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

American International Group, Inc. is an international insurance organization serving commercial, institutional and individual customers. AIG provides property-casualty insurance, life insurance, and retirement services.

Aig Claim Services, Inc was founded in 1977. The company's line of business includes providing insurance agent and broker services for a range of insurance types.

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

Introduction Late in 2023, I was considering writing an article about Acorn Labs' work simplifying Kubernetes containers. I even started it and wrote a few paragraphs, but then I got busy with other things. At the time, I was going to revisit it in February 2024, they made a couple of announcements; one was for Rubra, and the other was more of a personal project by one of the co-founders, Darren Shepherd, called GPTScript. This project was so exciting that a month later, Acorn announced they were changing their company focus to GPTScript (not sure what would happen to Rubra). I’ve been following all the developments on this project, closer than I typically do, because I find it absolutely amazing as enablement technology. It reminds me of the philosophy behind a product I worked with back in the 80s and 90s called MPEX from VESOFT, written by Eugen Volokh. What Darren has done with GPTScript is kind of hard to wrap your head around at first, especially for a programmer. I haven’t spent enough time with it yet to fully understand all the implications, but I have a lot of thoughts about it. In this article, I’ll walk through some basic introductions and examples and give you some thoughts about where you could take it. Getting Started I worked off the docs and various videos and examples; I gave this a go on my Windows 11 laptop. Let’s get this sucker installed: winget install gptscript-ai.gptscript
$env:OPENAI_API_KEY = 'your-api-key' The docs suggest running a “hello world” example at this point, but that isn’t very interesting, so let’s try the “bob” example. Also, note that there are many built-in examples you can get a list of by issuing the gptscript—-list-tools command and an online ‘examples’ directory. What does “bob.gpt” look like? This presents us with some obvious questions, like what this all means and how a value of ‘How are you doing?’ ended up in the ‘question’ argument. GPTScript operates around the notion of tools where a tool is described in a GPT file. Each file can be made up of more than one named tool, separated by three dashes. Borrowing from the docs, Tool parameters are key-value pairs defined at the beginning of a tool block before any instructional text. They are specified in the format key: value. The parser recognizes the following keys (case-insensitive and spaces are ignored): A tool starts with a preamble that defines the tool's name, description, args, available tools, and additional parameters. The preamble is followed by the tool's body, which contains the instructions for the tool. Comments in the preamble are lines starting with # and are ignored by the parser. Comments are not really encouraged as the text is typically more useful in the description, argument descriptions, or instructions. I’m still not sure why Bob was giving a default value for the argument. It would change if I gave it an argument, so that was a bit of a mystery. I decided to try the samples-readme.gpt script, which looks like this: Reading this, I understood that it would give me a summary of each gpt file in the examples directory. When I first tried it without an argument, it failed to find any input, so I tried with an example file as an argument, and it worked. Then I tried it with their larger example script for hacker-news-headlines, and instead, it did what I thought it should have done the first time. It created a readme.md file in the examples directory with a summary of each example. I’ve seen people in the community starting to generate some very cool tools that are being added to the GPTScript tools web page. I had a little bit of mixed results with my tests. Most things worked as expected, but I had a few things that didn’t, and I didn’t have the opportunity to dig deeper. That said, this is clearly powerful, and it is just scratching the surface. Summary As a lifelong programmer, this feels weird; it involves doing things in ways that seem like something is missing. This makes GPTScript fascinating and scary as if black magic is involved somehow. The natural language and loose feel of it take a little getting used to, but as you think about it, you start to realize how amazing this can be as an enablement technology. One of the first things that came to mind, but I didn’t test, was could this be used to drive OpenFoam? OpenFoam is open-source CFD software that is rather notorious for how difficult it is to use, which is why I never learned how to use it. What if I could just describe what I wanted to do with it, and it worked? Imagine doing this with any software with an API you could talk to. My mind starts to explode thinking about it. Acorn has created an online “tools” catalog that you can utilize, and they are iterating at an insane pace. This is really exciting. Check out my other What the Heck is… articles at the links below: What The Heck Is DuckDB?
