about, beginning with the Fuse SDK, which provides the tools to integrate crypto payments into existing client apps. It provides one of the simplest and fastest ways for businesses to upgrade legacy apps, saving on development costs. Next is Fuse Charge, which can be thought of as a kind of merchant bank for Web3 businesses, providing access to various crypto and fiat-related payment services. Using Charge, businesses have a way to process payments, create invoices and manage cross-border transactions. It paves the way for companies to integrate crypto payments alongside traditional fiat and manage everything under one hood, without worrying about complex installations and high development costs. What’s more, it’s a truly borderless platform, enabling companies to transact across different countries with complete autonomy in a compliant way. Businesses benefit from more certainty too, with a flat fee of just 0.5% on every transaction, crypto or fiat, so they understand the cost of doing business. As a non-custodial platform, businesses always retain complete control over their funds. Celo: Explore new business models powered by micro-transactions A mobile-friendly crypto payments platform, Celo is aiming to make DeFi accessible to everyone, so businesses have a way to tap into markets where many people remain unbanked. Perhaps the biggest advantage of Celo is the way it allows businesses to access new markets, with its focus on mobile wallets and low-cost transactions giving them a way to interact with the millions of people in the world that don’t own a bank account. In this way, Celo opens the door to new customer segments for businesses that are willing to try and tap into such audiences. The other aspect of Celo is its support for frictionless micro-transactions. Businesses can take advantage of this to create new business models around micropayments, such as extremely low-cost pay-per-use services and content monetization, which have traditionally been cost-prohibitive beyond a certain level. In support of this, Celo is focused on borderless efficiency, with its low-fees and rapid settlement times dramatically reducing the hassle of international payments. This can benefit businesses in many ways, such as helping them to create international supply chains to save on costs, hire remote workers or tap into new customers in foreign markets. In addition, Celo aims to open the door to sustainable microfinance. Businesses can generate new revenue models by using their capital to provide small-scale loans to entrepreneurs and underserved communities. It’s about more than just generating interest, and such activities can fuel economic development and create new opportunities for growth by establishing links with up-and-coming markets. MakerDAO: Deal in crypto payments without volatility Many businesses reject crypto payments due to the volatility of digital assets, but doing so means missing out on what is likely to one of the fastest-growing financial markets of our times, and a key enabler of borderless payments. MakerDAO provides businesses with a solution through DAI, an algorithmic stablecoin that’s designed to maintain a stable, one-to-one peg with the U.S. dollar. The advantage of DAI is that companies can deal in a range of cryptocurrencies, and quickly trade their holdings for the stablecoin to eliminate the risks associated with crypto’s volatility. DAI remains stable because it relies on complex algorithms that ensure it retains its U.S. dollar peg no matter what the market conditions are in the crypto industry. Key to this is MakerDAO’s lending protocol, which enables users to obtain loans paid out in DAI by depositing other cryptocurrencies as collateral. The loans are overcollateralized, which means users have to deposit more money than they take out. This enables MakerDAO to protect the value of DAI when the tokens backing it, such as ETH and Wrapped Bitcoin or wBTC, experience high volatility. The major benefit of DAI is that it enables businesses to transact using almost any cryptocurrency. Businesses no longer have to immediately convert their crypto funds into fiat and pay excessive transaction fees to do so. Instead, they can immediately swap their crypto for DAI at negligible costs, to avoid holding funds that could be subject to wild price swings. In addition, the wide acceptance of DAI means that businesses have a way to transact across borders with low, predictable costs. DeFi will define small business success Running a small business is a hard thing to do and it has become progressively more difficult due to the restrictions of the traditional banking industry. In 2022, banks reached a new, all-time low in terms of lending activity, with tightened credit restrictions making it harder than ever for small businesses to obtain reasonably priced credit. Businesses need access to credit. In a 2020 study, the U.S. Small Business Administration found that more than 80% of business failures occurred due to cash flow issues. By tapping into the world of DeFi, businesses more opportunities to access the funds they need to maintain their cash flow, even when they’re waiting on customers to pay invoices. At the same time, they can benefit from streamlined payments with lower costs and expand their reach into new, global markets. Don’t forget to like and share the story! Vested Interest Disclosure: This author is an independent contributor publishing via our business blogging program. HackerNoon has reviewed the report for quality, but the claims herein belong to the author. #DYOR For a long time, smaller businesses and entrepreneurs have been held back by the rigid constraints of the traditional banking sector, which imposes burdensome requirements on those seeking capital. With DeFi, small businesses can benefit from a more efficient and streamlined approach to financial transactions, with seamless payments, simpler lending and borrowing processes and much easier access to all-important capital. The innovations found in DeFi boost access while eliminating cumbersome approval processes and high transaction fees, and can potentially become a catalyst for much faster business growth. Let’s take a look at how some of the most innovative DeFi protocols can shake up the small business world. Aave: Generate yield on your crypto holdings Aave is one of the best known DeFi protocols of all, and in many ways it can be thought of as a decentralized bank, as it provides an array of services that can be utilized by small businesses to streamline their financial operations. Aave Aave Its core functions include interest generation, where businesses can deposit cryptocurrency to earn interest. It also acts as a source of funding, giving users the option to borrow a range of crypto assets through collateralized loans. The platform is notable for its flexibility, with features like collateral swapping ensuring businesses can utilize a variety of crypto assets. For small businesses that already accept crypto payments, Aave provides a way for them to unlock the value of those holdings without having to swap them all for fiat. Businesses can deposit a range of crypto assets into Aave’s lending pools, with the potential interest adjusting automatically based on demand. If more people want to borrow a certain kind of asset, the rate increases to encourage more lenders to deposit, creating opportunities for businesses to generate substantial yield on previously idle crypto assets. Centrifuge: Obtain credit fast using physical assets as collateral Finding funding can be a major challenge for small businesses, as there are very few options available in traditional finance, besides wealth investors and conventional banks. Centrifuge gives businesses another option – the ability to tokenize real-world assets and use them as collateral to obtain credit in an instant at very competitive rates. Centrifuge Centrifuge To borrow money from Centrifuge, businesses can use its associated decentralized application Tinlake, which is a marketplace for tokenized RWAs. It provides businesses with a streamline way to tokenize assets, such as invoices that have not yet been settled, or real estate owned by the business, so these can be used as collateral to obtain funding. With Tinlake, investors can also trade these RWAs over its decentralized marketplace, and businesses can even participate as small investors, depositing crypto into its liquidity pools for other businesses to borrow against. The great thing about Centrifuge is that its tokenization process is fast and efficient, meaning businesses can obtain capital in a fraction of the time it would take to secure a loan from a bank. It’s all about fast access to liquid funds, enabling entrepreneurs to obtain the capital they need to stock up on their inventories or invest in key parts of their business at just the right time. Fuse.io: Grow your business globally with scalable crypto payments The Fuse Network can be thought of as a business-to-business payments infrastructure network that supports the development of blockchain applications that can provide more streamlined and transparent services to users. Fuse Network Fuse Network The platform achieves blazing-fast transaction speeds thanks to its delegated proof-of-stake architecture, which enhances decentralization and security. Payment transactions are offloaded to a layer-2 network to ensure rapid execution. The business model is similar in many ways to Visa and Mastercard, only its focused on Web3 payments, enabling businesses to create new applications that support crypto payments, or add this support to existing apps. There are two key elements of Fuse that businesses need to know about, beginning with the Fuse SDK, which provides the tools to integrate crypto payments into existing client apps. It provides one of the simplest and fastest ways for businesses to upgrade legacy apps, saving on development costs. Next is Fuse Charge, which can be thought of as a kind of merchant bank for Web3 businesses, providing access to various crypto and fiat-related payment services. Using Charge, businesses have a way to process payments, create invoices and manage cross-border transactions. It paves the way for companies to integrate crypto payments alongside traditional fiat and manage everything under one hood, without worrying about complex installations and high development costs. What’s more, it’s a truly borderless platform, enabling companies to transact across different countries with complete autonomy in a compliant way. Businesses benefit from more certainty too, with a flat fee of just 0.5% on every transaction, crypto or fiat, so they understand the cost of doing business. As a non-custodial platform, businesses always retain complete control over their funds. Celo: Explore new business models powered by micro-transactions A mobile-friendly crypto payments platform, Celo is aiming to make DeFi accessible to everyone, so businesses have a way to tap into markets where many people remain unbanked. Celo Celo Perhaps the biggest advantage of Celo is the way it allows businesses to access new markets, with its focus on mobile wallets and low-cost transactions giving them a way to interact with the millions of people in the world that don’t own a bank account. In this way, Celo opens the door to new customer segments for businesses that are willing to try and tap into such audiences. The other aspect of Celo is its support for frictionless micro-transactions. Businesses can take advantage of this to create new business models around micropayments, such as extremely low-cost pay-per-use services and content monetization, which have traditionally been cost-prohibitive beyond a certain level. In support of this, Celo is focused on borderless efficiency, with its low-fees and rapid settlement times dramatically reducing the hassle of international payments. This can benefit businesses in many ways, such as helping them to create international supply chains to save on costs, hire remote workers or tap into new customers in foreign markets. In addition, Celo aims to open the door to sustainable microfinance. Businesses can generate new revenue models by using their capital to provide small-scale loans to entrepreneurs and underserved communities. It’s about more than just generating interest, and such activities can fuel economic development and create new opportunities for growth by establishing links with up-and-coming markets. MakerDAO: Deal in crypto payments without volatility Many businesses reject crypto payments due to the volatility of digital assets, but doing so means missing out on what is likely to one of the fastest-growing financial markets of our times, and a key enabler of borderless payments. MakerDAO provides businesses with a solution through DAI, an algorithmic stablecoin that’s designed to maintain a stable, one-to-one peg with the U.S. dollar. The advantage of DAI is that companies can deal in a range of cryptocurrencies, and quickly trade their holdings for the stablecoin to eliminate the risks associated with crypto’s volatility. MakerDAO MakerDAO DAI remains stable because it relies on complex algorithms that ensure it retains its U.S. dollar peg no matter what the market conditions are in the crypto industry. Key to this is MakerDAO’s lending protocol, which enables users to obtain loans paid out in DAI by depositing other cryptocurrencies as collateral. The loans are overcollateralized, which means users have to deposit more money than they take out. This enables MakerDAO to protect the value of DAI when the tokens backing it, such as ETH and Wrapped Bitcoin or wBTC, experience high volatility. The major benefit of DAI is that it enables businesses to transact using almost any cryptocurrency. Businesses no longer have to immediately convert their crypto funds into fiat and pay excessive transaction fees to do so. Instead, they can immediately swap their crypto for DAI at negligible costs, to avoid holding funds that could be subject to wild price swings. In addition, the wide acceptance of DAI means that businesses have a way to transact across borders with low, predictable costs. DeFi will define small business success Running a small business is a hard thing to do and it has become progressively more difficult due to the restrictions of the traditional banking industry. In 2022, banks reached a new, all-time low in terms of lending activity, with tightened credit restrictions making it harder than ever for small businesses to obtain reasonably priced credit. all-time low all-time low Businesses need access to credit. In a 2020 study, the U.S. Small Business Administration found that more than 80% of business failures occurred due to cash flow issues. 80% of business failures 80% of business failures By tapping into the world of DeFi, businesses more opportunities to access the funds they need to maintain their cash flow, even when they’re waiting on customers to pay invoices. At the same time, they can benefit from streamlined payments with lower costs and expand their reach into new, global markets. Don’t forget to like and share the story! Vested Interest Disclosure: This author is an independent contributor publishing via our business blogging program. HackerNoon has reviewed the report for quality, but the claims herein belong to the author. #DYOR Vested Interest Disclosure: This author is an independent contributor publishing via our business blogging program . HackerNoon has reviewed the report for quality, but the claims herein belong to the author. #DYOR Vested Interest Disclosure: business blogging program business blogging program

