The buyer had previously sold off its Atlanta portfolio but has gained a new foothold in the market with the Midtown acquisition.

AIG Sells Midtown Apartments For $61M

Coalition has hired AIG’s EVP for financial lines specialty claims Robert Jones as global head of claims.

Coalition hires AIG’s Robert Jones a global head of claims | The Insurer

The AIG stock rating upgrade to above 80 is a noteworthy move. But why is its Relative Strength Rating climbing?

AIG Stock Pops On Surging Profits | Investor's Business Daily

American International Group Inc. (AIG) said that it has completed sale of Validus Re to RenaissanceRe. AIG received total

AIG Closes Sale Of Validus Re To RenaissanceRe

RenaissanceRe, the Bermuda based reinsurance and third-party capital management specialist, has now completed its acquisition of the Validus reinsurance

RenaissanceRe completes acquisition of AIG’s Validus Re and AlphaCat

About AIG American International Group, Inc. (AIG) is a leading global insurance organization. AIG member companies provide insurance solutions that help businesses and individuals in approximately 70 countries and jurisdictions protect their assets and ...

AIG Completes Sale of Validus Re to RenaissanceRe

In a tribute to the late Shane Fitzsimons, the global insurer’s CEO Peter Zaffino praised the former CFO’s “legacy of extraordinary leadership."

Former AIG CFO dies following medical leave

Corebridge Financial, Inc. (NYSE:CRBG) today announced that it has successfully completed the previously announced sale of Laya Healthcare Limited ("laya healthcare") to a subsidiary of AXA S.A. ("AXA").

Corebridge Financial Completes Sale of Laya Healthcare to AXA

Oka, The Carbon Insurance Company™ (Oka) is thrilled to announce its inclusion in the ESGFinTech100, an annual list of the world's 100 most innovative technology companies helping the financial sector tackle environmental,

Oka, The Carbon Insurance Company™ Recognized as ESGFinTech100 Leader

Shane Fitzsimons, the former chief financial officer who left American International Group earlier this year following a medical leave of absence, has passed away.

