The funds will help students achieve their personal and professional goals.

UGA raises almost $10 million for graduate and professional student support - UGA Today

Jump To Top of Page MGOLF: UGA assigned to NCAA Austin Regional The University of Georgia men’s golf team will ... read article

Daily Dawg Thread: May 02, 2024 – Bulldawg Illustrated

The University of Georgia in Athens has released a statement regarding the arrest of protesters on its campus on Monday.

UGA says 16 arrested at Monday's protest; group knew the rules, school says

Live coverage of campus protests in metro Atlanta and Georgia from The Atlanta Journal-Constitution

Georgia campus protests over war in Gaza - Live updates

UGLA helps our partners become leaders wherever they choose to compete. We believe that the stronger our partners, the stronger we are. UGLA Cloud SaaS offers high performance, security and savings in resource usage, does not require additional hardware. UGLA is a very powerful and flexible solution, easy to use, which combines ERP and CRM. We help you easily innovate and achieve sustainable growth. Millions of people, from nonprofits to companies of all sizes, can use our tools to optimize supply chains, increase employee loyalty, move quickly to a new financial plan, and connect data and people around the world. Share the news, events and innovations you need to change the world forever. #UGLA#KYIV#UKRAINE

UGLA

UGLA ERP was founded in 2022 in Kyiv with the goal of facilitating business process management, increasing productivity, and enhancing the competitiveness of enterprises. Ukrainian ERP offers an intuitive and powerful toolkit for business process automation, as well as data analysis and visualization to help companies make more informed decisions. StartUps2023 Runner-up Tell us how your startup is changing the world. ERP systems have been an undeniable trend in recent years despite "black swans" such as pandemics and wars. Every year, ERP demonstrates steady growth. Today, even large companies offer their employees hybrid or fully online work formats - after the global pandemic of recent years, working from home or from anywhere in the world has become a daily norm. In Ukraine, the impact of the pandemic in 2022 was amplified by the war-forced migration within the country and externally became a significant factor in cementing remote work formats. Some large companies, especially those without a physical production cycle, may not have a single office or even coworking space, so all collaboration takes place online. This should also be taken into account when building business processes. Therefore, cloud solutions are becoming a cornerstone for most companies already. In addition to the almost traditional cloud solutions, their mobile versions are also gaining momentum - in today's fast-paced world, more and more people are doing their work on the go or simultaneously from multiple devices. Thus, UGLA ERP is changing the world precisely because it is accessible from anywhere, allowing you to provide your employees with the flexibility to work whenever and wherever they want. Not to mention that cloud solutions are generally more accessible than local systems. What sets you apart from the competition? UGLA ERP is a next-generation system that combines all the tools for managing and planning business processes. We developed our program in collaboration with advisors from various industries, focusing on business needs and user convenience for all employees. Additionally, UGLA ERP has a workflow feature that allows you to customize the business processes you need. By choosing UGLA ERP, you get all the necessary tools right away, with no additional costs. The cost of our software is affordable for any business. What do you love about your team, and why are you the ones to solve this problem? First of all, our team consists of very brave guys and girls, as we constantly work in Kyiv during the war. We have a great combination of experience and creativity, which is why we manage to implement what sometimes seems impossible. That's why I think we're the ones to solve the problem of business process planning because we've managed to combine experience, intelligence, and creativity. And, of course, we're 100% fearless. If you weren’t building your startup, what would you be doing? I think we would create something else but 100% Ukrainian)) At the moment, how do you measure success? What are your core metrics? Of course, the number of satisfied clients is essential to us. But it's also important for us to receive feedback and understand that our partners confirm that the main goal of UGLA ERP is being achieved. It involves saving time for employees and management for strategic tasks, personal development, and time with family and friends. As for success metrics, we use standard business metrics such as CAC (Customer Acquisition Cost), MRR (Monthly Recurring Revenue), ARR (Annual Recurring Revenue), LTV (Customer Lifetime Value), ARPU (Average Revenue Per User), Revenue, and others. What goals are you looking forward to accomplishing in 2024? Firstly, to create solutions for other industries such as pharmaceuticals, retail, manufacturing, etc. Secondly, we have specific plans to enter new markets, namely the USA, UK, and some European countries. Of course, there are plans regarding the number of our partners. Which trend(s) are you most excited about in 2024? Share your reasons. I believe that the biggest trends will be associated with even greater integration of ERP into all business processes and planning. Undoubtedly, the application of AI, IoT, and GenAI. Of course, data security will also be crucial. 2023 had been another crazy year, especially in tech, with layoffs and the Generative AI takeover! Which trend are you most concerned about? Be as brief or as detailed as you like. The development of AI, ML, and GenAI. Share your biggest success so far and/or your biggest failure so far. Oh, I'm sure our biggest success is that we continue to work despite all the obstacles. Overall, we had a successful year; we participated in international conferences such as WEB SUMMIT and Viva Tech, where we were able to represent Ukraine and our software. We would love your feedback on HackerNoon as a tech publication! How has your experience been with us? We're just starting our journey with you, but we're already in love! There's so much interesting information, so many opportunities for startups, convenience, and creativity. Any words of wisdom you’d like to share with us? Perhaps "be brave no matter what". Startups of The Year is HackerNoon’s Flagship community-driven event celebrating startups that survived and thrived in 2023. 30,000 startups across 4200+ cities and six continents participated this year to be crowned the best startup in their city. See our global winners announcement . here Companies that blog more get 67% more leads - Jumpstart your company’s blogging journey by purchasing our . Business Blogging Package The 2023 Startups Of The Year is sponsored by: . . The winners will get a free .Tech Domain, a HackerNoon NFT, and a Tech Company News Page. TECH Domains

Meet UGLA ERP, Runner-up of the Startups of the Year in Kyiv.

This year's Upland’s Genesis Hackathon intends to set new standards in the Web3 and blockchain gaming sectors. Upland is organising the Upland’s Genesis Week event, which will shift away from its conventional methodology in favour of a more idea-centric competition. In an exciting change, the 2024 iteration will no longer need participants to present a functional prototype, allowing for a greater emphasis on developing company ideas and planning methods. Main Features of the Hackathon Upland is introducing SPARKLET, a solution designed to facilitate the integration of blockchain applications with blockchain technology. This new information comes at a time when the Web3 domain is seeing resuscitation, with blockchain gaming at the forefront of this trend. The Genesis Hackathon aims to capitalise on these successes by providing participants with a platform that allows them to establish and enhance the capabilities of their own digital communities. The hackathon challenges participants to use the Upland platform and its developer tools to build a new service, game, or experience. Participants will have ten days to prepare a business strategy that defines their aims and techniques. Because they are focused on clarity, originality, and practicality, these plans are critical in determining which teams will advance to the final round. Entries must be submitted by May 10th, 9 AM PT, with finalists notified by May 13th, 2024. Those who make it to the final round will submit detailed presentations, aiming for a spot to pitch live at the Genesis Week event. Form Submission for Hackathon. Prizes and Incentives The hackathon offers substantial incentives, with prizes structured across multiple phases: Phase