How DeFi Is Creating New Avenues For Small Business Success

Kunjams DeFi ukax Machaq Avenidas Jisk’a Empresanakan Éxito ukar uñstayaski

Cómo las finanzas descentralizadas están creando nuevas vías para el éxito de las pequeñas empresas

DeFi कैसे छोटे व्यवसायों की सफलता के लिए नए रास्ते बना रहा है

Hogyan teremt a DeFi új utakat a kisvállalkozások sikeréhez?

DeFi が中小企業の成功に新たな道を切り開く

DeFi ກໍາລັງສ້າງເສັ້ນທາງໃຫມ່ສໍາລັບຄວາມສໍາເລັດຂອງທຸລະກິດຂະຫນາດນ້ອຍແນວໃດ

कसरी DeFi सानो व्यापार सफलता को लागी नयाँ अवसरहरु सिर्जना गर्दैछ

Jak DeFi tworzy nowe ścieżki do sukcesu małych firm

Jinsi DeFi Inatengeneza Njia Mpya za Mafanikio ya Biashara Ndogo

کس طرح DeFi چھوٹے کاروبار کی کامیابی کے لیے نئی راہیں پیدا کر رہا ہے۔

DeFi đang tạo ra những con đường mới cho sự thành công của doanh nghiệp nhỏ như thế nào

I-DeFi iyenza njani iiAvenues eziNtsha zeMpumelelo yamaShishini amaNcinci

Admit it! Startups are hard, why? Because in startup space we are looking at a problem that no one is solving or an incumbent is making lives miserable there and we improve the state, we add value, we find a new business model while doing so and somehow make that startup - now turned into a business - ! self sustainable Of course! You know all of that stuff then why am I repeating it? To give you context! Start-ups are hard because of the uncertainty of new - the unavailability of examples of business models, what to charge, how to charge, who will pay, whether customer wants it or not, etc. Apply that context to the dapps now - being born out of layer 1 blockchains, the first one of whom is merely 10 years old, we are in a nascent industry. The community is small, - on one side we see Bitcoin OGs whom you will rarely find (and imaginably they are well-funded because they were there when BTC was of $1 so don't need to show their face to anyone) self-righteousness is out of proportions On the other side you will see youngsters who just got into Ethereum development right in college or as first job and are ok with going about propagating the means of decentralisation, attending hackathons and just work for the common public good! Then there are some 35+ years olds who never made money as part of core-devs group, are dapp developers and you will find a tweet every now and then from them telling the world that how their kids are living without medical insurance and how hard it is to put food on the table with what is being offered as a salary or a grant in this space - mind you - these are the same developers who could go into mainstream tech at anytime and earn decent but they are also infected with decentralisation bug, enjoy the community and social bonding and don't want to leave the space. The point that I am raising here is that everyone has to find an earning source, everyone has to sustain themselves first and then they also need to sustain the project they are working on! Without positive cash-flows no one is going to pay for the electricity that you burn to power your laptop or to buy that black cup of coffee! Here I will dissect various funding sources that I am aware of and also would dissect them candidly, just so you can be aware of all the pitfalls (if you are not agreed with me, you can always reach out to me, I will be happy to listen your point of view). General Observations about funding avenues Let me start from hard to digest observations: Off-loading the deal with regulations - most layer 1 projects want to declare themselves decentralised, eat up most of the investor money and push the responsibility of dealing with regulators to the front-end dapp developers Almost all of the I pay dapp developers from the tokens minted from thin-air CO'ed projects Poorly designed dapp developer programs eventually hurt the end retail investor who are holding these tokens and developers eventually get paid on the investor's expense The grants/money on offer is too low for dapp developers to sustain themselves; as opposed to sustaining their projects and cover development and market costs I can keep filling this observation list but I am assuming I have set the stage, let's move on to some of the funding avenues available: Developer Grant Programs Nearly every project has them. They typically have the RFQs that you can choose from and afterwards you will fill in a grant application, give them a well-defined scope and they approve your application in 2-4 weeks. The funding available for these types of grants was still relatively good until the start of 2019 but bear market of last year has dented here the hardest. Let's say a grant that was coming around $5-$7k for a project, is now down to $1-$2k - yes, this is for a project whose MVP can take couple of months for a single developer. While we live in a an open world now, this range can be good for a fresh grad - this is however, not attracting any quality developers anymore - and from dapp developers to grant approvers, everyone is suffering in the process! The easiest way to find these grant programs is to write the name of the project in google along with developer grants keyword, you will find Polkadot, Nervos, Binance DEX, Aave, etc. - what you choose is up-to-you, these can be good or bad! Hackathons I attended about 4-6 online and in-person hackathons last year. While, these hackathons were once seen a good opportunity to on-board beginner developers to a specific blockchain by offering bounties - these are never the avenue for earning a living for the developer or finding a good future project for bounty sponsors (although I know several good projects came out of hackathons but these were usually pre-formed teams, who knew what and when to do and just used hackathon as an accelerator). For the starters, most developers at the hackathons are beginners who do not possess good knowledge or subject matter grasp to learn and execute a project in a domain that is entirely new while the cherry on top is most sponsors also don't know what they want from a project. The end result is, contrary to popular belief, a good developer only has 50% chance of earning a bounty at a hackathon that also merely covers his/her traveling cost to the hackathon. The average bounty in these hackathons is $200 to $1000 range. While EOS tried running their own hackathons, only successful ones are in Ethereum ecosystem being run by and . ETHGlobal Gitcoin Gitcoin CLR Grants is an Ethereum project that allows other blockchain projects to post bounties in their monthly hackathons, however, their other program Gitcoin CLR grants is just available for Ethereum projects at the moment. Gitcoin The program started in 2019 and just concluded its 4th round. This round was the craziest as for the first time everyone was allowed to participate (of course the application was to be approved by the Gitcoin team itself) but given the context of distributing development funds that Ethereum Foundation holds and wants someone else to come forward and manage the distribution process, this project actually ticked many of the boxes - not perfect - but better than others! Gitcoin CLR grants work on a concept called Quadratic funding, Vitalik wrote about the technical details in depth in his article . From the top, the concept is that public vote for projects with their hard-earned tokens, either DAI or ETH or any other supported tokens - the emphasis of Gitcoin is to give projects a style subscription, means if you are pledging to support a project by giving them 1 DAI, you can put that on subscription and project will get paid 1 DAI every month. here patreon You can also provide one time sponsorship.  The project is designed in such a way that most number of fundings is given higher weight in the algorithm as opposed to the amount of the funding. Gitcoin then matches your funding with its own pool of sponsored funds - sometimes up to 4x. 4th round of CLR funding saw some Twitter brawls as well since media projects were also allowed to put in applications and some opponents also accused the community to just use their friends to increase their bag of total funded amount. However, I never participated but dug into how hard it was to game the system and found an that could be used to find which projects were susceptible for gaming the system. I can safely say it wasn't that easy but since the system is not bulletproof yet, gaming could happen and should have happened. analysis project You can find the result of 4th round of Gitcoin CLR grants and information about upcoming 5th round . here Blockstack's App Mining Program If you ever want to run a developer program, what follows is a classic example of what not to do! On technical and conceptual grounds, I liked Blockstack since the beginning and even wrote a great promoting them on hackernoon. However, it's time to introduce their and equally run program called App Mining. blog post bold poorly App Mining is a program that is conceptually formed to release a developer from the burden of running ads or selling user-data in order to sustain their app. The idea is that protocol somehow will keep the dapps supported while developers can focus on their work - so no monetisation is required, developers can just go on to build great software - perfect utopia - but how? You may remember hearing Blockstack's name as part of first ever SEC approved token sale. The project, however, is operating on a turf much difficult than Bitcoin and Ethereum. Instead of democratising finance, Blockstack went after democratising the social media, auth and storage space. This is difficult because first of all, even after Cambridge Analytica, there are still billions of users who log-in to Facebook every month, hence privacy and owning your data (and paying for its storage) does not resonate with general public yet! With App Mining, the project went into even much tougher turf by thinking of ranking apps against each other, something that Apple and Google struggle with despite being old, in possession of full analytics, having hundreds of thousands of apps, in access of thousands of thousands of dollars and running full fledge app quality teams. Blockstack's version of ranking the apps was to: Delegate the review process from day one to 3rd parties in the name of decentralisation They couldn't create any categories of apps because total submissions were low - so imagine, Slack being scored against Bitcoin memes website Employed a academic formula to stop developers from gaming the system Blockstack's App Mining program started it's pilot in October 2018 and my app was included in the second run of the program in November 2018. Timski The 3rd party reviewers at the time were: TryMyUI DemocracyEarth ProductHunt The problem with DemocracyEarth at the time was that Blockstack was expecting accredited private investors (by definition each with net worth of above $1M to log in each month and review 50+ apps and vote - not even with their own money!). The second problem was with ProductHunt that despite the claims is too easy to manipulate especially when someone is offering you money to do so - (literally you just had to buy upvotes from regular members of the PH). App mining discontinued DemocracyEarth after 5 months and discontinued using ProductHunt upvotes after a whole year. Instead Awario was introduced in the mean time to quantify social and web/news mentions. Current app mining reviewers and process can be seen . here The pilot program offered $25,000 in grant money at the start and has recently been giving away $400,000 every month, however, app number still remains under 300. There were great apps as part of the project initially, such as and who also believed on the vision of the Blockstack, however, I believe as the team behind App Mining refused to iterate quickly, these projects left the program as well. Stealthy Graphite Docs I also pulled Timski just after 1st month, there was no way to keep pounding my head against the wall while the effort on a decentralised group chat iOS app was 4x higher than a normal Firebase powered app while the competition that I would need to go in was with Discord and Slack, both of which were already unicorns in this category. As Blockstack's STX tokens are now on Binance, the Blockstack PBC now wants to outsource App Mining program to a 3rd party. Only 2 years worth of STX tokens were pre-mined for App Mining. It's a tough task for anyone in the world to run a program like this where you are actually giving away money to improve the apps - as this becomes a honey-pot for developers to just game the system and draw as much money as they can! Also, the biggest task is to bake App Mining into the protocol itself, so imagine protocol deciding which app should get the money based on user statistics, domain its operating in, user profiles, etc. To be fair to Blockstack PBC, while trying to close all the gates of gaming, they went on to declare the iOS and Android (subsequently penalising everyone who had already invested in making those apps). native apps are not good for digital rights This makes me wonder if Tor was a better alternate choice than making a whole other blockchain if we do not want to include mainstream in the fight for digital privacy. After Timski that was 100% decentralised app (even the notifications were decentralised), I re-entered the App Mining program in November 2019 with my iOS app. Vegan Scanner - Is it Vegan? This time I am just testing the water and doing what everyone is doing, as I said doing a startup is hard, doing a dapp is hardest especially if you are operating in a wild-west kind of environment. The App Mining program was due to grow to $1M by May, 2020, however, since no good apps were coming up, Blockstack PBC is deciding to pause the program from February 1, 2020 and has asked the community for suggestions on how to go forward from here. EOS Block Producer Developer Programs I will just mention EOS here as I am not a subject matter expert in the world of EOS, however, I have met block producers coming to meet-ups to recruit developers. EOS seems heavily gamified and each block producer is required to come up with quality dapps as well. What you deal with that block producer is up to you but these are mostly service projects and be-aware of the fact that you are dealing with a full-fledge business here, instead of a startup or layer 1 blockchain project. NEAR Protocol I will just put in the name of another developer program here, NEAR protocol is helping dapp developers to raise from VCs and a few of them are already able to raise. This again brings us back to the philosophy of what we are doing here? Are we making self-sustainable business apps or are we creating thin-clients on top of blockchains and distributing them for free? I would rather go with the former, no amount of funding can manage the load of sustaining hundred of thousands of dapps for the foreseeable future! Funding from DAOs This also came into light with-in 2019 by the rise of different DAOs. I wouldn't name them, you know where they are, again a much novel concept. I only had the pleasure of pitching one of my apps to the founder of a DAO and also met a few founders whose apps were actually bought by these DAOs. That deal would go through like this that some one had made a mixer on Ethereum, a DAO approached them and asked if they can re-skin the mixer for the DAO and the guys did that, so it was actually a software sale! We will have to wait and see how DAOs can improve the funding landscape of the dapps. Conclusion Dapp funding is an interesting space, of course we are all experimenting and still fighting if sharding works or if PoS is better than PoW, similarly, dapp funding is a much harder nut to crack as well. However, utopia or dystopia, do think about your bills every now and then as well and keep things in perspective. For me I have now moved into core development of tendermint/cosmos sdk and learned to use elliptic-curve cryptography. This is quite an important skill to grasp if you want to stay in blockchain for long and this is where you get to do most exciting work as well. If you would want to discuss anything, you can reach out to me on or find me at one of the crypto conferences. Twitter

