Indonesian multinational telecommunications conglomerate Telkom Indonesia, and South Korean telecommunications giant KT Corp have signed a memorandum of understanding (MoU) to collaborate on a smart city project in what will soon be the country’s new capital,

Telkom Indonesia and KT announce MoU for Indonesian smart city project

Major 5G services market participants include AT&T, Inc., BT Group plc, China Mobile Ltd., China Telecom Corporation Ltd., Bharti Airtel Ltd., NTT Docomo, KT Corp., Saudi Telecom Company, Vodafone Group,

5G Services Market to exceed $1 Tn by 2032, Says Global Market Insights Inc.

South Korean telecommunications giant KT Corp. announced Wednesday that it has secured a partnership with Telkom Indonesia, pushing development for the smart city project in Indonesia’s new capital, Nusantara.

KT, Telkom join hands for Indonesia smart city project

South Korea's leading mobile carrier KT Corp. said Wednesday it has signed an initial agreement with Indonesia's state-owned telecommunications network Telkom Indonesia to cooperate for a smart city project in the Southeast Asian country's new capital.

KT signs MOU for smart city project in Indonesia's new capital

South Korean telecommunications giant KT Corp. on Tuesday unveiled Mi:dm, a customizable large language model aimed at corporate clients looking to develop their own advanced AI services in the booming field of generative AI technologies.

KT unveils large language model Mi:dm for corporate customization

KT unveiled its hyperscale AI platform Mi:dm and will expand its AI business in business-to-business (B2B) markets at a time when increasing numbers of organizations are considering adopting the emerging technologies to improve efficiency and productivity,

KT launches hyperscale AI platform Mi:dm

KT launches hyperscale AI Mi:dm

SEOUL, Oct. 31 (Yonhap) -- South Korea's leading mobile carrier KT Corp. on Tuesday unveiled its large artificial intelligence (AI) model, Mi:dm, in a bid to expand its AI business to the manufacturing, financial, education and public sectors, as well as the global market.

(LEAD) KT launches 200 bln-parameter large AI model Mi:dm

South Korean network operator KT Corp. has teamed up with LG Electronics and Corning to develop reconfigurable intelligent surface (RIS) technology that can be used in multiple candidate 6G radio frequencies; backhaul equipment specialist Ceragon Networks ...

What’s up with… KT, Ceragon, Vodafone

Advanced Micro Devices (AMD) and Korean telco KT are among the investors of Moreh, which builds an AI software tool that optimizes and creates AI models.

