4 Reasons AI in 2024 is On An Exponential: Data, Mamba, and More

1. Data quality is crucial for AI performance.

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Data quality plays a significant role in AI performance, even with new architectures like Mamba. Improving data quality can lead to significant gains in AI capabilities.

• Scaling laws show that different architectures have similar slopes, and data quality is the main factor that changes the slope.
• Maximizing the quality of data fed into models is essential for maximizing AI performance.

2. Mamba is a new architecture that improves processing efficiency.

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Mamba is a new architecture that aims to process sequences more efficiently than traditional Transformers. It achieves this by using a state of fixed size that is updated step by step, reducing the complexity of attention.

• Mamba's architecture reduces the quadratic complexity of attention in Transformers, making it more scalable for longer sequences.
• The state in Mamba is updated in the GPU SRAM, allowing for faster processing.

3. Inference time compute allows models to think longer.

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Inference time compute refers to the ability of models to allocate more compute to certain problems, allowing them to think longer and improve reasoning capabilities.

• Models that can think longer have the potential for better reasoning and decision-making.
• Inference time compute may come with some trade-offs, such as slower inference and higher costs, but the benefits can be significant.

4. AI capabilities can be improved without expensive retraining.

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New methods and techniques can significantly improve AI capabilities without requiring expensive retraining. Approaches like prompting, scaffolding, and data quality enhancements can provide substantial gains.

• Methods like verifier checking, self-consistency, and majority voting can be combined to achieve even better results.
• Scaling models up and combining different techniques can lead to compounding gains in AI performance.

5. Prompt optimization can dramatically improve AI performance.

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Language models can optimize their own prompts, leading to significantly better results even from existing models.

• Manual methods of prompt optimization are feeble compared to the potential of language models.
• Optimizing prompts can enhance performance in various domains, such as high school mathematics and movie recommendations.

6. Scaling models to trillions of parameters can further enhance AI capabilities.

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Increasing the size of models to 10 trillion or even 100 trillion parameters can lead to significant improvements.

• Larger models have the potential to achieve photorealistic text-to-video outputs.
• Scaling models can result in outputs that are indistinguishable from real images.

7. 2024 is predicted to be a transformative year for AI.

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Advancements in AI technology are expected to reach a point where outputs can fool most humans.

• The progress in AI development, as demonstrated by the Walt team at Google, is highly consistent.
• By the end of 2024, photorealistic text-to-video outputs that are difficult to distinguish from reality may become a reality.