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AI, David Shapiro, Synthetic Intelligence -

I built a thinking machine. Happy birthday, ACE!

I built a thinking machine. Happy birthday, ACE!
Patreon: https://www.patreon.com/daveshap LinkedIn: https://www.linkedin.com/in/dave-shap-automator/ Consulting: https://www.daveshap.io/Consulting GitHub: https://github.com/daveshap Medium: https://medium.com/@dave-shap The ACE Framework (Autonomous Cognitive Entity) is a software blueprint to use LLMs and LMMs in a hierarchical manner to create a "cognition first" model of artificial intelligence. This version is powered by OpenAI ChatGPT and GPT. It is barely an MVP.

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AI, David Shapiro, Synthetic Intelligence -

The AGI Moloch: Nash Equilibrium, Attractor States, and Heuristic Imperatives: How to Achieve Utopia

The AGI Moloch: Nash Equilibrium, Attractor States, and Heuristic Imperatives: How to Achieve Utopia
My Patreon: https://www.patreon.com/daveshap - Exclusive Discord - Consultations - Insider updates - Support my research, videos, and Open Source work My Homepage: https://www.daveshap.io/ - All my links - My books - Philosophy, etc

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AI, David Shapiro, Synthetic Intelligence -

GPT Masterclass: 4 Years of Prompt Engineering in 16 Minutes

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, David Shapiro -

GPT Masterclass: 4 Years of Prompt Engineering in 16 Minutes

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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David Shapiro, Gender -

When AGI?

When AGI?
My Patreon: https://www.patreon.com/daveshap - Exclusive Discord - Consultations - Insider updates - Support my research, videos, and Open Source work My Homepage: https://www.daveshap.io/ - All my links - My books - Philosophy, etc

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