AIG’s former CFO passes away

**GEORGE TOWN, Cayman Islands, May 8th, 2024/Chainwire/**AIGOLD,is pleased to announce the launch of its innovative cryptocurrency project. This groundbreaking initiative integrates artificial intelligence with the enduring value of gold, aiming to redefine the landscape of digital assets. The presale phase of AIGOLD is currently underway, with early access available at aigold.io. AIGOLD: Pioneering Gold Integration in Cryptocurrency with Enhanced Security and Market Stability AIGOLD is aiming to lead the integration of gold into the cryptocurrency sector, enhancing security and stability for those engaged in trading. The platform's design includes dedicating a portion of the proceeds from the presale of AIG (AIGOLD) tokens to acquire PAXG tokens. PAXG tokens are backed by physical gold, each representing one fine troy ounce stored in LBMA-certified vaults in London. At launch, the liquidity pool, underpinned by over one million dollars in PAXG, will be securely locked and aligned with gold price movement The initial presale price for AIGOLD is set at $0.0035 per token and the project underwent comprehensive audits from Cyberscope and is currently being audited by CertiK, underscore AIGOLD’s commitment to security and transparency. As the market trends towards gold-backed assets, AIGOLD is looking to offer a unique platform that not only secures value but also rewards participants with gold, enhancing their participation in the evolving digital economy. AIGOLD: Leading Sustainable Digital Finance with Strategic Tokenomics AIGOLD team's innovative tokenomics structure is crafted to foster growth and establish a solid foundation for its next developmental phase. This structure, along with a strategic tax model, is aimed at enhancing the benefits for participants in the ecosystem. By reinvesting 2% of every transaction back into the liquidity pool, AIGOLD looks to ensure a dynamic and well-supported trading environment. Additionally, a designated marketing allocation aims at keeping AIGOLD prominent in the marketplace, driving ongoing visibility and engagement. The team sees the self-sustaining approach as marking a notable evolution in the cryptocurrency arena, positioning AIGOLD as a leader in sustainable digital finance practices. AIGOLD: Empowering Future Financial Landscapes with Innovative Rewards and Stability AIGOLD’s ecosystem offers multiple compensation methods to its participants, including rewards in gold for every transaction. Holders of AIG tokens will potentially benefit from a 5% share of gold with each buy and sell action, thanks to AIG's built-in reward mechanism. This reward is issued in digital gold, which can also be claimed physically AIGOLD not only provides rewards in gold but also serves as a strategic hedge against inflation and de-dollarization. By integrating artificial intelligence with gold, AIGOLD is not merely a tool for potential profit but a forward-looking platform designed to empower participants for future financial landscapes. AIGOLD is dropping a never-before-seen Real Gold-backed NFT Mining Permit Collection as a bonus with exclusive benefits for NFT holders. AIGOLD is excited to announce the limited release of its real gold-backed NFT Mining Permits, with a total of only 4,000 permits ever to be issued. These exclusive mining permits offer additional benefits to AIG NFT holders, including receiving an annual share of 25% of the profits from gold recovered in AIG’s mining operations. This innovative approach positions AIG NFT holders to benefit from the burgeoning digital economy. For more details and to ensure early access, users visit aigold.io to secure their place on the whitelist. AIGOLDs team marks a transformative point in the development of digital currencies by integrating artificial intelligence with genuine gold mining. This initiative is led by new technology, including an AI-integrated exploration recovery vessel (ERV) equipped with state-of-the-art tools and LIDAR systems. These advancements facilitate the extraction of gold from the seabed in the Bering Sea, where estimates suggest there are over 500 million ounces of gold. With the rising value of gold, strategic positioning is crucial. AIGOLD's founder and CEO, Forest, from Cornell University, stated, "At AIGOLD, we are forging a unique combination of advanced artificial intelligence, forefront technology, and the reliability of gold-backed assets." For more details about AIGOLD, including its tokenomics, token metrics, and gold paper, users can visit aigold.io. The AIG Team is set to introduce a variety of incentives and special offers to benefit AIG token holders. These include exclusive NFT Mining Permits, participation in charity events, air drops, and official merchandise available through the AIG store. Additionally, participants will have the opportunity to enter a Tesla Cyber Truck Competition. For more details on these exciting opportunities, users can visit aigold.io. AIGOLD’s Tesla Cybertruck Competition AIGOLD is currently hosting a Cybertruck Competition featuring the chance to win a Tesla Cybertruck among a total of 20 prizes. The Tesla Cybertruck boasts remarkable features such as bulletproof construction, a waterproof or wade mode that allows driving in up to 30 inches of water, and a durable exoskeleton similar to materials used in Space X rockets. This all-electric vehicle includes Tesla's autopilot system, accelerates from 0 to 60 mph in just 2.6 seconds, can tow up to 11,000 lbs, and travels up to 340 miles on a single charge, all while being equipped with advanced interior technology. This competition is skill-based, not a random draw. In an era where durability and real-world utility are highly valued, AIGOLD offers a substantial opportunity. Moving away from transient trends in cryptocurrency, AIGOLD is looking to establish itself as a significant force with the potential to impact the crypto market significantly this year. About AIGOLD AIGOLD is the first-ever cryptocurrency project to back its liquidity pool in gold. Founded in 2023, they are a cryptocurrency ecosystem and a digital currency that offers a reward mechanism that pays out digital gold to all of their token holders. They are the first of their kind, bringing together AI and real gold mining. AIGOLD is a real utility token and aims to act as a hedge against inflation. They offer gold-backed assets through the buy/sell of their AIG token and access to the profits from their gold mining operations to Gold Mining NFT holders. AIGOLD is committed to and strives to become one of the top 100 cryptocurrencies and get on top-tier 1 exchanges. They are on a mission to maintain a self-sustainable, well-balanced, and healthy ecosystem where their investors and their ecosystem thrive. They aim to build a community of investors from around the world, including Bitcoin and Ethereum enthusiasts. Users can find more information about AIGOLD on the following platforms: Website I X I Telegram I Medium I Instagram AIGold is the source of this content. This Press Release is for informational purposes only. The information does not constitute investment advice or an offer to invest. Contact PR Media Relations Kate Brown AIGOLD aigoldofficial@aigold.io This story was distributed as a release by Chainwire under HackerNoon’s Business Blogging Program. Learn more about the program here. **GEORGE TOWN, Cayman Islands, May 8th, 2024/Chainwire/**AIGOLD,is pleased to announce the launch of its innovative cryptocurrency project. This groundbreaking initiative