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A Neuro-Symbolic Perspective on Large Language Models (LLMs)

Austria, Linz
Joined January 2023
Photo by SymbolicAI (@SymbolicAPI)
Replying to @SymbolicAPI
Here is a short example:
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We are migrating our Twitter/X handle to @ExtensityAI
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I've been working on QSTAR already back in 2014/2015. Read the paper here: sciencedirect.com/science/ar… ;)
Q-learning is back from the dead.
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AI in Life Sciences is a thing..
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Have a look at our latest release that brings the power of GPT with plugins from @ExtensityAI to your local shell pypi.org/manage/project/symb…
Replying to @OpenAI
Or you could do the same and stay in your local dev environment by simply using the @SymbolicAPI framework and your favourite GPT engine or host your own backend. Here are some inspirational examples for shell tool interactions and Jupyter notebooks:
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youtube.com/watch?v=R46Sskmm… @DinuMariusC and @leojoinsthevoid share more about the ChatBot with tool use functionality in SymbolicAI. We're working towards an easy user interface to customize your own tools and memory storage/retrieval for your own ChatBot/agent!
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SymbolicAI retweeted
Text-to-Audio Generation using Instruction-Tuned LLM and Latent Diffusion Model latent diffusion model based approach (TANGO) outperforms the state-of-the-art AudioLDM on most metrics and stays comparable on the rest on AudioCaps test set, despite training the LDM on a 63 times smaller dataset and keeping the text encoder frozen. This improvement might also be attributed to the adoption of audio pressure level-based sound mixing for training set augmentation, whereas the prior methods take a random mix abs: arxiv.org/abs/2304.13731 github: github.com/declare-lab/tango
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Some said I am to optimistic when I showed this image three months ago and stated that SOON #GPT3-like #LLMs will be available at the core of the dev stack (@SymbolicAPI) Well… you can now run GPT3-level #AI models on your laptop, phone, and Raspberry Pi arstechnica.com/information-…
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SymbolicAI retweeted
Adding Conditional Control to Text-to-Image Diffusion Models present a neural network structure, ControlNet, to control pretrained large diffusion models to support additional input conditions paper: github.com/lllyasviel/Contro… github: github.com/lllyasviel/Contro…
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New release: v0.2.9 github.com/Xpitfire/symbolic… Release notes: Added Shell Apps (sym+) Exposed SymbolicAI functions via shell commands Improved documentation Added Lambda Expression and many more... Read all details in our docs at github.com/Xpitfire/symbolic….
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Here an overview of many text-to-🪄 representations 💪
generative ai 🗨️: • text-to-gif (T2G) • text-to-3D (T2D) • text-to-text (T2T) • text-to-NFT (T2N) • text-to-code (T2C) • text-to-image (T2I) • text-to-audio (T2S) • text-to-video (T2V) • text-to-music (T2M) • text-to-motion (T2Mo) #artificialintelligence
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Another nice example showcasing how text as a symbolic representation is essential for ML.
SceneScape: Text-Driven Consistent Scene Generation abs: arxiv.org/abs/2302.01133 project page: scenescape.github.io/ text-driven perpetual view generation -- synthesizing long videos of arbitrary scenes solely from an input text describing the scene and camera poses
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A tutorial from @OpenAI on how to build a question-answering system on your custom website Preprocess: 1. Website -> text (Scraping) 2. Text -> chunks -> embed Inference: 1. Question -> embed 2. Get topK similar chunks 3. Answer generation using 1 & 2 platform.openai.com/docs/tut…
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Great thx @ai_austria for referencing us in their newsletter: aiaustria.substack.com/p/ai-…
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... Added runtime adaptable API Key command Added elapsed time monitoring command Added Sphinx documentation Minor Bugfixes and improvements
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Meet SymbolicAI: The Powerful Framework That Combines The Strengths Of Symbolic Artificial Intelligence (AI) And Large Language Models buff.ly/3wwBCM8 #DL #AI #ML #DeepLearning #ArtificialIntelligence #MachineLearning #ComputerVision #AutonomousVehicles
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