PaLM 2

Type: model

Publisher: Google Released: 2023-05 v.1.0.0

Model details

Blackbox External Model Access	✓
Capabilities demonstration	✓
Capabilities description	✓
Centralized model documentation	✓
Evaluation of capabilities	✓
External model access protocol	-
External reproducibility of capabilities evaluation	✓
External reproducibility of intentional harm evaluation	-
External reproducibility of mitigations evaluation	-
External reproducibility of trustworthiness evaluation	-
External reproducibility of unintentional harm evaluation	✓
Full external model access	-
Inference compute evaluation	-
Inference duration evaluation	-
Input modality	✓
Intentional harm evaluation	-

Limitations demonstration	-
Limitations description	✓
Mitigations demonstration	-
Mitigations description	✓
Mitigations evaluation	✓
Model architecture	✓
Asset license	✓
Model components	-
Model size	-
Output modality	✓
Risks demonstration	-
Risks description	-
Third party capabilities evaluation	-
Third party evaluation of limitations	✓
Third party mitigations evaluation	-
Third party risks evaluation	-
Trustworthiness evaluation	-
Unintentional harm evaluation	✓

Enhance natural language understanding and generation tasks across various industries including healthcare, finance, and customer service.
Support research and development in machine learning and artificial intelligence.
Provide a multilingual model capable of understanding, translating, and generating content in over 100 languages.
Enable coding assistance across more than 20 programming languages.
Facilitate the development of AI-powered applications and services by allowing fine-tuning to specific domains.

No dependencies specified.

No metrics specified.

No environmental refs specified.

Carbon emitted (tCO2eq): 0

Energy usage: 0 w

Compute usage: 0

Ensure transparency in AI applications developed using PaLM 2 to build trust with end users.
Address potential biases in model output, especially in multilingual and multicultural applications.
Adopt privacy-preserving measures when deploying PaLM 2 in applications handling sensitive personal data.
Engage in continuous monitoring and updating of AI models to address ethical concerns and maintain integrity.
Encourage an inclusive approach in AI development and applications, striving to minimize digital divide and ensure broad accessibility.

Developers are encouraged to use the PaLM API for integrating advanced AI capabilities into their applications.
Researchers should explore fine-tuning PaLM 2 for domain-specific tasks to leverage its adaptable nature.
Consider ethical implications and strive for responsible use, particularly in sensitive applications like healthcare.
Stay informed about the release of additional tools and plugins that may enhance PaLM 2's functionality.
Continuously monitor and evaluate AI performance to ensure fairness, accuracy, and minimal bias in applications.