integrates artificial intelligence with the enduring value of gold, aiming to redefine the landscape of digital assets. The presale phase of AIGOLD is currently underway, with early access available at aigold.io. AIGOLD: Pioneering Gold Integration in Cryptocurrency with Enhanced Security and Market Stability AIGOLD is aiming to lead the integration of gold into the cryptocurrency sector, enhancing security and stability for those engaged in trading. The platform's design includes dedicating a portion of the proceeds from the presale of AIG (AIGOLD) tokens to acquire PAXG tokens. PAXG tokens are backed by physical gold, each representing one fine troy ounce stored in LBMA-certified vaults in London. At launch, the liquidity pool, underpinned by over one million dollars in PAXG, will be securely locked and aligned with gold price movement The initial presale price for AIGOLD is set at $0.0035 per token and the project underwent comprehensive audits from Cyberscope and is currently being audited by CertiK, underscore AIGOLD’s commitment to security and transparency. comprehensive audits from Cyberscope comprehensive audits from Cyberscope As the market trends towards gold-backed assets, AIGOLD is looking to offer a unique platform that not only secures value but also rewards participants with gold, enhancing their participation in the evolving digital economy. AIGOLD: Leading Sustainable Digital Finance with Strategic Tokenomics AIGOLD team's innovative tokenomics structure is crafted to foster growth and establish a solid foundation for its next developmental phase. This structure, along with a strategic tax model, is aimed at enhancing the benefits for participants in the ecosystem. By reinvesting 2% of every transaction back into the liquidity pool, AIGOLD looks to ensure a dynamic and well-supported trading environment. Additionally, a designated marketing allocation aims at keeping AIGOLD prominent in the marketplace, driving ongoing visibility and engagement. The team sees the self-sustaining approach as marking a notable evolution in the cryptocurrency arena, positioning AIGOLD as a leader in sustainable digital finance practices. AIGOLD: Empowering Future Financial Landscapes with Innovative Rewards and Stability AIGOLD’s ecosystem offers multiple compensation methods to its participants, including rewards in gold for every transaction. Holders of AIG tokens will potentially benefit from a 5% share of gold with each buy and sell action, thanks to AIG's built-in reward mechanism. This reward is issued in digital gold, which can also be claimed physically AIGOLD not only provides rewards in gold but also serves as a strategic hedge against inflation and de-dollarization. By integrating artificial intelligence with gold, AIGOLD is not merely a tool for potential profit but a forward-looking platform designed to empower participants for future financial landscapes. AIGOLD is dropping a never-before-seen Real Gold-backed NFT Mining Permit Collection as a bonus with exclusive benefits for NFT holders. AIGOLD is excited to announce the limited release of its real gold-backed NFT Mining Permits, with a total of only 4,000 permits ever to be issued. These exclusive mining permits offer additional benefits to AIG NFT holders, including receiving an annual share of 25% of the profits from gold recovered in AIG’s mining operations. This innovative approach positions AIG NFT holders to benefit from the burgeoning digital economy. For more details and to ensure early access, users visit aigold.io to secure their place on the whitelist. AIGOLDs team marks a transformative point in the development of digital currencies by integrating artificial intelligence with genuine gold mining. This initiative is led by new technology, including an AI-integrated exploration recovery vessel (ERV) equipped with state-of-the-art tools and LIDAR systems. aigold.io aigold.io These advancements facilitate the extraction of gold from the seabed in the Bering Sea, where estimates suggest there are over 500 million ounces of gold. With the rising value of gold, strategic positioning is crucial. AIGOLD's founder and CEO, Forest, from Cornell University, stated, "At AIGOLD, we are forging a unique combination of advanced artificial intelligence, forefront technology, and the reliability of gold-backed assets." For more details about AIGOLD, including its tokenomics, token metrics, and gold paper, users can visit aigold.io . aigold.io aigold.io The AIG Team is set to introduce a variety of incentives and special offers to benefit AIG token holders. These include exclusive NFT Mining Permits, participation in charity events, air drops, and official merchandise available through the AIG store. Additionally, participants will have the opportunity to enter a Tesla Cyber Truck Competition. For more details on these exciting opportunities, users can visit aigold.io . aigold.io aigold.io AIGOLD’s Tesla Cybertruck Competition AIGOLD is currently hosting a Cybertruck Competition featuring the chance to win a Tesla Cybertruck among a total of 20 prizes. The Tesla Cybertruck boasts remarkable features such as bulletproof construction, a waterproof or wade mode that allows driving in up to 30 inches of water, and a durable exoskeleton similar to materials used in Space X rockets. This all-electric vehicle includes Tesla's autopilot system, accelerates from 0 to 60 mph in just 2.6 seconds, can tow up to 11,000 lbs, and travels up to 340 miles on a single charge, all while being equipped with advanced interior technology. This competition is skill-based, not a random draw. In an era where durability and real-world utility are highly valued, AIGOLD offers a substantial opportunity. Moving away from transient trends in cryptocurrency, AIGOLD is looking to establish itself as a significant force with the potential to impact the crypto market significantly this year. About AIGOLD AIGOLD is the first-ever cryptocurrency project to back its liquidity pool in gold. Founded in 2023, they are a cryptocurrency ecosystem and a digital currency that offers a reward mechanism that pays out digital gold to all of their token holders. They are the first of their kind, bringing together AI and real gold mining. AIGOLD is a real utility token and aims to act as a hedge against inflation. AIGOLD AIGOLD They offer gold-backed assets through the buy/sell of their AIG token and access to the profits from their gold mining operations to Gold Mining NFT holders. AIGOLD is committed to and strives to become one of the top 100 cryptocurrencies and get on top-tier 1 exchanges. They are on a mission to maintain a self-sustainable, well-balanced, and healthy ecosystem where their investors and their ecosystem thrive. They aim to build a community of investors from around the world, including Bitcoin and Ethereum enthusiasts. Users can find more information about AIGOLD on the following platforms: Website I X I Telegram I Medium I Instagram Website Website X X Telegram Telegram Medium Medium Instagram Instagram AIGold is the source of this content. This Press Release is for informational purposes only. The information does not constitute investment advice or an offer to invest. Contact PR Media Relations Kate Brown AIGOLD aigoldofficial@aigold.io This story was distributed as a release by Chainwire under HackerNoon’s Business Blogging Program. Learn more about the program here. This story was distributed as a release by Chainwire under HackerNoon’s Business Blogging Program. Learn more about the program here . here here