AMD and Korean telco KT back AI software developer Moreh in $22M Series B

KT CORPORATION

ktcorporation

Now that we've covered the US presidential elections and most of the Q3 earnings, I want to take a moment to discuss Intel and some of the excellent reporting from Reuters about the company's downfall. Intel, as it is today, appears very insignificant when compared to AMD and Nvidia, both of whom have made some very shrewd bets on artificial intelligence, especially Nvidia. Though in Nvidia's case, I've argued that its technology was simply at the right place at the right time. As I said so in March: Nvidia never really started as an artificial intelligence company the way OpenAI and so many others like it did. It started out as a company that produced graphic cards for video games, and for a long, long time, this is what Nvidia was known for. But as an offshoot of all the research it did in producing better and better graphic cards, it ended up creating tensor cores for real-time deep learning image enhancement and upscaling technologies for use in video games. Little did Nvidia know that this technology would then serve as the backbone of LLMs, instantly transforming it as the arms dealer of choice in corporations' pursuit of the perfect AI. At the time Nvidia was developing its technology, however, Intel engineers were scoffing at video gaming chip architecture and believed that a CPU could more effectively handle processing tasks needed to build and run AI models. As time has bore witness, Intel and its engineers were wrong. Very, very wrong. So when demand for AI chips skyrocketed, Intel was left with its pants down, scrambling to get a product out. Ironically, Intel was this close to hitting the jackpot that Nvidia eventually ended up striking. In 2017 and 2018, the company had the opportunity to invest in OpenAI but decided against it because then-CEO Bob Swan did not believe generative AI would make it to the market soon enough for it to be worthwhile the $1 billion Intel would have had to pay for a 15% stake. OpenAI also offered a further 15% stake if Intel agreed to make hardware for it at cost price — but the company refused, leaving OpenAI to rely on Nvidia's chips instead. GET PUBLISHED ON HACKERNOON.
Join a global network of 45,000+ published devs, builders,   founders, makers, VCs, hodlrs, and hackers. Start submitting your tech   stories and tutorials to get published FREE on HackerNoon — no pop-ups,   no paywalls. But that was then. Intel hasn't fared any better under current CEO Pat Gelsinger's leadership either. Intel rehired Gelsinger in 2021 to help lead the company out of a tough spot: it was losing out to the likes of Taiwan Semiconductor Manufacturing and Samsung Electronics on chip-making technology, and the idea was to have Gelsinger, who had been with Intel for most of his career and helped develop some of its most successful personal-computer chips, lead a turn around. While Gelsinger's heart was in the right place, his turnaround plan didn't really do much to turn the company's fortunes around. In fact, things came to a head this past summer when Intel stunned both employees and investors with its poor quarterly results and announced sweeping measures (cost cuts, headcount reduction, etc.) to become profitable again. All of this could have been avoided had it not been for some key mistakes Gelsinger made after taking reins of the company. According to Reuters, soon after becoming CEO, Gelsinger offended Taiwan Semiconductor Manufacturing by calling Taiwan unstable. Gelsinger's "Taiwan is not a stable place" comment had more to do with his push to develop a US-based supply chain for chip manufacturing, but was so poorly received by TSMC that it reneged on a 40% discount it was willing to offer Intel on chips it designed but could not produce. That discount would have really helped Intel's margins, but, nope, Gelsinger was too busy commenting on Taiwan. Making matters worse, Gelsinger kept doubling down on his plan to develop a manufacturing footprint that could rival TSMC's, but on American and European soil instead. Problem is, customers have no reason to shy away from TSMC, particularly considering that Intel's foundry ambitions have run into technical challenges. Combined with the fact that Intel kept sticking to producing chips for desktop computers and servers, and you have a clearer picture of why the company is in such dire straits today. The list of fumbles goes on, but what must really make this sting for Gelsinger is that just a few weeks ago it was announced that Nvidia would take the company's place on the Dow Jones Industrial Average. Today, Nvidia is the most valuable company in the world. The company has a pile of cash. And CEO Jensen Huang's 3.8% stake in Nvidia is worth more than Intel's market value combined, Reuters reports. And to think Intel could have prevented all of this from happening. The way I see it, Intel is either going to be bought or Gelsinger will be fired, possibly both. I'll be here to report on it when it happens. Intel was #198 on HackerNoon's Tech Company Rankings this week. In Other News.. 📰 Bitcoin ETF Options Attract $2B on Day One, Shifting BTC’s Market Structure — via CoinDesk
H, the AI startup that raised $220M, launches its first product: Runner H for ‘agentic’ applications — via TechCrunch
With Musk in tow, Trump eyes changes to government policies on AI and its dangers — via CNN
Apple to urge judge to end US smartphone monopoly case — via Reuters
Trump 2.0 will face Big Tech antitrust test — via Axios
FTX co-founder Gary Wang avoids prison time for role in crypto fraud — via CNBC And that's a wrap! Don't forget to share this newsletter with your family and friends! See y'all next week. PEACE! ☮️ — Sheharyar Khan, Editor, Business Tech @ HackerNoon *All rankings are current as of Monday. To see how the rankings have changed, please visit HackerNoon's Tech Company Rankings page. Tech, What the Heck!? is a once-weekly newsletter written by HackerNoon editors that combine HackerNoon's proprietary data with news-worthy tech stories from around the internet. Humorous and insightful, the newsletter recaps trending events that are shaping the world of tech. Subscribe here. Now that we've covered the US presidential elections and most of the Q3 earnings , I want to take a moment to discuss Intel and some of the excellent reporting from Reuters about the company's downfall. US presidential elections US presidential elections Q3 earnings Q3 earnings Intel Intel Intel, as it is today, appears very insignificant when compared to AMD and Nvidia , both of whom have made some very shrewd bets on artificial intelligence, especially Nvidia. Though in Nvidia's case, I've argued that its technology was simply at the right place at the right time. AMD AMD Nvidia Nvidia As I said so in March: said said Nvidia never really started as an artificial intelligence company the way OpenAI and so many others like it did. It started out as a company that produced graphic cards for video games, and for a long, long time, this is what Nvidia was known for. But as an offshoot of all the research it did in producing better and better graphic cards, it ended up creating tensor cores for real-time deep learning image enhancement and upscaling technologies for use in video games. Little did Nvidia know that this technology would then serve as the backbone of LLMs, instantly transforming it as the arms dealer of choice in corporations' pursuit of the perfect AI. Nvidia never really started as an artificial intelligence company the way OpenAI and so many others like it did. It started out as a company that produced graphic cards for video games, and for a long, long time, this is what Nvidia was known for. But as an offshoot of all the research it did in producing better and better graphic cards, it ended up creating tensor cores for real-time deep learning image enhancement and upscaling technologies for use in video games. Little did Nvidia know that this technology would then serve as the backbone of LLMs, instantly transforming it as the arms dealer of choice in corporations' pursuit of the perfect AI. Nvidia never really started as an artificial intelligence company the way OpenAI and so many others like it did. It started out as a company that produced graphic cards for video games, and for a long, long time, this is what Nvidia was known for. But as an offshoot of all the research it did in producing better and better graphic cards, it ended up creating tensor cores for real-time deep learning image enhancement and upscaling technologies for use in video games. Little did Nvidia know that this technology would then serve as the backbone of LLMs, instantly transforming it as the arms dealer of choice in corporations' pursuit of the perfect AI. At the time Nvidia was developing its technology, however, Intel engineers were scoffing at video gaming chip architecture and believed that a CPU could more effectively handle processing tasks needed to build and run AI models. scoffing scoffing As time has bore witness, Intel and its engineers were wrong. Very, very wrong. So when demand for AI chips skyrocketed, Intel was left with its pants down, scrambling to get a product out. Ironically, Intel was this close to hitting the jackpot that Nvidia eventually ended up striking. In 2017 and 2018, the company had the opportunity to invest in OpenAI but decided against it because then-CEO Bob Swan did not believe generative AI would make it to the market soon enough for it to be worthwhile the $1 billion Intel would have had to pay for a 15% stake. this OpenAI OpenAI OpenAI also offered a further 15% stake if Intel agreed to make hardware for it at cost price — but the company refused, leaving OpenAI to rely on Nvidia's chips instead. GET PUBLISHED ON HACKERNOON. Join a global network of 45,000+ published devs, builders,   founders, makers, VCs, hodlrs, and hackers. Start submitting your tech   stories and tutorials to get published FREE on HackerNoon — no pop-ups,   no paywalls. GET PUBLISHED ON HACKERNOON. GET PUBLISHED ON HACKERNOON . GET PUBLISHED ON HACKERNOON Join a global network of 45,000+ published devs, builders,   founders, makers, VCs, hodlrs, and hackers. Start submitting your tech   stories and tutorials to get published FREE on HackerNoon — no pop-ups,   no paywalls. Join a global network of 45,000+ published devs, builders,   founders, makers, VCs, hodlrs, and hackers. Start submitting your tech   stories and tutorials to get published FREE on HackerNoon — no pop-ups,   no paywalls. But that was then. Intel hasn't fared any better under current CEO Pat Gelsinger's leadership either. Intel rehired Gelsinger in 2021 to help lead the company out of a tough spot: it was losing out to the likes of Taiwan Semiconductor Manufacturing and Samsung Electronics on chip-making technology, and the idea was to have Gelsinger, who had been with Intel for most of his career and helped develop some of its most successful personal-computer chips, lead a turn around. rehired rehired While Gelsinger's heart was in the right place, his turnaround plan didn't really do much to turn the company's fortunes around. In fact, things came to a head this past summer when Intel stunned both employees and investors with its poor quarterly results and announced sweeping measures (cost cuts, headcount reduction, etc.) to become profitable again. stunned stunned All of this could have been avoided had it not been for some key mistakes Gelsinger made after taking reins of the company. According to Reuters, soon after becoming CEO, Gelsinger offended Taiwan Semiconductor Manufacturing by calling Taiwan unstable. According According Gelsinger's "Taiwan is not a stable place" comment had more to do with his push to develop a US-based supply chain for chip manufacturing, but was so poorly received by TSMC that it reneged on a 40% discount it was willing to offer Intel on chips it designed but could not produce. That discount would have really helped Intel's margins, but, nope, Gelsinger was too busy commenting on Taiwan. Making matters worse, Gelsinger kept doubling down on his plan to develop a manufacturing footprint that could rival TSMC's, but on American and European soil instead. Problem is, customers have no reason to shy away from TSMC, particularly considering that Intel's foundry ambitions have run into technical challenges. Combined with the fact that Intel kept sticking to producing chips for desktop computers and servers, and you have a clearer picture of why the company is in such dire straits today. The list of fumbles goes on, but what must really make this sting for Gelsinger is that just a few weeks ago it was announced that Nvidia would take the company's place on the Dow Jones Industrial Average. announced announced Today, Nvidia is the most valuable company in the world. The company has a pile of cash. And CEO Jensen Huang's 3.8% stake in Nvidia is worth more than Intel's market value combined, Reuters reports . reports reports And to think Intel could have prevented all of this from happening. The way I see it, Intel is either going to be bought or Gelsinger will be fired, possibly both. I'll be here to report on it when it happens. Intel was #198 on HackerNoon's Tech Company Rankings this week. In Other News.. 📰 In Other News.. 📰 Bitcoin ETF Options Attract $2B on Day One, Shifting BTC’s Market Structure — via CoinDesk H, the AI startup that raised $220M, launches its first product: Runner H for ‘agentic’ applications — via TechCrunch With Musk in tow, Trump eyes changes to government policies on AI and its dangers — via CNN Apple to urge judge to end US smartphone monopoly case — via Reuters Trump 2.0 will face Big Tech antitrust test — via Axios FTX co-founder Gary Wang avoids prison time for role in crypto fraud — via CNBC Bitcoin ETF Options Attract $2B on Day One, Shifting BTC’s Market Structure — via CoinDesk CoinDesk CoinDesk H, the AI startup that raised $220M, launches its first product: Runner H for ‘agentic’ applications — via TechCrunch TechCrunch TechCrunch With Musk in tow, Trump eyes changes to government policies on AI and its dangers — via CNN CNN CNN Apple to urge judge to end US smartphone monopoly case — via Reuters Reuters Reuters Trump 2.0 will face Big Tech antitrust test — via Axios Axios Axios FTX co-founder Gary Wang avoids prison time for role in crypto fraud — via CNBC CNBC CNBC And that's a wrap! Don't forget to share this newsletter with your family and friends! See y'all next week. PEACE! ☮️ — Sheharyar Khan , Editor, Business Tech @ HackerNoon Sheharyar Khan Sheharyar Khan *All rankings are current as of Monday. To see how the rankings have changed, please visit HackerNoon's Tech Company Rankings page. *All rankings are current as of Monday. To see how the rankings have changed, please visit HackerNoon's Tech Company Rankings page. *All rankings are current as of Monday. To see how the rankings have changed, please visit HackerNoon's Tech Company Rankings page. Tech Company Rankings Tech Company Rankings Tech, What the Heck!? is a once-weekly newsletter written by HackerNoon editors that combine HackerNoon's proprietary data with news-worthy tech stories from around the internet. Humorous and insightful, the newsletter recaps trending events that are shaping the world of tech. Subscribe here. Tech, What the Heck!? is a once-weekly newsletter written by HackerNoon editors that combine HackerNoon's proprietary data with news-worthy tech stories from around the internet. Humorous and insightful, the newsletter recaps trending events that are shaping the world of tech. Subscribe here. Tech, What the Heck!? is a once-weekly newsletter written by HackerNoon editors that combine HackerNoon's proprietary data with news-worthy tech stories from around the internet. Humorous and insightful, the newsletter recaps trending events that are shaping the world of tech. Subscribe here . here here

