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Rivet Ai: Create Complex Ai Agents For FREE Better Than Langflow & Flowise (Installation Guide)

Rivet Ai: Create Complex Ai Agents For FREE Better Than Langflow & Flowise (Installation Guide)
Welcome to our channel! In this video, we dive deep into the fascinating world of Rivet AI, an innovative tool revolutionizing AI agent creation using Large Language Models (LLMs). Discover how Rivet AI can empower your AI development journey! 🔥 Become a Patron (Private Discord): https://patreon.com/WorldofAi ☕ To help and Support me, Buy a Coffee or Donate to Support the Channel: https://ko-fi.com/worldofai - It would mean a lot if you did! Thank you so much, guys! Love yall 🧠 Follow me on Twitter: https://twitter.com/intheworldofai Business Inquires: intheworldzofai@gmail.com [MUST WATCH]: PromptFlow: Create LLM Apps In SECONDS with NO Code FOR FREE! (Installation Guide) - https://youtu.be/nveTxm9TS1Q?si=J8NCcJ6cUN6KUAVq CREATE and SELL Ai Models WIth A Single Prompt - $500/Month (Installation Tutorial) - https://youtu.be/XZjN4HRwqEk?si=AzC8suG1B-HlIsM0 DevOpsGPT: Autonomous Ai Agents Build SOFTWARES For FREE! - https://youtu.be/lyJKG04Kvl4?si=gmbL2L8SnomnWGAJ [Link's Used]: Rivet Website: https://rivet.ironcladapp.com/ Github Repo: https://rivet.ironcladapp.com/ Doc: https://rivet.ironcladapp.com/docs/getting-started/installation In this video, we unravel the potential of Rivet AI by highlighting its key attributes: 1. Visual Programming Environment: Discover how Rivet AI simplifies the AI agent creation process. We delve into its intuitive visual programming environment, which empowers users to design intricate LLM prompt graphs. Whether you're a seasoned developer or a newcomer, Rivet AI's interface makes AI agent creation accessible to all. 2. Direct Integration: We showcase Rivet's remarkable ability to seamlessly integrate LLM prompt graphs into applications. This streamlined approach ensures that your AI agents are primed for real-world deployment, bridging the gap between development and production effortlessly. 3. Debugging Capabilities: Rivet AI's user-friendly debugger takes the spotlight as we demonstrate its critical role in identifying and resolving issues in AI applications. Real-time observation of prompt chains enhances debugging efficiency, bolstering the overall reliability of AI agents. 4. Collaboration and Version Control: Learn how Rivet promotes collaboration within teams by representing AI graphs as YAML files. Developers can effortlessly manage version control for their AI projects using standard code versioning tools. This feature fosters teamwork and maintains the efficiency of AI development. 5. Practical Applications: We present a compelling testimonial from Ironclad, a leading digital contracting platform. Ironclad's experience underscores the practical value of Rivet AI, demonstrating how it can streamline AI agent development, potentially revolutionizing processes like contract review and legal team support. Rivet is not merely a theoretical concept; it boasts real-world applications. 6. Developed and Used by Research: Gain insights into Rivet AI's development and utilization by an organization known as "Research." This indicates rigorous testing and refinement in research and development environments, contributing to its robust functionality and practicality. In summary, Rivet AI emerges as an invaluable tool for organizations and developers aiming to harness the capabilities of LLMs in their applications. Its unique blend of visual programming, debugging prowess, seamless integration, and collaboration features positions it as a prime choice for crafting sophisticated AI agents. The testimonial from Ironclad further validates its potential for practical use, especially in industries like legal tech, where AI can make a significant impact. Don't miss out on the opportunity to supercharge your AI development journey with Rivet AI. Like, subscribe, and share this video to stay updated with the latest insights in the world of AI. For more AI-related content, visit our [website here]. Additional Tags and Keywords: Rivet AI, AI agents, Large Language Models, LLM prompt graphs, visual programming, debugging, collaboration, version control, Ironclad, practical applications, AI development, artificial intelligence, open-source AI, real-world AI. Hashtags: #RivetAI #AIDevelopment #LLM #AIProgramming #AIIntegration #Debugging #Collaboration #IroncladTestimonial #PracticalAI #AIInnovation

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The Dawn of Superintelligence - Nick Bostrom on ASI

