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AI Course Creator
AI Course Creator
An AI Course Creator which makes it easy to create a course with AI in minutes. Try Coursebox, the best AI Course Generator to build an online course with AI for free today.
·coursebox.ai·
AI Course Creator
Text - H.R.6466 - 118th Congress (2023-2024): AI Labeling Act of 2023
Text - H.R.6466 - 118th Congress (2023-2024): AI Labeling Act of 2023
Text for H.R.6466 - 118th Congress (2023-2024): AI Labeling Act of 2023
IN THE HOUSE OF REPRESENTATIVES November 21, 2023 Mr. Kean of New Jersey introduced the following bill; which was referred to the Committee on Energy and Commerce, and in addition to the Committee on Science, Space, and Technology, for a period to be subsequently determined by the Speaker, in each case for consideration of such provisions as fall within the jurisdiction of the committee concerned A BILL To require disclosures for AI-generated content, and for other purposes. Be it enacted by the Senate and House of Representatives of the United States of America in Congress assembled, SECTION 1. Short title.This Act may be cited as the “AI Labeling Act of 2023”. SEC. 2. Disclosures for AI-generated content. (a) Consumer disclosures.— (1) IMAGE, VIDEO, AUDIO, OR MULTIMEDIA AI-GENERATED CONTENT.— (A) IN GENERAL.—Each generative artificial intelligence system that, using any means or facility of interstate or foreign commerce, produces image, video, audio, or multimedia AI-generated content shall include on such AI-generated content a clear and conspicuous disclosure that meets the requirements of subparagraph (B). (B) DISCLOSURE REQUIREMENTS.—A disclosure required under subparagraph (A) shall meet each of the following criteria: (i) The disclosure shall include a clear and conspicuous notice, as appropriate for the medium of the content, that identifies the content as AI-generated content. (ii) The output's metadata information shall include an identification of the content as being AI-generated content, the identity of the tool used to create the content, and the date and time the content was created. (iii) The disclosure shall, to the extent technically feasible, be permanent or unable to be easily removed by subsequent users. (2) TEXT AI-GENERATED CONTENT.—Each artificial intelligence system that, using any means or facility of interstate or foreign commerce, produces text AI-generated content (including through an artificial intelligence chatbot) shall include a clear and conspicuous disclosure that identifies the content as AI-generated content and that is, to the extent technically feasible, permanent or unable to be easily removed by subsequent users. (3) OTHER OBLIGATIONS.— (A) DEVELOPERS OF GENERATIVE ARTIFICIAL INTELLIGENCE SYSTEMS.—Any entity that develops a generative artificial intelligence system shall implement reasonable procedures to prevent downstream use of such system without the disclosures required under this section, including by— (i) requiring by contract that end users and third-party licensees of the system refrain from removing any required disclosure; (ii) requiring certification that end users and third-party licensees will not remove any such disclosure; and (iii) terminating access to the system when the entity has reason to believe that an end user or third-party licensee has removed the required disclosure. (B) THIRD-PARTY LICENSEES.—Any third-party licensee of a generative artificial intelligence system shall implement reasonable procedures to prevent downstream use of such system without the disclosures required under this section, including by— (i) requiring by contract that users of the system refrain from removing any required disclosure; (ii) requiring certification that end users will not remove any such disclosure; and (iii) terminating access to the system when the third-party licensee has reason to believe that an end user has removed the required disclosure. (4) ENFORCEMENT BY THE COMMISSION.— (A) UNFAIR OR DECEPTIVE ACTS OR PRACTICE.—A violation of this subsection shall be treated as a violation of a rule defining an unfair or deceptive act or practice under section 18(a)(1)(B) of the Federal Trade Commission Act (15 U.S.C. 57a(a)(1)(B)). (B) POWERS OF THE COMMISSION.— (i) IN GENERAL.—The Commission shall enforce this subsection in the same manner, by the same means, and with the same jurisdiction, powers, and duties as though all applicable terms and provisions of the Federal Trade Commission Act (15 U.S.C. 41 et seq.) were incorporated into and made a part of this subsection. (ii) PRIVILEGES AND IMMUNITIES.—Any person who violates this subsection or a regulation promulgated thereunder shall be subject to the penalties and entitled to the privileges and immunities provided in the Federal Trade Commission Act (15 U.S.C. 41 et seq.). (iii) AUTHORITY PRESERVED.—Nothing in this Act shall be construed to limit the authority of the Commission under any other provision of law. (b) AI-Generated Content Consumer Transparency Working Group.— (1) ESTABLISHMENT.—Not later than 90 days after the date of enactment of this section, the Director of the National Institute of Standards and Technology (in this section referred to as the “Director”), in coordination with the heads of other relevant Federal agencies, shall form a working group to assist platforms in identifying AI-generated content. (2) MEMBERSHIP.—The working group shall include members from the follow
·congress.gov·
Text - H.R.6466 - 118th Congress (2023-2024): AI Labeling Act of 2023
Sourcely | Find Academic Sources with AI
Sourcely | Find Academic Sources with AI
AI-powered literature sourcing tool that quickly retrieves relevant texts based on user input. With advanced natural language processing techniques, it provides easy access to diverse information sources, saving time and effort. Get help from Sourcely AI.