Intel (No Longer) Inside, or How One CEO Messed Everything Up

ইন্টেল (আর নেই) ভিতরে, বা কীভাবে একজন সিইও সবকিছুকে গোলমাল করেছে

Intel (nicht mehr) Inside oder wie ein CEO alles vermasselt hat

Intel (ya no está) dentro, o cómo un CEO lo arruinó todo

Intel (n'est plus) à l'intérieur, ou comment un PDG a tout gâché

इंटेल (अब नहीं) अंदर, या कैसे एक सीईओ ने सब कुछ गड़बड़ कर दिया

インテル（もはや）内部、あるいはある CEO がいかにしてすべてを台無しにしたか

Intel(더 이상) 내부 또는 한 CEO가 모든 것을 어떻게 망쳤는지

Intel (não mais) dentro, ou como um CEO bagunçou tudo

Intel (больше не) Inside, или как один генеральный директор все испортил

Intel (Artık) İçeride Değil veya Bir CEO Her Şeyi Nasıl Mahvetti

Intel (Không Còn) Bên Trong, hay Một CEO Đã Làm Hỏng Mọi Thứ Như Thế Nào

英特尔（不再）内部，或者一位首席执行官如何把一切都搞砸了

Newer technology is always a game-changing thing. In the case of video games, it’s literally so. And what other new piece of tech to talk about than what’s all the rage at the moment—artificial intelligence or AI? Companies have been quick to adopt AI for various purposes. Whereas the majority of corporations from Bloomberg to Disney see it as a tool to cut costs, the game development industry has a very different take on all this. This take is more creative in nature. It’s being used to develop new story arcs, generate new gameplay ideas, and analyze existing player data at a whole other level. Some companies are even experimenting with AI-based NPCs (non-playable characters) where these seemingly harmless folks can now hold entire conversations and retain context throughout their adventures in-game. We’re at a very early level here. In this post, we’ll focus on a new aspect that can be revolutionized with AI—virtual economies. Let’s hop right into it, shall we? What are Virtual Economies? Believe it or not, virtual economies are one of the most lucrative businesses today. A lot of gamers are a part of at least one such economy, sometimes unknowingly. A virtual economy is an in-game trading system. It ranges from the player-based economy of trading posts in classic MMOs such as World of Warcraft and Elder Scrolls Online to Loot Boxes in CS:GO and Valorant. A strong virtual economy is at the heart of these games. These don’t necessarily have to involve real money transactions. There’s a lot of in-game currency transactions, trading, and item barter involved here. But yes, the most popular forms of virtual economies are tied to real money. The best case would be CS:GO skins that you can buy from reputed sellers. These are skins that you buy with real money for a nice change of clothes or a weapon loot box. It’s an active, thriving community of transactions and items. AI in Virtual Economies Developers are quickly realizing the potential of leveraging AI-based technologies and deep learning models to optimize these in-game virtual economies. The only current problem is the scale. To really scale it up, a developer will need to make some serious investments in AI. Today, suffice it to say that justifying this cost in front of stakeholders isn’t going to go very smoothly. But the potential is there, and we all know it. AI has already shown us how we can build better games. Smart NPCs, dynamic procedural environments, choice-based challenges, matchmaking balances, data analysis, anti-cheat, etc., are all very serious real-world use cases where AI is already doing a lot of work or is poised to be a strong factor in the coming years. Now, let’s shift our focus toward virtual economies. What exactly can AI do in this space? Players trade in-game items ranging from armor and weapons in some games to skins and cosmetics in others. These are facilitated mainly with the help of real-world money or some kind of virtual currency that can only be purchased using real money. With that in mind, here’s how AI can help. Dynamic Pricing AI models can be trained to be good at understanding the virtual economy’s market demand. Many financial models are already available, often proprietarily, to do this for real-world markets. These algorithms will be able to maintain pricing stability just by looking at the market trends, user demand, and trading history of these virtual items. Trend Prediction With all that data, such algorithms or models can easily predict the rise and fall in demand for certain skins or cosmetics, which will allow the marketplace to adjust the listings and prices accordingly. For example, we know that a lot of people buy bundles and treasures in Dota around The International, only to sell them at a higher price later. But when you make all this formulaic, suddenly you can predict a lot more trends than just that. Preventing Fraud AI can also help in detecting instances of fraudulent transactions and suspicious activities. This is a huge benefit for any marketplace moderator or game developer. The strength of an AI model is the training or inferencing part. This is why AI can predict weather better than a supercomputer. It relies on past data. The more data it is fed, the more accurate it will be. So, if a company like Valve trains an AI algorithm on fraudulent transactions and suspicious users, it will quickly learn to spot future offenders, which will be an immense help for all the other players. Leading CS:GO skin selling platforms can develop their own algorithms this way to make sure that their marketplace remains spotless. Such a case has already been implemented by sites such as skinsmonkey. Where Do We Go From Here? The times are changing fast. The future is full of AI, even in the world of video games. Though there’s still time before we can reach a world akin to Cyberpunk 2077, it’s much easier to accept a worldview where we will have AI-controlled virtual economies that can be easily scaled. AI-driven price predictions, automated price setting, authenticity verification, highly optimized trade algorithms, and so on. Artificial intelligence models and algorithms can bring about a net positive change in the world of in-game economies that so many gamers rely on. It won’t be an overstatement to say that games will be much better for player transactions and economies with AI in the picture. Major Concerns in AI Gaming AI Needs Data: AI requires heaps of data and training. Any company that wishes to make a foray here needs to properly label and deconstruct its existing data and then train a model on it. If it’s done in a shallow or incorrect way, such an algorithm will be able to do nothing good for the players.