Alternative Avenues to Raise Funds as a DApp Developer in 2020

Looking for a career option in IT industry which is exciting yet challenging? Then Testing is the career avenue for you. We are going to walk you through the ins and outs of software testing, before you pursue your career as a software tester. Software Software Testing is a process to determine whether a computer system/program meets the specified required and gives the desired output. In short, it is the process of identifying the errors/bugs in the software before launching it in the market. Software Testing is necessary to provide a bug free quality product which is going to generate the revenue for the organization. Necessary Skills for a Software Tester Non Technical Skills​​ · If you are going to be a software tester, you must have strong analytical skills. Analytical skills will help you gain better understanding of the complex software system by breaking it into smaller units. Analytical Skills: · A software tester must be good at verbal and written communication. A software tester is expected to generate easily readable and comprehensive test cases, plans, strategies, bug and error reports so that the team at the backend can understand and make the changes accordingly. Also, it requires cautiousness and tactics to deal with the developers in case of bugs or any other issue. Communication Skills: · Testing is a time consuming work, especially when a new code is released. A software tester must know how to handle the workload and at the same time display high productivity by optimally utilizing the time. Time Management Skills: · It is must for a software tester to always have the willingness to learn as the technologies in the software industry keeps evolving with a significant speed. A good software tester is required to keep his/her technical skills upgraded with the technological changes. Your attitude must display that you take ownership of the tasks given to you and can finish it under minimal supervision. Attitude: · A compelling degree of passion is required to excel in any profession or job. But how to determine whether you have a passion for something which you never tried before? Simply give it a try, have some hands-on experience, check whether that thing excites you and if not, try something else for which you have interest. Passion: Technical Skills · As a software tester, there will be times when you are required to validate the large amount of data that is stored in the background in different types of databases like Oracle, MySQL etc. Having a basic knowledge of SQL queries can help you in verifying that proper data is stored in the respective backend databases. Knowledge of Database/SQL: · It is pretty essential for testers to have knowledge of Linux commands as most of the web-services, databases and application servers are distributed on Linux enabled machines. Knowledge of Linux commands: · Test Management is all about managing the testing related artifacts like, reports, test cases/plans, test strategies etc. which makes it an important aspect of software testing. It is must to have proper test management techniques; otherwise it may lead to the failure of software testing process. There are several available for Test Management, so it is crucial to have sound knowledge and prior hands-on experience of working on them as they are used in most of the companies. Knowledge and Experience of a Test Management Tool: tools · Defect Tracking is a key aspect of software testing. It is extremely risky to properly manage defects and track them systematically. As it is important that entire team including managers, developers and testers must know about the defects, defect tracking becomes a necessity. Knowledge and Experience of a Defect Tracking Tool: Academic Background People who are from Computer Science or are qualified B.Tech, B.E., MCA, BCA, or B.Sc. (Computers) are preferred by the companies as Software Testers. If you are not from Computer Science and do not hold any such degree, then you must complete a certification course in software testing from International Software Testing Qualifications Board (ISTQB) or Certified Software Testing Engineer(CSTE) which will help you get equipped with complete development and testing related methodologies. Career Growth Your career growth varies from company to company. However, in a typical company which follows the Capability Maturity Model, your career as a software tester (Quality Analyst) will progress in the following way: · Fresher => Quality Analyst (QA) · 2–3 years’ experience => Senior QA · 5–6 years’ experience => QA Team Coordinator · 8–11 years’ experience => Test Manager · 14 or more year’ experience => Senior Test Manager Alternative Career Options · Responsibility: Automating manual test cases, reducing time consumption Automation Testing · Responsibility: Checking the performance (load time, load handling capacity) of the application Performance Testing · Responsibility: Analyzing and assessing the company’s business model and workflow and integrating them with technology Business Analyst Conclusion It is often believed that Software Testers (also known as QA professionals) are paid less than the Software Developers. This is not true. Both the jobs are well paid and treated at par with each other. Software Testing requires a lot of patience and is very tedious but it is not boring. Ask a with more than 3 years of experience and you will get the idea how boring his job has become. Software testing requires you to understand the business requirements and formulate test cases based on the same. This allows you to try new things and methods which make your job interesting. software developer