What the Heck Is Malloy?
What the Heck is PRQL?
What the Heck is GlareDB?
What the Heck is SeaTunnel?
What the Heck is LanceDB?
What the heck is SDF?
What the Heck is Paimon?
What the Heck is Proton?
What the Heck is PuppyGraph? Introduction Introduction Late in 2023, I was considering writing an article about Acorn Labs ' work simplifying Kubernetes containers. I even started it and wrote a few paragraphs, but then I got busy with other things. At the time, I was going to revisit it in February 2024, they made a couple of announcements; one was for Rubra, and the other was more of a personal project by one of the co-founders, Darren Shepherd , called GPTScript . Acorn Labs Acorn Labs Darren Shepherd Darren Shepherd GPTScript GPTScript This project was so exciting that a month later, Acorn announced they were changing their company focus to GPTScript (not sure what would happen to Rubra). announced announced I’ve been following all the developments on this project, closer than I typically do, because I find it absolutely amazing as enablement technology. It reminds me of the philosophy behind a product I worked with back in the 80s and 90s called MPEX from VESOFT, written by Eugen Volokh. What Darren has done with GPTScript is kind of hard to wrap your head around at first, especially for a programmer. I haven’t spent enough time with it yet to fully understand all the implications, but I have a lot of thoughts about it. In this article, I’ll walk through some basic introductions and examples and give you some thoughts about where you could take it. Getting Started I worked off the docs and various videos and examples; I gave this a go on my Windows 11 laptop. Let’s get this sucker installed: docs docs winget install gptscript-ai.gptscript
$env:OPENAI_API_KEY = 'your-api-key' winget install gptscript-ai.gptscript
$env:OPENAI_API_KEY = 'your-api-key' The docs suggest running a “hello world” example at this point, but that isn’t very interesting, so let’s try the “bob” example. Also, note that there are many built-in examples you can get a list of by issuing the gptscript—-list-tools command and an online ‘ examples ’ directory. examples examples What does “bob.gpt” look like? This presents us with some obvious questions, like what this all means and how a value of ‘How are you doing?’ ended up in the ‘question’ argument. GPTScript operates around the notion of tools where a tool is described in a GPT file. Each file can be made up of more than one named tool, separated by three dashes. tools Borrowing from the docs, Tool parameters are key-value pairs defined at the beginning of a tool block before any instructional text. They are specified in the format key: value. The parser recognizes the following keys (case-insensitive and spaces are ignored): Tool parameters are key-value pairs defined at the beginning of a tool block before any instructional text. They are specified in the format key: value. The parser recognizes the following keys (case-insensitive and spaces are ignored): A tool starts with a preamble that defines the tool's name, description, args, available tools, and additional parameters. The preamble is followed by the tool's body, which contains the instructions for the tool. Comments in the preamble are lines starting with # and are ignored by the parser. Comments are not really encouraged as the text is typically more useful in the description, argument descriptions, or instructions. A tool starts with a preamble that defines the tool's name, description, args, available tools, and additional parameters. The preamble is followed by the tool's body, which contains the instructions for the tool. Comments in the preamble are lines starting with # and are ignored by the parser. Comments are not really encouraged as the text is typically more useful in the description, argument descriptions, or instructions. I’m still not sure why Bob was giving a default value for the argument. It would change if I gave it an argument, so that was a bit of a mystery. I decided to try the samples-readme.gpt script, which looks like this: Bob samples-readme.gpt Reading this, I understood that it would give me a summary of each gpt file in the examples directory. When I first tried it without an argument, it failed to find any input, so I tried with an example file as an argument, and it worked. Then I tried it with their larger example script for hacker-news-headlines , and instead, it did what I thought it should have done the first time. It created a readme.md file in the examples directory with a summary of each example. hacker-news-headlines readme.md I’ve seen people in the community