Constant Learning: At its core, AI models are all about constant learning. If they are not allowed to hook into the economies in a friendly manner, then trends will change before they can pick up on them. This is especially challenging because so far, virtual economy data hasn’t really been intended for public use through APIs. Such APIs will be crucial if we want to use AI for continuous learning. This might require major rewrites of the game’s code before they can even be prepared for something like this. We Wrap Things Up Here It's all exciting, that’s for sure. But only time will tell how practical these AI use cases are, or when we finally achieve something valuable for the gamer. Until then, happy gaming. Newer technology is always a game-changing thing. In the case of video games, it’s literally so. And what other new piece of tech to talk about than what’s all the rage at the moment—artificial intelligence or AI? Companies have been quick to adopt AI for various purposes. Whereas the majority of corporations from Bloomberg to Disney see it as a tool to cut costs, the game development industry has a very different take on all this. Bloomberg Disney This take is more creative in nature. It’s being used to develop new story arcs, generate new gameplay ideas, and analyze existing player data at a whole other level. Some companies are even experimenting with AI-based NPCs (non-playable characters) where these seemingly harmless folks can now hold entire conversations and retain context throughout their adventures in-game. We’re at a very early level here. In this post, we’ll focus on a new aspect that can be revolutionized with AI— virtual economies . Let’s hop right into it, shall we? revolutionized virtual economies What are Virtual Economies? Believe it or not, virtual economies are one of the most lucrative businesses today. A lot of gamers are a part of at least one such economy, sometimes unknowingly. A virtual economy is an in-game trading system. It ranges from the player-based economy of trading posts in classic MMOs such as World of Warcraft and Elder Scrolls Online to Loot Boxes in CS:GO and Valorant . virtual economies MMOs World of Warcraft Elder Scrolls Online Loot Boxes CS:GO Valorant A strong virtual economy is at the heart of these games. These don’t necessarily have to involve real money transactions. There’s a lot of in-game currency transactions, trading, and item barter involved here. But yes, the most popular forms of virtual economies are tied to real money. The best case would be CS:GO skins that you can buy from reputed sellers. These are skins that you buy with real money for a nice change of clothes or a weapon loot box. It’s an active, thriving community of transactions and items. AI in Virtual Economies Developers are quickly realizing the potential of leveraging AI-based technologies and deep learning models to optimize these in-game virtual economies. The only current problem is the scale . To really scale it up, a developer will need to make some serious investments in AI. only current problem is the scale Today, suffice it to say that justifying this cost in front of stakeholders isn’t going to go very smoothly. But the potential is there, and we all know it. AI has already shown us how we can build better games. Smart NPCs , dynamic procedural environments , choice-based challenges , matchmaking balances , data analysis , anti-cheat , etc., are all very serious real-world use cases where AI is already doing a lot of work or is poised to be a strong factor in the coming years. Smart NPCs dynamic procedural environments choice-based challenges matchmaking balances data analysis anti-cheat Now, let’s shift our focus toward virtual economies. What exactly can AI do in this space? What exactly can AI do in this space? Players trade in-game items ranging from armor and weapons in some games to skins and cosmetics in others. These are facilitated mainly with the help of real-world money or some kind of virtual currency that can only be purchased using real money. With that in mind, here’s how AI can help. Dynamic Pricing AI models can be trained to be good at understanding the virtual economy’s market demand. Many financial models are already available, often proprietarily, to do this for real-world markets. These algorithms will be able to maintain pricing stability just by looking at the market trends , user demand , and trading history of these virtual items. market trends user demand trading history Trend Prediction With all that data, such algorithms or models can easily predict the rise and fall in demand for certain skins or cosmetics, which will allow the marketplace to adjust the listings and prices accordingly. For example, we know that a lot of people buy bundles and treasures in Dota around The International, only to sell them at a higher price later. Dota around The International, But when you make all this formulaic, suddenly you can predict a lot more trends than just that. Preventing Fraud AI can also help in detecting instances of fraudulent transactions and suspicious activities. This is a huge benefit for any marketplace moderator or game developer. The strength of an AI model is the training or inferencing part. This is why AI can predict weather better than a supercomputer. It relies on past data. The more data it is fed, the more accurate it will be. So, if a company like Valve trains an AI algorithm on fraudulent transactions and suspicious users, it will quickly learn to spot future offenders, which will be an immense help for all the other players. So, if a company like Valve trains an AI algorithm on fraudulent transactions and suspicious users, it will quickly learn to spot future offenders, which will be an immense help for all the other players. Leading CS:GO skin selling platforms can develop their own algorithms this way to make sure that their marketplace remains spotless. Such a case has already been implemented by sites such as skinsmonkey . skinsmonkey skinsmonkey . Where Do We Go From Here? The times are changing fast. The future is full of AI, even in the world of video games. Though there’s still time before we can reach a world akin to Cyberpunk 2077, it’s much easier to accept a worldview where we will have AI-controlled virtual economies that can be easily scaled. AI-driven price predictions , automated price setting, authenticity verification , highly optimized trade algorithms , and so on. Artificial intelligence models and algorithms can bring about a net positive change in the world of in-game economies that so many gamers rely on. AI-driven price predictions automated price setting, authenticity verification highly optimized trade algorithms It won’t be an overstatement to say that games will be much better for player transactions and economies with AI in the picture. Major Concerns in AI Gaming AI Needs Data: AI requires heaps of data and training. Any company that wishes to make a foray here needs to properly label and deconstruct its existing data and then train a model on it. If it’s done in a shallow or incorrect way, such an algorithm will be able to do nothing good for the players. Constant Learning: At its core, AI models are all about constant learning. If they are not allowed to hook into the economies in a friendly manner, then trends will change before they can pick up on them. This is especially challenging because so far, virtual economy data hasn’t really been intended for public use through APIs. Such APIs will be crucial if we want to use AI for continuous learning. This might require major rewrites of the game’s code before they can even be prepared for something like this. AI Needs Data: AI requires heaps of data and training. Any company that wishes to make a foray here needs to properly label and deconstruct its existing data and then train a model on it. If it’s done in a shallow or incorrect way, such an algorithm will be able to do nothing good for the players. AI Needs Data: Constant Learning: At its core, AI models are all about constant learning. If they are not allowed to hook into the economies in a friendly manner, then trends will change before they can pick up on them. This is especially challenging because so far, virtual economy data hasn’t really been intended for public use through APIs. Such APIs will be crucial if we want to use AI for continuous learning. This might require major rewrites of the game’s code before they can even be prepared for something like this. Constant Learning: We Wrap Things Up Here It's all exciting, that’s for sure. But only time will tell how practical these AI use cases are, or when we finally achieve something valuable for the gamer. Until then, happy gaming.

AI in Gaming: A Case Study in Virtual Economies 

Presto SQL + S3 Data + Superset = Data Lake

Interaction, Repetition and Trust: Are These Opt-out?

