Deep Thought

AI, Synthetic Intelligence - October 07, 2023

Promptbreeder: Self-Referential Self-Improvement Via Prompt Evolution (Paper Explained)

#ai #promptengineering #evolution Promptbreeder is a self-improving self-referential system for automated prompt engineering. Give it a task description and a dataset, and it will automatically come up with appropriate prompts for the task. This is achieved by an evolutionary algorithm where not only the prompts, but also the mutation-prompts are improved over time in a population-based, diversity-focused approach. OUTLINE: 0:00 - Introduction 2:10 - From manual to automated prompt engineering 10:40 - How does Promptbreeder work? 21:30 - Mutation operators 36:00 - Experimental Results 38:05 - A walk through the appendix Paper: https://arxiv.org/abs/2309.16797 Abstract: Popular prompt strategies like Chain-of-Thought Prompting can dramatically improve the reasoning abilities of Large Language Models (LLMs) in various domains. However, such hand-crafted prompt-strategies are often sub-optimal. In this paper, we present Promptbreeder, a general-purpose self-referential self-improvement mechanism that evolves and adapts prompts for a given domain. Driven by an LLM, Promptbreeder mutates a population of task-prompts, and subsequently evaluates them for fitness on a training set. Crucially, the mutation of these task-prompts is governed by mutation-prompts that the LLM generates and improves throughout evolution in a self-referential way. That is, Promptbreeder is not just improving task-prompts, but it is also improving the mutationprompts that improve these task-prompts. Promptbreeder outperforms state-of-the-art prompt strategies such as Chain-of-Thought and Plan-and-Solve Prompting on commonly used arithmetic and commonsense reasoning benchmarks. Furthermore, Promptbreeder is able to evolve intricate task-prompts for the challenging problem of hate speech classification. Authors: Chrisantha Fernando, Dylan Banarse, Henryk Michalewski, Simon Osindero, Tim Rocktäschel Links: Homepage: https://ykilcher.com Merch: https://ykilcher.com/merch YouTube: https://www.youtube.com/c/yannickilcher Twitter: https://twitter.com/ykilcher Discord: https://ykilcher.com/discord LinkedIn: https://www.linkedin.com/in/ykilcher If you want to support me, the best thing to do is to share out the content :) If you want to support me financially (completely optional and voluntary, but a lot of people have asked for this): SubscribeStar: https://www.subscribestar.com/yannickilcher Patreon: https://www.patreon.com/yannickilcher Bitcoin (BTC): bc1q49lsw3q325tr58ygf8sudx2dqfguclvngvy2cq Ethereum (ETH): 0x7ad3513E3B8f66799f507Aa7874b1B0eBC7F85e2 Litecoin (LTC): LQW2TRyKYetVC8WjFkhpPhtpbDM4Vw7r9m Monero (XMR): 4ACL8AGrEo5hAir8A9CeVrW8pEauWvnp1WnSDZxW7tziCDLhZAGsgzhRQABDnFy8yuM9fWJDviJPHKRjV4FWt19CJZN9D4n

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AI, David Shapiro, Synthetic Intelligence - October 07, 2023

GPT Masterclass: 4 Years of Prompt Engineering in 16 Minutes

Medium article: https://medium.com/@dave-shap/become-a-gpt-prompt-maestro-943986a93b81 Slide Deck: https://github.com/daveshap/YouTube_Slide_Decks/blob/main/Business%20and%20Product/LLM%20Prompt%20Taxonomy.pdf Large language models (LLMs) like GPT-4 have shown impressive abilities to generate humanlike text, have conversations, and demonstrate knowledge across many domains. However, there is still confusion around exactly how LLMs work and what capabilities they currently possess. This passage aims to provide a high-level taxonomy of LLM abilities and limitations. LLMs are deep learning neural networks trained on massive text datasets to predict the next word in a sequence. This allows them to build complex statistical representations of language and accumulate world knowledge from their training data. LLMs have no explicit rules or knowledge - their capabilities emerge from recognizing patterns. LLMs excel at reductive operations like summarization, distillation, and extraction which condense large inputs down by identifying salient information. Summarization produces concise overviews of documents. Distillation extracts key facts and principles. Extraction retrieves targeted information like names, dates, or figures. Transformational techniques like paraphrasing, translation, and restructuring reshape text without losing meaning. Paraphrasing rewrites text with different words/phrasing while preserving meaning. Translation converts between languages. Restructuring improves logical flow and readability. Transformations leverage LLMs' understanding of linguistic conventions and narrative flow. Generative tasks like drafting, planning, brainstorming, and amplifying synthesize new content from limited input. Drafting can expand prompts into coherent documents. Planning formulates step-by-step strategies to achieve goals based on parameters. Brainstorming produces creative possibilities from prompts. Amplification adds explanatory details to existing text. Generative abilities are more variable but rapidly improving. Examined through Bloom's Taxonomy, LLMs exhibit skills from basic remembering of facts to highest-level creating original content. Their statistical learning acts as a knowledge repository to query. LLMs also demonstrate strong abilities in understanding concepts, applying knowledge, analyzing passages, and evaluating content. With the right prompting, they can create novel stories, articles, and dialogue. LLMs have vast latent knowledge not contained in their explicit training. This includes memorized facts, general world knowledge, and learned cognitive skills for tasks like translation. Latent knowledge forms a dense reservoir that requires careful probing with prompts and techniques to extract. While promising, reliance on latent knowledge highlights LLMs' need to better index and activate their own internal knowledge. Emergent capabilities like theory of mind, implied cognition, logical reasoning, and in-context learning have arisen from recognizing intricate patterns, not hardcoded rules. Theory of mind suggests models can distinguish their own and others' perspectives. Implied cognition points to dynamic reasoning when generating text. Logical reasoning abilities hint at inferring abstract principles from data. Rapid in-context learning demonstrates knowledge acquisition abilities. Rather than a bug, LLMs' ability to fabricate plausible statements represents a core feature of intelligence. Humans also exhibit a spectrum from creativity to hallucination based on uncontrolled pattern generation. The ideal is not suppressing but responsibly directing generation. Research into alignment and ethics can allow beneficial creativity to flourish while minimizing harms. Maintaining factual grounding and conveying uncertainty are key precautions. In summary, LLMs have diverse capabilities and limitations requiring continued research. With responsible development focused on augmenting human intelligence, LLMs offer exciting potential while managing risks. Their latent knowledge and emergent properties highlight promising directions to elevate reasoning, creativity, and understanding.