Award Type

Details



1

Kickoff Grant

$2,500



2

MVP Deployment Bonus

$15,000 upon successful integration



3

Sparklet Grant Program

Opportunity to pitch for additional support Finalists will compete for these prizes during the Upland Awards Show on June 7th, where the top projects will be showcased. Logistics and Additional Benefits Finalists will travel to Las Vegas, with Upland covering hotel expenses and providing travel stipends of up to $500 for domestic and $1,000 for international flights. This arrangement not only alleviates financial burdens but also enhances the overall experience, allowing participants to focus fully on their presentations. The Genesis 2024 Hackathon is a launchpad for tomorrow's innovators in the Web3 space. With significant prizes, strategic opportunities, and the chance to influence the burgeoning blockchain gaming market, this event is a cornerstone for technological advancement. Don’t miss out—mark your calendar, prepare your submission, and take part in shaping the future of the digital world. Don’t forget to like and share the story! Vested Interest Disclosure: This author is an independent contributor publishing via our business blogging program. HackerNoon has reviewed the report for quality, but the claims herein belong to the author. #DYOR. This year's Upland’s Genesis Hackathon intends to set new standards in the Web3 and blockchain gaming sectors. Upland is organising the Upland’s Genesis Week event, which will shift away from its conventional methodology in favour of a more idea-centric competition. In an exciting change, the 2024 iteration will no longer need participants to present a functional prototype, allowing for a greater emphasis on developing company ideas and planning methods. Main Features of the Hackathon Main Features of the Hackathon Upland is introducing SPARKLET, a solution designed to facilitate the integration of blockchain applications with blockchain technology. This new information comes at a time when the Web3 domain is seeing resuscitation, with blockchain gaming at the forefront of this trend. The Genesis Hackathon aims to capitalise on these successes by providing participants with a platform that allows them to establish and enhance the capabilities of their own digital communities. The hackathon challenges participants to use the Upland platform and its developer tools to build a new service, game, or experience. Participants will have ten days to prepare a business strategy that defines their aims and techniques. Because they are focused on clarity, originality, and practicality, these plans are critical in determining which teams will advance to the final round. Entries must be submitted by May 10th, 9 AM PT, with finalists notified by May 13th, 2024. Those who make it to the final round will submit detailed presentations, aiming for a spot to pitch live at the Genesis Week event. submitted Form Submission for Hackathon. Form Submission Prizes and Incentives Prizes and Incentives The hackathon offers substantial incentives, with prizes structured across multiple phases: Phase

Award Type

Details



1

Kickoff Grant

$2,500



2

MVP Deployment Bonus

$15,000 upon successful integration



3

Sparklet Grant Program

Opportunity to pitch for additional support Phase

Award Type

Details 1

Kickoff Grant

$2,500 2

MVP Deployment Bonus

$15,000 upon successful integration 3

Sparklet Grant Program

Opportunity to pitch for additional support Phase Award Type Details Phase Phase Award Type Award Type Details Details 1 Kickoff Grant $2,500 1 1 Kickoff Grant Kickoff Grant $2,500 $2,500 2 MVP Deployment Bonus $15,000 upon successful integration 2 2 MVP Deployment Bonus MVP Deployment Bonus $15,000 upon successful integration $15,000 upon successful integration 3 Sparklet Grant Program Opportunity to pitch for additional support 3 3 Sparklet Grant Program Sparklet Grant Program Opportunity to pitch for additional support Opportunity to pitch for additional support Finalists will compete for these prizes during the Upland Awards Show on June 7th, where the top projects will be showcased. Logistics and Additional Benefits Logistics and Additional Benefits Finalists will travel to Las Vegas, with Upland covering hotel expenses and providing travel stipends of up to $500 for domestic and $1,000 for international flights. This arrangement not only alleviates financial burdens but also enhances the overall experience, allowing participants to focus fully on their presentations. The Genesis 2024 Hackathon is a launchpad for tomorrow's innovators in the Web3 space. With significant prizes, strategic opportunities, and the chance to influence the burgeoning blockchain gaming market, this event is a cornerstone for technological advancement. Don’t miss out—mark your calendar, prepare your submission, and take part in shaping the future of the digital world. Don’t forget to like and share the story! Vested Interest Disclosure: This author is an independent contributor publishing via our business blogging program. HackerNoon has reviewed the report for quality, but the claims herein belong to the author. #DYOR. Vested Interest Disclosure: This author is an independent contributor publishing via our business blogging program . HackerNoon has reviewed the report for quality, but the claims herein belong to the author. #DYOR. Vested Interest Disclosure: business blogging program business blogging program

Unlock $15,000 for Your Web3 Project: Compete in Upland's 2024 Genesis Hackathon

আপনার ওয়েব3 প্রকল্পের জন্য $15,000 আনলক করুন: আপল্যান্ডের 2024 জেনেসিস হ্যাকাথনে প্রতিযোগিতা করুন

Sichern Sie sich 15.000 $ für Ihr Web3-Projekt: Nehmen Sie am Genesis Hackathon 2024 von Upland teil.

Desbloquee $ 15,000 para su proyecto Web3: compita en el Hackathon Genesis 2024 de Upland

Débloquez 15 000 $ pour votre projet Web3 : participez au hackathon Genesis 2024 d'Upland

अपने वेब3 प्रोजेक्ट के लिए $15,000 अनलॉक करें: अपलैंड के 2024 जेनेसिस हैकाथॉन में प्रतिस्पर्धा करें

Web3 プロジェクトで 15,000 ドルを獲得: Upland の 2024 Genesis Hackathon に参加しましょう

Web3 프로젝트를 위해 $15,000 잠금 해제: Upland의 2024 Genesis Hackathon에서 경쟁하세요

Desbloqueie US$ 15.000 para seu projeto Web3: compita no Genesis Hackathon 2024 da Upland

Разблокируйте 15 000 долларов для своего проекта Web3: примите участие в хакатоне Genesis 2024 Upland

Web3 Projeniz için 15.000 Doların Kilidini Açın: Upland'in 2024 Genesis Hackathon'unda yarışın

Mở khóa 15.000 đô la cho Dự án Web3 của bạn: Cạnh tranh trong Genesis Hackathon 2024 của Upland

