Evolving with AI: Real-time AIs Require Real-time Applications
Tags: ai
Published: 2023-07-05 14:48:47

**TL;DR:** Large language models like GPT-4, Bard, and other models are evolving at an unprecedented rate in the fast advancing field of artificial intelligence (AI). However, in order to take full advantage of their potential, we must adopt real-time applications that match their real-time processing capabilities. REST APIs, with their inherent delays, are becoming less effective, especially in light of many SaaS' API response limits. Instead, technologies such as WebSockets provide unique ways for rapidly delivering AI results to user interfaces, transforming the user experience. As we usher in the AI revolution, we must build a healthy ecosystem in which advanced AI models and real-time applications can live and thrive.

## Adopting new Solutions for Better Interactivity

In the dynamic and rapidly evolving field of artificial intelligence (AI), we are on the verge of a transformative epoch. A period marked by the rise of large language models (LLMs) like as OpenAI's ChatGPT/GPT-4, Google's Bard, and an array of models such as LLama hosted on Hugging Face.

These models are fascinating not only for their ability to read and write human-like writing or solve hard problems, but also for the astounding rate at which they evolve. Newer, more powerful models are being produced and trained at an incredible rate, with each iteration pushing the boundaries of what AI is capable of. It's a never-ending cycle of development, with today's cutting-edge AI potentially being superseded by a more advanced model in the blink of an eye.

However, the rapid growth of AI models presents a challenge. The challenge is to ensure that our ways of providing AI-generated insights to user interfaces (UIs) keep up with these AI models' real-time capabilities. The days of REST APIs, which can lead to a response delay of up to 30 seconds, appear to be over in the face of modern AI models' real-time processing prowess.

Moreover, many Software as a Service (SaaS) platforms have a time limit for API responses, which is frequently set at 30 seconds. This can be an issue when immediate interaction with AI systems is required for an optimal user experience.
This is where moving to real-time apps driven by WebSockets can make a significant difference. WebSockets, which enable two-way interactive communication between a user's browser and a server, can offer AI responses in real time, significantly improving user experience. 

Now, If you're worried about the complexity involved in developing such real-time apps, serverless computing is here to help. Platforms like Amazon AWS API Gateway, with their inherent support for sockets and auto-scaling capabilities, make this work surprisingly simple.

Here's a Python example showing the simple process of sending a message via WebSocket using Amazon AWS API Gateway:

```
import boto
import json

client = boto3.client('apigatewaymanagementapi', 
                      endpoint_url="wss://{your-app-id}.execute-api.{your-region}.amazonaws.com/")

data = {
    'message': 'Hello'
}

client.post_to_connection(
    ConnectionId=connectionId,
    Data=json.dumps(data).encode('utf-8')
)
```

Also, let me show you an example using typescript

```
import 
  ApiGatewayManagementApiClient,
  PostToConnectionCommand,
} from '@aws-sdk/client-apigatewaymanagementapi';

const apiGatewayManagementApi = new ApiGatewayManagementApiClient({
      apiVersion: '2018-11-29',
      endpoint: "wss://{your-app-id}.execute-api.{your-region}.amazonaws.com/",
});

const data = {
     'message': 'Hello'
}

const msg = new PostToConnectionCommand({
      ConnectionId: connectionId,
      Data: Buffer.from(JSON.stringify(data)),
 });

await this.apiGatewayManagementApi.send(msg);
```

These code snippets demonstrates how WebSocket communication can work, ensuring real-time, seamless user interaction with AI processes.

**GraphQL Pub/Sub** is another possible option for developing real-time applications. Its event-driven architecture enables data to be delivered from the server to the client in real time, offering an important boost to responsive application development.

To conclude, we must do more than only design and leverage the power of fast evolving AI models like GPT-4 and others. We also need to focus on developing real-time applications that can effectively interact with these AI models.

It is not enough to create advanced AI models in the future of AI; it is also essential to create a balanced ecosystem in which AI models and real-time applications may coexist and thrive. We can lead the charge towards this exciting future by leveraging technologies like WebSockets and making the most of the AI revolution that is now underway.