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LLMs are great for human in the loop applications, but fail at deterministic developer tasks. Interfaze (YC P26) is a new AI model that outperforms general LLMs on high accuracy tasks like: OCR, Object Detection, Web scraping, Speech-to-text, Classification and more. Congrats on the launch, Yoeven and Harsha!

69,326 Aufrufe • vor 2 Monaten •via X (Twitter)

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3D-LLM: Injecting the 3D World into Large Language Models paper page: Large language models (LLMs) and Vision-Language Models (VLMs) have been proven to excel at multiple tasks, such as commonsense reasoning. Powerful as these models can be, they are not grounded in the 3D physical world, which involves richer concepts such as spatial relationships, affordances, physics, layout, and so on. In this work, we propose to inject the 3D world into large language models and introduce a whole new family of 3D-LLMs. Specifically, 3D-LLMs can take 3D point clouds and their features as input and perform a diverse set of 3D-related tasks, including captioning, dense captioning, 3D question answering, task decomposition, 3D grounding, 3D-assisted dialog, navigation, and so on. Using three types of prompting mechanisms that we design, we are able to collect over 300k 3D-language data covering these tasks. To efficiently train 3D-LLMs, we first utilize a 3D feature extractor that obtains 3D features from rendered multi- view images. Then, we use 2D VLMs as our backbones to train our 3D-LLMs. By introducing a 3D localization mechanism, 3D-LLMs can better capture 3D spatial information. Experiments on ScanQA show that our model outperforms state-of-the-art baselines by a large margin (e.g., the BLEU-1 score surpasses state-of-the-art score by 9%). Furthermore, experiments on our held-in datasets for 3D captioning, task composition, and 3D-assisted dialogue show that our model outperforms 2D VLMs. Qualitative examples also show that our model could perform more tasks beyond the scope of existing LLMs and VLMs.

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After taking some time off post-Rapid, I'm excited to share what I’ve been up to since: Datawizz AI! We’ve raised a $12.5M Seed led by Human Capital to make AI 10x cheaper, 2x more accurate and 15x faster by transitioning from LLMs to SLMs. AI is eating the world. But unit economics are eating AI. Looking at the fastest growing AI products, they all share two traits - growing fast, and painful inference bills. General-purpose LLMs are just too expensive to run. A big reason for that is we train LLMs to be good at everything - answer any question, be an expert on any topic. The big labs dub this "generalisation", but for real-world applications, it is unnecessary. In reality - many AI applications need models to be experts in one thing - and do that thing extremely well. Your coding model doesn’t need to memorize ancient recipes for Garum sauce. This is where Datawizz comes in - we sit between the AI applications and automatically create smaller (100x-1,000x) specialized models to handle specific aspects of your work. By focusing the model and combining industry-data in the distillation process - we end up with models that beat SOTA LLMs at a fraction of the cost. We created Datawizz to make AI specialized and scalable. We’re early in the journey, but have already been able to save companies 90%+ on their inference bill and speed up their apps by 10x. Excited to build better AI platforms? Join the Datawizz team (link in first comment)

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