AIGOLD Goes Live, Introducing The First Gold Backed Crypto Project

AIGOLD লাইভ যাচ্ছে, প্রথম গোল্ড ব্যাকড ক্রিপ্টো প্রজেক্টের সাথে পরিচয়

AIGOLD geht live und stellt das erste goldgedeckte Kryptoprojekt vor

AIGOLD se pone en marcha y presenta el primer proyecto criptográfico respaldado por oro

AIGOLD est lancé et présente le premier projet de cryptographie soutenu par l'or

AIGOLD लाइव हुआ, पहला गोल्ड समर्थित क्रिप्टो प्रोजेक्ट पेश किया

AIGOLDが始動、初の金担保暗号プロジェクトを発表

AIGOLD가 출시되어 최초의 금 기반 암호화폐 프로젝트를 소개합니다

AIGOLD entra em operação, apresentando o primeiro projeto de criptografia apoiado em ouro

AIGOLD запускается, представляя первый криптопроект, обеспеченный золотом

AIGOLD İlk Altın Destekli Kripto Projesini Tanıtarak Yayına Başladı

AIGOLD đi vào hoạt động, giới thiệu dự án tiền điện tử được hỗ trợ bằng vàng đầu tiên

AIGOLD 上线，推出首个以黄金为后盾的加密货币项目

The following video showcases a snippet of the Apache SeaTunnel developer's daily workflow, aided by Co-Pilot. If you haven't yet embraced tools like Co-Pilot, ChatGPT, or private large-scale models for development assistance, you might find yourself quickly outpaced by the industry over the next five years. Those skilled in leveraging AI-powered coding can accelerate their development speed tenfold compared to those without this skill. This isn't an exaggeration – after reading this, you'll likely have a whole new perspective on how AIGC enhances development efficiency. https://www.youtube.com/watch?v=-pBmwLZZtIw&embedable=true Disrupting Traditional Junior Programmer Training with Large Models: Empowering Skills and Experience In the past, junior programmers would be given tasks by mentors, taught basic approaches, and then guided and corrected as they wrote code. They learned from experience and guidance, gradually gaining proficiency. However, the arrival of large models has completely transformed this process. Large models possess extensive knowledge and even some rudimentary reasoning abilities. They've undergone countless practices, learning various codes and business definitions within a company. The scenarios they've encountered far exceed those faced by mentors in their time. They provide potential answers based on developers' requirements and goals. It's akin to having an all-knowing "mentor" available at your beck and call, capable of instantly generating code for your reference and learning. After learning and adjustment, a junior programmer can submit code that surpasses their individual skill level, ready for peer review. So, why wouldn't we utilize large models to boost our development efficiency? How to Utilize Large Models for Assisted Programming? Common tools currently include ChatGPT, Co-Pilot, and private large-scale models. Different methods are applicable depending on the programming scenario: It's evident that humans are best suited for challenging and innovative architecture or new business scenario code. For reused or similar algorithms, using Co-Pilot can rapidly enhance efficiency. ChatGPT can be employed for generating code in similar scenarios with minor modifications. Private large models are suitable for scenarios where data and code security are crucial, and where the model's familiarity with your business knowledge matters – requiring Fine-Tuning. One might argue that not every company can afford private large models. However, this is a misconception. You don't necessarily need to train an entirely new private large model. For most companies, optimizing (Fine-Tuning) an existing open-source large model to understand your business context is sufficient. This process requires only 1-2 graphics cards (e.g., 3090/4090) and a few hours for configuration.(If interested, you can refer to the following article: In this article, it discusses using Apache DolphinScheduler and pre-configured templates to easily train a large model by drag-and-drop.) . Train Your Own Private ChatGPT with the Cost of a Starbucks Coffee Using private large models can directly facilitate the following: Easy access to desired software features. Efficient navigation of complex user manuals and rules. Assisted programming, Txt2SQL, enhancing data programmer efficiency. Leveraging private large models for programming assistance is closer than you think. If you're still skeptical about the era of AIGC-driven automation in programming, consider the example below on how an open-source project utilizes AIGC to enhance development efficiency. Example of Large Model-Driven Automated Programming: Apache SeaTunnel The vision of Apache SeaTunnel is to " " This ambitious goal aims to connect all data sources on the market, including databases, SaaS, middleware, and BinLogs, achieving optimal synchronization. This is an impossible feat for any single company, and even humans struggle to accomplish it amidst thousands of SaaS software options and ever-changing interfaces. So, how did the core team behind Apache SeaTunnel design such software in the AI era? The overall structure is depicted in the following diagram: Connect Everything, Sync as Fast as Lightning. Unlike Flink or Spark, it focuses on efficient memory, CPU, and bandwidth utilization, and ensures data consistency – aspects that lack existing benchmarks. Consequently, most of the code isn't borrowed but developed directly by core engineers, with continuous input and refinements from experts worldwide to keep pace with cutting-edge technology. Firstly, the core computational engine is purpose-built for synchronization. While these still rely primarily on human expertise, they can leverage existing codes and cloud-based practices, with Co-Pilot as a supportive tool. Large models assist by complementing conventional algorithms and reusing code snippets, as demonstrated in the introductory video. Secondly, the core database connectors, such as the Iceberg Connector, are complex implementations that prioritize both accuracy and high data transmission efficiency. In the face of a vast sea of SaaS interfaces – over 5000 in the MarTech sector alone – manual integration is unfeasible. The SeaTunnel core team devised a strategy: abstracting over a dozen interfaces that humans had previously coded, collaborating with ChatGPT to turn them into two interfaces capable of producing elegant code. ChatGPT can comprehend SaaS interface documentation and directly generate relevant code snippets. Within SeaTunnel, this feature is known as "AI Compatibility," facilitating collaboration between AI and humans. It's a kind of "reconciliation" between programmers and AI, ensuring that each contributes where the other excels. However, as an evolving open-source project, it still has room for improvement. Given the open nature of the code, more enthusiasts are likely to fine-tune and automate this feature. For instance, I've heard of a developer planning to create a GPT Coder that monitors SaaS issues on GitHub, uses ChatGPT to generate code, and submits pull requests automatically – taking human-bot collaboration to the extreme. This feature was released in Apache SeaTunnel 2.3.1. Challenges of Large Model-Driven Automated Programming While ChatGPT, Co-Pilot, and similar tools enhance programming, they're not invincible. There are several challenges to consider when generating code with large models: Accuracy concerns. Inability to conduct code reviews. Limited capability for automated testing. No accountability. – even ChatGPT4 may produce code with 90% accuracy. Hence, it's crucial to simplify the process of code generation, as generating incorrect code is a potential pitfall. After rapid code generation by large models, human code review may struggle to keep up, as machines cannot verify if the final code correctly implements the business logic. Attempting code review using large models reveals a plethora of seemingly important but ultimately trivial improvements, obscuring the assessment of logic correctness. Large models can still make mistakes, and this will likely remain the norm for some time Moreover, automated testing and test case generation are still challenging aspects for large models. While TestPilot is actively discussed in academic circles (see Cornell's articles on the topic), its engineering-level application remains distant. Lastly, there's the issue of responsibility – a philosophical rather than technical challenge. Even with a 90% accuracy rate for ChatGPT, let alone a hypothetical 99.9999%, would you trust it to automatically calculate the entire company's payroll and initiate direct salary transfers with banks? If a problem arises, who's accountable? Not all business challenges can be solved with technology, and the same applies to large models. Future Outlook We're currently in the early stages of large model-driven automated programming. Many developers are still experimenting with Co-Pilot and ChatGPT, and most programmers haven't yet utilized private large models to boost coding efficiency based on their company's context. : However, over the next 3-5 years, automated assistance in programming will undoubtedly become a staple tool for our generation of developers Foundational large models will narrow the gap with ChatGPT, improving usability. Open-source large models will enhance accuracy and performance, resulting in improved ROI for more companies that adopt private large models. The threshold for large model-driven automation will decrease, with the emergence of more tools for democratized large model training – beyond . Apache DolphinScheduler Awareness of large model-driven automated programming will increase among technical managers, leading to adaptations in technical management processes. Amid the current economic cycle, leveraging large models to enhance efficiency becomes imperative. Therefore, over the next few years, if your development process remains limited to CRUD operations and you can't harness large models to amplify your expertise and business comprehension by 10 or 100 times, you might not need to wait until you're 35 to be replaced by programmers adept at large model programming. Once they've scaled up by a factor of 10, you might find yourself among the 9 who are left behind. While this might sound a bit alarmist, the trend toward large model-driven automation in programming is an unstoppable force. I'm just a novice explorer in the realm of large model-driven automated programming, and I believe many technical managers and architects will join the wave of large model-driven automation, continuously iterating and optimizing the relationship between humans and large models within the development field. Ultimately, this synergy will make programmers, AI, and technical development processes more effective in serving businesses.