starting to generate some very cool tools that are being added to the GPTScript tools web page. I had a little bit of mixed results with my tests. Most things worked as expected, but I had a few things that didn’t, and I didn’t have the opportunity to dig deeper. That said, this is clearly powerful, and it is just scratching the surface. Summary As a lifelong programmer, this feels weird; it involves doing things in ways that seem like something is missing. This makes GPTScript fascinating and scary as if black magic is involved somehow. The natural language and loose feel of it take a little getting used to, but as you think about it, you start to realize how amazing this can be as an enablement technology. One of the first things that came to mind, but I didn’t test, was could this be used to drive OpenFoam ? OpenFoam is open-source CFD software that is rather notorious for how difficult it is to use, which is why I never learned how to use it. What if I could just describe what I wanted to do with it, and it worked? Imagine doing this with any software with an API you could talk to. OpenFoam OpenFoam My mind starts to explode thinking about it. Acorn has created an online “ tools ” catalog that you can utilize, and they are iterating at an insane pace. This is really exciting. tools tools Check out my other What the Heck is… articles at the links below: What The Heck Is DuckDB? What the Heck Is Malloy? What the Heck is PRQL? What the Heck is GlareDB? What the Heck is SeaTunnel? What the Heck is LanceDB? What the heck is SDF? What the Heck is Paimon? What the Heck is Proton? What the Heck is PuppyGraph? What The Heck Is DuckDB? What The Heck Is DuckDB? What the Heck Is Malloy? What the Heck Is Malloy? What the Heck is PRQL? What the Heck is PRQL? What the Heck is GlareDB? What the Heck is GlareDB? What the Heck is SeaTunnel? What the Heck is SeaTunnel? What the Heck is LanceDB? What the Heck is LanceDB? What the heck is SDF? What the heck is SDF? What the Heck is Paimon? What the Heck is Paimon? What the Heck is Proton? What the Heck is Proton? What the Heck is PuppyGraph? What the Heck is PuppyGraph?

What the Heck Is GPTScript?

This paper is under CC 4.0 license. available on arxiv Authors: (1) Prajwal Niraula, Department of Earth, Atmospheric and Planetary Sciences; (2) Julien de Wit, Department of Earth, Atmospheric and Planetary Sciences; (3) Benjamin V. Rackham, Department of Earth, Atmospheric and Planetary Sciences; (4) Elsa Ducrot, Astrobiology Research Unit, University of Li`ege; (5) Artem Burdanov, Department of Earth, Atmospheric and Planetary Sciences; (6) Ian J. M. Crossfield, Kansas University Department of Physics and Astronomy; (7) Valerie Van Grootel´, Space Sciences, Technologies and Astrophysics Research (STAR) Institute, University of Li`ege; (8) Catriona Murray, 5Cavendish Laboratory; (9) Lionel J. Garcia, Astrobiology Research Unit, University of Li`ege; (10) Roi Alonso, Instituto de Astrof´ısica de Canarias & Dpto. de Astrof´ısica, Universidad de La Laguna; (11) Corey Beard, Department of Physics & Astronomy, The University of California; (12) Yilen Gomez Maqueo Chew, Instituto de Astronom´ıa, Universidad Nacional Aut´onoma de M´exico, Ciudad Universitaria; (13) Laetitia Delrez, Astrobiology Research Unit, University of Li`ege, Space Sciences, Technologies and Astrophysics Research (STAR) Institute, University of Li`ege & 0Observatoire de lUniversit´e de Gen`eve; (14) Brice-Olivier Demory, University of Bern, Center for Space and Habitability; (15) Benjamin J. Fulton, NASA Exoplanet Science Institute/Caltech-IPAC; (16) Michael Gillon, Astrobiology Research Unit, University of Li`ege; (17) Maximilian N. Gunther, Department of Physics, and Kavli Institute for Astrophysics and Space Research, Massachusetts Institute of Technology; (18) Andrew W. Howard, California Institute of Technology; (19) Howard Issacson, Department of Astronomy, University of California Berkeley; (20) Emmanuel Jehin, Space Sciences, Technologies and Astrophysics Research (STAR) Institute, University of Li`ege; (21) Peter P. Pedersen, Cavendish Laboratory; (22) Francisco J. Pozuelos, Astrobiology Research Unit, University of Li`ege & Space Sciences, Technologies and Astrophysics Research (STAR) Institute, University of Li`ege; (23) Didier Queloz, Cavendish Laboratory; (24) Rafael Rebolo-Lopez, Instituto de Astrof´ısica de Canarias & Dpto. de Astrof´ısica, Universidad de La Laguna'; (25) Sairam Lalitha, School of Physics & Astronomy, University of Birmingham; (26) Daniel Sebastian, Astrobiology Research Unit, University of Li`ege (27) Samantha Thompson, Cavendish Laboratory; (28) Amaury