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AI, Synthetic Intelligence - October 07, 2023

ChatGPT-4 Prompt Engineering: The Tree of Thoughts Method - WOW!

ChatGPT-4 Prompt Engineering: The Tree of Thoughts Process 𝐅𝐢𝐧𝐝 𝐭𝐡𝐞 𝐏𝐫𝐨𝐦𝐩𝐭 𝐓𝐞𝐦𝐩𝐥𝐚𝐭𝐞 𝐇𝐞𝐫𝐞: https://www.allabtai.com/the-tree-of-thoughts-prompt-template/ Paper: https://arxiv.org/abs/2203.02155 Get a FREE 45+ ChatGPT Prompts PDF here: 📧 Join the newsletter: https://www.allabtai.com/newsletter/ 👊 Become a member: https://www.youtube.com/c/AllAboutAI/join 🌐 My website: https://www.allabtai.com Explore how AI can mimic human-like problem-solving with the revolutionary Tree of Thoughts (ToT) approach. Traditional AI methods fall short - ToT is the future, considering multiple reasoning paths like exploring a forest, not just one trail. Witness how Prompt Engineering with ToT tackles complex problems more efficiently than ever before. 00:00 ChatGPT-4 Prompt Engineering: ToT Intro 00:25 What is the Tree of Thoughts Prompt Approach? 01:36 The Brainstorming Phase 03:59 The Evaluation Phase 06:19 The Expansion Phase 07:59 The Decision Phase

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AI, Synthetic Intelligence - October 07, 2023

AI and the quest for immortality - are we defeating death? | DW Documentary

Can artificial intelligence, or AI, make it possible for us to live forever? Or at least, be preserved for posterity? What are the current developments in the fields of artificial intelligence and biotechnology? Will humanity exist without biological bodies, in the near future? Could humans and AI merge into one being? This documentary explores these questions, and more. The film also explores current advances in AI, robotics and biotechnology. What is the essence of human existence? Can that essence be replicated? Technological development in these fields is rapid. It is also increasingly urgent, as people's lives play out more and more online. Visionaries, authors, and theorists such as Nick Bostrom, Hiroshi Ishiguro, Douglas Rushkoff and Deepak Chopra are questioning how a humanity without a biological body might evolve. The scientific community is fascinated by the idea of merging human and machine. However, leading minds are also pondering the question of whether AI might just be the last thing humans ever create. #documentary #dwdocumentary ______ DW Documentary gives you knowledge beyond the headlines. Watch top documentaries from German broadcasters and international production companies. Meet intriguing people, travel to distant lands, get a look behind the complexities of daily life and build a deeper understanding of current affairs and global events. Subscribe and explore the world around you with DW Documentary. Subscribe to: ⮞ DW Documentary (English): https://www.youtube.com/dwdocumentary ⮞ DW Documental (Spanish): https://www.youtube.com/dwdocumental ⮞ DW Documentary وثائقية دي دبليو (Arabic): https://www.youtube.com/dwdocarabia ⮞ DW Doku (German): https://www.youtube.com/dwdoku ⮞ DW Documentary हिन्दी (Hindi): https://www.youtube.com/dwdochindi For more visit: http://www.dw.com/en/tv/docfilm/s-3610 Follow DW Documentary on Instagram: https://www.instagram.com/dwdocumentary/ Follow DW Documental on Facebook: https://www.facebook.com/dwdocumental We kindly ask viewers to read and stick to the DW netiquette policy on our channel: https://p.dw.com/p/MF1G

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AI, Synthetic Intelligence - October 07, 2023