Authors:
(1) Martin Kleppmann, University of Cambridge, Cambridge, UK (martin.kleppmann@cst.cam.ac.uk);
(2) Paul Frazee, Bluesky Social PBC United States;
(3) Jake Gold, Bluesky Social PBC United States;
(4) Jay Graber, Bluesky Social PBC United States;
(5) Daniel Holmgren, Bluesky Social PBC United States;
(6) Devin Ivy, Bluesky Social PBC United States;
(7) Jeromy Johnson, Bluesky Social PBC United States;
(8) Bryan Newbold, Bluesky Social PBC United States;
(9) Jaz Volpert, Bluesky Social PBC United States. Table of Links Abstract and 1 Introduction 2 The Bluesky Social App 2.1 Moderation Features 2.2 User Handles 2.3 Custom Feeds and Algorithmic Choice 3 The at Protocol Architecture 3.1 User Data Repositories 3.2 Personal Data Servers (PDS) 3.3 Indexing Infrastructure 3.4 Labelers and Feed Generators 3.5 User Identity 4 Related Work 5 Conclusions, Acknowledgments, and References 2 THE BLUESKY SOCIAL APP Bluesky presents itself to users as a straightforward microblogging application in the style of Twitter/X (see Figure 1). The “official” client app is available on iOS, Android, and the web; several independently developed client apps are also available, such as Graysky [42] and deck.blue [24]. Users can make public posts containing up to 300 characters of text, and up to four images, and they can interact with posts by replying, reposting, or liking. A user can also follow other users, and the default feed shows posts by accounts that the user is following in reverse chronological order. There are also alternative feeds that show content on various topics, without the user needing to follow the poster (see Section 2.3), which helps users discover each other. Bluesky launched an invite-only beta release in February 2023, and has grown to over 3 million registered users in January 2024, as shown in Figure 2. Bluesky Social PBC (a public-benefit corporation) develops the official client app and operates the core services; the client and several server-side components are open source under the MIT license [8]. The protocols they use are defined by open specifications [7]. Several parts of the system, such as feed generators (Section 2.3) and various alternative clients [6] are developed and operated by independent third parties. This paper is available on arxiv under CC BY 4.0 DEED license. Authors: (1) Martin Kleppmann, University of Cambridge, Cambridge, UK (martin.kleppmann@cst.cam.ac.uk); (2) Paul Frazee, Bluesky Social PBC United States; (3) Jake Gold, Bluesky Social PBC United States; (4) Jay Graber, Bluesky Social PBC United States; (5) Daniel Holmgren, Bluesky Social PBC United States; (6) Devin Ivy, Bluesky Social PBC United States; (7) Jeromy Johnson, Bluesky Social PBC United States; (8) Bryan Newbold, Bluesky Social PBC United States; (9) Jaz Volpert, Bluesky Social PBC United States. Authors: Authors: (1) Martin Kleppmann, University of Cambridge, Cambridge, UK (martin.kleppmann@cst.cam.ac.uk); (2) Paul Frazee, Bluesky Social PBC United States; (3) Jake Gold, Bluesky Social PBC United States; (4) Jay Graber, Bluesky Social PBC United States; (5) Daniel Holmgren, Bluesky Social PBC United States; (6) Devin Ivy, Bluesky Social PBC United States; (7) Jeromy Johnson, Bluesky Social PBC United States; (8) Bryan Newbold, Bluesky Social PBC United States; (9) Jaz Volpert, Bluesky Social PBC United States. Table of Links Abstract and 1 Introduction Abstract and 1 Introduction 2 The Bluesky Social App 2 The Bluesky Social App 2.1 Moderation Features 2.1 Moderation Features 2.2 User Handles 2.2 User Handles 2.3 Custom Feeds and Algorithmic Choice 2.3 Custom Feeds and Algorithmic Choice 3 The at Protocol Architecture 3 The at Protocol Architecture 3.1 User Data Repositories 3.1 User Data Repositories 3.2 Personal Data Servers (PDS) 3.2 Personal Data Servers (PDS) 3.3 Indexing Infrastructure 3.3 Indexing Infrastructure 3.4 Labelers and Feed Generators 3.4 Labelers and Feed Generators 3.5 User Identity 3.5 User Identity 4 Related Work 4 Related Work 5 Conclusions, Acknowledgments, and References 5 Conclusions, Acknowledgments, and References 2 THE BLUESKY SOCIAL APP Bluesky presents itself to users as a straightforward microblogging application in the style of Twitter/X (see Figure 1). The “official” client app is available on iOS, Android, and the web; several independently developed client apps are also available, such as Graysky [42] and deck.blue [24]. Users can make public posts containing up to 300 characters of text, and up to four images, and they can interact with posts by replying, reposting, or liking. A user can also follow other users, and the default feed shows posts by accounts that the user is following in reverse chronological order. There are also alternative feeds that show content on various topics, without the user needing to follow the poster (see Section 2.3), which helps users discover each other. Bluesky launched an invite-only beta release in February 2023, and has grown to over 3 million registered users in January 2024, as shown in Figure 2. Bluesky Social PBC (a public-benefit corporation) develops the official client app and operates the core services; the client and several server-side components are open source under the MIT license [8]. The protocols they use are defined by open specifications [7]. Several parts of the system, such as feed generators (Section 2.3) and various alternative clients [6] are developed and operated by independent third parties. This paper is available on arxiv under CC BY 4.0 DEED license. This paper is available on arxiv under CC BY 4.0 DEED license. available on arxiv