The Dawn of Superintelligence - Nick Bostrom on ASI
The Dawn of Superintelligence - Nick Bostrom on ASI Dive into the cosmic intersection of human cognition and machine intelligence as we explore the paradigm-shifting rise of Artificial General Intelligence (AGI) and its potential evolution into Artificial Superintelligence (ASI). Using astrophysicist Neil deGrasse Tyson's hypothesis of an alien encounter, we unpack the profound cognitive chasm between beings. How does a Bonobo’s linguistic prowess compare to a human intellectual titan? And as we've witnessed the evolution of ChatGPT from its first iteration to ChatGPT-4, are we brushing the fringes of true AGI? Philosophers like Bostrom speculate on a potential "intelligence explosion" when AI begins to improve itself. As we stand at the dawn of a new era, where machines might eclipse human intellect, we ponder our place in the vast intelligence tapestry. Beyond the philosophical, the practical implications are vast: from power dynamics to potential harm if AI goals misalign with ours. Yet, amidst these uncertainties, there's optimism. This journey offers a profound insight into the most consequential technological evolution in our history and the pivotal choices we must make. Subscribe to Science Time: https://www.youtube.com/sciencetime24 #artificialintelligence #ai #science

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An Actually Big Week in AI: AutoGen, The A-Phone, Mistral 7B, GPT-Fathom and Meta Hunts CharacterAI

An Actually Big Week in AI: AutoGen, The A-Phone, Mistral 7B, GPT-Fathom and Meta Hunts CharacterAI
From dramatic new use cases for GPT Vision, Meta bringing language models to billions of people, Autogen as the new AutoGPT, to what I’m calling the Altman Phone, this is a huge time in AI. I’ll also cover Mistral’s 7B model, the new CIA-bot, Orca potentially replacing OpenAI models at Microsoft, and yesterday’s fascinating GPT-Fathom paper. https://www.metaculus.com/ai/?utm_source=ai_explained&utm_campaign=ai_explained https://www.patreon.com/AIExplained Chapters: 0:35 – GPT Vision Use Cases 1:32 – Meta AI to 4 Billion People? 3:00 – CIA-Bot 3:48 – The Altman Phone 4:47 – AutoGen 8:26 – Mistral 7B 9:58 – Orca @ MSFT 11:20 – GPT-Fathom GPT 4V Agent: https://twitter.com/mattshumer_/status/1707480439793840402 GPT Vision UI: https://twitter.com/skirano/status/1706823089487491469 Meta Models ft. Mr Beast: https://about.fb.com/news/2023/09/introducing-ai-powered-assistants-characters-and-creative-tools/ Character.AI Valuation: https://www.bloomberg.com/news/articles/2023-09-28/character-ai-in-early-talks-for-funding-at-more-than-5-billion-valuation AutoGen: https://www.microsoft.com/en-us/research/blog/autogen-enabling-next-generation-large-language-model-applications/ Altman Tweet Timelines: https://twitter.com/sama/status/1705752292484624863 The Altman phone, The Verge - https://www.theverge.com/2023/9/28/23893939/jony-ive-openai-sam-altman-iphone-of-artificial-intelligence-device CIA Bot: https://www.bloomberg.com/news/articles/2023-09-26/cia-builds-its-own-artificial-intelligence-tool-in-rivalry-with-china?leadSource=reddit_wall LLMs for Censorship: https://www.lesswrong.com/posts/oqvsR2LmHWamyKDcj/large-language-models-will-be-great-for-censorship PRISM: https://en.wikipedia.org/wiki/PRISM Mistral 7B: https://mistral.ai/news/announcing-mistral-7b/ Perplexity Labs: https://labs.perplexity.ai/ Orca at Microsoft: https://www.theinformation.com/articles/how-microsoft-is-trying-to-lessen-its-addiction-to-openai-as-ai-costs-soar?utm_source=ti_app&rc=sy0ihq My Orca Video: https://www.youtube.com/watch?v=Dt_UNg7Mchg&t=842s My Phi-1 Video: https://www.youtube.com/watch?v=7S68y6huEpU&t=88s GPT-Fathom: https://arxiv.org/pdf/2309.16583.pdf https://www.patreon.com/AIExplained

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AI BENCHMARKS ARE BROKEN! [Prof. MELANIE MITCHELL]