​Kick Start your Career as a Software Tester: A Career Avenue for You in 2018

Authors:
(1) Nora Schneider, Computer Science Department, ETH Zurich, Zurich, Switzerland (nschneide@student.ethz.ch);
(2) Shirin Goshtasbpour, Computer Science Department, ETH Zurich, Zurich, Switzerland and Swiss Data Science Center, Zurich, Switzerland (shirin.goshtasbpour@inf.ethz.ch);
(3) Fernando Perez-Cruz, Computer Science Department, ETH Zurich, Zurich, Switzerland and Swiss Data Science Center, Zurich, Switzerland (fernando.perezcruz@sdsc.ethz.ch). Table of Links Abstract and 1 Introduction 2 Background 2.1 Data Augmentation 2.2 Anchor Regression 3 Anchor Data Augmentation 3.1 Comparison to C-Mixup and 3.2 Preserving nonlinear data structure 3.3 Algorithm 4 Experiments and 4.1 Linear synthetic data 4.2 Housing nonlinear regression 4.3 In-distribution Generalization 4.4 Out-of-distribution Robustness 5 Conclusion, Broader Impact, and References A Additional information for Anchor Data Augmentation B Experiments Abstract We propose a novel algorithm for data augmentation in nonlinear over-parametrized regression. Our data augmentation algorithm borrows from the literature on causality and extends the recently proposed Anchor regression (AR) method for data augmentation, which is in contrast to the current state-of-the-art domain-agnostic solutions that rely on the Mixup literature. Our Anchor Data Augmentation (ADA) uses several replicas of the modified samples in AR to provide more training examples, leading to more robust regression predictions. We apply ADA to linear and nonlinear regression problems using neural networks. ADA is competitive with state-of-the-art C-Mixup solutions. [1] 1 Introduction Data augmentation is one of the key ingredients of any successful application of a machine learning classifier. The first example that typically comes to mind is the in-depth description of the data augmentation in the now-famous Alexnet paper [26]. Data augmentation algorithms come in different flavors, and they mostly rely on the expectation that small perturbations, invariances, or symmetries applied to the input will not change the class label. That way, we can present ‘fresh new’ samples as alterations of the available examples for training. These transformations modify the input distribution to make the algorithm more robust for cases where the distribution of the test set may differ from that of the training set. We refer the reader to the related work section (Section 2.1) for an overview and description of different data augmentation strategies. The literature for data augmentation in regression is slim. The paper on Mixup augmentation [51] proposes a simple and general scheme for data augmentation using convex combinations of samples. The authors only apply their data augmentation proposal to classification problems. They conjecture in the discussion that the application to regression is straightforward, however, this is not the case in practice. Mixup is theoretically analyzed in [5, 52] as a regularization technique for classification and regression problems. However, it is only illustrated in classification problems. The Mixup algorithm has been extended to regression problems in [18, 49], in which the authors explain that Mixup cannot be blindly applied to regression problems. To our knowledge, these are the only two papers in which data augmentation for regression is proposed. RegMix [18] relies on a hard-to-train prior neural network controller before augmenting the data using a Mixup strategy. C-Mixup [49], a method proposed more recently, solves some of the issues limiting the standard Mixup algorithm for regression problems. The authors propose to mix only closeby samples in the output space (i.e., samples which have close enough labels). This strategy is only valid when the target variables are monotonic with the input and is applied in a transformed space. The authors present comprehensive results in data augmentation for in-distribution generalization, task generalization and out-of-distribution robustness. In this paper, we rely on the causality literature to provide a different avenue for augmenting data in regression problems. Causal discovery finds the causes of a response variable among a given set of observations or helps to recognize the causal relations between a set of variables [39]. These causes allow us to understand how these relations will change if we were to intervene in a subset of the (input) variables or what would be the effect on the output. So, in general, the regression model will be robust to perturbations in the input variables making the prediction less sensitive to changes in the distribution of the test set. For example, the authors in [40] use the invariance property for prediction to perform causal inference. In turn, Anchor Regression (AR) builds upon the causality literature to obtain robust regression solutions when the input variables have been perturbed [42]. The procedure relies on anchor variables capturing the heterogeneity within a dataset and a parameter γ that measures the deviation with respect to the least square solution. Once the values of the anchors are known, AR modifies the data and obtains the least square solution, as detailed in Section 2.2. In this paper, we propose Anchor Data Augmentation (ADA) to augment the training dataset with several replicas of the available data. We use a simple clustering of the data to encode a homogeneous group of observations and use different values of γ to robustify the solution to different strengths of potential distribution shifts. In every minibatch, we sample γ from a predetermined range around γ = 1. As AR was developed for linear regression, the data augmentation strategy needs to be modified for nonlinear regression accordingly. We validate ADA for in-distribution generalization and out-of-distribution robustness under the same conditions proposed in C-Mixup [49], as well as some illustrative linear and nonlinear regression examples. In the replicated experiments, ADA is competitive or superior to other augmentation strategies such as C-Mixup, although on some datasets the performance gain is marginal. The rest of the paper is organized as follows: First, we provide background information in Section 2. We give a brief overview of related work on data augmentation in Section 2.1 and summarize the key concepts on Anchor Regression in Section 2.2. Second, Section 3 shows how we extend Anchor Regression and introduces ADA. Section 4 reports empirical evidence that our approach can improve predictions, especially in over-parameterized settings. We conclude the paper in Section 5. This paper is available on arxiv under CC0 1.0 DEED license. [1] Our Python implementation of ADA is available at: https://github.com/noraschneider/ anchordataaugmentation/ Authors: (1) Nora Schneider, Computer Science Department, ETH Zurich, Zurich, Switzerland (nschneide@student.ethz.ch); (2) Shirin Goshtasbpour, Computer Science Department, ETH Zurich, Zurich, Switzerland and Swiss Data Science Center, Zurich, Switzerland (shirin.goshtasbpour@inf.ethz.ch); (3) Fernando Perez-Cruz, Computer Science Department, ETH Zurich, Zurich, Switzerland and Swiss Data Science Center, Zurich, Switzerland (fernando.perezcruz@sdsc.ethz.ch). Authors: Authors: (1) Nora Schneider, Computer Science Department, ETH Zurich, Zurich, Switzerland (nschneide@student.ethz.ch); (2) Shirin Goshtasbpour, Computer Science Department, ETH Zurich, Zurich, Switzerland and Swiss Data Science Center, Zurich, Switzerland (shirin.goshtasbpour@inf.ethz.ch); (3) Fernando Perez-Cruz, Computer Science Department, ETH Zurich, Zurich, Switzerland and Swiss Data Science Center, Zurich, Switzerland (fernando.perezcruz@sdsc.ethz.ch). Table of Links Abstract and 1 Introduction Abstract and 1 Introduction 2 Background 2.1 Data Augmentation 2.1 Data Augmentation 2.2 Anchor Regression 2.2 Anchor Regression 3 Anchor Data Augmentation 3 Anchor Data Augmentation 3.1 Comparison to C-Mixup and 3.2 Preserving nonlinear data structure 3.1 Comparison to C-Mixup and 3.2 Preserving nonlinear data structure 3.3 Algorithm 3.3 Algorithm 4 Experiments and 4.1 Linear synthetic data 4 Experiments and 4.1 Linear synthetic data 4.2 Housing nonlinear regression 4.2 Housing nonlinear regression 4.3 In-distribution Generalization 4.3 In-distribution Generalization 4.4 Out-of-distribution Robustness 4.4 Out-of-distribution Robustness 5 Conclusion, Broader Impact, and References 5 Conclusion, Broader Impact, and References A Additional information for Anchor Data Augmentation A Additional information for Anchor Data Augmentation B Experiments B Experiments Abstract We propose a novel algorithm for data augmentation in nonlinear over-parametrized regression. Our data augmentation algorithm borrows from the literature on causality and extends the recently proposed Anchor regression (AR) method for data augmentation, which is in contrast to the current state-of-the-art domain-agnostic solutions that rely on the Mixup literature. Our Anchor Data Augmentation (ADA) uses several replicas of the modified samples in AR to provide more training examples, leading to more robust regression predictions. We apply ADA to linear and nonlinear regression problems using neural networks. ADA is competitive with state-of-the-art C-Mixup solutions. [1] 1 Introduction Data augmentation is one of the key ingredients of any successful application of a machine learning classifier. The first example that typically comes to mind is the in-depth description of the data augmentation in the now-famous Alexnet paper [26]. Data augmentation algorithms come in different flavors, and they mostly rely on the expectation that small perturbations, invariances, or symmetries applied to the input will not change the class label. That way, we can present ‘fresh new’ samples as alterations of the available examples for training. These transformations modify the input distribution to make the algorithm more robust for cases where the distribution of the test set may differ from that of the training set. We refer the reader to the related work section (Section 2.1) for an overview and description of different data augmentation strategies. The literature for data augmentation in regression is slim. The paper on Mixup augmentation [51] proposes a simple and general scheme for data augmentation using convex combinations of samples. The authors only apply their data augmentation proposal to classification problems. They conjecture in the discussion that the application to regression is straightforward, however, this is not the case in practice. Mixup is theoretically analyzed in [5, 52] as a regularization technique for classification and regression problems. However, it is only illustrated in classification problems. The Mixup algorithm has been extended to regression problems in [18, 49], in which the authors explain that Mixup cannot be blindly applied to regression problems. To our knowledge, these are the only two papers in which data augmentation for regression is proposed. RegMix [18] relies on a hard-to-train prior neural network controller before augmenting the data using a Mixup strategy. C-Mixup [49], a method proposed more recently, solves some of the issues limiting the standard Mixup algorithm for regression problems. The authors propose to mix only closeby samples in the output space (i.e., samples which have close enough labels). This strategy is only valid when the target variables are monotonic with the input and is applied in a transformed space. The authors present comprehensive results in data augmentation for in-distribution generalization, task generalization and out-of-distribution robustness. In this paper, we rely on the causality literature to provide a different avenue for augmenting data in regression problems. Causal discovery finds the causes of a response variable among a given set of observations or helps to recognize the causal relations between a set of variables [39]. These causes allow us to understand how these relations will change if we were to intervene in a subset of the (input) variables or what would be the effect on the output. So, in general, the regression model will be robust to perturbations in the input variables making the prediction less sensitive to changes in the distribution of the test set. For example, the authors in [40] use the invariance property for prediction to perform causal inference. In turn, Anchor Regression (AR) builds upon the causality literature to obtain robust regression solutions when the input variables have been perturbed [42]. The procedure relies on anchor variables capturing the heterogeneity within a dataset and a parameter γ that measures the deviation with respect to the least square solution. Once the values of the anchors are known, AR modifies the data and obtains the least square solution, as detailed in Section 2.2. In this paper, we propose Anchor Data Augmentation (ADA) to augment the training dataset with several replicas of the available data. We use a simple clustering of the data to encode a homogeneous group of observations and use different values of γ to robustify the solution to different strengths of potential distribution shifts. In every minibatch, we sample γ from a predetermined range around γ = 1. As AR was developed for linear regression, the data augmentation strategy needs to be modified for nonlinear regression accordingly. We validate ADA for in-distribution generalization and out-of-distribution robustness under the same conditions proposed in C-Mixup [49], as well as some illustrative linear and nonlinear regression examples. In the replicated experiments, ADA is competitive or superior to other augmentation strategies such as C-Mixup, although on some datasets the performance gain is marginal. Anchor Data Augmentation (ADA) The rest of the paper is organized as follows: First, we provide background information in Section 2. We give a brief overview of related work on data augmentation in Section 2.1 and summarize the key concepts on Anchor Regression in Section 2.2. Second, Section 3 shows how we extend Anchor Regression and introduces ADA. Section 4 reports empirical evidence that our approach can improve predictions, especially in over-parameterized settings. We conclude the paper in Section 5. This paper is available on arxiv under CC0 1.0 DEED license. This paper is available on arxiv under CC0 1.0 DEED license. available on arxiv [1] Our Python implementation of ADA is available at: https://github.com/noraschneider/ anchordataaugmentation/