为您的 Web3 项目解锁 15,000 美元：参加 Upland 的 2024 年 Genesis Hackathon

TLDR: Effective code comments serve to elucidate the reasoning behind code design choices, rather than merely describing its functionality while avoiding redundancy and confusion. They should be concise yet informative, providing clarity for non-obvious code segments and explanations for unconventional approaches. Following established rules such as avoiding code duplication and ensuring comments do not obscure unclear code is essential. This text is a resume of the Clean Code Chapter 4 - Comments, with some insights from me and other articles. The Uncle Bob book helps you question your comments to understand if the comment is good or bad, and here you can find these questions more easily. After some time reflecting on the comments, these questions become obvious practices that we internalize, becoming something natural. This kind of knowledge and evolution marks a significant milestone in a developer's growth, demonstrating more maturity and mastery over what they're doing. Uncle Bob has a more purist view and describes comments as failures most of the time because they represent a failure on the part of the developer to create code that doesn't need explanation. I don't believe comments are failures, but rather a tool that, when misused, can hinder more than help. As we'll see, some comments are extremely useful. However, to better understand what constitutes good comments, let's first look at what to avoid when we're creating comments. Bad Explaining the Code Itself When you need to create a comment to explain what the code is doing, your comment is useless because anyone reading your code can already understand what it does. If your code is bad, and you need to explain, you should check the names of variables, functions, and the organization of the code itself. When the code uses variables and functions with explanatory names, following an organized structure, it is easy to understand what is being done. TODO Comments as "notes" of what needs to be done are comments that can be useful but should be avoided. You need to verify if this type of note is something that needs to be explicitly stated in the code if it is something that anyone will understand the need, and if you should not do this or if you cannot do it in this delivery One way to avoid the existence of TODO is to create an ESLint Rule. Mumbling Worse than an unnecessary comment is a comment that only makes sense to the author. You can understand what is commented on, but it makes no sense to anyone other than the author. The comment needs to be easily understandable for other people. Misleading When you make a comment that contradicts your code, for example, when a function should return a boolean false, but in your comment, you say that the function returns a boolean true, or that the variables used are different from the ones that are truly used. This usually occurs when you try to explain the code that shouldn't need an explanation of what it does. Mandated and Noise When your comment has no value but is made out of a "need" to organize something that should not be described through the code. These are comments that only create noise and their existence makes no difference. Comments that can be ignored; their existence can only cause some misunderstanding or error because to understand what the code does its existence is not necessary. Journal, Atrributions and Bylines The Developer should not describe how they reached the final product of their code, informing the journey they followed to reach that code is irrelevant to the code. Another problem is the comment made to inform who made this addition, something that is extremely unnecessary since we have version control tools that make it clear who modified where the code. Position Markers Some developers like to divide a file with one line per comment, just to separate what is being done. This goes against the organization and clarity of the code, bringing only more pollution. If new features need to be created in the code, this comment will confuse instead of guiding other developers on the organization of the code. Too Much Information If there is too much information that should be described in the created code, this should be in the PR, not in the code. Including too much information, with all certainty, will add irrelevant information, the comment should be made with the necessary information, nothing more. Good This is a bit about comments that should be avoided, but now, we enter those that should be questioned to see if the comment is necessary. Informative Comments that provide necessary information that cannot be demonstrated through the code itself, such as the type of time formatting that is used. Although the code explains what it does, the comment that shows time formatting is useful. Clarification When the code, even if well done, is still a bit obscure, or returns something that is too large or easily confused, it is also useful to create a comment that clarifies what the code does. Warning of Consequences It is also useful a comment that warns other developers of the consequences of that code, the problems it can generate, or that the change in it can generate changes in other parts of the code that are not evident in the code itself. Amplifications When you need to amplify the importance of something that is done by the code since the code itself cannot explain what is most important in it. Using these tips to write code comments creates more readable and maintainable code. Helpful comments in your code enable other developers to more quickly understand the design of your codebase.

Comments: The Good, the Bad and the Ugly

United States v. Apple INC Court Filing, retrieved on March 21, 2024 is part of . You can jump to any part in this filing . This part is 25 of 25. HackerNoon’s Legal PDF Series here X. Request for Relief 232. To remedy these illegal acts, Plaintiffs request that the Court: Adjudge and decree that Apple has acted unlawfully to monopolize, or, in the alternative, attempt to monopolize, the smartphone market in the United States in violation of Section 2 of the Sherman Act, 15 U.S.C. § 2; Adjudge and decree that Apple has acted unlawfully to monopolize, or, in the alternative, attempt to monopolize, the performance smartphone market in the United States in violation of Section 2 of the Sherman Act, 15 U.S.C. § 2, the New Jersey Antitrust Act, N.J.S.A. 56:9-1 to -19, Wisconsin’s Antitrust Act, Wis. Stat. Ch. § 133.03 et seq.; Enter relief as needed to cure any anticompetitive harm; Enjoin Apple from continuing to engage in the anticompetitive practices described herein and from engaging in any other practices with same purpose or effect as the challenged practices, including but not limited to: a. preventing Apple from using its control of app distribution to undermine cross-platform technologies such as super apps and cloud streaming apps, among others; b. preventing Apple from using private APIs to undermine cross platform technologies like messaging, smartwatches, and digital wallets, among others; and c. preventing Apple from using the terms and conditions of its contracts with developers, accessory makers, consumers, or others to obtain, maintain, extend, or entrench a monopoly. 5. Enter any other preliminary or permanent relief necessary and appropriate to restore competitive conditions in the markets affected by Apple’s unlawful conduct; 6. Enter any additional relief the Court finds just and proper; and 7. Award each Plaintiff, as applicable, an amount equal to its costs, including reasonable attorneys’ fees, incurred in bringing this action. Dated: March 21, 2024 Respectfully, FOR PLAINTIFF UNITED STATES OF AMERICA: FOR PLAINTIFF STATE OF NEW JERSEY: FOR PLAINTIFF STATE OF ARIZONA: FOR PLAINTIFF STATE OF CALIFORNIA: FOR PLAINTIFF STATE THE DISTRICT OF COLUMBIA: FOR PLAINTIFF STATE THE DISTRICT OF CONNECTICUT: FOR PLAINTIFF STATE OF MAINE: FOR PLAINTIFF PEOPLE OF THE STATE OF MICHIGAN: FOR PLAINTIFF OF STATE OF MINNESOTA: FOR PLAINTIFF STATE OF NEW HAMPSHIRE: FOR PLAINTIFF STATE OF NEW YORK: FOR PLAINTIFF STATE OF NORTH DAKOTA: FOR PLAINTIFF STATE OF OKLAHOMA: FOR PLAINTIFF STATE OF OREGON: FOR PLAINTIFF STATE OF TENNESSEE: FOR PLAINTIFF STATE OF WISCONSIN: FOR PLAINTIFF STATE OF VERMONT: About HackerNoon Legal PDF Series: We bring you the most important technical and insightful public domain court case filings. This court case retrieved on March 21, 2024, from is part of the public domain. The court-created documents are works of the federal government, and under copyright law, are automatically placed in the public domain and may be shared without legal restriction. justice.gov

Plaintiffs Call for Remedies Against Apple's Unlawful Monopolization in Smartphone Market