Bluesky: The Decentralized Twitter-Like Social App You Need to Know

Authors:
(1) Martin Kleppmann, University of Cambridge, Cambridge, UK (martin.kleppmann@cst.cam.ac.uk);
(2) Paul Frazee, Bluesky Social PBC United States;
(3) Jake Gold, Bluesky Social PBC United States;
(4) Jay Graber, Bluesky Social PBC United States;
(5) Daniel Holmgren, Bluesky Social PBC United States;
(6) Devin Ivy, Bluesky Social PBC United States;
(7) Jeromy Johnson, Bluesky Social PBC United States;
(8) Bryan Newbold, Bluesky Social PBC United States;
(9) Jaz Volpert, Bluesky Social PBC United States. Table of Links Abstract and 1 Introduction 2 The Bluesky Social App 2.1 Moderation Features 2.2 User Handles 2.3 Custom Feeds and Algorithmic Choice 3 The at Protocol Architecture 3.1 User Data Repositories 3.2 Personal Data Servers (PDS) 3.3 Indexing Infrastructure 3.4 Labelers and Feed Generators 3.5 User Identity 4 Related Work 5 Conclusions, Acknowledgments, and References ABSTRACT Bluesky is a new social network built upon the AT Protocol, a decentralized foundation for public social media. It was launched in private beta in February 2023, and has grown to over 3 million registered users in the following year. In this paper we introduce the architecture of Bluesky and the AT Protocol, which is inspired by the web itself, but modernized to include streams of real-time updates and cryptographic authentication. We explain how the technical design of Bluesky is informed by our goals: to enable decentralization by having multiple interoperable providers for every part of the system; to make it easy for users to switch providers; to give users agency over the content they see; and to provide a simple user experience that does not burden users with complexity arising from the system’s decentralized nature. The system’s openness allows anybody to contribute to content moderation and community management, and we invite the research community to use Bluesky as a dataset and testing ground for new approaches in social media moderation. 1 INTRODUCTION Over the last two decades, social media services have evolved from a fun curiosity into a cornerstone of civic life [5]. This development has been accompanied by increasing unease that mainstream “digital town squares”, such as Twitter/X or Facebook, are under the control of a single corporation, and may change their policies on the whim of their leaders [62]. Their operations are opaque (e.g. regarding which content is recommended to users), and their users lack agency over their user experience. As a result, there has been increasing interest in decentralized social networks, of which the fediverse around the ActivityPub protocol [34] and the Mastodon software [39] is perhaps the best known (we review a selection of decentralized social networks in Section 4). However, decentralization also introduces new challenges. For example, in the case of Mastodon, a user needs to choose a server when creating an account. This choice is significant because the server name becomes part of the username; migrating to another server implies changing username, and preserving one’s followers during such a migration requires the cooperation of the old server. If a server is shut down without warning, accounts on that server cannot be recovered – a particular risk with volunteer-run servers. In principle, a user can host their own server, but only a small fraction of social media users have both the technical skills and the inclination to do so. The distinction between servers in Mastodon introduces complexity for users that does not exist in centralized services. For example, a user viewing a thread of replies in the web interface of one server may see a different set of replies compared to viewing the same thread on another server, because a server only shows those replies that it knows about [2]. As another example, when viewing the web profile of an account on another server, clicking the “follow” button does not simply follow that account; instead, the user needs to enter the hostname of their own server and be redirected to a URL on their home server before they can follow the account. In our opinion, it is undesirable to burden users with such complexity arising from the federated architecture. In this paper we introduce the AT Protocol (atproto), a decentralized foundation for social networking, and Bluesky, a Twitter-style social app built upon it. A core design goal of atproto and Bluesky is to enable a user experience of the same or better quality as centralized services, while being open and decentralized on a technical level. We introduce the user-facing features of Bluesky in Section 2, and in Section 3 we explain the underlying systems architecture. The AT Protocol is designed such that for every part of the system there are multiple competing operators providing interoperable services, making it easy to switch from one provider to another. Decentralization alone is not able to solve some of the thorniest problems of social media, such as misinformation, harassment, and hate speech [46]. However, by opening up the internals of a service to contributors who are not employees of a particular company, decentralization can enable a marketplace of approaches to these problems [38]. For example, Bluesky allows anybody to run moderation services that make subjective decisions of selecting desirable content or flagging undesirable content, and users can choose which moderation services they want to subscribe to. Moderation services are decoupled from hosting providers, making it easy for users to switch moderation services until they find ones that match their preferences. Our hope is that this architectural openness enables communities to develop their own approaches to managing problematic content, independently of what any particular service operator implements [38]. For example, researchers wanting to identify disinformation campaigns can easily get access to all content being posted, the social graph, and user profiles on Bluesky. If they are able construct an algorithm to label suspected disinformation, they can publish their labels in real time, and users who wish to see those labels can enable them in their client software. One goal of this paper is to bring Bluesky and the AT Protocol to the attention of researchers working on such algorithms, and to invite them to use the rapidly growing dataset of Bluesky content as a basis for their work. This paper is available on arxiv under CC BY 4.0 DEED license. Authors: (1) Martin Kleppmann, University of Cambridge, Cambridge, UK (martin.kleppmann@cst.cam.ac.uk); (2) Paul Frazee, Bluesky Social PBC United States; (3) Jake Gold, Bluesky Social PBC United States; (4) Jay Graber, Bluesky Social PBC United States; (5) Daniel Holmgren, Bluesky Social PBC United States; (6) Devin Ivy, Bluesky Social PBC United States; (7) Jeromy Johnson, Bluesky Social PBC United States; (8) Bryan Newbold, Bluesky Social PBC United States; (9) Jaz Volpert, Bluesky Social PBC United States. Authors: Authors: (1) Martin Kleppmann, University of Cambridge, Cambridge, UK (martin.kleppmann@cst.cam.ac.uk); (2) Paul Frazee, Bluesky Social PBC United States; (3) Jake Gold, Bluesky Social PBC United States; (4) Jay Graber, Bluesky Social PBC United States; (5) Daniel Holmgren, Bluesky Social PBC United States; (6) Devin Ivy, Bluesky Social PBC United States; (7) Jeromy Johnson, Bluesky Social PBC United States; (8) Bryan Newbold, Bluesky Social PBC United States; (9) Jaz Volpert, Bluesky Social PBC United States. Table of Links Abstract and 1 Introduction Abstract and 1 Introduction 2 The Bluesky Social App 2 The Bluesky Social App 2.1 Moderation Features 2.1 Moderation Features 2.2 User Handles 2.2 User Handles 2.3 Custom Feeds and Algorithmic Choice 2.3 Custom Feeds and Algorithmic Choice 3 The at Protocol Architecture 3 The at Protocol Architecture 3.1 User Data Repositories 3.1 User Data Repositories 3.2 Personal Data Servers (PDS) 3.2 Personal Data Servers (PDS) 3.3 Indexing Infrastructure 3.3 Indexing Infrastructure 3.4 Labelers and Feed Generators 3.4 Labelers and Feed Generators 3.5 User Identity 3.5 User Identity 4 Related Work 4 Related Work 5 Conclusions, Acknowledgments, and References 5 Conclusions, Acknowledgments, and References ABSTRACT Bluesky is a new social network built upon the AT Protocol, a decentralized foundation for public social media. It was launched in private beta in February 2023, and has grown to over 3 million registered users in the following year. In this paper we introduce the architecture of Bluesky and the AT Protocol, which is inspired by the web itself, but modernized to include streams of real-time updates and cryptographic authentication. We explain how the technical design of Bluesky is informed by our goals: to enable decentralization by having multiple interoperable providers for every part of the system; to make it easy for users to switch providers; to give users agency over the content they see; and to provide a simple user experience that does not burden users with complexity arising from the system’s decentralized nature. The system’s openness allows anybody to contribute to content moderation and community management, and we invite the research community to use Bluesky as a dataset and testing ground for new approaches in social media moderation. 1 INTRODUCTION Over the last two decades, social media services have evolved from a fun curiosity into a cornerstone of civic life [5]. This development has been accompanied by increasing unease that mainstream “digital town squares”, such as Twitter/X or Facebook, are under the control of a single corporation, and may change their policies on the whim of their leaders [62]. Their operations are opaque (e.g. regarding which content is recommended to users), and their users lack agency over their user experience. As a result, there has been increasing interest in decentralized social networks, of which the fediverse around the ActivityPub protocol [34] and the Mastodon software [39] is perhaps the best known (we review a selection of decentralized social networks in Section 4). However, decentralization also introduces new challenges. For example, in the case of Mastodon, a user needs to choose a server when creating an account. This choice is significant because the server name becomes part of the username; migrating to another server implies changing username, and preserving one’s followers during such a migration requires the cooperation of the old server. If a server is shut down without warning, accounts on that server cannot be recovered – a particular risk with volunteer-run servers. In principle, a user can host their own server, but only a small fraction of social media users have both the technical skills and the inclination to do so. The distinction between servers in Mastodon introduces complexity for users that does not exist in centralized services. For example, a user viewing a thread of replies in the web interface of one server may see a different set of replies compared to viewing the same thread on another server, because a server only shows those replies that it knows about [2]. As another example, when viewing the web profile of an account on another server, clicking the “follow” button does not simply follow that account; instead, the user needs to enter the hostname of their own server and be redirected to a URL on their home server before they can follow the account. In our opinion, it is undesirable to burden users with such complexity arising from the federated architecture. In this paper we introduce the AT Protocol (atproto), a decentralized foundation for social networking, and Bluesky, a Twitter-style social app built upon it. A core design goal of atproto and Bluesky is to enable a user experience of the same or better quality as centralized services, while being open and decentralized on a technical level. We introduce the user-facing features of Bluesky in Section 2, and in Section 3 we explain the underlying systems architecture. The AT Protocol is designed such that for every part of the system there are multiple competing operators providing interoperable services, making it easy to switch from one provider to another. Decentralization alone is not able to solve some of the thorniest problems of social media, such as misinformation, harassment, and hate speech [46]. However, by opening up the internals of a service to contributors who are not employees of a particular company, decentralization can enable a marketplace of approaches to these problems [38]. For example, Bluesky allows anybody to run moderation services that make subjective decisions of selecting desirable content or flagging undesirable content, and users can choose which moderation services they want to subscribe to. Moderation services are decoupled from hosting providers, making it easy for users to switch moderation services until they find ones that match their preferences. Our hope is that this architectural openness enables communities to develop their own approaches to managing problematic content, independently of what any particular service operator implements [38]. For example, researchers wanting to identify disinformation campaigns can easily get access to all content being posted, the social graph, and user profiles on Bluesky. If they are able construct an algorithm to label suspected disinformation, they can publish their labels in real time, and users who wish to see those labels can enable them in their client software. One goal of this paper is to bring Bluesky and the AT Protocol to the attention of researchers working on such algorithms, and to invite them to use the rapidly growing dataset of Bluesky content as a basis for their work. This paper is available on arxiv under CC BY 4.0 DEED license. This paper is available on arxiv under CC BY 4.0 DEED license. available on arxiv