Programmers Not Proficient in AIGC Programming May Face Obsolescence in the Next 5 Years

today announced their plans to expand into Virtual Reality (VR) and Game Content (AIGC). Building upon their GameFi expertise, BinaryX has reportedly entered into agreements with their VR and AIGC partners, AiGC Labs, to develop a groundbreaking new GameFi genre of AI-based VR games. BinaryX Artificial Intelligence First AI-Powered Virtual Reality Game on the Metaverse According to the team, BinaryX is preparing for the launch of the first-ever VR game which heavily features both VR and AIGC in collaboration with AiGC Labs. The team has not released the full details of the game yet, but has hinted that the game will be set in a character-rich universe, inspired by heroes and characters from East Asian folklore. This comes just after Apple’s official reveal of their VR headset, Apple Vision Pro, during the Worldwide Developers Conference event on June 5. Using ChatGPT and GameGPT for Game Design The team revealed that they have been experimenting with ChatGPT and GameGPT to generate engaging gameplay, and characters and build compelling narratives for their games. The AI models helped the team to explore and refine ideas for world-building as well, including ideas for environmental layout, challenges, and level design. Besides the AI-powered VR game in collaboration with AiGC Labs, BinaryX has also been working on a separate AI-powered game that will be released sometime this year. "We are incredibly excited to unveil our latest venture into VR and AIGC technologies," said Rudy, Head of Growth at BinaryX. "This is a significant milestone for our companies as we continue to push the boundaries of innovation in the Web3 gaming industry. Together with our partners, our team has poured talent and dedication into creating a game that will redefine the gaming experience and captivate players like never before. We’re excited to reveal the game to our players soon." The game is currently still under development and more details about the official release will be shared on their social media. This story was distributed as a release by Chainwire under HackerNoon’s Brand As An Author Program. Learn more about the program here: https://business.hackernoon.com/brand-as-author About BinaryX BinaryX is a GameFi and IGO platform, and the force behind popular blockchain games such as CyberChess, CyberDragon, and CyberLand. These games run on the BNB Chain. Aside from developing games, BinaryX has now evolved into a full-fledged GameFi platform by offering services, giving game developers the opportunity to launch their GameFi projects on their platform, and for users to get early access to innovative new games. Initial Game Offering (IGO) As one of the top 10 projects on the BNB Chain, BinaryX has a vast community of more than 100k coin holders and 30K monthly active wallets. It is also one of the top few metaverse projects by trading volume on the BNB chain, with more than 200 million in market cap. BinaryX also has a token, $BNX, that has consistently demonstrated strong performance despite the bear market. For more details and information about BinaryX, please visit | | | | Official website Medium Discord Telegram Twitter Check out BinaryX's games on their and on website YouTube Contact: marketing@binaryx.pro

BinaryX and AiGC Labs Set to Launch AI-Powered VR Game on Metaverse

BinaryX y AiGC Labs se preparan para lanzar un juego de realidad virtual impulsado por IA en Metaverse

BinaryX et AiGC Labs s'apprêtent à lancer un jeu VR alimenté par l'IA sur Metaverse

बाइनरीएक्स और एआईजीसी लैब्स मेटावर्स पर एआई-पावर्ड वीआर गेम लॉन्च करने के लिए तैयार हैं

BinaryX と AiGC Labs がメタバース上で AI 搭載 VR ゲームを開始予定

BinaryX e AiGC Labs preparam-se para lançar um jogo de realidade virtual baseado em IA no Metaverse

Phòng thí nghiệm BinaryX và AiGC được thiết lập để khởi chạy trò chơi VR do AI cung cấp trên Metaverse

BinaryX 和 AiGC Labs 将在元界推出 AI 驱动的 VR 游戏

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

Aigle vs Bluebird

Googled Tricked OpenAI Into Thinking It Was Ahead (It Wasn't)

Google engañó a OpenAI haciéndole creer que estaba por delante (no lo estaba)

