Exploring Real-Time AI: Shaping the Future of Conversational Interfaces, World Generation, and Robotics

Join us as we explore the exciting intersection of real-time intelligence, conversational interfaces, and multimodal AI, where we're building models that can compress information to power new experiences and transform industries.

1. The last four to five years of AI have focused on batch intelligence, which involves building AI systems that can reason for long periods of time to solve problems.
2. However, there is also a need for real-time or streaming applications, such as generating video or audio, or understanding sensor streams in real-time.
3. Real-time applications require low latency and the ability to constantly query and return responses quickly.
4. Examples of real-time intelligence applications include conversational interfaces, such as voice assistants that can understand and respond to queries in real-time, and video game graphics that are
5. Real-time intelligence is important for making AI more accessible and useful in a variety of applications, such as customer support and robotics.
6. Caria is building foundational models for real-time deep learning, using technology developed over the past four or five years.
7. This technology includes the ability to compress information as it comes into the model, which is important for streaming applications.
8. The technology was developed during a PhD program and has evolved from early iterations that are no longer in use to more modern versions.
9. The goal of Caria's work is to improve and push the boundaries of this technology, rather than settling for a single way of doing things.
10. Efficiently modeling long context is a key challenge in AI, as much of practical data is sequence data.
11. Compression is fundamental to intelligence, as it allows us to process and understand large amounts of information.
12. Current AI systems are built on the principle of retrieval rather than compression, which can be limiting.
13. Multimodal AI, such as that used in customer support or robotics, will remain challenging as long as it is based on the retrieval paradigm.
14. Humans are an "extremely amazing machine" that process and understand information in a compressed way that current AI models cannot replicate.
15. The best AI models today can handle contexts in the 10 million to 100 million token range, but there is still a long way to go before they can truly understand information over long periods of tim
16. Current AI models are built for data centers and may not be efficient or practical for real-world applications that require low power and high efficiency.
17. Quadratic scaling in context length can make it difficult to process large amounts of context efficiently, as the amount of computation required increases with the length of the context.
18. The predominant approach to addressing this issue is to throw more compute at the problem, but this may not be practical or sustainable for real-world applications.
19. Caria's hypothesis is that new architectures are needed to address the challenge of long context in AI models.
20. Caria is working on fundamentally efficient architectures with compression at their core, which can scale more linearly with context length and be implemented at low power.
21. Streaming Transformers (ssms) are an example of this technology, and they have the potential to be more efficient than traditional Transformer models for certain applications.
22. ssms use a streaming system to update an internal memory as tokens come into the system, rather than keeping all past tokens around, which simplifies the system and makes it more efficient.
23. ssms are gaining popularity as an alternative way of doing things that is more oriented around recurrence, rather than retrieval.
24. Caria is working on scaling up these models and making them more interesting for a variety of applications, particularly those involving multimodal data.

Source: AI Engineer via YouTube

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