The Metaverse is a virtual world that is constantly expanding and evolving.
As more and more people enter the Metaverse, the need for realistic artificial intelligence (AI) that can interact with users becomes increasingly important.
But training realistic machine learning models to interact in a simulated world inhabited by human and non-human players (AI or simple bots) is complex: the action space can be huge, as well as the variety of possible situations.
Unity ML Agents
To tackle this problem, we need the ability to create a world simulation close to the Metaverse. Also, we need to develop agents that can interact with the simulated world to gather data to train our models.
This is where Unity ML agents intervene.
It is a toolkit for developing intelligent agents who can learn to solve tasks independently.
It is designed to be used with the Unity game engine to create agents that can learn to navigate 3D environments, play games, and more.
The toolkit includes several features that make it easy to develop and train agents, including:
- A flexible architecture that allows for a variety of learning algorithms to be used
- A set of sample environments that can be used to train and test agents - A set of tools for visualizing and debugging agent behavior
- A Python API that allows for easy integration with popular machine learning libraries
Typical workflow: create a world simulation in Unity ML agents:
- Implement an instance of the Agent class
- Get the agent sensor inputs (image, sound, ...)
- Feed the inputs into your model (PyTorch, Tensorflow, ...)
- Get the next action to take as input from your model,
- Execute the action in the game and gather the new world state
- Based on the new state determines if the agent performed well or not
- Restart from 2 until the model is good enough
A perfect use case for High-Performance Computing
Unity's ML agents offer a powerful solution for creating realistic AI that can inhabit the Metaverse.
However, it usually takes a lot of time to train agents. It's not a task for your laptop or a simple virtual machine in the cloud.
HPC clusters are the way to go. They offer the computational power and flexibility needed to train ML agents to behave realistically in a reasonable amount of time.
Some of the benefits of training ML agents on HPC clusters include:
Increased realism
Advanced AI requires complex models with many parameters. As a result, they take a lot of memory and computational power to train. Thanks to the high computational capabilities of HPC clusters, we can train advanced AI models to create much more realistic and believable AI.
The increased computational power of HPC clusters allows for more detailed and realistic simulations. This results in AI that can better interact with users in the Metaverse.
Increased flexibility
HPC clusters offer the flexibility needed to train ML agents to behave realistically.
The ability to run multiple simulations simultaneously on an HPC cluster allows for a greater variety of training scenarios.
This flexibility results in AI that can better adapt to the ever-changing Metaverse.
Increased scalability
The scalability of HPC clusters makes them ideal for training ML agents. Furthermore, adding more computational resources as needed ensures that training can keep pace with the increasing demand for AI in the Metaverse.
Reduced costs
Training ML agents on HPC clusters can save money in the long run.
The cost of training on an HPC cluster is much lower than the cost of training on a traditional supercomputer. This cost reduction can help make AI more affordable for businesses and individuals. Furthermore, by training ML agents on high-performance computing clusters, we can create realistic artificial intelligence that can inhabit the ever-expanding Metaverse.
DeepSquare and Unity ML agents: the perfect match
"Training artificial intelligence is an energy intensive process . New estimates suggest that the carbon footprint of training a single AI is as much as 284 tonnes of carbon dioxide equivalent – five times the lifetime emissions of an average car [...] To measure the environmental impact of this approach, the researchers trained four different AIs – Transformer, ELMo, BERT, and GPT-2 – for one day each, and sampled the energy consumption throughout." (source)
It depends on the specific machine learning algorithm being used, as well as the size and complexity of the data set. Generally speaking, however, training a machine learning algorithm can be pretty energy-intensive. This is because many machine learning algorithms require many iterations to converge on a solution. But each iteration can require a significant amount of computational resources, which can require a lot of energy.
DeepSquare is a decentralized sustainable cloud ecosystem that enables organizations to keep the ownership of their data while benefiting from a scalable, optimized cloud computing infrastructure. It is powered by renewable energy, blockchain technology, and artificial intelligence. DeepSquare is designed to be highly energy-efficient and to minimize the environmental impact of cloud computing.
Using the DeepSquare HPC resources, you can run the most demanding workload while significantly reducing your carbon footprint: indeed, DeepSquare is 100% powered by renewable energy. Moreover, it uses the heat produced by the cluster to share it with habitations and power industries in the cluster surroundings.
Train your Unity ML agents in DeeSquare clusters to build the future sustainably: an immersive Metaverse powered by realistic AI trained without harming the environment.
Sources
Unity ML Agents: https://unity.com/products/machine-learning-agents DeepSquare: https://deepsquare.io/
Artificial intelligence energy consumption: https://www.newscientist.com/article/2205779-creating-an-ai-can-be-five-times-worse-for-the-planet-than-a-car/
Facebook is trying to advance AI to 'human levels' in order to build its metaverse: https://www.businessinsider.com/facebook-metaverse-challenges-include-ai-development-to-human-level-2022-2?r=US&IR=T