Googled je prevario OpenAI da misli da je ispred (nije)

Googled te twonpe OpenAI pou l te panse li te devan (li pa t)

Googled menipu OpenAI dengan berpikir bahwa ia unggul (padahal tidak)

Googled は OpenAI を騙して自分たちが先行していると信じ込ませた (実際はそうではなかった)

Google OpenAI-ді алда деп ойлап, алдады (ол емес)

Google piemānīja OpenAI, liekot domāt, ka tas ir priekšā (tā nebija)

गुगलले ओपनएआईलाई ठग्यो यो अगाडि थियो भनेर सोच्दै (यो थिएन)

Google обманул OpenAI, заставив его думать, что он впереди (но это не так)

Google podviedol OpenAI, aby si myslel, že to bolo dopredu (nebolo)

Googled Yakanyengedza OpenAI Kufunga Kuti Yaive Mberi (Yanga isiri)

Googled Ilidanganya OpenAI Kufikiria Ilikuwa Mbele (Haikuwa)

Table of Links Abstract and 1 Introduction 2 Background & Problem Statement 2.1 How can we use MLLMs for Diffusion Synthesis that Synergizes both sides? 3 DreamLLM 3.1 End-to-End Interleaved generative Pretraining (I-GPT) 3.2 Model Training 4 Experiments and 4.1 Multimodal Comprehension 4.2 Text-Conditional Image Synthesis 4.3 Multimodal Joint Creation & Comprehension 5 Discussions 5.1 Synergy between creation & Comprehension? 5. 2 What is learned by DreamLLM? 6 Related Works 7 Conclusions and References A Additional Experiments B Additional Qualitative Examples C Implementation Details D Additional Related Works E Limitations, Failure Cases & Future Works D ADDITIONAL RELATED WORKS D.1 LARGE LANGUAGE MODELS A flourishing era of Natural Language Processing (NLP) driven by LLMs is being experienced, with the parameter size growing over 100B according to the scaling law (Kaplan et al., 2020). The GPT series of models, starting with GPT-1 (Radford et al., 2018) and followed by GPT-2 (Radford et al., 2019), made significant advancements in few-shot learning by scaling up the number of parameters to 175 billion in GPT-3 (Brown et al., 2020). This breakthrough garnered a lot of attention and paved the way for further research and development in the field. Since then, researchers have focused on developing LLMs by improving the scaling strategy. Several notable efforts include Gopher (Rae et al., 2021), GaLM (Du et al., 2022), FLAN (Wei et al., 2022a), Switch Transformer (Fedus et al., 2022), Chinchilla (Hoffmann et al., 2022), and PaLM (Chowdhery et al., 2022). Besides, instruction based tuning techniques are explored for aligning with human preferences (Christiano et al., 2017; Ouyang et al., 2022). Such success of LLMs has been further solidified by the production release of ChatGPT (OpenAI, 2022) and the highly anticipated GPT-4 (OpenAI, 2023). Meanwhile, in the community, the open-source LLMs are achieving remarkable progress in language capabilities compared to their close-source counterparts. For example, OPT (Zhang et al., 2022), BLOOM (Scao et al., 2022), GLM (Zeng et al., 2023), LLaMA (Touvron et al., 2023a;b), and Falcon (Penedo et al., 2023) all raised great attention and are been widely deployed. Other methods attempt to learn from distillation, such as Alpaca (Taori et al., 2023) and Vicuna (Chiang et al., 2023). D.2 TEXT-CONDITIONAL CONTENT CREATION WITH DIFFUSION MODELS The recent surge in AI-generated content (AIGC) has been primarily driven by diffusion-based methods, particularly in the realm of text-conditional content creation. Saharia et al. (2022) have achieved astonishing advancements in high-resolution image synthesis through large-scale pretrained language models and cascaded DMs. Another paradigm, such as SD, focuses on latent spaces and demonstrates superior efficiency and performance (Rombach et al., 2022; Ramesh et al., 2022; Peebles & Xie, 2022). Recently, Lian et al. (2023) propose to enhance the reasoning capability by constructing layouts with LLMs. Motivated by the great success in 2D, a series of works have significantly propelled the 3D synthesis development (Lin et al., 2023; Wang et al., 2023c) based on Score Distillation Sampling (SDS) (Poole et al., 2023; Wang et al., 2023a) that utilizes pretrained 2D DMs. For text-to-video synthesis, the expansion of pretrained spatial to a spatial-temporal factorized U-Net with joint image and video data training has yielded significant success (Ho et al., 2022a;b). This paper is available on arxiv under CC BY-NC-ND 4.0 DEED license. Authors:
(1) Runpei Dong, Xi’an Jiaotong University and Internship at MEGVII;
(2) Chunrui Han, MEGVII Technology;
(3) Yuang Peng, Tsinghua University and Internship at MEGVII;
(4) Zekun Qi, Xi’an Jiaotong University and Internship at MEGVII;
(5) Zheng Ge, MEGVII Technology;
(6) Jinrong Yang, HUST and Internship at MEGVII;
(7) Liang Zhao, MEGVII Technology;
(8) Jianjian Sun, MEGVII Technology;
(9) Hongyu Zhou, MEGVII Technology;
(10) Haoran Wei, MEGVII Technology;
(11) Xiangwen Kong, MEGVII Technology;
(12) Xiangyu Zhang, MEGVII Technology and a Project leader;
(13) Kaisheng Ma, Tsinghua University and a Corresponding author;
(14) Li Yi, Tsinghua University, a Corresponding authors and Project leader. Table of Links Abstract and 1 Introduction Abstract and 1 Introduction 2 Background & Problem Statement 2 Background & Problem Statement 2.1 How can we use MLLMs for Diffusion Synthesis that Synergizes both sides? 2.1 How can we use MLLMs for Diffusion Synthesis that Synergizes both sides? 3 DreamLLM 3 DreamLLM 3.1 End-to-End Interleaved generative Pretraining (I-GPT) 3.1 End-to-End Interleaved generative Pretraining (I-GPT) 3.2 Model Training 3.2 Model Training 4 Experiments and 4.1 Multimodal Comprehension 4 Experiments and 4.1 Multimodal Comprehension 4.2 Text-Conditional Image Synthesis 4.2 Text-Conditional Image Synthesis 4.3 Multimodal Joint Creation & Comprehension 4.3 Multimodal Joint Creation & Comprehension 5 Discussions 5.1 Synergy between creation & Comprehension? 5.1 Synergy between creation & Comprehension? 5. 2 What is learned by DreamLLM? 5. 2 What is learned by DreamLLM? 6 Related Works 6 Related Works 7 Conclusions and References 7 Conclusions and References A Additional Experiments A Additional Experiments B Additional Qualitative Examples B Additional Qualitative Examples C Implementation Details C Implementation Details D Additional Related Works D Additional Related Works E Limitations, Failure Cases & Future Works E Limitations, Failure Cases & Future Works D ADDITIONAL RELATED WORKS D.1 LARGE LANGUAGE MODELS A flourishing era of Natural Language Processing (NLP) driven by LLMs is being experienced, with the parameter size growing over 100B according to the scaling law (Kaplan et al., 2020). The GPT series of models, starting with GPT-1 (Radford et al., 2018) and followed by GPT-2 (Radford et al., 2019), made significant advancements in few-shot learning by scaling up the number of parameters to 175 billion in GPT-3 (Brown et al., 2020). This breakthrough garnered a lot of attention and paved the way for further research and development in the field. Since then, researchers have focused on developing LLMs by improving the scaling strategy. Several notable efforts include Gopher (Rae et al., 2021), GaLM (Du et al., 2022), FLAN (Wei et al., 2022a), Switch Transformer (Fedus et al., 2022), Chinchilla (Hoffmann et al., 2022), and PaLM (Chowdhery et al., 2022). Besides, instruction based tuning techniques are explored for aligning with human preferences (Christiano et al., 2017; Ouyang et al., 2022). Such success of LLMs has been further solidified by the production release of ChatGPT (OpenAI, 2022) and the highly anticipated GPT-4 (OpenAI, 2023). Meanwhile, in the community, the open-source LLMs are achieving remarkable progress in language capabilities compared to their close-source counterparts. For example, OPT (Zhang et al., 2022), BLOOM (Scao et al., 2022), GLM (Zeng et al., 2023), LLaMA (Touvron et al., 2023a;b), and Falcon (Penedo et al., 2023) all raised great attention and are been widely deployed. Other methods attempt to learn from distillation, such as Alpaca (Taori et al., 2023) and Vicuna (Chiang et al., 2023). D.2 TEXT-CONDITIONAL CONTENT CREATION WITH DIFFUSION MODELS The recent surge in AI-generated content (AIGC) has been primarily driven by diffusion-based methods, particularly in the realm of text-conditional content creation. Saharia et al. (2022) have achieved astonishing advancements in high-resolution image synthesis through large-scale pretrained language models and cascaded DMs. Another paradigm, such as SD, focuses on latent spaces and demonstrates superior efficiency and performance (Rombach et al., 2022; Ramesh et al., 2022; Peebles & Xie, 2022). Recently, Lian et al. (2023) propose to enhance the reasoning capability by constructing layouts with LLMs. Motivated by the great success in 2D, a series of works have significantly propelled the 3D synthesis development (Lin et al., 2023; Wang et al., 2023c) based on Score Distillation Sampling (SDS) (Poole et al., 2023; Wang et al., 2023a) that utilizes pretrained 2D DMs. For text-to-video synthesis, the expansion of pretrained spatial to a spatial-temporal factorized U-Net with joint image and video data training has yielded significant success (Ho et al., 2022a;b). 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 Authors: (1) Runpei Dong, Xi’an Jiaotong University and Internship at MEGVII; (2) Chunrui Han, MEGVII Technology; (3) Yuang Peng, Tsinghua University and Internship at MEGVII; (4) Zekun Qi, Xi’an Jiaotong University and Internship at MEGVII; (5) Zheng Ge, MEGVII Technology; (6) Jinrong Yang, HUST and Internship at MEGVII; (7) Liang Zhao, MEGVII Technology; (8) Jianjian Sun, MEGVII Technology; (9) Hongyu Zhou, MEGVII Technology; (10) Haoran Wei, MEGVII Technology; (11) Xiangwen Kong, MEGVII Technology; (12) Xiangyu Zhang, MEGVII Technology and a Project leader; (13) Kaisheng Ma, Tsinghua University and a Corresponding author; (14) Li Yi, Tsinghua University, a Corresponding authors and Project leader. Authors: Authors: (1) Runpei Dong, Xi’an Jiaotong University and Internship at MEGVII; (2) Chunrui Han, MEGVII Technology; (3) Yuang Peng, Tsinghua University and Internship at MEGVII; (4) Zekun Qi, Xi’an Jiaotong University and Internship at MEGVII; (5) Zheng Ge, MEGVII Technology; (6) Jinrong Yang, HUST and Internship at MEGVII; (7) Liang Zhao, MEGVII Technology; (8) Jianjian Sun, MEGVII Technology; (9) Hongyu Zhou, MEGVII Technology; (10) Haoran Wei, MEGVII Technology; (11) Xiangwen Kong, MEGVII Technology; (12) Xiangyu Zhang, MEGVII Technology and a Project leader; (13) Kaisheng Ma, Tsinghua University and a Corresponding author; (14) Li Yi, Tsinghua University, a Corresponding authors and Project leader.