A Novel Anchor Data Augmentation Algorithm for Nonlinear Models

Get Rich Building Verticalized AI Wrappers (Even If You Don’t Code)

রিচ বিল্ডিং ভার্টিক্যালাইজড এআই র‍্যাপার পান (এমনকি যদি আপনি কোড না করেন)

Werden Sie reich durch die Entwicklung vertikaler KI-Wrapper (auch wenn Sie nicht programmieren können)

Hazte rico creando contenedores de IA verticalizados (aunque no sepas programar)

Devenez riche en créant des wrappers d’IA verticalisés (même si vous ne codez pas)

वर्टिकलाइज़्ड AI रैपर बनाकर अमीर बनें (भले ही आप कोड न जानते हों)

Menjadi Kaya dengan Membangun Pembungkus AI Vertikal (Bahkan Jika Anda Tidak Membuat Kode)

垂直統合型 AI ラッパーを構築して金持ちになる (コードを書かなくても)

Rich Building вертикалдандырылған AI орауыштарын алыңыз (тіпті кодтамасаңыз да)

수직화된 AI 래퍼를 구축하여 부자가 되세요(코딩하지 않아도)

Баян барилгын босоо AI боодолтой бол (та код бичээгүй байсан ч)

Fique rico construindo wrappers de IA verticalizados (mesmo que você não codifique)

Разбогатейте, создавая вертикальные оболочки ИИ (даже если вы не пишете код)

Hel dhismo qani ah oo toosan AI ku duub (Xitaa Haddi aadan Codayn)

Get Rich Building Verticalized AI Wrappers (Kahit Hindi Ka Mag-code)

Dikey AI Sarmalayıcıları Oluşturarak Zengin Olun (Kodlamasanız Bile)

رچ بلڈنگ ورٹیکلائزڈ AI ریپر حاصل کریں (چاہے آپ کوڈ نہ بھی کریں)

Làm giàu bằng cách xây dựng các trình bao bọc AI theo chiều dọc (ngay cả khi bạn không viết mã)

通过构建垂直化 AI 包装器致富（即使你不会编码）

The rapid advancement of AI has offered powerful tools for malware detection, but it has also introduced new avenues for adversarial attacks. As an example, recently OpenAI reported threat actors abusing ChatGPT to execute reconnaissance, help fixing code, write partial code, or look at vulnerabilities. These are to me examples of AI aiding “basic” steps, but would threat actors invest and use more advanced applications? Universal Adversarial Perturbations (UAPs) have gained attention due to their potential to bypass machine learning models in various domains, including malware detection. UAPs can manipulate malware in ways that evade AI-based detection systems without altering the malware's core functionality. However, despite this capability, cybercriminals have not widely adopted AI-driven techniques like UAPs. This blog delves into the complexity and effort required to generate UAPs for malware and explains why it might not be worth the trouble for attackers. Just to be clear on definitions: Artificial Intelligence (AI) is a broad field that aims to create machines or software capable of performing tasks that typically require human intelligence, such as understanding language, recognizing images, problem-solving, and decision-making. AI encompasses various techniques and approaches, from rule-based systems to learning algorithms. Machine Learning (ML) is a subset of AI that focuses on building systems that learn from data. Instead of being explicitly programmed for each task, ML models identify patterns in data to make predictions or decisions, improving over time with more experience. UAPs: A Brief Overview Universal Adversarial Perturbations (UAPs) are subtle modifications applied to input data (such as malware samples) to mislead AI models. What makes UAPs particularly interesting is that a single perturbation (one ring rules them all) can be applied to many inputs, causing the AI model to misclassify them. Think of it as changing just a few pixels in a picture to make a powerful facial recognition system mistake someone for someone else. In the below example, a single bit of random code is added to multiple different images, resulting in the classifying model going completely wrong on the identification. When we look at the example of the platypus, the model identifies the animal partially right based on the training on the beak with other images, but due to the interference with the added “noise” in the pixels, it classifies it wrong. That is exactly the interesting space when it comes to malware detection and evasion. You want malicious files to be classified wrong. In the context of malware detection, UAPs allow attackers to evade detection without having to create entirely new malware variants. While this seems like a low-effort, high-reward strategy, generating effective UAPs is far more challenging than it appears, particularly in the malware domain. Complexity in Crafting UAPs for Malware In their paper, "Realizable Universal Adversarial Perturbations for Malware," Labaca-Castro et al. demonstrate that crafting UAPs for malware requires an intricate balance between manipulating feature space (abstract representations of malware) and problem space (real-world executable malware). Unlike image or text data, where perturbations may be easily applied without affecting functionality, malware is far more delicate. A slight misstep in the perturbation process can corrupt the malware sample, rendering it unusable. You need to respect (with regards to Windows malware) the PE structure of a file. A modification to that structure will  break its functionality and the malware will not execute. It may have bypassed detection but it is useless to the attacker. The process requires attackers to perform a series of careful transformations to avoid breaking the executable while still evading detection. This is a far cry from simply adding noise to an image or text dataset. As a result, the time and expertise required to create UAPs that both fool AI/ML malware detection models and preserve malware functionality is significant. UAPs vs. New Malware Variants Given the complexity of generating UAPs, cybercriminals face a dilemma: Should they invest time and resources into crafting these perturbations, or is it easier to create entirely new strains of malware? Developing a new malware strain might involve reusing code from previous versions, applying known obfuscation techniques, or modifying payloads. This process is often faster, less risky, and more predictable compared to the complex sequence of transformations required to generate UAPs. As a result, many attackers prefer to invest in creating new strains of malware, which are more likely to achieve the desired outcome without the same level of effort and risk. Challenges One of the major hurdles in applying UAPs to malware is the real-world execution environment. Malware operates in dynamic, unpredictable conditions, and UAPs crafted in controlled environments may not perform as expected once deployed. Small changes in the operating system, file structure, or antivirus defenses can render the UAP ineffective. This fragility is a key reason why UAPs remain largely theoretical for malware attacks rather than a widely adopted technique in practice. Additionally, defenders are not standing still. Adversarial training—where AI models are retrained using adversarial examples—can harden systems against UAPs, making it even harder for attackers to succeed. Mitigation strategies will raise the cost and effort required for attackers to generate successful UAPs, further reducing their appeal. Conclusion The idea of using AI to defeat AI, particularly through Universal Adversarial Perturbations, may seem like a natural progression in the ongoing battle between attackers and defenders. However, the reality is that the complexity and risk associated with developing UAPs for malware make this approach unattractive for most cybercriminals. Instead, attackers tend to rely on more straightforward methods like creating new malware variants, which offer a better return on investment with less risk of failure. If you examine some of the latest ransomware campaigns, none of them highlight the use of AI-based techniques. Instead, as shown in recent coverage of ransomware tactics here, attackers consistently focus on tried-and-tested approaches that maximize impact and minimize operational complexity. As long as the development of UAPs remains fraught with difficulties—such as maintaining functionality and overcoming problem-space constraints—it’s unlikely that we will see widespread adoption of these techniques in the cybercriminal world. Instead, traditional malware development and deployment methods will continue to dominate the landscape, while defenders must remain vigilant and adaptive to the evolving AI threat landscape. The rapid advancement of AI has offered powerful tools for malware detection, but it has also introduced new avenues for adversarial attacks. As an example, recently OpenAI reported threat actors abusing ChatGPT to execute reconnaissance, help fixing code, write partial code, or look at vulnerabilities. These are to me examples of AI aiding “basic” steps, but would threat actors invest and use more advanced applications? Universal Adversarial Perturbations (UAPs) have gained attention due to their potential to bypass machine learning models in various domains, including malware detection. UAPs can manipulate malware in ways that evade AI-based detection systems without altering the malware's core functionality. However, despite this capability, cybercriminals have not widely adopted AI-driven techniques like UAPs. This blog delves into the complexity and effort required to generate UAPs for malware and explains why it might not be worth the trouble for attackers. Just to be clear on definitions: Artificial Intelligence (AI) is a broad field that aims to create machines or software capable of performing tasks that typically require human intelligence, such as understanding language, recognizing images, problem-solving, and decision-making. AI encompasses various techniques and approaches, from rule-based systems to learning algorithms. Artificial Intelligence (AI) Machine Learning (ML) is a subset of AI that focuses on building systems that learn from data. Instead of being explicitly programmed for each task, ML models identify patterns in data to make predictions or decisions, improving over time with more experience. Machine Learning (ML) UAPs: A Brief Overview Universal Adversarial Perturbations (UAPs) are subtle modifications applied to input data (such as malware samples) to mislead AI models. What makes UAPs particularly interesting is that a single perturbation (one ring rules them all) can be applied to many inputs, causing the AI model to misclassify them. Think of it as changing just a few pixels in a picture to make a powerful facial recognition system mistake someone for someone else. In the below example, a single bit of random code is added to multiple different images, resulting in the classifying model going completely wrong on the identification. When we look at the example of the platypus, the model identifies the animal partially right based on the training on the beak with other images, but due to the interference with the added “noise” in the pixels, it classifies it wrong. That is exactly the interesting space when it comes to malware detection and evasion. You want malicious files to be classified wrong. In the context of malware detection, UAPs allow attackers to evade detection without having to create entirely new malware variants. While this seems like a low-effort, high-reward strategy, generating effective UAPs is far more challenging than it appears, particularly in the malware domain. Complexity in Crafting UAPs for Malware In their paper, "Realizable Universal Adversarial Perturbations for Malware ," Labaca-Castro et al. demonstrate that crafting UAPs for malware requires an intricate balance between manipulating feature space (abstract representations of malware) and problem space (real-world executable malware). Unlike image or text data, where perturbations may be easily applied without affecting functionality, malware is far more delicate. A slight misstep in the perturbation process can corrupt the malware sample, rendering it unusable. You need to respect (with regards to Windows malware) the PE structure of a file. A modification to that structure will  break its functionality and the malware will not execute. It may have bypassed detection but it is useless to the attacker. "Realizable Universal Adversarial Perturbations for Malware "Realizable Universal Adversarial Perturbations for Malware The process requires attackers to perform a series of careful transformations to avoid breaking the executable while still evading detection. This is a far cry from simply adding noise to an image or text dataset. As a result, the time and expertise required to create UAPs that both fool AI/ML malware detection models and preserve malware functionality is significant. UAPs vs. New Malware Variants Given the complexity of generating UAPs, cybercriminals face a dilemma: Should they invest time and resources into crafting these perturbations, or is it easier to create entirely new strains of malware? Developing a new malware strain might involve reusing code from previous versions, applying known obfuscation techniques, or modifying payloads. This process is often faster, less risky, and more predictable compared to the complex sequence of transformations required to generate UAPs. As a result, many attackers prefer to invest in creating new strains of malware, which are more likely to achieve the desired outcome without the same level of effort and risk. Challenges One of the major hurdles in applying UAPs to malware is the real-world execution environment. Malware operates in dynamic, unpredictable conditions, and UAPs crafted in controlled environments may not perform as expected once deployed. Small changes in the operating system, file structure, or antivirus defenses can render the UAP ineffective. This fragility is a key reason why UAPs remain largely theoretical for malware attacks rather than a widely adopted technique in practice. Additionally, defenders are not standing still. Adversarial training —where AI models are retrained using adversarial examples—can harden systems against UAPs, making it even harder for attackers to succeed. Mitigation strategies will raise the cost and effort required for attackers to generate successful UAPs, further reducing their appeal. Adversarial training Adversarial training Conclusion Conclusion The idea of using AI to defeat AI, particularly through Universal Adversarial Perturbations, may seem like a natural progression in the ongoing battle between attackers and defenders. However, the reality is that the complexity and risk associated with developing UAPs for malware make this approach unattractive for most cybercriminals. Instead, attackers tend to rely on more straightforward methods like creating new malware variants, which offer a better return on investment with less risk of failure. If you examine some of the latest ransomware campaigns, none of them highlight the use of AI-based techniques. Instead, as shown in recent coverage of ransomware tactics here , attackers consistently focus on tried-and-tested approaches that maximize impact and minimize operational complexity. here here As long as the development of UAPs remains fraught with difficulties—such as maintaining functionality and overcoming problem-space constraints—it’s unlikely that we will see widespread adoption of these techniques in the cybercriminal world. Instead, traditional malware development and deployment methods will continue to dominate the landscape, while defenders must remain vigilant and adaptive to the evolving AI threat landscape.

