Discover how #GenerativeAI is revolutionizing #videogame #development by #automating content creation, enhancing realism, and #streamlining #testing processes. Explore the #transformative role of generative #AI in shaping the #future of #gaming. https://
forbes.com/sites/bernardm
arr/2024/04/18/the-role-of-generative-ai-in-video-game-development/
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GENERATIVE AI
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Generative AI Revolutionizing Video Game Development and Content Creation
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Training LLMs: Avoiding Default Mode Through Data Curation
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First you’d teach the model quite a bit from recent content (w/ tons of LLM outputs in it it’s likely, if we were to train this in normally, the LLM will likely get stuck in that default LLM ‘mode’ we know so well + make it harder to break out of this w/ post-training) So
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Training Strategy: Pre-2021 Data Priority Over AI-Generated Content
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With all the AI-generated content flooding the web There might be something to first training on content from 2021-on And then continuing to train on pre-2021 content
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Llama 3 Open Source Release Announced Within 24 Hours
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less than 24 hours till llama 3 is open-sourced
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API Bug Prevents Token Streaming Implementation
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Sadly it doesn't support streaming tokens yet – I nearly got that working but ran into an API bug that I couldn't work around
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Quick Prompt to Assess Model Capability Level
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Anyone got a good short go-to prompt for running against a model that instantly gives an indicator of how capable that model is? I'm trying to figure out a neat way of determining if a model feels gpt-3.5-turbo or gpt-4 level with the minimum amount of work (and API token spend)
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RAG and Token Optimization with Expanded Context Windows
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I was playing with maximizing RAG content last year, but that was when models still had tiny context lengths – 4,000 or 8,000 wasn't a lot to play with Token optimization like that is less interesting now we have 100,000+ tokens to play with even with the less expensive models
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Cost estimation and context optimization for advanced RAG queries
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I want to be able to give users a cost estimate before they run larger prompts, but I'm also interested in knowing how much content I can cram into the context for advanced RAG queries
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Anthropic Needs Public Tokenizer API Like Google Gemini
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Anthropic don't release any form of their tokenizer at all – not even an API endpoint that lets you count tokens before prompting with them (Google Gemini has that) It's a small thing but it's the feature I most want from them at the moment
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Mixtral Tokenizer Solves OpenAI’s JSON Token Counting Problem
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OpenAI's tokenizer is available (as the tiktoken Python library) but you're on your own when it comes to guessing how many tokens will be used by the additional JSON syntax used for previous messages and tool definitions Mixtral's tokenizer here solves that problem