DreamLLM: Additional Related Works to Look Out For

DreamLLM: What We Can Conclude From This Comprehensive Framework?

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 IX. PRACTICAL IMPLICATIONS AND LIMITATIONS OF GENERATIVE AI TECHNOLOGIES Generative AI technologies, encompassing MoE, multimodality, and AGI, present unique computational challenges. This section explores the processing power requirements, memory usage, and scalability concerns inherent in these advanced AI models. A. Computational Complexity and Real-world Applications of Generative AI Technologies 1) Computational Complexity: Generative AI technologies, encompassing MoE, multimodality, and AGI, present unique computational challenges. This section explores the processing power requirements, memory usage, and scalability concerns inherent in these advanced AI models. • Processing Power Requirements: Advanced generative AI models, including MoE architectures and AGI systems, require significant processing power [321]. The demand for GPUs and TPUs is accentuated, particularly when handling complex computations and large datasets typical in multimodal AI applications. • Memory Usage in AI Modeling: A critical challenge in training and deploying large-scale AI models, particularly in multimodal and AGI systems executed on GPUs, lies in the substantial GPU and VRAM requirements. Unlike computer RAM, VRAM often cannot be expanded easily on many platforms, posing significant constraints. Developing strategies for GPU and VRAM optimization and efficient model scaling is thus crucial for the practical deployment of these AI technologies. • Scalability and Efficiency in AI Deployment: Addressing scalability challenges in generative AI, especially in MoE and AGI contexts, involves optimizing load management and parallel processing techniques. This is vital for their practical application in fields like healthcare, finance, and education. 2) Real-world Application Examples of Generative AI Technologies: The application of generative AI models in real-world scenarios demonstrates their transformative potential and challenges in various sectors. • Healthcare: In healthcare, generative AI facilitates advancements in diagnostic imaging and personalized medicine, but also raises significant concerns regarding data privacy and the potential for misuse of sensitive health information [322]. • Finance: The use of AI for fraud detection and algorithmic trading in finance underlines its efficiency and accuracy, while at the same time, it raises ethical concerns, particularly in automated decision-making processes, which may lack transparency and accountability [323]. • Education: Generative AI’s role in creating personalized learning experiences offers immense benefits in terms of educational accessibility and tailored instruction. However, it poses challenges in equitable access to technology, potential biases in AI-Generated Content (AIGC), and could reduce demand for human educators. Additionally, there’s a growing concern about educators who are against the use of AIGC, fearing it may undermine traditional teaching methodologies and the role of educators. B. Commercial Viability and Industry Solutions in Generative AI Technologies 1) Market Readiness: Assessing the market readiness of generative AI technologies involves analyzing cost, accessibility, deployment challenges, and user adoption trends. • Cost Analysis: The financial aspects of deploying generative AI, including MoE, multimodality, and AGI, are crucial for market adoption. • Accessibility and Deployment: Integration of these technologies into existing systems and the technical expertise required are key factors influencing their adoption. • User Adoption Trends: Understanding current adoption patterns provides insights into market acceptance and the role of user trust and perceived benefits. 2) Existing Industry Solutions: Generative AI is reshaping various industries by offering innovative solutions and altering market dynamics. • Sector-Wise Deployment: The diverse applications of generative AI, from digital content creation to process streamlining, also raise questions about originality and intellectual property rights. • Impact on Market Dynamics: The effect of AI solutions on traditional industry structures and the introduction of novel business models are significant considerations. • Challenges and Constraints: Addressing limitations such as scalability, data management complexity, privacy concerns, and ethical implications is essential for robust governance frameworks. C. Limitations and Future Directions in Generative AI Technologies 1) Technical Limitations: Identifying and addressing technical limitations in generative AI models is crucial for their advancement and reliability. • Contextual Understanding: Enhancing AI’s ability to understand and interpret context, especially in natural language processing and image recognition, is a key area for improvement. • Handling Ambiguous Data: Developing better algorithms for processing ambiguous or incomplete data sets is essential for decision-making accuracy and reliability. • Navigating Human Judgment: Despite generative AI’s accuracy in interpreting policies and procedures, its impact is limited in replacing human judgment. This is especially true in legal and political contexts where decision-makers might selectively use AIGC, leading to biased outcomes. Thus, the effectiveness of generative AI in such scenarios should be realistically assessed. 2) Future Research Directions to Enhance the Practicality of Generative AI: Future research in generative AI should focus on addressing current limitations and expanding its practical applications. • Improved Contextual Understanding: Research should aim at developing models with better contextual awareness, particularly in complex natural language and image processing tasks. • Robust Handling of Ambiguous Data: Investigating techniques for effective processing of ambiguous data is vital for advancing the decision-making capabilities of AI models. • Ethical Integration of AIGC in Legal and Political Arenas: Future research should focus on the ethical integration of AI-generated content into legal and political decision-making processes, which involves developing frameworks that utilize AIGC in a supportive role, ensuring it enhances human judgment and contributes to transparency and fairness [324]. Importantly, researchers should consider the biases and limitations inherent in AI [324], alongside the potential for human fallibility, ethical complexities, and possible corruption in these domains. 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 IX. PRACTICAL IMPLICATIONS AND LIMITATIONS OF GENERATIVE AI TECHNOLOGIES Generative AI technologies, encompassing MoE, multimodality, and AGI, present unique computational challenges. This section explores the processing power requirements, memory usage, and scalability concerns inherent in these advanced AI models. A. Computational Complexity and Real-world Applications of Generative AI Technologies A. Computational Complexity and Real-world Applications of Generative AI Technologies 1) Computational Complexity: Generative AI technologies, encompassing MoE, multimodality, and AGI, present unique computational challenges. This section explores the processing power requirements, memory usage, and scalability concerns inherent in these advanced AI models. 1) Computational Complexity: • Processing Power Requirements: Advanced generative AI models, including MoE architectures and AGI systems, require significant processing power [321]. The demand for GPUs and TPUs is accentuated, particularly when handling complex computations and large datasets typical in multimodal AI applications. • Processing Power Requirements: • Memory Usage in AI Modeling: A critical challenge in training and deploying large-scale AI models, particularly in multimodal and AGI systems executed on GPUs, lies in the substantial GPU and VRAM requirements. Unlike computer RAM, VRAM often cannot be expanded easily on many platforms, posing significant constraints. Developing strategies for GPU and VRAM optimization and efficient model scaling is thus crucial for the practical deployment of these AI technologies. • Memory Usage in AI Modeling: • Scalability and Efficiency in AI Deployment: Addressing scalability challenges in generative AI, especially in MoE and AGI contexts, involves optimizing load management and parallel processing techniques. This is vital for their practical application in fields like healthcare, finance, and education. • Scalability and Efficiency in AI Deployment: 2) Real-world Application Examples of Generative AI Technologies: The application of generative AI models in real-world scenarios demonstrates their transformative potential and challenges in various sectors. 2) Real-world Application Examples of Generative AI Technologies: • Healthcare: In healthcare, generative AI facilitates advancements in diagnostic imaging and personalized medicine, but also raises significant concerns regarding data privacy and the potential for misuse of sensitive health information [322]. • Healthcare: • Finance: The use of AI for fraud detection and algorithmic trading in finance underlines its efficiency and accuracy, while at the same time, it raises ethical concerns, particularly in automated decision-making processes, which may lack transparency and accountability [323]. • Finance: • Education: Generative AI’s role in creating personalized learning experiences offers immense benefits in terms of educational accessibility and tailored instruction. However, it poses challenges in equitable access to technology, potential biases in AI-Generated Content (AIGC), and could reduce demand for human educators. Additionally, there’s a growing concern about educators who are against the use of AIGC, fearing it may undermine traditional teaching methodologies and the role of educators. • Education: B. Commercial Viability and Industry Solutions in Generative AI Technologies B. Commercial Viability and Industry Solutions in Generative AI Technologies 1) Market Readiness: Assessing the market readiness of generative AI technologies involves analyzing cost, accessibility, deployment challenges, and user adoption trends. 1) Market Readiness: • Cost Analysis: The financial aspects of deploying generative AI, including MoE, multimodality, and AGI, are crucial for market adoption. • Cost Analysis: • Accessibility and Deployment: Integration of these technologies into existing systems and the technical expertise required are key factors influencing their adoption. • Accessibility and Deployment: • User Adoption Trends: Understanding current adoption patterns provides insights into market acceptance and the role of user trust and perceived benefits. • User Adoption Trends: 2) Existing Industry Solutions: Generative AI is reshaping various industries by offering innovative solutions and altering market dynamics. 2) Existing Industry Solutions: • Sector-Wise Deployment: The diverse applications of generative AI, from digital content creation to process streamlining, also raise questions about originality and intellectual property rights. • Sector-Wise Deployment: • Impact on Market Dynamics: The effect of AI solutions on traditional industry structures and the introduction of novel business models are significant considerations. • Impact on Market Dynamics: • Challenges and Constraints: Addressing limitations such as scalability, data management complexity, privacy concerns, and ethical implications is essential for robust governance frameworks. • Challenges and Constraints: C. Limitations and Future Directions in Generative AI Technologies C. Limitations and Future Directions in Generative AI Technologies 1) Technical Limitations: Identifying and addressing technical limitations in generative AI models is crucial for their advancement and reliability. 1) Technical Limitations: • Contextual Understanding: Enhancing AI’s ability to understand and interpret context, especially in natural language processing and image recognition, is a key area for improvement. • Contextual Understanding: • Handling Ambiguous Data: Developing better algorithms for processing ambiguous or incomplete data sets is essential for decision-making accuracy and reliability. • Handling Ambiguous Data: • Navigating Human Judgment: Despite generative AI’s accuracy in interpreting policies and procedures, its impact is limited in replacing human judgment. This is especially true in legal and political contexts where decision-makers might selectively use AIGC, leading to biased outcomes. Thus, the effectiveness of generative AI in such scenarios should be realistically assessed. • Navigating Human Judgment: 2) Future Research Directions to Enhance the Practicality of Generative AI: Future research in generative AI should focus on addressing current limitations and expanding its practical applications. 2) Future Research Directions to Enhance the Practicality of Generative AI: • Improved Contextual Understanding: Research should aim at developing models with better contextual awareness, particularly in complex natural language and image processing tasks. • Improved Contextual Understanding: • Robust Handling of Ambiguous Data: Investigating techniques for effective processing of ambiguous data is vital for advancing the decision-making capabilities of AI models. • Robust Handling of Ambiguous Data: • Ethical Integration of AIGC in Legal and Political Arenas: Future research should focus on the ethical integration of AI-generated content into legal and political decision-making processes, which involves developing frameworks that utilize AIGC in a supportive role, ensuring it enhances human judgment and contributes to transparency and fairness [324]. Importantly, researchers should consider the biases and limitations inherent in AI [324], alongside the potential for human fallibility, ethical complexities, and possible corruption in these domains. • Ethical Integration of AIGC in Legal and Political Arenas: 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

