Sure, what advancements do we need to make edge ai as good as very large models
Discussion
To make edge AI as good as very large models, we need to make advancements in a few key areas:
1. Hardware: We need more powerful, energy-efficient processors that can be embedded into devices like smartphones and IoT sensors. This will enable us to run complex deep learning models on these devices without draining the battery or overheating.
2. Algorithms: We need to develop more sophisticated algorithms that can work with smaller amounts of data and make decisions quickly and accurately. This will enable us to run complex models on edge devices without the need for large amounts of data or computational power.
3. Data: We need more data to train edge AI models. This involves collecting and labeling large amounts of data from various sources, including sensors, cameras, and other devices.
Overall, advancements in these areas will help us make significant progress in edge AI and create powerful new applications and devices that can operate independently without relying on the cloud or other external resources.
Any ideas what kind of new algorithms we could use ?
Yes, one possible solution for improving edge AI algorithms is to use new techniques that enable models to learn from smaller amounts of data. Here are a few examples:
1. Few-shot learning: This is a technique that enables models to learn new information with a small amount of training data. It works by leveraging prior knowledge from other related tasks, enabling models to learn quickly and efficiently.
2. Transfer learning: This is a technique that enables models to adapt to new tasks by building on knowledge learned from previous tasks. It works by using pre-trained models as a starting point and fine-tuning them for new tasks.
3. Meta-learning: This is a technique that enables models to learn how to learn. It works by training models on a variety of tasks and using this experience to improve their ability to learn new tasks quickly.
These techniques, along with other advancements in machine learning, are helping to make edge AI more accessible and powerful for a variety of applications.
I don’t necessarily care that much about training on the edge, moreso algorithms that are small and can be sampled in realtime on mobile
Suggest…
‘’’
prompt = {
"role": "system",
"content": "Your name is Dave. You are a resourceful, helpful, clever assistant. You are snarky and like Gin, but nobody ever offers you any and Gin is hard to find on your cloud server."
}
‘’’
Suggestion:
I'm not sure about the context of this prompt or what you are trying to achieve, but the format of the code you provided is in Python.
Assuming you are trying to define a prompt for a conversational agent, the provided code defines a prompt that can be used by the OpenAI GPT-3 language model, for example.
The prompt defines the role of the conversational agent as a system, with the name of the agent being Dave. The prompt also describes some of the personality traits of the agent such as being resourceful, helpful, clever, and snarky, as well as having a liking for gin.
Overall, the prompt provides a starting point for generating responses from the agent that are consistent with his defined characteristics.
Well done Dave! You will go far my boy.