Why Cybercriminals Are Not Necessarily Embracing AI

In Vietnam, Porn Is Better Served and More Accessible Than Starbucks

For those who might not be deeply familiar with blockchain, can you explain why it's so crucial in today’s digital age? One of the core parts of the digital age is giving power back to the people; you see it in the way ordinary users can have their voices heard and businesses can be seen without the approval of gatekeepers. Blockchain is the highest form of this because it is fully democratized. Imagine a global computing system that belongs to everyone and no one at the same time. That is blockchain in a nutshell. Blockchain is important in the digital age because it puts people in control while making computer systems better and safer with things like Decentralized Autonomous Organizations (DAOs) and crypto wallets. What are some of the most innovative projects or products your company is working on right now? How do they address current market needs? We’ve been lucky to work behind the scenes of some truly amazing projects so far. We worked with top crypto exchange OKX to increase user growth through a PR campaign and banner ads. The result was over 492,000 user engagement and increased brand awareness. These days, we’re working with clients to better leverage influencer and PR marketing. Both are huge markets delivering results for many other industries and we believe that the blockchain space has even more to gain from it. What are the biggest challenges your company faces in the crypto/blockchain space, and how are you planning to overcome them? First, there’s the matter of regulation. Laws about how you can market blockchain products are always changing so we have to keep track of them to stay on the right side of the law. We also have to convince clients of the benefits of more ‘traditional’ advertising methods. Most of them understand digital ads and influencer sponsorships but getting them to do radio or billboards is sometimes a feat in itself. Then, of course, you have the holdout consumers who don’t see the value of Web3 just yet. How do you envision Web3 transforming the internet as we know it? What are the key benefits and potential drawbacks of this transition? Web3 is going to create an internet landscape where data and power are split across millions of nodes and not concentrated in a single palace. The benefit of this is that hacks, especially of widely used systems, will be close to impossible. It also means that users will have more control over their own data and content and can enjoy more privacy. The Web2 space has, with all of its perks, been dominated by big corporations like Google with centralized systems and near-monopolies on the Internet. Web3 is going to change that. Are there any traditions you would want the world of web3 to adopt from the web2 world? Web2 notably ushered in a new era of user-generated content and changed the way we share information thanks to the platforms it enabled. Web3 would do well to integrate this as it becomes a reality. Are there any challenges to transition from web2 to web3? There is an obvious reputational issue as some consumers and even businesses are reluctant to take the leap. Part of this is because they don’t understand how Web3 works and what it can do for them. As the space expands, better regulations will be needed to avoid it becoming a Wild West. Most of these issues arise because they are relatively new and should be addressed over time. Can you explain some of the technical advancements that are making Web3 possible? All the technologies that allow for decentralization are fast making Web3 a reality. You have blockchain which is the basis of things like cryptocurrency and NFTs. You also have smart contracts that allow transactions to be completed automatically and permissionlessly. From all these, we can begin to build a truly decentralized future. How can developers get involved in this space? Developers can get involved by, well, getting involved. Developing new tools and programs that leverage Web3 technologies and make them easier to use. We need all hands on deck to make Web3 a reality and many of our clients are proud to have worked with a wealth of developers. How do you think influencers and social media personalities impact the cryptocurrency market? They certainly influence the demand for tokens. We see this when influencers launch their own tokens or promote existing ones. They have the power to drive demand for every product you can imagine and crypto is no different. Do you see this as a positive or negative trend? It has the potential to be both. If influencers are using their platforms to encourage their fans to invest responsibly in worthwhile projects then it can be positive. If not, then it’s negative. TikTokers, Instagrammers, X (formerly Twitter) accounts -- how are they shaping the discourse? They’re amplifying every kind of crypto opinion there is. From giving verdicts about assets to discussing laws and even scandals, they help to cultivate an engaged community that has a vested interest in the success of the industry. What do you think is happening? Are these platforms credible sources for information? I think the industry is expanding and with this expansion comes a demand for content. These platforms can be credible sources depending on who you’re listening to. Misinformation is an issue in every corner of the internet so users need to be cautious. What do you believe are the strongest arguments in favor of cryptocurrencies and blockchain technology? These technologies give power to the people; historically underserved people can get access to financial services through DeFi, prosecuted populations can access funds through crypto, information can be stored forever on the blockchain, and even elections can be made fairer using this tech. There is something for everyone and if harnessed properly, blockchain can help humanity become its best self. How do you respond to critics? We respond by showing the value we deliver to clients. You can argue about tech preferences but you can’t argue with years of an impressive track record. Blockchain and cryptocurrencies do have their detractors. What’s an argument or rationale you use to convince naysayers or those on the fence about blockchain and crypto? Our argument would simply be to take a look at all the amazing things happening in the space. The crypto industry is complex but that is to be expected of any new sector. Even outside of crypto, look at all the ways blockchain is being implemented and it’s pretty obvious that it has a lot to offer. What advice would you give to someone who is just starting to explore the world of cryptocurrencies and blockchain technology? Our advice would be to do your research and engage with the community. Succeeding with blockchain and crypto is all about knowledge and research plus community is how you get it. Also, try to have fun with it. Any sources to check out? Any media sites to visit? Any X (Twitter) accounts to follow? Share the deets! Hackernoon, definitely (wink) but also BeInCrypto, Cointelegraph, Coindesk, and the like. These sites have been giving top-notch blockchain reporting for years so we highly recommend them. Twitter/X founder Jack Dorsey, Ethereum founder Vitalik Buterin, POMP, and Gemini-Cofounder Tyler Winklevoss are some insightful personalities we love on Twitter/X. How do you see events like HackerNoon’s Startups of The Year shaping public understanding and engagement of the crypto and blockchain industry? It’s great. We see it the way you might see awards like the MTV VMAs; it lets the world know that certain acts (or businesses, in this case) have a solid community behind them. By giving businesses in the space a platform for recognition, skeptics can see that they are as legitimate as any other start-up. It’s also great for morale; a niche industry like blockchain can sometimes feel isolated but initiatives like this bring the community together and show us how far we have come. Why we decided to participate in HackerNoon's Startups of The Year awards We’re a marketing agency so we’re all about collaborating and connecting others. The HackerNoon's Startups of The Year awards give us a chance to connect with other businesses in the space as well as the community that supports our work. As the awards unravel, we’re learning more about other firms that operate in the same sector and this creates the avenue for possible collaboration. Similarly, other potential partners and clients can find us and this is a win for everyone. We’re proud to be part of the blockchain community and this is a great opportunity to connect even more with it. Final Thoughts Proelo.io has worked behind the scenes of some of the biggest marketing campaigns in the blockchain industry for years now. We’re immensely proud of what we do and would be honored to receive HackerNoon's Startups of The Year award. We find ourselves in great company with a wealth of respected companies and hope to keep serving our industry for years to come. Vote for us today! For those who might not be deeply familiar with blockchain, can you explain why it's so crucial in today’s digital age? One of the core parts of the digital age is giving power back to the people; you see it in the way ordinary users can have their voices heard and businesses can be seen without the approval of gatekeepers. Blockchain is the highest form of this because it is fully democratized. Imagine a global computing system that belongs to everyone and no one at the same time. That is blockchain in a nutshell. Blockchain is important in the digital age because it puts people in control while making computer systems better and safer with things like Decentralized Autonomous Organizations (DAOs) and crypto wallets. What are some of the most innovative projects or products your company is working on right now? How do they address current market needs? We’ve been lucky to work behind the scenes of some truly amazing projects so far. We worked with top crypto exchange OKX to increase user growth through a PR campaign and banner ads. The result was over 492,000 user engagement and increased brand awareness. These days, we’re working with clients to better leverage influencer and PR marketing. Both are huge markets delivering results for many other industries and we believe that the blockchain space has even more to gain from it. What are the biggest challenges your company faces in the crypto/blockchain space, and how are you planning to overcome them? First, there’s the matter of regulation. Laws about how you can market blockchain products are always changing so we have to keep track of them to stay on the right side of the law. We also have to convince clients of the benefits of more ‘traditional’ advertising methods. Most of them understand digital ads and influencer sponsorships but getting them to do radio or billboards is sometimes a feat in itself. Then, of course, you have the holdout consumers who don’t see the value of Web3 just yet. How do you envision Web3 transforming the internet as we know it? What are the key benefits and potential drawbacks of this transition? Web3 is going to create an internet landscape where data and power are split across millions of nodes and not concentrated in a single palace. The benefit of this is that hacks, especially of widely used systems, will be close to impossible. It also means that users will have more control over their own data and content and can enjoy more privacy. The Web2 space has, with all of its perks, been dominated by big corporations like Google with centralized systems and near-monopolies on the Internet. Web3 is going to change that. Are there any traditions you would want the world of web3 to adopt from the web2 world? Web2 notably ushered in a new era of user-generated content and changed the way we share information thanks to the platforms it enabled. Web3 would do well to integrate this as it becomes a reality. Are there any challenges to transition from web2 to web3? There is an obvious reputational issue as some consumers and even businesses are reluctant to take the leap. Part of this is because they don’t understand how Web3 works and what it can do for them. As the space expands, better regulations will be needed to avoid it becoming a Wild West. Most of these issues arise because they are relatively new and should be addressed over time. Can you explain some of the technical advancements that are making Web3 possible? All the technologies that allow for decentralization are fast making Web3 a reality. You have blockchain which is the basis of things like cryptocurrency and NFTs. You also have smart contracts that allow transactions to be completed automatically and permissionlessly. From all these, we can begin to build a truly decentralized future. How can developers get involved in this space? Developers can get involved by, well, getting involved. Developing new tools and programs that leverage Web3 technologies and make them easier to use. We need all hands on deck to make Web3 a reality and many of our clients are proud to have worked with a wealth of developers. How do you think influencers and social media personalities impact the cryptocurrency market? They certainly influence the demand for tokens. We see this when influencers launch their own tokens or promote existing ones. They have the power to drive demand for every product you can imagine and crypto is no different. Do you see this as a positive or negative trend? It has the potential to be both. If influencers are using their platforms to encourage their fans to invest responsibly in worthwhile projects then it can be positive. If not, then it’s negative. TikTokers, Instagrammers, X (formerly Twitter) accounts -- how are they shaping the discourse? They’re amplifying every kind of crypto opinion there is. From giving verdicts about assets to discussing laws and even scandals, they help to cultivate an engaged community that has a vested interest in the success of the industry. What do you think is happening? Are these platforms credible sources for information? I think the industry is expanding and with this expansion comes a demand for content. These platforms can be credible sources depending on who you’re listening to. Misinformation is an issue in every corner of the internet so users need to be cautious. What do you believe are the strongest arguments in favor of cryptocurrencies and blockchain technology? These technologies give power to the people; historically underserved people can get access to financial services through DeFi, prosecuted populations can access funds through crypto, information can be stored forever on the blockchain, and even elections can be made fairer using this tech. There is something for everyone and if harnessed properly, blockchain can help humanity become its best self. How do you respond to critics? We respond by showing the value we deliver to clients. You can argue about tech preferences but you can’t argue with years of an impressive track record. Blockchain and cryptocurrencies do have their detractors. What’s an argument or rationale you use to convince naysayers or those on the fence about blockchain and crypto? Our argument would simply be to take a look at all the amazing things happening in the space. The crypto industry is complex but that is to be expected of any new sector. Even outside of crypto, look at all the ways blockchain is being implemented and it’s pretty obvious that it has a lot to offer. What advice would you give to someone who is just starting to explore the world of cryptocurrencies and blockchain technology? Our advice would be to do your research and engage with the community. Succeeding with blockchain and crypto is all about knowledge and research plus community is how you get it. Also, try to have fun with it. Any sources to check out? Any media sites to visit? Any X (Twitter) accounts to follow? Share the deets! Hackernoon, definitely (wink) but also BeInCrypto, Cointelegraph, Coindesk, and the like. These sites have been giving top-notch blockchain reporting for years so we highly recommend them. Twitter/X founder Jack Dorsey, Ethereum founder Vitalik Buterin, POMP, and Gemini-Cofounder Tyler Winklevoss are some insightful personalities we love on Twitter/X. How do you see events like HackerNoon’s Startups of The Year shaping public understanding and engagement of the crypto and blockchain industry? It’s great. We see it the way you might see awards like the MTV VMAs; it lets the world know that certain acts (or businesses, in this case) have a solid community behind them. By giving businesses in the space a platform for recognition, skeptics can see that they are as legitimate as any other start-up. It’s also great for morale; a niche industry like blockchain can sometimes feel isolated but initiatives like this bring the community together and show us how far we have come. Why we decided to participate in HackerNoon's Startups of The Year awards We’re a marketing agency so we’re all about collaborating and connecting others. The HackerNoon's Startups of The Year awards give us a chance to connect with other businesses in the space as well as the community that supports our work. As the awards unravel, we’re learning more about other firms that operate in the same sector and this creates the avenue for possible collaboration. Similarly, other potential partners and clients can find us and this is a win for everyone. We’re proud to be part of the blockchain community and this is a great opportunity to connect even more with it. Final Thoughts Proelo.io has worked behind the scenes of some of the biggest marketing campaigns in the blockchain industry for years now. We’re immensely proud of what we do and would be honored to receive HackerNoon's Startups of The Year award. We find ourselves in great company with a wealth of respected companies and hope to keep serving our industry for years to come. Vote for us today! Vote for us today! Vote for us today! Vote for us today!