H.M.J. Triaud, School of Physics & Astronomy, University of Birmingham. Table of Links Abstract & Introduction Observation Analysis and Validation Future Prospects & References 4. FUTURE PROSPECTS The search for transiting planets around small stars has been motivated in large part by their potential for atmospheric characterization. Owing to the size and proximity of its host, K2-315b is thus one of the few known terrestrial exoplanets possibly amenable for atmospheric characterization in the next two decades. In order to quantify and contextualize its prospects for atmospheric study, we followed the same approach as for TRAPPIST-1 in Gillon et al. (2016) (see de Wit & Seager 2013), focusing here on all known terrestrial planets. We selected terrestrial planets as planets with a reported radius below 1.6 R⊕ in the NASA Exoplanet Archive[4] (Rogers 2015; Fulton et al. 2017). We thus derive the amplitude of the planets’ signals in transmission as: where Rp is the planetary radius, R∗ is the stellar radius, and heff is the effective atmospheric height, µ is the atmospheric mean molecular mass, T is the atmospheric temperature and g is the local gravity. We assume heff to cover seven atmospheric scale heights, µ the atmospheric mean molecular mass to be 20 amu, and the atmospheric temperature to be the equilibrium temperature for a Bond albedo of 0. For the planets with missing masses, we estimated g using the model of Chen & Kipping (2017). The signal amplitudes are reported in Figure 7 together with the SNR relative to TRAPPIST-1 b’s, calculated by scaling the signal amplitude with the hosts’ brightness in J band. We find that K2-315b fares closely to the outer planets of TRAPPIST-1 in terms of potential for atmospheric exploration with JWST— its warmer and thus larger atmosphere compensating for its larger star. In fact, its relative SNR for transmission spectroscopy is half those of TRAPPIST-1 f–h, meaning that assessing the presence of a µ ∼ 20 atmosphere around the planet would require of the order of 40 transits—four times the ∼10 transits required for a similar assessment for TRAPPIST-1 f–h (Lustig-Yaeger et al. 2019). K2-315b is thus at the very edge of JWST’s capability for atmospheric characterization, mostly due to its “large” host star. The derivation above allows us to rank planets in terms of relative potential for atmospheric characterization, assuming a similar atmospheric scenario. In practice, a significant difference in atmospheric mean molecular mass, surface pressure, and/or cloud/haze altitude will strongly affect the actual potential of a planet for characterization (Lustig-Yaeger et al. 2019). For example, in some cases clouds could render the characterization of the favorable TRAPPIST-1 e difficult even with the generation of instruments following the JWST (i.e., LUVOIR, Pidhorodetska et al. 2020). With an estimated radial velocity semi-amplitude of 1.3 m s−1 (assuming a mass comparable to that of Earth), the planet could be accessible for mass measurements using modern ultra-precise radial velocity instruments. Such possibilities and a ranking amongst the 10 best-suited Earth-sized planets for atmospheric study, EPIC 249631677 b will therefore play an important role in the upcoming era of comparative exoplanetology for terrestrial worlds. It will surely be a prime target for the generation of observatories to follow JWST and bring the field fully into this new era. This project makes use of publicly available K2 data. P.N. would like to acknowledge funding for Kerr Fellowship and Elliot Fellowship at MIT. J.d.W. and MIT gratefully acknowledge financial support from the Heising-Simons Foundation, Dr. and Mrs. Colin Masson and Dr. Peter A. Gilman for Artemis, the first telescope of the SPECULOOS network situated in Tenerife, Spain. B.V.R. thanks the Heising-Simons Foundation for support. V.V.G. is a F.R.S.-FNRS Research Associate. M.G. and E.J. are F.R.S.-FNRS Senior Research Associates. This work was also partially supported by a grant from the Simons Foundation (PI Queloz, grant number 327127), and funding from the European Research Council under the European Union’s Seventh Framework Programme (FP/2007-2013) ERC Grant Agreement Number 336480, from the ARC grant for Concerted Research Actions financed by the Wallonia-Brussels Federation, from the Balzan Prize Foundation, from F.R.S-FNRS (Research Project ID T010920F), from the European Research Council (ERC) under the European Unions Horizon 2020 research and innovation programme (grant agreement n◦ 803193/BEBOP), and from a Leverhulme Trust Research Project Grant n◦ RPG-2018-418. 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π Earth: a 3.14-day Earth-sized Planet from K2's Kitchen Served Warm: Future Prospects & References