·sourcely.net·
Sourcely | Find Academic Sources with AI
Researchers from Stanford and OpenAI Introduce 'Meta-Prompting': An Effective Scaffolding Technique Designed to Enhance the Functionality of Language Models in a Task-Agnostic Manner
Researchers from Stanford and OpenAI Introduce 'Meta-Prompting': An Effective Scaffolding Technique Designed to Enhance the Functionality of Language Models in a Task-Agnostic Manner
Language models (LMs), such as GPT-4, are at the forefront of natural language processing, offering capabilities that range from crafting complex prose to solving intricate computational problems. Despite their advanced functionalities, these models need fixing, sometimes yielding inaccurate or conflicting outputs. The challenge lies in enhancing their precision and versatility, particularly in complex, multi-faceted tasks. A key issue with current language models is their occasional inaccuracy and limitation in handling diverse and complex tasks. While these models excel in many areas, their efficacy could improve when confronted with tasks that demand nuanced understanding or specialized knowledge beyond their general capabilities.
·marktechpost.com·
Researchers from Stanford and OpenAI Introduce 'Meta-Prompting': An Effective Scaffolding Technique Designed to Enhance the Functionality of Language Models in a Task-Agnostic Manner
AI Literacy
AI Literacy
AIandYou is a community-facing platform fostering a more inclusive and empathetic artificial intelligence ecosystem through free online resources, global dialogues with diverse communities, and the expert advice of a diverse leadership.
·aiandyou.org·
AI Literacy
Should I cite the AI tool that I used? *** by Dr. Kristin Terrill, Iowa State University — Academic Insight Lab
Should I cite the AI tool that I used? *** by Dr. Kristin Terrill, Iowa State University — Academic Insight Lab
First, let’s disambiguate between two questions: should I cite the AI tool that I used, and how should I cite the AI tool that I used? The first question rests on the nature of your AI tool use, and to answer it, I will break down aspects of research into parts. The second question is proced
·academicinsightlab.org·
Should I cite the AI tool that I used? *** by Dr. Kristin Terrill, Iowa State University — Academic Insight Lab
Meta-Prompting: Enhancing Language Models with Task-Agnostic Scaffolding
Meta-Prompting: Enhancing Language Models with Task-Agnostic Scaffolding
We introduce meta-prompting, an effective scaffolding technique designed to enhance the functionality of language models (LMs). This approach transforms a single LM into a multi-faceted conductor, adept at managing and integrating multiple independent LM queries. By employing high-level instructions, meta-prompting guides the LM to break down complex tasks into smaller, more manageable subtasks. These subtasks are then handled by distinct "expert" instances of the same LM, each operating under specific, tailored instructions. Central to this process is the LM itself, in its role as the conductor, which ensures seamless communication and effective integration of the outputs from these expert models. It additionally employs its inherent critical thinking and robust verification processes to refine and authenticate the end result. This collaborative prompting approach empowers a single LM to simultaneously act as a comprehensive orchestrator and a panel of diverse experts, significantly enhancing its performance across a wide array of tasks. The zero-shot, task-agnostic nature of meta-prompting greatly simplifies user interaction by obviating the need for detailed, task-specific instructions. Furthermore, our research demonstrates the seamless integration of external tools, such as a Python interpreter, into the meta-prompting framework, thereby broadening its applicability and utility. Through rigorous experimentation with GPT-4, we establish the superiority of meta-prompting over conventional scaffolding methods: When averaged across all tasks, including the Game of 24, Checkmate-in-One, and Python Programming Puzzles, meta-prompting, augmented with a Python interpreter functionality, surpasses standard prompting by 17.1%, expert (dynamic) prompting by 17.3%, and multipersona prompting by 15.2%.
·arxiv.org·
Meta-Prompting: Enhancing Language Models with Task-Agnostic Scaffolding
Cheat Sheet: Mastering Temperature and Top_p in ChatGPT API - API - OpenAI Developer Forum
Cheat Sheet: Mastering Temperature and Top_p in ChatGPT API - API - OpenAI Developer Forum
Hello everyone! Ok, I admit had help from OpenAi with this. But what I “helped” put together I think can greatly improve the results and costs of using OpenAi within your apps and plugins, specially for those looking to guide internal prompts for plugins… @ruv I’d like to introduce you to two important parameters that you can use with OpenAI’s GPT API to help control text generation behavior: temperature and top_p sampling. These parameters are especially useful when working with GPT for tas...
·community.openai.com·
Cheat Sheet: Mastering Temperature and Top_p in ChatGPT API - API - OpenAI Developer Forum
Practical GAI Strategies
Practical GAI Strategies
The Practical Strategies… collection provides educators with clear, practical advice on using Generative AI
·leonfurze.com·
Practical GAI Strategies
List of Resources
List of Resources
Books Book: AI Super-Powers: China, Silicon Valley, and The New World Order Book: Architects of Intelligence: The truth about AI from the people building it Book: Artificial Intelligence: A Guide f…
·ai4k12.org·
List of Resources