Bluesky and the AT Protocol: Decentralized Social Media that Works

Bluesky и AT протоколът: Децентрализирани социални медии, които работят

Bluesky y el protocolo AT: redes sociales descentralizadas que funcionan

Bluesky と AT プロトコル: 機能する分散型ソーシャル メディア

Bluesky ແລະ AT Protocol: Decentralized Social Media ທີ່ເຮັດວຽກ

Bluesky un AT protokols: decentralizēti sociālie mediji, kas darbojas

ब्लूस्की र एटी प्रोटोकल: विकेन्द्रीकृत सोशल मिडिया जसले काम गर्दछ

Bluesky și protocolul AT: rețele sociale descentralizate care funcționează

Bluesky na AT Protokole: Itangazamakuru ryegerejwe abaturage rikora

புளூஸ்கி மற்றும் ஏடி புரோட்டோகால்: பரவலாக்கப்பட்ட சமூக ஊடகங்கள் செயல்படுகின்றன

Bluesky และโปรโตคอล AT: โซเชียลมีเดียแบบกระจายอำนาจที่ใช้งานได้จริง

Bluesky at ang AT Protocol: Desentralisadong Social Media na Gumagana

Bluesky và Giao thức AT: Phương tiện truyền thông xã hội phi tập trung có hiệu quả