320 Stories To Learn About Deep Learning

The news of The New York Times filing a lawsuit against Microsoft and OpenAI for copyright infringement struck the internet like a rogue comet, sparking a fierce debate about the ‘inherent dangers’ of AI. One thing is clear: there has been no shortage of lawsuits contesting the legality or fair use of a work of art in the digital space, with intellectual property being the subject matter. Meanwhile, concerns over copyright infringement in AI have recently increased, highlighting the need to protect the ownership of AI-generated content. Ringfence, a web3-based platform for Generative AI creators, is building the infrastructure for creators to protect and monetize various media, including photos, images, videos, documents, and music. In this interview, Ringfence CEO offered valuable insights into Artificial Intelligence Generated Content(AIGC)and the need to incentivise creators. Whitney Gibbs Mr. Gibbs, can you tell us about yourself and your background? My journey in web3 began in 2017, buying the top of the bull market.  Over the next year, I researched different projects and communities and built relationships in the space. In 2018, I found my way to GPU mining and all things Proof of Work. That year, I began what would evolve into the HASHR8 Podcast (later Compass Mining Podcast, now The Mining Pod). For the next two years, I focused on providing the crypto mining community with valuable insights, and in 2020, the podcast was recognized by Forbes as one of crypto’s most important podcasts. Around this time, I started Compass Mining with two friends. As CEO from 2020-2022, I lead Compass’s growth from zero to becoming the world’s largest marketplace for hardware and hosting. Bitcoin mining Compass was (and still is) an amazing business doing hundreds of millions of dollars in annual sales, and we built a great team of nearly 100 people around the world. The coolest thing about Compass was that we created the “retail mining” category, opening Bitcoin mining up to people who would have otherwise been excluded. In June 2022, I stepped down as CEO of Compass Mining to begin researching new market opportunities to see how I could best impact the global adoption of web3. It was clear to me that artificial intelligence was the most important technological advancement in our world since the internet, so all research was focused on where blockchain technologies and AI might intersect. The most immediate, clear intersection is the establishment of digital provenance and attribution in the training of neural networks. There are millions of people around the world currently providing their data to large companies like FOR FREE. While I don't think this is a malicious action on the part of those companies, there is a better way to do things. This is ultimately what led to the formation of Ringfence. OpenAI and Stable Diffusion Congratulations on your fundraising success. I would like to know what you think of the ongoing lawsuit against Microsoft and OpenAI. Thank you! The New York Times’ lawsuit is a landmark legal case—there’s little doubt its outcomes will shape the future landscape of generative AI. The way some see it, The New York Times is fighting on behalf of journalists and authors worldwide to get answers. The way we see it, it’s a major case among many where creators—both companies and individuals—are fighting for their creative rights. But you can’t ignore that it’s incredibly nuanced. In my opinion, the New York Times case has merit: These companies train their data on billions and billions of data points, including from media outlets, and these chatbots are perfectly capable of regenerating information strikingly similar to what a journalist produces daily—even articles behind paywalls. But NYT is also alleging that these chatbots can regurgitate articles word-for-word. It remains to be seen exactly how these tools, in this case, ChatGPT specifically, were able to do this and whether this process relied more on external sources via web searching rather than an internal training set. This will be an important point in the case—if it's the former, it might be fair to say it comes under Fair Use. And if it’s the latter? The case might just raise more questions than it answers. Ultimately, I think what’s most troubling to creators is that some of these AI companies have claimed that their work is transformative enough of the original training data that it comes under Fair Use. It seems unfair to creators to be