How to analyze comment sentiment and react automatically using AI Today, we're digging into a crucial topic for anyone who’s creating content for YouTube: How to analyze and respond to video comments quickly, using the sentiment in those comments. This tutorial is also handy for anyone who's running a YouTube channel and wants to grow engagement. Who cares about comments? We all know that the comments section on YouTube is where real magic happens. That’s where you have a chance to engage with your audience, hear their honest opinions, and build loyalty over time. Yes, nice comments are awesome. But the not-so-nice comments are important, too. they’re the ones you need to respond to the fastest. And An ideal system would work something like the one below. If you’re serious about growing your channel, you’ll want a deeper understanding of your audience. Analyzing comment sentiment gives a good gauge of the overall mood of your viewers—which helps you tailor your content. Truly, this could be a game-changer. Let's jump right in. Part 1: The tools for the job For this project, we’re using a platform called MindsDB—the platform for building custom AI. It basically connects real-time data—like tons of YouTube comments— to . It lets companies prototype and deploy AI-powered enterprise solutions quickly and at scale. AI models In this case, it acts as an orchestration layer where we bring everything together, using AI to analyze the sentiment in the comments. Today, we’ll be using models from OpenAI, which makes ChatGPT. Then we’ll set up some MindsDB automation and alerts—those will come from Slack—to help with instant responses. Here's what the workflow will look like in the end. Part 2: How to get everything set up Step 1. Connect YouTube to MindsDB To connect your YouTube account to MindsDB and be able to set up a chatbot, you need to first generate OAuth2 credentials in the Google Cloud Console and use it to connect YouTube to MindsDB. Find detailed instructions in our documentation . here Let’s name the connection mindsdb_youtube_oauth, which is referred to in the following tutorial. CREATE DATABASE mindsdb_youtube_oauth
WITH ENGINE = 'youtube',
PARAMETERS = {
    "credentials_file": "/path-to-creds-file/credentials.json"
    -- alternatively, use the credentials_url parameter
}; Once you connect YouTube to MindsDB successfully using the above statement, the mindsdb_youtube_oauth connection comes with the following tables: The comments table stores all data related to the comments left by the viewers, including when the comment was added, who added the comment, and the comment’s content. The videos table stores all data related to the videos, including title, description, and transcript. Next, we'll use OpenAI models to analyze the sentiment of the comments and generate replies. Step 2. Use an OpenAI model to analyze the sentiment of YouTube comments Before you can deploy OpenAI models in MindsDB, you should create an engine, providing an OpenAI API key as below. CREATE ML_ENGINE openai_engine
FROM openai
USING
    api_key = 'sk-xxx'; Now you can plug in any of OpenAI’s models into MindsDB using the CREATE MODEL statement and this engine. Let’s create a model that analyzes the sentiment of the comments. CREATE MODEL yt_sentiment_classifier
PREDICT sentiment
USING
    engine = 'openai_engine',
    model_name = 'gpt-4',
    prompt_template = 'describe the sentiment of the comments
                        strictly as "positive", "spam", or "negative".
                        "I love this video":positive
                        "It is not a helpful content":negative
                        "{{comment}}.":'; This model takes the {{comment}} value (from the comments table provided by the mindsdb_youtube_oauth connection created in Step 1) as input and gives the sentiment value as output. Join the comments table (from mindsdb_youtube_oauth) with the model to analyze the sentiment of the comments. Provide the channel_id value in the WHERE clause. SELECT c.comment, m.sentiment
FROM mindsdb_youtube_oauth.comments AS c
JOIN yt_sentiment_classifier AS m
WHERE c.channel_id="UC5_wBOLCWath6q1iTgPPD5A"; Here is sample output: comment sentiment This video explains the subject so well. positive Subscribe to this channel asap. spam It didn’t help me understand the subject. negative Step 3. Use an OpenAI model to generate replies to YouTube comments Create a model to generate replies to comments. Here the model will utilize the comment content (from the comments table) and transcript of the video (from the videos table) to come up with a reply. CREATE MODEL yt_reply_model
PREDICT reply
USING
    engine = 'openai_engine',
    model_name = 'gpt-4',
    prompt_template = 'briefly respond to the youtube comment "{{comment}}";
                       as a context, use the video transcript "{{transcript}}"'; This model takes the {{comment}} value (from the comments table) and the {{transcript}} value (from the videos table) as input and gives the reply value as output. Join the comments and videos tables on their common column being the video_id column. Then, join it with the sentiment classifier model and the reply generator model. This is how you can accomplish the chaining of models in MindsDB. (Before executing the below command, add at least one new comment to the channel, so that it can fulfill the LAST keyword condition.) SELECT c.comment AS comment,
       m1.sentiment AS sentiment,
       m2.reply AS reply
FROM mindsdb_youtube_oauth.comments c
LEFT JOIN mindsdb_youtube_oauth.videos v
ON c.video_id = v.video_id
JOIN yt_sentiment_classifier AS m1
JOIN yt_reply_model AS m2
WHERE c.channel_id="UC5_wBOLCWath6q1iTgPPD5A"
AND v.channel_id="UC5_wBOLCWath6q1iTgPPD5A"
AND c.published_at > LAST; Please note that for long videos the transcript will be long and that may result in the OpenAI token limit being exceeded. Alternatively, to pass long transcript values in the prompt to the model, you can use the feature of MindsDB. RAG Here is a sample output: comment sentiment reply This video explains the subject so well. positive Thank you for your comment. We’re glad you enjoyed the video. It didn’t help me understand the subject. negative Thank you for your comment. Please let us know your feedback on what could be improved. The above query joins the comments and videos tables on their common column, which is the video_id column. The reason for joining these tables is to provide input to both the models—the sentiment classifier model requires the comment column from the comments table, and the reply generator model requires the transcript column from the videos table. Next, the two models are joined using the custom JOIN statement that does not require any ON clause condition because each model is provided with the required input from the data tables. You can select the predictions made by the models as shown in the SELECT clause. Finally, we define the channel_id value in the WHERE clause. The condition that uses the LAST keyword on the published_at column enables you to fetch only the recently added comments. Now that the sentiment of comments is analyzed and replies are generated, let’s add some automation. Step 4. Post replies to positive comments This is the first component of the job that we’ll create in Step 7. Here is how you can insert replies to recent comments whose sentiment has been classified as positive: (Before executing the below command, add at least one new (positive) comment to the channel, so that it can fulfill the LAST keyword condition.) INSERT INTO mindsdb_youtube_oauth.comments (comment_id, reply)
  SELECT c.comment_id AS comment_id,
         m2.reply AS reply
  FROM mindsdb_youtube_oauth.comments c
  LEFT JOIN mindsdb_youtube_oauth.videos v
  ON c.video_id = v.video_id
  JOIN yt_sentiment_classifier AS m1
  JOIN yt_reply_model AS m2
  WHERE c.channel_id="UC5_wBOLCWath6q1iTgPPD5A"
  AND v.channel_id="UC5_wBOLCWath6q1iTgPPD5A"
  AND c.published_at > LAST
  AND m1.sentiment = 'positive'; The LAST keyword ensures that only the newly added comments are returned by the query. Step 5. Notify about spam comments This is the second component of the job that we’ll create in Step 7. Let’s automate notifications to Slack about spam comments. A notification will include all details about the comment, its sentiment (predicted by the model in Step 2), and a sample reply (generated by the model in Step 3). Set up your Slack channel and connect it to MindsDB following . this instruction CREATE DATABASE yt_slack
WITH
  ENGINE = 'slack',
  PARAMETERS = {
      "token": "xoxb-xxx"
    }; Test the connection by inserting a message into the Slack channel. INSERT INTO yt_slack.channels (channel, text)
VALUES("channel-name", "Test message."); Here is how you can send notifications to Slack about recent comments whose sentiment has been classified as spam: (Before executing the below command, add at least one new (spam) comment to the channel, so that it can fulfill the LAST keyword condition.) INSERT INTO yt_slack.channels (channel, text)
    SELECT "channel-name" AS channel,
           concat('Video: https://www.youtube.com/watch?v=', c.video_id, chr(10),
                  'Spam Comment: https://www.youtube.com/watch?v=', c.video_id, 
                        '&lc=', c.comment_id, chr(10),
                  'Author: ', c.display_name, chr(10),
                  'Comment: ', c.comment) AS text
    FROM mindsdb_youtube_oauth.comments c
    LEFT JOIN mindsdb_youtube_oauth.videos v
    ON c.video_id = v.video_id
    JOIN yt_sentiment_classifier AS m
    WHERE c.channel_id="UC5_wBOLCWath6q1iTgPPD5A"
    AND v.channel_id="UC5_wBOLCWath6q1iTgPPD5A"
    AND c.published_at > LAST
    AND m.sentiment = 'spam'; The LAST keyword ensures that only the newly added comments are returned by the query. Step 6. Notify about negative comments This is the third component of the job that we’ll create in Step 7. Now, what about comments with negative sentiment? As these require analysis and thoughtful answers, let’s automate notifications to Slack about negative comments. A notification will include all details about the comment, its sentiment (predicted by the model in Step 2), and a sample reply (generated by the model in Step 3). Set up your Slack channel and connect it to MindsDB following . this instruction CREATE DATABASE yt_slack
WITH
  ENGINE = 'slack',
  PARAMETERS = {
      "token": "xoxb-xxx"
    }; Test the connection by inserting a message into the Slack channel. INSERT INTO yt_slack.channels (channel, text)

