“Artificial Intelligence Weekly Round-up: April 15, 2024”

The world of artificial intelligence is ever-evolving, and every week brings new research, news, resources, and perspectives. In this article, we will dive into the most recent developments and provide Python coding examples using OpenAI’s API.

One of the big news this week is Google’s expansion of the Gemma family with models tailored for developers and researchers. The new addition, CodeGemma, is designed for code completion and generation tasks, while RecurrentGemma provides an efficiency-optimized architecture for research experimentation.

How can we utilize this in Python? Let’s look at an example using OpenAI’s API with the "gpt-3.5-turbo" model. Users can adjust the model to "gpt-4" or "gpt-4-32k" as per their requirement.

import openai

openai.api_key = 'your-api-key'

response = openai.ChatCompletion.create(
  model="gpt-3.5-turbo",
  messages=[
        {"role": "system", "content": "You are a helpful assistant."},
        {"role": "user", "content": "Translate the following English text to French: 'Hello, how are you?'"}
    ]
)

print(response['choices'][0]['message']['content'])

This example translates English text to French. The model parameter specifies the model to use. The messages parameter is a list of message objects. Each object has a role that can be ‘system’, ‘user’, or ‘assistant’, and content which is the text of the message from that role. The system role sets the behavior of the assistant, the user role provides instructions to the assistant, and the assistant role stores prior responses from the assistant.

Another exciting development is the release of the Qwen1.5–32B and Qwen1.5–32B-Chat models as part of the Qwen1.5 language model series. These models are considered the "sweet spot" for achieving both strong performance and manageable resource requirements.

Exploring these developments through Python can help users grasp their potential applications and functionality. For instance, the following Python code can be used to generate a message using the Qwen1.5–32B model:

response = openai.ChatCompletion.create(
  model="gpt-3.5-turbo",  # Change this to "Qwen1.5–32B" or "Qwen1.5–32B-Chat" when available
  messages=[
        {"role": "system", "content": "You are a creative writer."},
        {"role": "user", "content": "Write a short story about a brave knight."}
    ]
)

print(response['choices'][0]['message']['content'])

This code generates a short story about a brave knight. The system message sets the behavior of the assistant as a creative writer, and the user message instructs the assistant to write a story.

These Python code examples provide a glimpse into the vast potential of AI models. As the field continues to develop, it is essential for users to stay updated with the latest models and functionalities. The gpt-3.5-turbo model serves as a starting point, and users can change it to use different models like "gpt-4" or "gpt-4-32k" as they become available.

Remember, the future of AI is not just about the technology, but also about how we use it. As such, learning to code with OpenAI’s API provides a valuable skill set for navigating this future.

Stay tuned for more weekly AI news, research, resources, and perspectives.