# Things we learned about LLMs in 2024

- **In 2024, the GPT-4 barrier was decisively surpassed**, with 18 organizations now boasting models that outperform it, including Google's Gemini 1.5 Pro, which introduced unprecedented capabilities like a 2 million token input context and video input.

- **Deepseek's R1 model has outperformed OpenAI’s o1 on reasoning benchmarks**, showcasing its potential as a formidable player in the AI landscape, driven by a commitment to foundational technology and open-source principles.

- **Coconut** introduces a novel reasoning method for LLMs, allowing them to operate in a **continuous latent space**, enhancing their problem-solving capabilities beyond traditional word-based reasoning.

- **Nvidia is investing in robotics**, launching its Jetson Thor computers in 2025, aiming to lead the anticipated robotics revolution with a comprehensive "full stack" solution for AI-powered robots.

- **Putnam-AXIOM** introduces a **new benchmark** for evaluating **higher-level mathematical reasoning** in large language models (LLMs), highlighting significant performance gaps and the effects of data contamination.

- The **Large Concept Models (LCM)** utilize a **higher-level semantic representation** called "concepts," enabling language- and modality-agnostic sentence modeling across **200 languages** in text and **57 languages** in speech, leveraging the [SONAR](https://github.com/facebookresearch/SONAR) embedding space.

- The **RT-2 model** demonstrates advanced capabilities in **vision-language-action** tasks, successfully executing commands like "pick up the extinct animal" through integrated perception and action.

- **Submerging electrical circuits in nonconductive liquid coolant** can capture 100% of generated heat, potentially revolutionizing data-center design by eliminating the need for traditional cooling systems.

- A **new state-of-the-art (SOTA)** Text to Audio model utilizes **rectified flow matching** and **FLUX architecture**, achieving rapid inference times of about **3 seconds on a GPU**.

- The current **SOTA** for biomedical encoder models includes **PubMedBERT** and **BioBERT**, which serve as strong baselines for tasks like **sentence similarity** and **document retrieval**.

- **Multihead latent attention** compresses the entire sequence into a **latent vector**, allowing the model to access information from the whole sequence, which disrupts traditional autoregressive constraints.

- **CNN with regression heads** is proposed for predicting **absolute clearance distances** without reference objects, addressing the challenge of varying object dimensions and camera intrinsics.

- **Multimodal models**, particularly **Vision-Language Models (VLMs)**, are increasingly utilized for **content moderation**, addressing challenges like detecting _hateful memes_ and _violence in images_.