VALUES("channel-name", "Test message."); Here is how you can send notifications to Slack about recent comments whose sentiment has been classified as negative: (Before executing the below command, add at least one new (negative) comment to the channel, so that it can fulfill the LAST keyword condition.) INSERT INTO yt_slack.channels (channel, text)
    SELECT "channel-name" AS channel,
           concat('Video: https://www.youtube.com/watch?v=', c.video_id, chr(10),
                  'Negative Comment: https://www.youtube.com/watch?v=', c.video_id, 
                        '&lc=', c.comment_id, chr(10),
                  'Author: ', c.display_name, chr(10),
                  'Comment: ', c.comment, chr(10),
                  'Sample reply: ', m2.reply) AS text
    FROM mindsdb_youtube_oauth.comments c
    LEFT JOIN mindsdb_youtube_oauth.videos v
    ON c.video_id = v.video_id
    JOIN yt_sentiment_classifier AS m
    JOIN yt_reply_model AS m2
    WHERE c.channel_id="UC5_wBOLCWath6q1iTgPPD5A"
    AND v.channel_id="UC5_wBOLCWath6q1iTgPPD5A"
    AND c.published_at > LAST
    AND m.sentiment = 'negative'; The LAST keyword ensures that only the newly added comments are returned by the query. Step 7. Automate replies to positive comments and notifications about negative and spam comments Let’s create a job that will reply to recent positive comments, send notifications about recent negative comments with draft responses, and send notifications about recent spam comments. To do that, we need to connect a database to MindsDB in order to save the recent comments into a table. (The reason for doing so is that every run of a query that includes the condition with the LAST keyword returns only the newly added comments, which haven’t been returned in the previous run of such a query. To be able to do different actions for positive and negative comments, we need to save the output of the query with LAST. To save a batch of recently added comments returned by the query with LAST, we need a database table. Learn more about the .) LAST keyword here You can connect a database to MindsDB using the CREATE DATABASE statement and providing specific parameters. Choose one of the on how to connect it to MindsDB. supported databases and follow the instructions Here, we connect a PostgreSQL database as below: CREATE DATABASE psql_datasource
WITH ENGINE = 'postgres',
PARAMETERS = {
    "host": "host-name-or-ip-address",
    "port": 5432,
    "database": "database_name",
    "schema": "schema_name",
    "user": "postgres",
    "password": "password"
}; To create a table and insert into it the output of the query with LAST, use this command: CREATE OR REPLACE TABLE psql_datasource.recent_comments (
      SELECT *
      FROM mindsdb_youtube_oauth.comments c
      LEFT JOIN mindsdb_youtube_oauth.videos v
      ON c.video_id = v.video_id
      WHERE c.channel_id="UC5_wBOLCWath6q1iTgPPD5A"
      AND v.channel_id="UC5_wBOLCWath6q1iTgPPD5A"
      AND c.published_at > LAST
); Now, it’s time to create a job that automates steps 4, 5, and 6. CREATE JOB youtube_chatbot (

  -- save the recently added comments into a table

  CREATE OR REPLACE TABLE psql_datasource.recent_comments (
    SELECT *
    FROM mindsdb_youtube_oauth.comments c
    LEFT JOIN mindsdb_youtube_oauth.videos v
    ON c.video_id = v.video_id
    WHERE c.channel_id="UC5_wBOLCWath6q1iTgPPD5A"
    AND v.channel_id="UC5_wBOLCWath6q1iTgPPD5A"
    AND c.published_at > LAST
  );

  -- automate replies for positive comments (from Step 4)

  INSERT INTO mindsdb_youtube_oauth.comments (comment_id, reply)
    SELECT c.comment_id AS comment_id,
           m2.reply AS reply
    FROM psql_datasource.recent_comments AS c
    JOIN yt_sentiment_classifier AS m
    JOIN yt_reply_model AS m2
    WHERE m.sentiment = 'positive';

   -- automate notifications about spam comments (from Step 5)

   INSERT INTO yt_slack.channels (channel, text)
     SELECT "channel-name" AS channel,
            concat('Video: https://www.youtube.com/watch?v=', c.video_id, chr(10),
                   'Spam Comment: https://www.youtube.com/watch?v=', c.video_id, 
                         '&lc=', c.comment_id, chr(10),
                   'Author: ', c.display_name, chr(10),
                   'Comment: ', c.comment) AS text
     FROM psql_datasource.recent_comments AS c
     JOIN yt_sentiment_classifier AS m
     WHERE m.sentiment = 'spam';

  -- automate notifications about negative comments (from Step 6)

