System: Generate search queries based on the following query and context.
                use the following format for search queries:

```search
{
  "queries": [
    "first search query",
    "second search query",
    "third search query",
  ]
}
                
User: Query: give me the list with urls of the latest most important research papers in AI in 2024, with small summaries for them
Context: Here is a list of significant AI research papers, along with their URLs and brief summaries:

**1. "Attention Is All You Need"**

- **Authors**: Vaswani et al., 2017
- **Summary**: Introduced the Transformer architecture, which relies entirely on self-attention mechanisms, eliminating the need for recurrent neural networks. This architecture has become foundational in natural language processing and other domains.
- **URL**: [https://arxiv.org/abs/1706.03762](https://arxiv.org/abs/1706.03762)

**2. "BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding"**

- **Authors**: Devlin et al., 2018
- **Summary**: Presented BERT, a Transformer-based model pre-trained on a large corpus in a bidirectional manner. BERT achieved state-of-the-art results on a variety of NLP tasks and set the stage for widespread use of pre-trained language models.
- **URL**: [https://arxiv.org/abs/1810.04805](https://arxiv.org/abs/1810.04805)

**3. "Language Models are Few-Shot Learners" (GPT-3)**

- **Authors**: Brown et al., 2020
- **Summary**: Introduced GPT-3, a large-scale language model with 175 billion parameters. Demonstrated that scaling up model size enables few-shot learning, where the model can perform tasks it was not explicitly trained on with minimal examples.
- **URL**: [https://arxiv.org/abs/2005.14165](https://arxiv.org/abs/2005.14165)

**4. "AlphaFold 2: Deep Learning-Based Protein Structure Prediction"**

- **Authors**: Jumper et al., 2021
- **Summary**: Developed by DeepMind, AlphaFold 2 demonstrated high accuracy in predicting protein structures, solving a long-standing challenge in biology and opening new avenues for scientific research.
- **URL**: [https://www.nature.com/articles/s41586-021-03819-2](https://www.nature.com/articles/s41586-021-03819-2)

**5. "NeRF: Representing Scenes as Neural Radiance Fields for View Synthesis"**

- **Authors**: Mildenhall et al., 2020
- **Summary**: Proposed Neural Radiance Fields (NeRF), a method for 3D scene representation that enables photorealistic novel view synthesis using deep learning.
- **URL**: [https://arxiv.org/abs/2003.08934](https://arxiv.org/abs/2003.08934)

**6. "High-Resolution Image Synthesis with Latent Diffusion Models"**

- **Authors**: Rombach et al., 2022
- **Summary**: Introduced Latent Diffusion Models, which enable high-resolution and efficient image synthesis by performing diffusion in a compressed latent space. This work underpins models like Stable Diffusion.
- **URL**: [https://arxiv.org/abs/2112.10752](https://arxiv.org/abs/2112.10752)

**7. "Deep Reinforcement Learning from Human Preferences"**

- **Authors**: Christiano et al., 2017
- **Summary**: Demonstrated how reinforcement learning agents can learn complex behaviors by incorporating human feedback, enhancing the alignment of AI systems with human values.
- **URL**: [https://arxiv.org/abs/1706.03741](https://arxiv.org/abs/1706.03741)

**8. "Scaling Laws for Neural Language Models"**

- **Authors**: Kaplan et al., 2020
- **Summary**: Explored how the performance of neural language models scales with model size, dataset size, and computation, providing guidelines for efficiently scaling models.
- **URL**: [https://arxiv.org/abs/2001.08361](https://arxiv.org/abs/2001.08361)

**9. "DALL·E: Creating Images from Text Descriptions"**

- **Authors**: Ramesh et al., 2021
- **Summary**: Introduced DALL·E, a Transformer-based model capable of generating images from textual descriptions, showcasing the ability to combine vision and language understanding.
- **URL**: [https://arxiv.org/abs/2102.12092](https://arxiv.org/abs/2102.12092)

**10. "Masked Autoencoders Are Scalable Vision Learners"**

- **Authors**: He et al., 2021
- **Summary**: Proposed Masked Autoencoders for self-supervised learning in computer vision, demonstrating that masking input patches and reconstructing them can lead to effective visual representations.
- **URL**: [https://arxiv.org/abs/2111.06377](https://arxiv.org/abs/2111.06377)

These papers have significantly contributed to advancements in artificial intelligence across various domains, including natural language processing, computer vision, and reinforcement learning. They offer valuable insights into modern AI techniques and are foundational for anyone interested in the field.
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