đź“ť Table of Contents
Introduction
- The Steep Part of the Exponential
- Four Reasons Why AI is Not Slowing Down
Data Quality
- The Importance of Data Quality
- Mamba Architecture
- Hardware-Aware State Expansion
Inference Time Compute
- Letting Models Think for Longer
- Prompt Optimization
Multimodal Progress
- Chain of Thought
- Structured State Spaces for Sequence Modeling
- Multimodality
Predictions for 2024
- Photorealistic Text Video Output
- Cartoon Dynamo
Conclusion
🤖 Introduction
Welcome to the world of AI, where the possibilities are endless. As we enter 2024, we are on the steep part of the exponential, and AI is not slowing down anytime soon. In this article, we will explore four clear reasons why AI is not slowing down and what we can expect in the future.
🚀 Four Reasons Why AI is Not Slowing Down
Data Quality
Data quality is crucial in AI, and it will change everything. The famed authors of Mamba and Mix Trial have emphasized the importance of data quality. The architecture is fun, but making the hardware efficient is about data. The scaling law curve shows that different architectures would generally have the same slope, and the only thing that changes the slope is the data quality. We will cover Mamba in a minute, but for language modeling, it performs better than the Transformer Plus+. With five or ten times as much compute, you could replicate the performance of Mamba with a Transformer. However, data quality still means more, and we are not even close to maximizing the quality of data fed into our models.
Inference Time Compute
The ability of the model to decide how much compute to allocate to certain problems is called inference time compute. Letting models think for longer is crucial for reasoning, and it can be combined with multimodality. Models can generate sequences of things before giving an answer that will resemble much more what we call reasoning. In the future, models will have this knowledge of the world, and this generation, which we call Chain of Thought in text, but multimodality, will be the key.
Multimodal Progress
Multimodal progress is occurring around us, and it includes listening to a version of my voice that you might find hard to distinguish from my real one. There is a lot of low-hanging fruit out there in AI that doesn't even require more compute. Structured State Spaces for Sequence Modeling is an example of this. The Mamba architecture is causing shock waves in AI circles, and it has been generating a lot of buzz. It is an architecture built with an awareness of the kind of GPUs it's going to run on.
Predictions for 2024
In 2024, we can expect a photorealistic text video output that could fool most humans. The progress season on season is quite something to watch, and we are not even close to being done with the exponential gains in AI. Cartoon Dynamo is a fascinating prediction made a hundred years ago, and it is interesting to see how far we have come.
🎉 Conclusion
In conclusion, AI is not slowing down anytime soon, and we can expect to see significant progress in the future. Data quality, inference time compute, multimodal progress, and prompt optimization are the four reasons why AI is not slowing down. As we enter 2024, we can expect to see a photorealistic text video output that could fool most humans. The possibilities are endless, and we are only scratching the surface of what AI can do.
🔍 Highlights
- Data quality is crucial in AI, and it will change everything.
- Letting models think for longer is crucial for reasoning, and it can be combined with multimodality.
- The Mamba architecture is causing shock waves in AI circles, and it has been generating a lot of buzz.
- In 2024, we can expect a photorealistic text video output that could fool most humans.
âť“ FAQ
Q: What is Mamba architecture?
A: Mamba architecture is a new architecture that has been generating a lot of buzz in AI circles. It is built with an awareness of the kind of GPUs it's going to run on.
Q: What is inference time compute?
A: Inference time compute is the ability of the model to decide how much compute to allocate to certain problems.
Q: What is prompt optimization?
A: Prompt optimization is the ability of language models to optimize their own prompts.
Q: What can we expect in 2024?
A: In 2024, we can expect a photorealistic text video output that could fool most humans.