  INSERT INTO yt_slack.channels (channel, text)
    SELECT "channel-name" AS channel,
           concat('Video: https://www.youtube.com/watch?v=', c.video_id, chr(10),
                  'Negative Comment: https://www.youtube.com/watch?v=', c.video_id, 
                        '&lc=', c.comment_id, chr(10),
                  'Author: ', c.display_name, chr(10),
                  'Comment: ', c.comment, chr(10),
                  'Sample reply: ', m2.reply) AS text
    FROM psql_datasource.recent_comments AS c
    JOIN yt_sentiment_classifier AS m
    JOIN yt_reply_model AS m2
    WHERE m.sentiment = 'negative';
)
EVERY 10 minutes; This job (re)creates the recent_comments table and stores only the recently added comments in it. This table is joined with the models to reply to only positive comments. Next, this table is joined with the sentiment classifier model to send notifications about spam comments. And finally, this table is again joined with both models to send notifications about negative comments with draft responses. Please note that for large workloads of comments data, it may be required to increase the . YouTube quota What a custom YouTube chatbot means With the above steps, you can connect YouTube to MindsDB to access your channel’s comments, analyze them with the help of AI, and automate replies to the recently added comments (or send notifications about the recently added comments). In this tutorial, we've shown how to set up a chatbot for a YouTube channel defined in the WHERE clause while querying the comments and videos table. You can customize this chatbot to your use case, utilizing other integrations and features provided by MindsDB. Why analyzing sentiment is so important Analyzing to determine whether it’s positive or negative is gold when it comes to understanding your audience's emotions. language in comments Believe it or not—negative comments, while not fun, are not the enemy. They're an opportunity. They give you valuable feedback that helps you grow as a creator or as a brand. Some of the most dedicated viewers start as critics. When you respond quickly and gracefully to their nastiest comments (which shows that you value their feedback), you really can turn critics into loyal fans. Remember, it's not about proving anyone wrong; it's about responding fast (in real-time, which is possible with MindsDB) and building a connection. Also published . here

AI-Driven YouTube Comment Management: The Good, Bad, and Ugly

USA v. Samuel Bankman-Fried Court Filing, retrieved on March 15, 2024 is part of . You can jump to any part in this filing . This part is 6 of 33. HackerNoon’s Legal PDF Series here II. The Defendant’s Scheme to Make Unlawful Political Contributions In addition to the offenses of conviction, Bankman-Fried was charged with conspiring to violate the Federal Election Campaign Act and defraud the Federal Election Commission. While Bankman-Fried consented to extradition on that count, the government of The Bahamas informed the United States after the extradition that Bankman-Fried had not been extradited on the campaign finance count. (Dkt. 181). For that reason alone, the Government was unable to proceed to trial on what was Count Eight of the original Indictment. Nonetheless, the Court ruled that the Government could present evidence of the campaign finance scheme as direct evidence of the other charged crimes, including to show motive and the relationship between co-conspirators. (Dkt. 289 at 3). At trial, the Government proved Bankman-Fried’s involvement in a conspiracy to make over $100 million in political donations to federal and state officials from both parties, as well as political action committees, all funded with funds from Alameda (and ultimately customers). As the trial evidence established, the defendant passed these donations through accounts belonging to Ryan Salame and Singh (both of whom pled guilty to conspiracy to violate the campaign finance laws). (GX-1088, 1089, 1090). All of the contributions that were funded by Alameda or the defendant, but reported in the names of Salame or Singh, violated the campaign finance laws. Likewise, all of the contributions that were funded by Alameda, a corporation, and made to individual candidates violated the prohibition on corporate contributions to candidates. The defendant’s scheme spanned several years and was plainly designed to permit the defendant to amass power and influence. Beginning in or around 2020, Bankman-Fried began donating large sums of funds to political candidates, at least in part to improve his personal standing in Washington, D.C., increase FTX’s profile, and curry favor with candidates that could help pass legislation favorable to FTX or Bankman-Fried’s personal agenda, including legislation concerning regulatory oversight over FTX and its industry. To accomplish these goals, BankmanFried caused substantial contributions to be made in support of candidates of both major political parties and across the political spectrum. Bankman-Fried, however, did not want to be known as a left-leaning partisan, or to have his name publicly attached to Republican candidates. (GX-477). In order to obscure his association with certain contributions, Bankman-Fried conspired with others including Salame and Singh, to make some of the contributions in the names of Salame and Singh, instead of in the names Bankman-Fried or Alameda. (Tr. 1420-23; 1427-36). As Salame explained in a private message to a confidant, the purpose of these bipartisan donations would be “to weed out anti crypto dems for pro crypto dems and anti crypto repubs for pro crypto repubs,” and that donations would likely be routed through Salame “to weed out that republican side.” (GX505). Between in or about the fall of 2021 and the November 2022 election, Salame, BankmanFried, and Singh collectively made tens of millions of dollars in contributions, including in “hard money” contributions to federal candidates from both major political parties. (GX-28). The money used to make these political donations or to compensate for donations made by credit card originated from Alameda or FTX bank accounts and included funds that had been deposited by FTX customers. (GX-1088, 1089, 1090). Notwithstanding his awareness of the campaign finance laws, in order to conceal the true source of the funds, Bankman-Fried agreed with others that funds for contributions would be transferred from Alameda’s bank accounts to bank accounts in the name of the political donors, and then quickly transferred from those individuals’ bank accounts to political campaigns. To further conceal the scheme, Bankman-Fried and his co-conspirators recorded the outgoing wire transfers from Alameda to individuals’ bank accounts for purposes of making contributions as Alameda “loans” or “expenses.” (GX-480A; Tr. 1458-61). But unlike other loans that were made to FTX executives, most of these outgoing wire payments were not documented in agreements or on term sheets, and there were no set interest rates, no interest payments, no collateral, and no evidence of repayment. In total, Bankman-Fried and his co-conspirators made over 300 political contributions, totaling tens of millions of dollars, that were unlawful because they were made in the name of a straw donor or paid for with corporate funds. The proposed preliminary order of forfeiture lists the contributions identified by the Government. In dozens of instances, use of straw donors allowed Bankman-Fried to evade contribution limits on individual donations to candidates to whom Bankman-Fried had already donated. As a result of this fraudulent conduct, Bankman-Fried and his co-conspirators caused false information to be reported by campaigns and PACs to the FEC, which had the result of impairing and impeding the FEC’s reporting and enforcement functions. The defendant argues that the Court should not consider the defendant’s unlawful political contributions for two reasons. (Def. Mem. at 27). First, he argues that the Government did not prove this conduct by a preponderance of the evidence at trial. But at trial Singh testified that funds were transferred from Alameda to his bank account and then Salame, as part of the conspiracy with the defendant, would transfer those funds to political campaigns as if those were donations from Singh. (Tr. 1420-38). The Government also introduced documentary evidence at trial of this scheme. Government Exhibit 475, the “Donation Processing” Signal chat, provides evidence that Bankman-Fried coordinated sending Alameda funds through the accounts of Singh, Salame, and Bankman-Fried. Government Exhibit 477, a Signal chat between Singh and another person working for the defendant, also shows that certain donations would be made in Singh’s name instead of the defendant’s name. Government Exhibit 28 is a spreadsheet that was maintained by the defendant to track contributions being made in his name, Singh’s name, and Salame’s name. The Government also traced funds from Alameda through bank accounts in the names of Singh, Salame, and Bankman-Fried, on to the accounts of political campaigns. Those tracing charts were admitted into evidence as Government Exhibits 1088, 1089, and 1090. Based on all of that evidence, there is sufficient proof for the Court to conclude by a preponderance of the evidence that the defendant committed violations of the campaign finance laws. To the extent the defendant’s willfulness is at issue, it was established by the inherent aspects of concealment involved in the crime, and the fact that, as several witnesses discussed, the defendant was sophisticated when it came to politics. Additionally, while it is not necessary here, the Court is not constrained by the trial record and may consider additional evidence in connection with sentencing, or hold a hearing, at which the Government would establish that the defendant acted willfully. Second, the defendant complains that he was not permitted to call Bradley Smith as an expert witness at trial. But the testimony for which he was noticed is irrelevant for this proceeding. The defendant says that “Professor Smith would have offered expert testimony concerning ‘[c]ommon, established, and well-known practices for large-scale political giving campaigns,’ as well as testimony ‘to rebut evidence which may be offered by the Government concerning issues of campaign finance and political donations.’” (Def. Mem. at 28 (quoting Def.’s Expert Notice)). But such general testimony would not undermine the clear evidence that money was being laundered through straw donors’ accounts to make illegal contributions. And under the Rules of Evidence, Professor Smith would not be permitted to testify about the defendant’s mental state, Fed. R. Evid. 704(b), so it is not clear how his noticed testimony would have been probative of any of the sentencing issues. If it were, the defendant would have presumably offered some of Professor Smith’s opinions as part of his sentencing submission. Continue Reading . Here About HackerNoon Legal PDF Series: We bring you the most important technical and insightful public domain court case filings. This court case retrieved on March 15, 2024, from is part of the public domain. The court-created documents are works of the federal government, and under copyright law, are automatically placed in the public domain and may be shared without legal restriction. storage.courtlistener