CEH v13 certification equips cybersecurity professionals with essential AI skills to prepare them for a new era of cybercrime TAMPA, FLORIDA September 23, 2024: EC-Council, a world leader in cybersecurity certification, education, training, today announced the launch of Certified Ethical Hacker CEH v13 with AI- powered capabilities, a first of its kind certification in cybersecurity. Long recognized as the #1 ethical hacking certification in the world, the new and improved groundbreaking program integrates artificial intelligence (AI) learning into ethical hacking practices, giving professionals advanced skills and tools to battle global cybercriminals. CEH v13 provides in-depth training by integrating AI into all five phases of ethical hacking, from reconnaissance and scanning to gaining access, maintaining access, and covering tracks to help bridge the AI Chasm. Through the course, learners will be equipped to leverage AI to enhance their hacking techniques and hack AI systems to automate their ethical hacking tasks while driving up to 40% efficiency in cyber defense and doubling their productivity. “AI has already begun to radically transform the cybersecurity landscape,” said Jay Bavisi, Group President of EC-Council. “The launch of CEH v13 represents a major leap forward in cybersecurity education, with AI taking center stage in the curriculum. As AI tools for both attackers and defenders continue to proliferate, building AI skills is already becoming a key factor for employers looking to hire and retain cybersecurity talent.” Inspired by the findings of EC-Council’s 2024 CEH Threat Report, the CEH v13 program is designed to leverage a unique four-phase learning framework based on the principles of Learn, Certify, Engage, and Compete, offering current and prospective cybersecurity professionals comprehensive knowledge of AI in cybersecurity through courses and hands-on learning experiences. The program includes 221 labs, attack vectors, hacking tools, a lab environment that simulates real-time environment, and a Capture the Flag global hacking competition that will run all year long. The year-long competition allows individuals to continually sharpen their skills as needed while keeping them prepared to operate and defend in an increasingly complex cyber environment. As part of the CEHv13 program, trainees will participate in 40 hours of rigorous training though instructional practice and education, followed by a yearlong, month-to-month competition across various technologies to help learners obtain world class operational skills in ethical hacking. Course participants will experience more than 550 attack techniques and 4000+ commercial-grade security tools. Key highlights of CEH v13 include World's first ethical hacking program harnessing the power of AI Master AI-driven skills and Learn Hacking AI systems: Learn to leverage AI for enhanced hacking techniques and how to identify and exploit vulnerabilities in AI systems. Core domains of Cybersecurity: CEH strengthens your core domains of cybersecurity with a practical learning approach, which enables CEH to map across 45+ job roles over many industries. Focus on the OWASP Top 10 AI Attacks: Gain expertise in mitigating threats like prompt injection, insecure output handling, training data poisoning, and more. 221 hands-on labs and 4000+ commercial-grade hacking tools: Develop practical skills in a real-time simulated environment. Knowledge-Based and Practical Exam: Validate your skills with both a 4-hour, knowledge-based exam and a 6-hour, fully practical exam as part of the CEH Master program. For more information about the Certified Ethical Hacker CEH v13, visit https://www.eccouncil.org/cehv13ai/. ABOUT EC-COUNCIL EC-Council invented the Certified Ethical Hacker. Founded in 2001 in response to 9/11, EC-Council's mission is to provide the training and certifications apprentice and experienced cybersecurity professionals need to keep corporations, government agencies, and others who employ them safe from attack. Best known for its Certified Ethical Hacker program, EC-Council today offers 200 different pieces of training, certificates, and degrees in everything from Computer Forensic Investigation and Security Analysis to Threat Intelligence and Information Security. An ISO/IEC 17024 Accredited Organization recognized under the U.S. Defense Department Directive 8140/8570 and many other authoritative cybersecurity bodies worldwide, EC-Council has certified over 350,000 professionals across the globe. EC-Council is the gold standard in cybersecurity education and certification, trusted by seven of the Fortune 10, half of the Fortune 100, and the intelligence communities of 150 nations. A truly global organization with a driving belief in bringing diversity, equity, and inclusion to the modern cybersecurity workforce, EC-Council maintains 11 offices in the US, the UK, India, Malaysia, Singapore, and Indonesia. The company can be reached online at https://www.eccouncil.org/
Press Contact: press@eccouncil.org CEH v13 certification equips cybersecurity professionals with essential AI skills to prepare them for a new era of cybercrime CEH v13 certification equips cybersecurity professionals with essential AI skills to prepare them for a new era of cybercrime TAMPA, FLORIDA September 23, 2024: EC-Council, a world leader in cybersecurity certification, education, training, today announced the launch of Certified Ethical Hacker CEH v13 with AI- powered capabilities, a first of its kind certification in cybersecurity. Long recognized as the #1 ethical hacking certification in the world, the new and improved groundbreaking program integrates artificial intelligence (AI) learning into ethical hacking practices, giving professionals advanced skills and tools to battle global cybercriminals. TAMPA, FLORIDA September 23, 2024: CEH v13 provides in-depth training by integrating AI into all five phases of ethical hacking, from reconnaissance and scanning to gaining access, maintaining access, and covering tracks to help bridge the AI Chasm. Through the course, learners will be equipped to leverage AI to enhance their hacking techniques and hack AI systems to automate their ethical hacking tasks while driving up to 40% efficiency in cyber defense and doubling their productivity. “AI has already begun to radically transform the cybersecurity landscape,” said Jay Bavisi, Group President of EC-Council. “The launch of CEH v13 represents a major leap forward in cybersecurity education, with AI taking center stage in the curriculum. As AI tools for both attackers and defenders continue to proliferate, building AI skills is already becoming a key factor for employers looking to hire and retain cybersecurity talent.” “AI has already begun to radically transform the cybersecurity landscape,” said Jay Bavisi, Group President of EC-Council. “The launch of CEH v13 represents a major leap forward in cybersecurity education, with AI taking center stage in the curriculum. As AI tools for both attackers and defenders continue to proliferate, building AI skills is already becoming a key factor for employers looking to hire and retain cybersecurity talent.” Inspired by the findings of EC-Council’s 2024 CEH Threat Report , the CEH v13 program is designed to leverage a unique four-phase learning framework based on the principles of Learn, Certify, Engage, and Compete, offering current and prospective cybersecurity professionals comprehensive knowledge of AI in cybersecurity through courses and hands-on learning experiences. The program includes 221 labs, attack vectors, hacking tools, a lab environment that simulates real-time environment, and a Capture the Flag global hacking competition that will run all year long. The year-long competition allows individuals to continually sharpen their skills as needed while keeping them prepared to operate and defend in an increasingly complex cyber environment. 2024 CEH Threat Report As part of the CEHv13 program, trainees will participate in 40 hours of rigorous training though instructional practice and education, followed by a yearlong, month-to-month competition across various technologies to help learners obtain world class operational skills in ethical hacking. Course participants will experience more than 550 attack techniques and 4000+ commercial-grade security tools. Key highlights of CEH v13 include World's first ethical hacking program harnessing the power of AI Master AI-driven skills and Learn Hacking AI systems: Learn to leverage AI for enhanced hacking techniques and how to identify and exploit vulnerabilities in AI systems. Core domains of Cybersecurity: CEH strengthens your core domains of cybersecurity with a practical learning approach, which enables CEH to map across 45+ job roles over many industries. Focus on the OWASP Top 10 AI Attacks: Gain expertise in mitigating threats like prompt injection, insecure output handling, training data poisoning, and more. 221 hands-on labs and 4000+ commercial-grade hacking tools: Develop practical skills in a real-time simulated environment. Knowledge-Based and Practical Exam: Validate your skills with both a 4-hour, knowledge-based exam and a 6-hour, fully practical exam as part of the CEH Master program. For more information about the Certified Ethical Hacker CEH v13, visit https://www.eccouncil.org/cehv13ai/ . https://www.eccouncil.org/cehv13ai/ ABOUT EC-COUNCIL EC-Council invented the Certified Ethical Hacker. Founded in 2001 in response to 9/11, EC-Council's mission is to provide the training and certifications apprentice and experienced cybersecurity professionals need to keep corporations, government agencies, and others who employ them safe from attack. Best known for its Certified Ethical Hacker program, EC-Council today offers 200 different pieces of training, certificates, and degrees in everything from Computer Forensic Investigation and Security Analysis to Threat Intelligence and Information Security. An ISO/IEC 17024 Accredited Organization recognized under the U.S. Defense Department Directive 8140/8570 and many other authoritative cybersecurity bodies worldwide, EC-Council has certified over 350,000 professionals across the globe. EC-Council is the gold standard in cybersecurity education and certification, trusted by seven of the Fortune 10, half of the Fortune 100, and the intelligence communities of 150 nations. A truly global organization with a driving belief in bringing diversity, equity, and inclusion to the modern cybersecurity workforce, EC-Council maintains 11 offices in the US, the UK, India, Malaysia, Singapore, and Indonesia. The company can be reached online at https://www.eccouncil.org/ Press Contact: press@eccouncil.org https://www.eccouncil.org/ press@eccouncil.org

EC-Council Introduces AI-Powered Ethical Hacking against Cybercrime

EC-Council በሳይበር ወንጀል ላይ AI-Powered Ethical Hacking አስተዋወቀ

EC-Consejo ukax Hacking Ético AI-Powered ukax Ciberdelincuencia ukar uñt’ayi

El Consejo de la CE presenta un hackeo ético basado en inteligencia artificial contra el cibercrimen

Le Conseil de l'UE présente le piratage éthique basé sur l'IA contre la cybercriminalité

EC評議会、サイバー犯罪対策としてAIを活用した倫理的ハッキングを導入

EC-Зөвлөл цахим гэмт хэргийн эсрэг хиймэл оюун ухаанаар ажилладаг ёс зүйн хакердалтыг нэвтрүүллээ.

Совет ЕС представляет этичную хакерскую атаку на основе искусственного интеллекта против киберпреступности

EC-කවුන්සිලය සයිබර් අපරාධවලට එරෙහිව AI-බලවත් සදාචාරාත්මක හැකිං හඳුන්වා දෙයි

EC-Council Inaanzisha Udukuzi wa Kimaadili unaoendeshwa na AI dhidi ya Uhalifu wa Mtandao

EC-کونسل نے سائبر کرائم کے خلاف AI سے چلنے والی اخلاقی ہیکنگ متعارف کرائی

EC-Council giới thiệu công nghệ hack đạo đức hỗ trợ bởi AI chống lại tội phạm mạng

I-EC-Council Yethula I-AI-Powered Ethical Hacking ngokumelene nobugebengu be-Cybercrime

Bitcoinized Fossil Fuel

Биткоинизированное выкапнёвае паліва

Combustible fósil bitcoinizado

Combustible fossile bitcoinisé

דלק מאובן שעבר ביטקוין

ビットコイン化された化石燃料

Bahan Api Fosil Bitcoin

Ibicanwa bya Bitcoin

பிட்காயின் செய்யப்பட்ட புதைபடிவ எரிபொருள்

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