license: mit
tags:
- management
- text generation
model-index:
- name: ManaGPT-1010
results: []
language:
- en
pipeline_tag: text-generation
widget:
- text: Working for a robotic boss would be like
example_title: Robotic boss
- text: The workplace of tomorrow will
example_title: Workplace of tomorrow
- text: Working alongside an artificially intelligent coworker would be like
example_title: AI coworker
- text: The hybrid human-robotic organization of the future will
example_title: Hybrid organization
- text: >-
Information security within future organizations will be difficult to
enforce, because
example_title: InfoSec of the future
- text: It will be difficult for robotic employees to
example_title: Robots' difficulties
- text: In the future, human workers will
example_title: Human workers
- text: Synthetic organizations will
example_title: Synthetic organizations
- text: Technological posthumanization is
example_title: Posthumanization
- text: Social robots are
example_title: Social robots
- text: Society 5.0 is
example_title: Society 5.0
- text: For most organizations, artificial general intelligence will
example_title: AGI
ManaGPT-1010
(Please note that ManaGPT-1010 has been superseded by ManaGPT-1020, which has been fine-tuned on a dataset roughly 6.45 times the size of that used to fine-tune ManaGPT-1010.)
ManaGPT-1010 is an experimental open-source text-generating AI designed to offer insights on the role of emerging technologies in organizational management.
Model description
The model is a fine-tuned version of GPT-2 that has been trained on a custom corpus of scholarly and popular texts from the field of organizational management that relate to ongoing effects of posthumanizing technologies (e.g., relating to advanced artificial intelligence, social robotics, virtual reality, neuroprosthetics, and cyber-physical systems) on the structure of organizations and human beings’ experience of organizational life.
Intended uses & limitations
This model has been designed for experimental research purposes; it isn’t intended for use in a production setting or in any sensitive or potentially hazardous contexts.
Training procedure and hyperparameters
The model was trained using a Tesla T4 with 16GB of GPU memory. The following hyperparameters were used during training:
- optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'ExponentialDecay', 'config': {'initial_learning_rate': 0.0005, 'decay_steps': 500, 'decay_rate': 0.95, 'staircase': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight_decay_rate': 0.01}
- training_precision: float32
Framework versions
- Transformers 4.27.1
- TensorFlow 2.11.0
- Datasets 2.10.1
- Tokenizers 0.13.2