SBF's Scheme to Curry Favor with  Unlawful Political Contributions Worth Over $100 Million

Get ready for an AI earthquake! A team of UCLA researchers ( n, , , ) have dropped some major keys to AGI. It's not only the code to seriously human-sounding AI, but they've also gone and open-sourced the whole thing. @zxche @Yihe__Deng @HuizhuoY @Kaixuan_Ji_19, @QuanquanGu Now you can develop better LLMs without needing to feed it tons of new, human-annotated data. First, let's focus on the game-changer here: a self-teaching language model. This method lets a language model becoming better and better without massive amounts of new, externally curated data. teach itself, Introducing SPIN: Self-Play Fine-Tuning Converts Weak Language Models to Strong Language Models I went full deep-dive mode – read their paper (" "),  scoured the insights on forums like , , and with Google Gemini Ultra and GPT-4 Turbo – and the core concept of SPIN knocked my tech-loving metaphorical socks off: Self-Play Fine-Tuning Converts Weak Language Models to Strong Language Models HackerNews X Reddit The 'Conversation Partner' Trick Imagine starting with a language model that has mastered basic skills (let's say conversational etiquette). With SPIN, the model building a dataset from what it already knows. generates internal 'conversations,' Instant knowledge expansion! Step two involves unleashing a new model and giving it one task: spot the difference between and genuine human communication. This forces the original model to up its game, getting more and more with every response to avoid detection. machine-generated chats human-like Here's where things get interesting. They started with (already fine-tuned with ). SPIN unleashed an iterative training system with this base model, improving it exponentially without relying on tons of new externally created data. zephyr-7b-sft-full UltraChat corpus SPIN vs. Traditional AI Training (DPO): A New Champion? We usually think machine learning, particularly for these huge language models, requires boatloads of carefully curated and labeled data. methods involve humans painstakingly rating AI responses against each other for training. Not only is this labor-intensive, but it also balloons costs as a dataset grows. Direct Preference Optimization (DPO) Direct Preference Optimization (DTO) is a training method where a model is fine-tuned using a dataset of preferences, often involving human judgments that decide which of the model-generated responses are preferred. This method requires collecting new data where each piece is labeled based on these preferences, which can be resource-intensive. In contrast, SPIN utilizes , significantly reducing the need for new data. iterative self-play By the first iteration, , highlighting its efficiency and effectiveness in leveraging existing data to enhance model performance. SPIN's performance already exceeds that of DPO in most cases SPIN showcases its strength by achieving . The process of , methodically enhances the model's performance across multiple iterations, , especially on challenging benchmarks like TruthfulQA and GSM8k. on-par performance with models trained on more extensive datasets iterative training showcasing substantial improvements So, SPIN outperforms conventional training methods, including DPO, by efficiently leveraging synthetic datasets generated through self-play, without the need for additional human-annotated data. What are SPIN's Strengths and Costs? SPIN throws a curveball with its self-play dynamic. Think of it like a language model sparring with itself in a linguistic boxing ring, with each round teaching it new tricks. SPIN's data efficiency bypasses the need for new human-annotated datasets. But more importantly, it , . accelerates the improvement loop making the model increasingly adept at generating human-like text Not only does SPIN seem to match models trained on larger external datasets, but its iterative power means consistent gains as it essentially studies its own output. Mindblowing, right? Okay, Let's Talk the Elephant in the Room – COST Nous Research co-founder has a point. These big ol' language models don't get smarter for free. Iteratively re-training with SPIN involves the expensive process of Supervised Fine-Tuning (SFT) each time. @Teknium1 However, he also mentions that "I think its worth it!". Also, the long-term benefits of quicker evolution and potentially less dependency on human-annotated data outweigh the initial investment? That's the exciting question! BOOM! It's Open-Source AI Time Just yesterday, , associate professor of computer science at UCLA and director of AI research at ByteDance, announced that . This doesn't just mean code and datasets, but pre-trained models to kickstart your own AI journeys. Quanquan Gu anyone can now use the SPIN model and dataset SPIN mirrors human thought processes. By generating text that feels human, SPIN hints at the foundational elements of reasoning that future AI could do. You know how some LLM outputs feel robotic right? Well, SPIN is different. It actually mirrors the way humans think. The way it writes feels so natural, it's like a peek into how future AI might be able to reason for themselves. This isn't just about making chatbots sound nicer. It's about creating a kind of digital thinking that works like ours. That kind of AI would be so much more flexible and capable of real understanding. While SPIN is a big leap forward in making language models sound more natural, . it's easy to get excited and overestimate what it means The text it produces is impressive (you can take a look to the database), but it's important to remember that AI doesn't yet have the capacity for true independent reasoning. While SPIN isn't true , the way it mimics human-like writing demonstrates impressive advances in how AI could process and use language in the future. AGI Even so, it does suggest amazing possibilities for how AI and language might develop in the future (if you remember that we are at the beginning of the hockey stick, the future is not far from today...) The ripple effects will be huge and here's your access pass: Code: Available on : GitHub https://github.com/uclaml/SPIN Data: Hosted on , the dataset is readily accessible for those eager to apply SPIN methodologies: Hugging Face https://huggingface.co/collections/UCLA-AGI/datasets-spin-65c3624e98d4b589bbc76f3a… Models: Pre-trained models are also available, offering a head start for experimenting with SPIN-enhanced language models: https://huggingface.co/collections/UCLA-AGI/zephyr-7b-sft-full-spin-65c361dfca65637272a02c40… Project Page: For comprehensive insights and further information, the project page is an invaluable resource: https://uclaml.github.io/SPIN/ To sum up, its iterative, self-improving methodology is a significant advancement towards creating LLM that can engage in genuinely human-like communication. Originally shared on my . X account