able to take their work en masse, train an AI tool, and then use that tool for commercial purposes without giving the creators due credit or compensation. This is what Ringfence is built to solve. But whatever the case and its outcome, I think the New York Times, like other creators before and after it, has every right to fight to regain creative and financial control over its intellectual property. How did copyright infringement become a burning issue in Generative AI? Copyright has long been a burning issue when it comes to Intellectual Property rights and digital ownership. As Generative AI has only made it easier to generate digital content, it’s no surprise that this issue has been exacerbated. The digital output of these models relies on the immense amount of information used to train the Large Language Model behind the Generative AI without consent or compensation to the original creators. There are countless cases, really since the inception of publicly available , of artists and authors having their work scraped from the internet and used to train them. More recently, we now have AI creating music imitating certain artists and even AI movies in the style of different directors. Generative AI models As these tools have become more advanced, they’ve been able to reproduce artistically similar images, text, and now even video and audio. With them also being commercialized, the copyright vs. Fair Use debate has only become more foggy. How do you leverage AI and blockchain technology? Blockchain is fundamental to Ringfence, providing the infrastructure for digital authentication and monetization. In our case, creations uploaded to the cloud can be tokenized, with their metadata also stored on-chain. This serves a few purposes. Firstly, blockchain provides a transparent and immutable ledger for tracking the digital provenance of creation, providing a clear record of ownership. Secondly, the tokenization also offers digital ownership itself, facilitating peer-to-peer transfer. We also make use of smart contracts to facilitate enforceable end-user agreements, enabling fair and seamless monetization. With these, creators can set clear rules for how AI gets to use their work, whether it can be personalized or used to train models, and how they prefer to be compensated when their work is used to create AI derivatives. Generative AI can be a tool to enable hyperproductivity. Artists, authors, and creatives of any kind should be able to make use of it in a way that respects and rewards their creativity. Creators should be able to use their work to rapidly iterate and create derivatives, but they should also be able to license their work to train AI models—and get rewarded. Why is content monetization one of your key objectives? Data is the most valuable commodity in the world, yet rarely are those supplying it the same ones benefiting from it. At present, this is most true with AI. Companies like OpenAI, , and Stable Diffusion garner billion-dollar valuations and generate million-dollar monthly profits while paying out NOTHING to those who have supplied the key data points without which they would cease to function. Midjourney For Ringfence, we believe that content monetization is a critical first step toward the monetization of all personal data. Through content monetization and the neural network needed to support it, we intend to answer important questions around attribution, compensation, and true fair use. Can you go into more detail about your recent fundraising success? Raising capital in 2023 was no small task, even with a compelling idea and sound business plan. The end of the era of zero-interest rates, coupled with global economic uncertainty, changed the venture landscape and made raising capital hard for many. Ringfence was fortunate to secure funding from a great cohort of investors who believe in our vision for the future and were excited about backing our experienced team. Any parting words? AI is a tool or technology like any other—it can have an immensely positive impact, but it might also be incredibly detrimental to creators without us taking a step back and assessing it closely. Ultimately, creatives shouldn't have to compromise or buckle under the threat of AI taking their work. They should be able to create freely, working alongside AI to improve their productivity while being fairly rewarded. At the risk of sounding a little too sentimental, I hope that we can all work towards a shared future where creatives can collaborate with AI fairly, bringing in a new age of creativity—but where innovation can continue.

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