February 2025

Using SAT solving to optimize quantum circuit mapping In experiments involving real quantum devices and algorithms, automated-reasoning-based method for mapping quantum computations onto quantum circuits is 26 times as fast as predecessors. Quantum technologies Jiong Yang February 28, 01:08 PM February 28, 04:20 PM Quantum computing (QC) is a new computational paradigm that promises significant speedup over classical computing on some problems. Quantum computations are often represented as complex circuits involving quantum gates, which are analogous to the […]

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Amazon announces Ocelot quantum chip Prototype is the first realization of a scalable, hardware-efficient quantum computing architecture based on bosonic quantum error correction. Quantum technologies Fernando Brando Oskar Painter February 27, 06:00 AM April 30, 08:32 PM Today we are happy to announce Ocelot, our first-generation quantum chip. Ocelot represents Amazon Web Services pioneering effort to develop, from the ground up, a hardware implementation of quantum error correction that is both resource efficient and scalable. Based on superconducting […]

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Training code generation models to debug their own outputs Using large language models to generate training data and updating models through both fine tuning and reinforcement learning improves the success rate of code generation by 39%. Conversational AI Varun Kumar February 20, 12:23 PM February 20, 01:51 PM Code generation automatically translating natural-language specifications into computer code is one of the most promising applications of large language models (LLMs). But the more complex the programming task, the more […]

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Benchmarking tool for graph-centric predictive modeling on databases 4DBInfer enables model comparison across datasets, predictive tasks, database-to-graph extraction methods, and graph-based predictive architectures. Cloud and systems Quan Gan February 14, 02:00 PM February 14, 02:00 PM Relational databases (RDBs) store vast amounts of structured data across multiple interconnected tables. This rich relational information has immense potential for predictive machine learning. However, the progress of predictive models on RDBs currently lags behind advancements in other domains like computer vision […]

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DeepSeek-V3 DeepSeek proposes novel LLM architectures (still based on transformers) and training strategies to create powerful open source LLMs with limited training costs. Model Architecture Similarly to DeepSeek-V2, V3 adopts Multi-Head Latent Attention (MLA) for efficient inference and mixture of experts (DeepSeekMoE) for economical training. The main addition to the architecture, from V2, is the auxiliary-loss-free load balancing. Multi-Head Latent Attention MLA aims to offer more efficient inference in autoregressive text generation (especially for long contexts), using a […]

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Enroll for free now: https://bit.ly/4aRnn7Z Github Repo: https://github.com/HandsOnLLM/Hands-On-Large-Language-Models We’re ecstatic to bring you “How Transformer LLMs Work” — a free course with ~90 minutes of video, code, and crisp visuals and animations that explain the modern Transformer architecture, tokenizers, embeddings, and mixture-of-expert models. Thanks for reading Language Models & Co.! Subscribe for free to receive new posts and support my work. and I have developed a lot of the visual language over the last several years (tens of […]

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    Paper    Code Large language models (LLMs) have shown remarkable capabilities in areas like problem-solving, knowledge retrieval, and code generation. Yet, these models still fail sometimes on surprisingly simple tasks. Two such examples that went viral recently were models such as ChatGPT and Claude failing on the questions “how many r’s are in the word strawberry?” and “which is greater, 9.11 or 9.9?” These examples might seem amusing but inconsequential. However, in safety-critical contexts such as […]

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