AGI Blueprint? UCLA Researchers Open-Source SPIN—A Self-Improving Language Model

এজিআই ব্লুপ্রিন্ট? UCLA গবেষকরা ওপেন-সোর্স স্পিন—একটি স্ব-উন্নতিশীল ভাষা মডেল

AGI-Blaupause? UCLA-Forscher Open-Source-SPIN – ein sich selbst verbesserndes Sprachmodell

¿Plan AGI? SPIN de código abierto de investigadores de UCLA: un modelo de lenguaje que se mejora a sí mismo

Plan AGI ? SPIN open source des chercheurs de l'UCLA : un modèle linguistique auto-améliorable

एजीआई ब्लूप्रिंट? यूसीएलए शोधकर्ता ओपन-सोर्स स्पिन-एक स्व-सुधार भाषा मॉडल

AGIブループリント? UCLA の研究者がオープンソースの SPIN — 自己改善型言語モデルを公開

AGI 청사진? UCLA 연구원 오픈 소스 SPIN - 자체 개선 언어 모델

Projeto AGI? SPIN de código aberto para pesquisadores da UCLA - um modelo de linguagem que se aprimora automaticamente

Проект AGI? Исследователи Калифорнийского университета в Лос-Анджелесе SPIN с открытым исходным кодом — самоусовершенствующаяся языковая модель

AGI Planı? UCLA Araştırmacıları Açık Kaynak SPIN—Kendini Geliştiren Bir Dil Modeli

Bản thiết kế AGI? Các nhà nghiên cứu của UCLA SPIN mã nguồn mở—Mô hình ngôn ngữ tự cải thiện

AGI 蓝图？加州大学洛杉矶分校研究人员开源 SPIN——一种自我改进的语言模型

The New York Times Company v. Microsoft Corporation Court Filing December 27, 2023 is part of . You can jump to any part in this filing . This is part 1 of 27. HackerNoon’s Legal PDF Series here Plaintiff The New York Times Company (“The Times”), by its attorneys Susman Godfrey LLP and Rothwell, Figg, Ernst & Manbeck, P.C., for its complaint against Defendants Microsoft Corporation (“Microsoft”) and OpenAI, Inc., OpenAI LP, OpenAI GP LLC, OpenAI LLC, OpenAI OpCo LLC, OpenAI Global LLC, OAI Corporation, LLC, OpenAI Holdings, LLC, (collectively “OpenAI” and, with Microsoft, “Defendants”), alleges as follows: I. NATURE OF THE ACTION 1. Independent journalism is vital to our democracy. It is also increasingly rare and valuable. For more than 170 years, The Times has given the world deeply reported, expert, independent journalism. Times journalists go where the story is, often at great risk and cost, to inform the public about important and pressing issues. They bear witness to conflict and disasters, provide accountability for the use of power, and illuminate truths that would otherwise go unseen. Their essential work is made possible through the efforts of a large and expensive organization that provides legal, security, and operational support, as well as editors who ensure their journalism meets the highest standards of accuracy and fairness. This work has always been important. But within a damaged information ecosystem that is awash in unreliable content, The Times’s journalism provides a service that has grown even more valuable to the public by supplying trustworthy information, news analysis, and commentary. 2. Defendants’ unlawful use of The Times’s work to create artificial intelligence products that compete with it threatens The Times’s ability to provide that service. Defendants’ generative artificial intelligence (“GenAI”) tools rely on large-language models (“LLMs”) that were built by copying and using millions of The Times’s copyrighted news articles, in-depth investigations, opinion pieces, reviews, how-to guides, and more. While Defendants engaged in widescale copying from many sources, they gave Times content particular emphasis when building their LLMs—revealing a preference that recognizes the value of those works. Through Microsoft’s Bing Chat (recently rebranded as “Copilot”) and OpenAI’s ChatGPT, Defendants seek to free-ride on The Times’s massive investment in its journalism by using it to build substitutive products without permission or payment. 3. The Constitution and the Copyright Act recognize the critical importance of giving creators exclusive rights over their works. Since our nation’s founding, strong copyright protection has empowered those who gather and report news to secure the fruits of their labor and investment. Copyright law protects The Times’s expressive, original journalism, including, but not limited to, its millions of articles that have registered copyrights. 4. Defendants have refused to recognize this protection. Powered by LLMs containing copies of Times content, Defendants’ GenAI tools can generate output that recites Times content verbatim, closely summarizes it, and mimics its expressive style, as demonstrated by scores of examples. See Exhibit J. These tools also wrongly attribute false information to The Times. 5. Defendants also use Microsoft’s Bing search index, which copies and categorizes The Times’s online content, to generate responses that contain verbatim excerpts and detailed summaries of Times articles that are significantly longer and more detailed than those returned by traditional search engines. By providing Times content without The Times’s permission or authorization, Defendants’ tools undermine and damage The Times’s relationship with its readers and deprive The Times of subscription, licensing, advertising, and affiliate revenue. 6. Using the valuable intellectual property of others in these ways without paying for it has been extremely lucrative for Defendants. Microsoft’s deployment of Times-trained LLMs throughout its product line helped boost its market capitalization by a trillion dollars in the past year alone. And OpenAI’s release of ChatGPT has driven its valuation to as high as $90 billion. Defendants’ GenAI business interests are deeply intertwined, with Microsoft recently highlighting that its use of OpenAI’s “best-in-class frontier models” has generated customers—including “leading AI startups”—for Microsoft’s Azure AI product.[1] 7. The Times objected after it discovered that Defendants were using Times content without permission to develop their models and tools. For months, The Times has attempted to reach a negotiated agreement with Defendants, in accordance with its history of working productively with large technology platforms to permit the use of its content in new digital products (including the news products developed by Google, Meta, and Apple). The Times’s goal during these negotiations was to ensure it received fair value for the use of its content, facilitate the continuation of a healthy news ecosystem, and help develop GenAI technology in a responsible way that benefits society and supports a well-informed public. 8. These negotiations have not led to a resolution. Publicly, Defendants insist that their conduct is protected as “fair use” because their unlicensed use of copyrighted content to train GenAI models serves a new “transformative” purpose. But there is nothing “transformative” about using The Times’s content without payment to create products that substitute for The Times and steal audiences away from it. Because the outputs of Defendants’GenAI models compete with and closely mimic the inputs used to train them, copying Times works for that purpose is not fair use. 9. The law does not permit the kind of systematic and competitive infringement that Defendants have committed. This action seeks to hold them responsible for the billions of dollars in statutory and actual damages that they owe for the unlawful copying and use of The Times’s uniquely valuable works. Continue Reading . Here [1] Microsoft Fiscal Year 2024 First Quarter Earnings Conference Call, MICROSOFT INVESTOR RELATIONS (Oct. 24, 2023), https://www.microsoft.com/en-us/Investor/events/FY-2024/earnings-fy-2024-q1.aspx. About HackerNoon Legal PDF Series: We bring you the most important technical and insightful public domain court case filings. This court case 1:23-cv-11195 retrieved on December 29, 2023, from is part of the public domain. The court-created documents are works of the federal government, and under copyright law, are automatically placed in the public domain and may be shared without legal restriction. nycto-assets.nytimes.com

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