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LLMs and Transformers from Scratch: the Decoder | by Luís Roque

Exploring the Transformer’s Decoder Architecture: Masked Multi-Head Attention, Encoder-Decoder Attention, and Practical Implementation This post was co-authored with Rafael Nardi. In this article, we delve into the decoder component of the transformer architecture, focusing on its differences and similarities with the encoder. The decoder’s unique feature is its loop-like, iterative nature, which contrasts with the…

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Meta GenAI Research Introduces ControlRoom3D: A Novel Artificial Intelligence Method to Generate High-Quality 3D Room Meshes Given a Textual Description of the Room Style

In the rapidly evolving domain of augmented and virtual reality, creating 3D environments is a formidable challenge, particularly due to the complexities of 3D modeling software. This situation often deters end-users from crafting personalized virtual spaces, an increasingly significant aspect in diverse applications ranging from gaming to educational simulations. Central to this challenge is the…

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A Generalist Agent – Google DeepMind

Inspired by progress in large-scale language modelling, we apply a similar approach towards building a single generalist agent beyond the realm of text outputs. The agent, which we refer to as Gato, works as a multi-modal, multi-task, multi-embodiment generalist policy. The same network with the same weights can play Atari, caption images, chat, stack blocks…

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Researchers from Tsinghua University Introduce LLM4VG: A Novel AI Benchmark for Evaluating LLMs on Video Grounding Tasks

Large Language Models (LLMs) have recently extended their reach beyond traditional natural language processing, demonstrating significant potential in tasks requiring multimodal information. Their integration with video perception abilities is particularly noteworthy, a pivotal move in artificial intelligence. This research takes a giant leap in exploring LLMs’ capabilities in video grounding (VG), a critical task in…

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Researchers from UCSD and NYU Introduced the SEAL MLLM framework: Featuring the LLM-Guided Visual Search Algorithm V ∗ for Accurate Visual Grounding in High-Resolution Images

The focus has shifted towards multimodal Large Language Models (MLLMs), particularly in enhancing their processing and integrating multi-sensory data in the evolution of AI. This advancement is crucial in mimicking human-like cognitive abilities for complex real-world interactions, especially when dealing with rich visual inputs. A key challenge in the current MLLMs is their need for…

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LLMs Are Dumber Than a House Cat. Can they replace you anyway? | by Nabil Alouani | Jan, 2024

Not to pick on Sebastian Bubeck in particular, but if auto-complete-on-steroid can “blow his mind,” imagine the effects on the average user. Developers and data practitioners use LLMs every day to generate code, synthetic data, and documentation. They too can be misled by inflated capabilities. It’s when humans over-trust their tools that mistakes happen. TL;DR:…

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