In the past, AI applications were built on single models. These models were trained on a specific dataset and were designed to perform a specific task. However, as AI has become more sophisticated, the limitations of single models have become apparent.
Compound AI systems are a new approach to AI that addresses the limitations of single models. Compound AI systems are made up of multiple models that work together to achieve a common goal. Each model in a compound AI system is specialized in a particular task, and the models communicate with each other to share information and make decisions. This approach has been discovered at Berkeley Artificial Intelligence Research (BAIR) is a leading research lab at the University of California, Berkeley. They recently wrote a paper with Databricks founder Matei Zaharia and others called “The Shift from Models to Compound AI Systems.” (https://bair.berkeley.edu/blog/2024/02/18/compound-ai-systems/)
There are several reasons why developers are using compound AI systems. First, compound AI systems can be more accurate and reliable than single models. This is because each model in a compound AI system is focused on a specific task, and the models can share information with each other to improve their accuracy. Second, compound AI systems can be more adaptable than single models. This is because the models in a compound AI system can be updated or replaced as needed, which allows the system to adapt to new data and new tasks.
However, there are also some challenges associated with using compound AI systems. First, compound AI systems can be more complex to design and develop than single models. This is because the models in a compound AI system need to be able to communicate with each other and work together effectively. Second, compound AI systems can be more expensive to train and deploy than single models. This is because the models in a compound AI system need to be trained on a larger amount of data, and the system may require more computing power.
Despite these challenges, compound AI systems are the future of AI. Compound AI systems have the potential to revolutionize the way we interact with the world around us. They can be used to create more accurate and reliable AI applications, and they can be used to develop new and innovative applications that were not possible with single models.
What is a Compound AI System?
A Compound AI system is a system that tackles AI tasks using multiple interacting components, including multiple calls to models, retrievers, or external tools. It's like a team of experts working together to solve a problem, each with their own specialty and area of expertise.
Why is a Compound AI System Better?
There are several reasons why compound AI systems are better than single models:
They can tackle tasks that are easier to improve via system design: By breaking down a complex task into smaller, more manageable subtasks, compound AI systems can be more easily improved by making changes to the system design rather than having to retrain the entire model.
They can incorporate timely data and improve control and trust: Compound AI systems can be designed to incorporate new data and information as it becomes available, which can improve their accuracy and reliability. They can also be designed to provide more control and transparency into how they make decisions, which can help to build trust in their outputs.
They can vary performance goals: Different parts of a compound AI system can be optimized for different performance goals, such as accuracy, speed, or interpretability. This allows the system to be tailored to the specific needs of the task at hand.
Real-time examples of compound AI systems:
AlphaCode 2: This system uses LLMs (large language models) to generate up to 1 million possible solutions for a coding task and then filters down the set. This is a great example of how compound AI systems can be used to tackle complex tasks that are difficult for single models to handle.
AlphaGeometry: This system combines an LLM with a traditional symbolic solver to tackle olympiad problems. This shows how compound AI systems can leverage the strengths of different approaches to achieve better results.
Medprompt: This system uses an LLM to answer medical questions by searching for similar examples to construct a few-shot prompt, adding model-generated chain-of-thought for each example, and generating and judging up to 11 solutions. This demonstrates how compound AI systems can be used to incorporate expert knowledge and reasoning into AI systems.
Self driving cars: Self-driving cars integrate multiple AI technologies and sensors to navigate and operate in the real world. This complexity requires the coordination of many components, including computer vision, predictive modelling and path finding.
Developing Compound AI systems can be complex, but it holds immense potential for creating powerful and adaptable AI solutions.
Remember, developing Compound AI systems requires expertise in various areas like AI models, machine learning, software engineering, and system design. Consider seeking help from experts or leveraging existing development frameworks and tools to streamline the process.
The big question is how to make all these elements work together.
Planning your group implies looking over numerous players (computer based intelligence models and apparatuses). You likewise settle on your play style (framework plan). You should consider how to get the best out of your group. Do you really want a sharpshooter who can extend your reach (like upgrading recovery quality)? Or on the other hand a strong focus to check your plays are compelling (further developing man-made intelligence yield)?
In enhancement, there's actually no need to focus on having headliners. It's tied in with ensuring they play well together. This could mean changing your plays to suit the qualities of your group. It's a piece like tuning your man-made intelligence parts to work in a state of harmony. Regardless of whether a few pieces of the framework, similar to specific game procedures, can't be changed mid-play.
Running this elite player group additionally brings functional difficulties. It's more perplexing than instructing a normal group. You need to oversee stars. You screen their exhibition and ensure the playbook is followed. You likewise watch out for group elements. This requires progressed systems. You need to monitor everything on the court. You should guarantee the information (or player details) is exact and cutting-edge. You should likewise safeguard against surprising plays (getting your computer based intelligence framework).
For business executives, it is significant to figure out these viewpoints. Building a top pick NBA group requires more than gathering the best players. Making successful compound computer based intelligence frameworks requests smart incorporation of parts. It requires key asset allotment, inventive advancement strategies, and refined functional administration. The objective is to guarantee your group — your simulated intelligence framework — performs at its ideal, adjusting to difficulties and conveying wins for your business.
I hope this helps! Feel free to ask if you have any specific questions about developing Compound AI systems for your particular needs.
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