AI BENCHMARKS ARE BROKEN! [Prof. MELANIE MITCHELL]
Patreon: https://www.patreon.com/mlst Discord: https://discord.gg/ESrGqhf5CB Pod version: https://podcasters.spotify.com/pod/show/machinelearningstreettalk/episodes/Prof--Melanie-Mitchell-2-0---AI-Benchmarks-are-Broken-e2959li Prof. Melanie Mitchell argues that the concept of "understanding" in AI is ill-defined and multidimensional - we can't simply say an AI system does or doesn't understand. She advocates for rigorously testing AI systems' capabilities using proper experimental methods from cognitive science. Popular benchmarks for intelligence often rely on the assumption that if a human can perform a task, an AI that performs the task must have human-like general intelligence. But benchmarks should evolve as capabilities improve. Large language models show surprising skill on many human tasks but lack common sense and fail at simple things young children can do. Their knowledge comes from statistical relationships in text, not grounded concepts about the world. We don't know if their internal representations actually align with human-like concepts. More granular testing focused on generalization is needed. There are open questions around whether large models' abilities constitute a fundamentally different non-human form of intelligence based on vast statistical correlations across text. Mitchell argues intelligence is situated, domain-specific and grounded in physical experience and evolution. The brain computes but in a specialized way honed by evolution for controlling the body. Extracting "pure" intelligence may not work. Other key points: - Need more focus on proper experimental method in AI research. Developmental psychology offers examples for rigorous testing of cognition. - Reporting instance-level failures rather than just aggregate accuracy can provide insights. - Scaling laws and complex systems science are an interesting area of complexity theory, with applications to understanding cities. - Concepts like "understanding" and "intelligence" in AI force refinement of fuzzy definitions. - Human intelligence may be more collective and social than we realize. AI forces us to rethink concepts we apply anthropomorphically. The overall emphasis is on rigorously building the science of machine cognition through proper experimentation and benchmarking as we assess emerging capabilities. TOC: [00:00:00] Introduction and Munk AI Risk Debate Highlights [00:05:00] Douglas Hofstadter on AI Risk [00:06:56] The Complexity of Defining Intelligence [00:11:20] Examining Understanding in AI Models [00:16:48] Melanie's Insights on AI Understanding Debate [00:22:23] Unveiling the Concept Arc [00:27:57] AI Goals: A Human vs Machine Perspective [00:31:10] Addressing the Extrapolation Challenge in AI [00:36:05] Brain Computation: The Human-AI Parallel [00:38:20] The Arc Challenge: Implications and Insights [00:43:20] The Need for Detailed AI Performance Reporting [00:44:31] Exploring Scaling in Complexity Theory Eratta: Note Tim said around 39 mins that a recent Stanford/DM paper modelling ARC “on GPT-4 got around 60%”. This is not correct and he misremembered. It was actually davinci3, and around 10%, which is still extremely good for a blank slate approach with an LLM and no ARC specific knowledge. Folks on our forum couldn’t reproduce the result. See paper linked below. Books (MUST READ): Artificial Intelligence: A Guide for Thinking Humans (Melanie Mitchell) https://www.amazon.co.uk/Artificial-Intelligence-Guide-Thinking-Humans/dp/B07YBHNM1C/?&_encoding=UTF8&tag=mlst00-21&linkCode=ur2&linkId=44ccac78973f47e59d745e94967c0f30&camp=1634&creative=6738 Complexity: A Guided Tour (Melanie Mitchell) https://www.amazon.co.uk/Audible-Complexity-A-Guided-Tour?&_encoding=UTF8&tag=mlst00-21&linkCode=ur2&linkId=3f8bd505d86865c50c02dd7f10b27c05&camp=1634&creative=6738 See rest of references in pinned comment. Show notes + transcript https://atlantic-papyrus-d68.notion.site/Melanie-Mitchell-2-0-15e212560e8e445d8b0131712bad3000?pvs=4

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AI and our future with Yuval Noah Harari and Mustafa Suleyman

AI and our future with Yuval Noah Harari and Mustafa Suleyman
The Economist brought together Yuval Noah Harari and Mustafa Suleyman to grapple with the biggest technological revolution of our times. They debate the impact of AI on our immediate futures, how the technology can be controlled and whether it could ever have agency. 00:00 - Harari and Suleyman discuss the future of AI 00:51 - What will the world look like in 2028? 03:35 - Is AI comparable to an alien invasion? 06:22 - The importance of regulation Sign up to The Economist’s daily newsletter: https://econ.st/3QAawvI Watch the full interview here: https://econ.st/48ajUzL Yuval Noah Harari argues that AI has hacked the operating system of human civilisation: https://econ.st/3PDyFUz Yuval Noah Harari argues that what’s at stake in Ukraine is the direction of human history: https://econ.st/3sRFNDF How scientists are using artificial intelligence: https://econ.st/3PkMRAc How artificial intelligence can revolutionise science: https://econ.st/3sMVDPQ How worried should you be about AI disrupting elections?: https://econ.st/48buN4d Your employer is (probably) unprepared for artificial intelligence: https://econ.st/48foCMI What are the chances of an AI apocalypse?: https://econ.st/3Lm2VR2 How Britain can become an AI superpower: https://econ.st/3sRLSA5 Why tech giants want to strangle AI with red tape: https://econ.st/4882bJj Large, creative AI models will transform lives and labour markets: https://econ.st/3ZeQLz4 The relationship between AI and humans: https://econ.st/3EDmKje Watch: How to stop AI going rogue: https://econ.st/46eekdX Watch: Beyond ChatGPT: what chatbots mean for the future: https://econ.st/3Piq0Fl Listen: Mustafa Suleyman on the coming age of AI: https://econ.st/45RxTbZ Listen: How will AI influence the 2024 election?: https://econ.st/3ZeEdaY

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