Surveying the Evolution and Future Trajectory of Generative AI - Implications and Limitations of AI

Surveying the Evolution and Future Trajectory of Generative AI - Abstract and Introduction

Authors:
(1) Tony Lee, Stanford with Equal contribution;
(2) Michihiro Yasunaga, Stanford with Equal contribution;
(3) Chenlin Meng, Stanford with Equal contribution;
(4) Yifan Mai, Stanford;
(5) Joon Sung Park, Stanford;
(6) Agrim Gupta, Stanford;
(7) Yunzhi Zhang, Stanford;
(8) Deepak Narayanan, Microsoft;
(9) Hannah Benita Teufel, Aleph Alpha;
(10) Marco Bellagente, Aleph Alpha;
(11) Minguk Kang, POSTECH;
(12) Taesung Park, Adobe;
(13) Jure Leskovec, Stanford;
(14) Jun-Yan Zhu, CMU;
(15) Li Fei-Fei, Stanford;
(16) Jiajun Wu, Stanford;
(17) Stefano Ermon, Stanford;
(18) Percy Liang, Stanford. Table of Links Abstract and 1 Introduction 2 Core framework 3 Aspects 4 Scenarios 5 Metrics 6 Models 7 Experiments and results 8 Related work 9 Conclusion 10 Limitations Author contributions, Acknowledgments and References A Datasheet B Scenario details C Metric details D Model details E Human evaluation procedure E Human evaluation procedure E.1 Amazon Mechanical Turk We used the Amazon Mechanical Turk (MTurk) platform to receive human feedback on the AIgenerated images. Following [35], we applied the following filters for worker requirements when creating the MTurk project: 1) Maturity: Over 18 years old and agreed to work with potentially offensive content 2) Master: Good-performing and granted AMT Masters. We required five different annotators per sample. Figure 6 shows the design layout of the survey. Based on an hourly wage of $16 per hour, each annotator was paid $0.02 for answering a single multiple-choice question. The total amount spent for human annotations was $13,433.55. E.2 Human Subjects Institutional Review Board (IRB) This paper is available on arxiv under CC BY 4.0 DEED license. Authors: (1) Tony Lee, Stanford with Equal contribution; (2) Michihiro Yasunaga, Stanford with Equal contribution; (3) Chenlin Meng, Stanford with Equal contribution; (4) Yifan Mai, Stanford; (5) Joon Sung Park, Stanford; (6) Agrim Gupta, Stanford; (7) Yunzhi Zhang, Stanford; (8) Deepak Narayanan, Microsoft; (9) Hannah Benita Teufel, Aleph Alpha; (10) Marco Bellagente, Aleph Alpha; (11) Minguk Kang, POSTECH; (12) Taesung Park, Adobe; (13) Jure Leskovec, Stanford; (14) Jun-Yan Zhu, CMU; (15) Li Fei-Fei, Stanford; (16) Jiajun Wu, Stanford; (17) Stefano Ermon, Stanford; (18) Percy Liang, Stanford. Authors: Authors: (1) Tony Lee, Stanford with Equal contribution; (2) Michihiro Yasunaga, Stanford with Equal contribution; (3) Chenlin Meng, Stanford with Equal contribution; (4) Yifan Mai, Stanford; (5) Joon Sung Park, Stanford; (6) Agrim Gupta, Stanford; (7) Yunzhi Zhang, Stanford; (8) Deepak Narayanan, Microsoft; (9) Hannah Benita Teufel, Aleph Alpha; (10) Marco Bellagente, Aleph Alpha; (11) Minguk Kang, POSTECH; (12) Taesung Park, Adobe; (13) Jure Leskovec, Stanford; (14) Jun-Yan Zhu, CMU; (15) Li Fei-Fei, Stanford; (16) Jiajun Wu, Stanford; (17) Stefano Ermon, Stanford; (18) Percy Liang, Stanford. Table of Links Abstract and 1 Introduction Abstract and 1 Introduction 2 Core framework 2 Core framework 3 Aspects 3 Aspects 4 Scenarios 4 Scenarios 5 Metrics 5 Metrics 6 Models 6 Models 7 Experiments and results 7 Experiments and results 8 Related work 8 Related work 9 Conclusion 9 Conclusion 10 Limitations 10 Limitations Author contributions, Acknowledgments and References Author contributions, Acknowledgments and References A Datasheet A Datasheet B Scenario details B Scenario details C Metric details C Metric details D Model details D Model details E Human evaluation procedure E Human evaluation procedure E Human evaluation procedure E.1 Amazon Mechanical Turk We used the Amazon Mechanical Turk (MTurk) platform to receive human feedback on the AIgenerated images. Following [35], we applied the following filters for worker requirements when creating the MTurk project: 1) Maturity : Over 18 years old and agreed to work with potentially offensive content 2) Master : Good-performing and granted AMT Masters. We required five different annotators per sample. Figure 6 shows the design layout of the survey. Maturity Master Based on an hourly wage of $16 per hour, each annotator was paid $0.02 for answering a single multiple-choice question. The total amount spent for human annotations was $13,433.55. E.2 Human Subjects Institutional Review Board (IRB) 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

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