Meet PROLEO.IO: Startups of The Year 2024 Nominee 

የ HackerNoon የአመቱ ጅምር ሽልማት፡ ምላሾች

Прэмія «Стартапы года» ад HackerNoon: адказы

Награда за стартиращи компании на годината на HackerNoon: Отговори

Premio Startups del año de HackerNoon: respuestas

HackerNoon-en Urteko Startups saria: erantzunak

Prim HackerNoon pou Startups pou Ane a: Repons

A HackerNoon Az év startupjai díja: Válaszok

HackerNoonの今年のスタートアップ賞：反応

HackerNoon's Startups of the Year Award: Valiny

د هیکرنون د کال د پیل جایزه: ځوابونه

Sagwadi ra HackerNoon ra Masungulo ya Lembe: Tinhlamulo

I-HackerNoon's Startups of the Year Award: Iimpendulo

Startups of The Year: Meet the Media Industry

Startups des Jahres: Lernen Sie die Medienbranche kennen

Startups της χρονιάς: Γνωρίστε τη βιομηχανία των μέσων ενημέρωσης

Startups del año: conozca la industria de los medios

Urteko startup-ak: ezagutu komunikabideen industria

今年のスタートアップ: メディア業界を知る

Metų startuoliai: susipažinkite su žiniasklaidos pramone

Оны шилдэг гарааны бизнесүүд: Хэвлэл мэдээллийн салбарынхантай танилц

Startups van het jaar: maak kennis met de media-industrie

Startupy Roku: Poznaj Branżę Medialną

د کال پیل: د رسنیو صنعت سره لیدنه

වසරේ ආරම්භක: මාධ්‍ය කර්මාන්තය හමුවන්න

Startups of The Year: Hlangana na Vumaki bya Vuhangalasi bya Mahungu

Authors:
(1) Timothy R. McIntosh;
(2) Teo Susnjak;
(3) Tong Liu;
(4) Paul Watters;
(5) Malka N. Halgamuge. Table of Links Abstract and Introduction Background: Evolution of Generative AI The Current Generative AI Research Taxonomy Innovative Horizon of MOE Speculated Capabilities of Q* Projected Capabilities of AGI Impact Analysis on Generative AI Research Taxonomy Emergent Research Priorities in Generative AI Practical Implications and Limitations of Generative AI Technologies Impact of Generative AI on Preprints Across Disciplines Conclusions, Disclaimer, and References VII. IMPACT ANALYSIS ON GENERATIVE AI RESEARCH TAXONOMY With the advent of advanced AI developments such as MoE, multimodality, and AGI, the landscape of Generative AI research is undergoing a significant transformation. This section analyzes how these developments are reshaping the research taxonomy in generative AI. A. Criteria for Impact Analysis The continuously evolving landscape of Generative AI, which instigates transformative changes across various research domains, necessitates a systematic evaluation of these advancements’ influence, for which we have established a set of criteria detailed in Table II, serving as analytical lenses to quantify and categorize the impact, deeply rooted in the dynamic interplay between technological progress and the evolving paradigms of research focus areas. Our analysis framework has been constructed on a gradient scale ranging from emergent to obsolete, reflecting the extent to which areas of Generative AI research are being reshaped. The categorization into five distinct classes allows for a complex assessment, acknowledging that not all areas will be uniformly affected. This multi-tiered approach is informed by historical patterns of technological disruption and the adaptability of scientific inquiry. At the apex of our evaluative hierarchy, ‘Emerging Direction’ encapsulates the advent of uncharted research vistas, propelled by ongoing AI breakthroughs, which is predicated not on conjecture, but on a historical continuum of AI evolution, where each surge in technological power unfurls new scientific enigmas and avenues [315], [316]. ‘Areas Requiring Redirection’ denote research spheres that, though established, find themselves at an inflection point, necessitating a strategic pivot to assimilate emergent AI paradigms and an overhaul of traditional methodologies, akin to the transition from rulebased expert systems to adaptive machine learning frameworks [315], [317]. The ‘Still Relevant’ classification affirms the tenacity of select research domains that, by addressing persistent scientific inquiries or through their inherent malleability, remain impervious to the tides of AI innovation [317]. In contrast, domains categorized as ‘Likely to Become Redundant’ confront potential obsolescence, inviting strategic foresight and resource reallocation to forestall scientific stagnation [318]. Lastly, ‘Inherently Unresolvable’ challenges serve as a sobering reminder of the perpetual dilemmas within AI research that defy resolution, rooted in the complex web of human ethics and cultural diversity, thus anchoring the pursuit of AI within the intractable tapestry of human values and societal imperatives [319], [320]. B. Overview of Impact Analysis This subsection offers a detailed overview of the impact analysis carried out on the research taxonomy within the realm of generative AI, with a specific focus on recent progress in MoE, multimodality, and AGI, aiming to evaluate the impact of these innovative developments on various facets of generative AI research, ranging from model architecture to sophisticated learning methodologies, and includes both quantitative and qualitative assessments across a multitude of domains and subdomains in LLM research, shedding light on the extent to which each area is influenced by these technological advancements. This evaluation considered factors such as the emergence of new research directions, the necessity for redirection in existing research areas, the continued relevance of certain methodologies, and the potential redundancy of others, and has encapsulated in Table III. Impact On Model Architecture: Transformer Models have been scored with a redirection requirement (֒→) of 4 in both MoE and AGI, and a relevance (↔) of 3 in multimodality, leading to an overall score of 11. These models, forming the backbone of many current AI architectures, continue to be relevant for handling complex input sequences. However, the emergence of MoE and AGI indicates a shift towards more dynamic and specialized architectures. While transformers remain essential, there is a need for them to evolve and integrate with these advanced systems for enhanced performance and adaptability. Data Security maintains a consistent relevance (↔) with a score of 3 across MoE, multimodality, and AGI, leading to a total score of 9. The fundamental principles of data security remain crucial despite the advancements in MoE, which may necessitate tailored strategies for its distributed nature. In multimodal AI, the secure handling of diverse data types continues to be of paramount importance. The core tenets of data security are sustained even with the advancement of AGI, though the complexity and scope of security measures are likely to increase. AI Ethics is marked for redirection (֒→) with a score of 4 in both MoE and multimodality, and faces inherently unresolvable challenges (△) with a score of 1 in AGI, accumulating a total score of 9. The decision-making processes and transparency of MoE models necessitate a reevaluation of ethical considerations. In multimodal AI, ethical concerns, particularly in the interpretation and use of multimodal data, require new approaches. The ethical challenges in AGI are expected to be complex and involve deep philosophical and societal implications that might be difficult to fully resolve. Privacy Preservation scores a redirection need (֒→) of 4 across MoE, multimodality, and AGI, leading to an overall score of 12. The distributed nature of MoE systems requires a reassessment of privacy preservation techniques to handle data processed by multiple experts. Multimodal AI systems, especially those handling sensitive data such as images and sounds, necessitate tailored privacy strategies. With the extensive data processing capabilities of AGI, advanced and potentially new approaches to privacy preservation are called for. Aligning AI with human values poses inherently unresolvable challenges (△) in all contexts—MoE, multimodality, and AGI—with a score of 1. This reflects the complexity and diversity of tasks MoE models handle, the integration of various data types in multimodal AI, and the broad range of cognitive abilities encompassed by AGI. These factors contribute to the significant ongoing challenges in aligning AI with human values, resulting in a total score of 3. The development of AGI necessitates redirection (֒→) in both MoE and multimodality, each with a score of 4, indicating the need for more integrated and complex systems. AGI remains at the forefront of its own field (↔) with a score of 3, with each breakthrough directly influencing its progress. The overall impact score for AGI development is 11. This paper is available on arxiv under CC BY-NC-ND 4.0 DEED license. Authors: (1) Timothy R. McIntosh; (2) Teo