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#DataGemma is an experimental set of #open#models designed to ground responses in #realworld#statistical#data from numerous #public#sources ranging from census and health bureaus to the #UN, resulting in more factual and trustworthy AI.
By integrating with Google’s #Data Commons, DataGemma’s early research advancements attempt to address the issue of #hallucination—a key challenge faced by language models #llm.
What is the Data Commons?
Google Data Commons: A Knowledge Graph for Public Data
Google Data Commons is a public knowledge graph that integrates and harmonizes data from various sources, making it easier to explore and analyze. It's designed to provide a unified view of the world's information, enabling users to discover insights and trends across different domains.
Key Features and Benefits:
Unified Dataset: Data Commons combines data from over 200 sources, including government statistics, academic research, and private sector data. This creates a comprehensive and interconnected dataset.
Knowledge Graph: The data is organized as a knowledge graph, where entities (e.g., countries, cities, people) are connected by relationships (e.g., location, affiliation). This structure makes it easier to explore data and discover connections.
Natural Language Queries: Users can query the data using natural language, making it accessible to a wider audience, even those without technical expertise.
Visualization Tools: Data Commons provides tools for visualizing data, such as charts and maps, making it easier to understand complex information.
API Access: Developers can access the data through an API, allowing them to integrate it into their applications and workflows.
Use Cases:
Research: Researchers can use Data Commons to explore trends, identify patterns, and test hypotheses.
Policy Making: Governments and policymakers can use the data to inform decisions and develop effective policies.
Journalism: Journalists can use Data Commons to investigate stories and uncover hidden trends.
Business: Businesses can use the data to understand their customers, identify market opportunities, and optimize their operations.
In essence, Google Data Commons is a valuable resource for anyone looking to explore and analyze public data. By providing a unified and accessible platform, it empowers users to discover insights and make informed decisions.
Generative AI models, such as generative adversarial networks (GANs) or autoregressive models, learn from large datasets and use the learned patterns to generate new and realistic content. These models have the ability to generate text, images, or other forms of data that possess similar characteristics to the training data. Generative AI has found applications in various fields, including creative arts, content generation, virtual reality, data augmentation, and more.
Few examples of how generative AI is applied in different domains:
1. Text Generation: Generative AI models can be used to generate creative and coherent text, such as writing stories, poems, or articles. They can also assist in chatbot development, where they generate responses based on user inputs.
2. Image Synthesis: Generative AI models like GANs can create realistic images from scratch or transform existing images into new forms, such as generating photorealistic faces or creating artistic interpretations.
3. Music Composition: Generative AI can compose original music pieces based on learned patterns and styles from a large music dataset. These models can generate melodies, harmonies, and even entire compositions in various genres.
4. Video Generation: Generative AI techniques can synthesize new videos by extending or modifying existing video content. They can create deepfakes, where faces are replaced or manipulated, or generate entirely new video sequences.
5. Virtual Reality and Gaming: Generative AI can enhance virtual reality experiences and game development by creating realistic environments, characters, and interactive elements.
6. Data Augmentation: Generative AI can generate synthetic data samples to augment existing datasets, helping to improve the performance and generalization of machine learning models.
These are just a few examples, and the applications of generative AI continue to expand as researchers and developers explore new possibilities in content generation and creativity.
Today I will show you how to use Google Cloud Vertex AI to know how to use it for different types of prompts and configurations.
Vertex AI Studio is a Google Cloud console tool for rapidly prototyping and testing generative AI models. You can test sample prompts, design your own prompts, and customize foundation models to handle tasks that meet your application’s needs.
Here are some of the features of Vertex AI Studio:
Chat interface: You can interact with Vertex AI Studio using a chat interface. This makes it easy to experiment with different prompts and see how they affect the output of the model.
Prompt design: You can design your own prompts to control the output of the model. This allows you to create specific outputs, such as poems, code, scripts, musical pieces, email, letters, etc.
Prompt tuning: You can tune the prompts that you design to improve the output of the model. This allows you to get the most out of the model and create the outputs that you want.