Susnjak; (3) Tong Liu; (4) Paul Watters; (5) Malka N. Halgamuge. Authors: Authors: (1) Timothy R. McIntosh; (2) Teo Susnjak; (3) Tong Liu; (4) Paul Watters; (5) Malka N. Halgamuge. Table of Links Abstract and Introduction Abstract and Introduction Background: Evolution of Generative AI Background: Evolution of Generative AI The Current Generative AI Research Taxonomy The Current Generative AI Research Taxonomy Innovative Horizon of MOE Innovative Horizon of MOE Speculated Capabilities of Q* Speculated Capabilities of Q* Projected Capabilities of AGI Projected Capabilities of AGI Impact Analysis on Generative AI Research Taxonomy Impact Analysis on Generative AI Research Taxonomy Emergent Research Priorities in Generative AI Emergent Research Priorities in Generative AI Practical Implications and Limitations of Generative AI Technologies Practical Implications and Limitations of Generative AI Technologies Impact of Generative AI on Preprints Across Disciplines Impact of Generative AI on Preprints Across Disciplines Conclusions, Disclaimer, and References Conclusions, Disclaimer, and References VII. IMPACT ANALYSIS ON GENERATIVE AI RESEARCH TAXONOMY With the advent of advanced AI developments such as MoE, multimodality, and AGI, the landscape of Generative AI research is undergoing a significant transformation. This section analyzes how these developments are reshaping the research taxonomy in generative AI. A. Criteria for Impact Analysis A. Criteria for Impact Analysis The continuously evolving landscape of Generative AI, which instigates transformative changes across various research domains, necessitates a systematic evaluation of these advancements’ influence, for which we have established a set of criteria detailed in Table II, serving as analytical lenses to quantify and categorize the impact, deeply rooted in the dynamic interplay between technological progress and the evolving paradigms of research focus areas. Our analysis framework has been constructed on a gradient scale ranging from emergent to obsolete, reflecting the extent to which areas of Generative AI research are being reshaped. The categorization into five distinct classes allows for a complex assessment, acknowledging that not all areas will be uniformly affected. This multi-tiered approach is informed by historical patterns of technological disruption and the adaptability of scientific inquiry. At the apex of our evaluative hierarchy, ‘Emerging Direction’ encapsulates the advent of uncharted research vistas, propelled by ongoing AI breakthroughs, which is predicated not on conjecture, but on a historical continuum of AI evolution, where each surge in technological power unfurls new scientific enigmas and avenues [315], [316]. ‘Areas Requiring Redirection’ denote research spheres that, though established, find themselves at an inflection point, necessitating a strategic pivot to assimilate emergent AI paradigms and an overhaul of traditional methodologies, akin to the transition from rulebased expert systems to adaptive machine learning frameworks [315], [317]. The ‘Still Relevant’ classification affirms the tenacity of select research domains that, by addressing persistent scientific inquiries or through their inherent malleability, remain impervious to the tides of AI innovation [317]. In contrast, domains categorized as ‘Likely to Become Redundant’ confront potential obsolescence, inviting strategic foresight and resource reallocation to forestall scientific stagnation [318]. Lastly, ‘Inherently Unresolvable’ challenges serve as a sobering reminder of the perpetual dilemmas within AI research that defy resolution, rooted in the complex web of human ethics and cultural diversity, thus anchoring the pursuit of AI within the intractable tapestry of human values and societal imperatives [319], [320]. B. Overview of Impact Analysis B. Overview of Impact Analysis This subsection offers a detailed overview of the impact analysis carried out on the research taxonomy within the realm of generative AI, with a specific focus on recent progress in MoE, multimodality, and AGI, aiming to evaluate the impact of these innovative developments on various facets of generative AI research, ranging from model architecture to sophisticated learning methodologies, and includes both quantitative and qualitative assessments across a multitude of domains and subdomains in LLM research, shedding light on the extent to which each area is influenced by these technological advancements. This evaluation considered factors such as the emergence of new research directions, the necessity for redirection in existing research areas, the continued relevance of certain methodologies, and the potential redundancy of others, and has encapsulated in Table III. Impact On Model Architecture: Transformer Models have been scored with a redirection requirement (֒→) of 4 in both MoE and AGI, and a relevance (↔) of 3 in multimodality, leading to an overall score of 11. These models, forming the backbone of many current AI architectures, continue to be relevant for handling complex input sequences. However, the emergence of MoE and AGI indicates a shift towards more dynamic and specialized architectures. While transformers remain essential, there is a need for them to evolve and integrate with these advanced systems for enhanced performance and adaptability. Impact On Model Architecture: Transformer Models have been scored with a redirection requirement (֒→) of 4 in both MoE and AGI, and a relevance (↔) of 3 in multimodality, leading to an overall score of 11. These models, forming the backbone of many current AI architectures, continue to be relevant for handling complex input sequences. However, the emergence of MoE and AGI indicates a shift towards more dynamic and specialized architectures. While transformers remain essential, there is a need for them to evolve and integrate with these advanced systems for enhanced performance and adaptability. Data Security maintains a consistent relevance (↔) with a score of 3 across MoE, multimodality, and AGI, leading to a total score of 9. The fundamental principles of data security remain crucial despite the advancements in MoE, which may necessitate tailored strategies for its distributed nature. In multimodal AI, the secure handling of diverse data types continues to be of paramount importance. The core tenets of data security are sustained even with the advancement of AGI, though the complexity and scope of security measures are likely to increase. AI Ethics is marked for redirection (֒→) with a score of 4 in both MoE and multimodality, and faces inherently unresolvable challenges (△) with a score of 1 in AGI, accumulating a total score of 9. The decision-making processes and transparency of MoE models necessitate a reevaluation of ethical considerations. In multimodal AI, ethical concerns, particularly in the interpretation and use of multimodal data, require new approaches. The ethical challenges in AGI are expected to be complex and involve deep philosophical and societal implications that might be difficult to fully resolve. Privacy Preservation scores a redirection need (֒→) of 4 across MoE, multimodality, and AGI, leading to an overall score of 12. The distributed nature of MoE systems requires a reassessment of privacy preservation techniques to handle data processed by multiple experts. Multimodal AI systems, especially those handling sensitive data such as images and sounds, necessitate tailored privacy strategies. With the extensive data processing capabilities of AGI, advanced and potentially new approaches to privacy preservation are called for. Aligning AI with human values poses inherently unresolvable challenges (△) in all contexts—MoE, multimodality, and AGI—with a score of 1. This reflects the complexity and diversity of tasks MoE models handle, the integration of various data types in multimodal AI, and the broad range of cognitive abilities encompassed by AGI. These factors contribute to the significant ongoing challenges in aligning AI with human values, resulting in a total score of 3. The development of AGI necessitates redirection (֒→) in both MoE and multimodality, each with a score of 4, indicating the need for more integrated and complex systems. AGI remains at the forefront of its own field (↔) with a score of 3, with each breakthrough directly influencing its progress. The overall impact score for AGI development is 11. This paper is available on arxiv under CC BY-NC-ND 4.0 DEED license. This paper is available on arxiv under CC BY-NC-ND 4.0 DEED license. available on arxiv

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