Foundation models: Vertex AI Studio provides a variety of foundation models that you can use to get started with generative AI. These models are pre-trained on large datasets, so you don’t need to train your own model from scratch.
Deployment: Once you are satisfied with the output of your model, you can deploy it to production. This allows you to use the model in your applications and make it available to other users.
Vertex AI Studio is a powerful tool for rapidly prototyping and testing generative AI models. It is easy to use and provides a variety of features that allow you to create the outputs that you want.
Here are some of the benefits of using Vertex AI Studio:
Quickly prototype and test generative AI models: Vertex AI Studio makes it easy to quickly prototype and test generative AI models. You can use the chat interface to experiment with different prompts and see how they affect the output of the model. You can also design your own prompts and tune them to improve the output of the model.
Deploy models to production: Once you are satisfied with the output of your model, you can deploy it to production. This allows you to use the model in your applications and make it available to other users.
Cost-effective: Vertex AI Studio is a cost-effective way to develop and deploy generative AI models. You only pay for the resources that you use, so you can save money by only running the model when you need it.
If you are looking for a way to quickly prototype and test generative AI models, or if you want to deploy a generative AI model to production, then Vertex AI Studio is a good option.
Lets start. Open your google cloud console. If you dont have account you can open a FREE account by clicking above.
Enable the Vertex AI API by
Create prompt by clicking on Generative AI Studio. Then select Promt from right pen and then select Language from the left pen.
Prompt design. There are three different types of prompt can be design
Zero-shot prompting
One-shot prompting
Few-shot prompting
You may also notice the FREE-FORM and STRUCTURED tabs.
Click on Text Prompt
You can see the right side several different parameters can be tuned. Let get know about them below.
Temperature is a hyperparameter that controls the randomness of the sampling process. A higher temperature will result in more random samples, while a lower temperature will result in more deterministic samples.
Token limit is the maximum number of tokens that can be generated at a time. This is useful for preventing the model from generating too much text, which can be helpful for preventing the model from going off topic.
Top k sampling is a sampling method that only considers the top k most likely tokens for each step in the generation process. This can help to improve the quality of the generated text by preventing the model from generating low-quality tokens.
Top p sampling is a sampling method that only considers tokens whose cumulative probability is greater than or equal to p. This can help to improve the diversity of the generated text by preventing the model from generating only the most likely tokens.
These hyperparameters can be used together to control the generation process and produce text that is both high-quality and diverse.
Here are some additional details about each hyperparameter:
Temperature
The temperature hyperparameter can be thought of as a measure of how “confident” the model is in its predictions. A higher temperature will result in more random samples, while a lower temperature will result in more deterministic samples.
For example, if the temperature is 1.0, the model will always choose the token with the highest probability. However, if the temperature is 0.5, the model will be more likely to choose tokens with lower probabilities.
Token limit
The token limit hyperparameter can be thought of as a measure of how long the generated text should be. A higher token limit will result in longer generated text, while a lower token limit will result in shorter generated text.
For example, if the token limit is 10, the model will generate a maximum of 10 tokens. However, if the token limit is 20, the model will generate a maximum of 20 tokens.
Top k sampling
Top k sampling is a sampling method that only considers the top k most likely tokens for each step in the generation process. This can help to improve the quality of the generated text by preventing the model from generating low-quality tokens.
For example, if k is 10, the model will only consider the 10 most likely tokens for each step in the generation process. This can help to prevent the model from generating tokens that are not relevant to the topic at hand.
Top p sampling
Top p sampling is a sampling method that only considers tokens whose cumulative probability is greater than or equal to p. This can help to improve the diversity of the generated text by preventing the model from generating only the most likely tokens.
For example, if p is 0.75, the model will only consider tokens whose cumulative probability is greater than or equal to 0.75. This can help to prevent the model from generating only the most common tokens.
I hope this helps!
Above created one prompt and when submit got the response. Now let try Structure Text prompting.
So you can provide some example of input and output to make the response much better.
If you want to get the python code by SDK then click on view code.
Now if you want to clean all by disabling the Vertext AI API then click on the Manage.
