Could 3D objects provide scalability in training AI?
*All images in article are created/owned by Nyla Worker
The use of synthetic programming datasets using 3D objects is a topic that is gaining increasing attention in the world of AI and machine learning. Recently, Nyla Worker, where she delivered a (deservedly) praised technical talk at Scale.AI about the potential of using synthetic data in the form of 3D graphic design assets to train AI models at scale and improve their performance.
On November 17, Nyla Worker delivered a highly-praised technical presentation at Scale.AI, where she illuminated the possibilities of using synthetic data in the form of 3D graphic design assets to train AI models at scale, ultimately enhancing their performance. Nyla, a talented product manager at NVIDIA, is helping lead the charge on synthetic data as part of the Omniverse Replicator team, a framework for generating synthetic data at NVIDIA.
Synthetic data, as the name suggests, is data that is artificially created and not derived from real-world sources. It is often used in machine learning and AI to train models in a controlled environment, allowing for more efficient and effective training. However, synthetic data has its limitations, as it can often be unrealistic and not reflective of the diversity and complexity of real-world data.
Nyla proposes a solution to this problem: the use of 3D graphic design assets to create more realistic and diverse synthetic data sets. By using 3D objects, AI models can be trained on data that is more representative of the real world, leading to improved performance and increased coverage.
Of course, using 3D graphic design assets to generate synthetic data comes with its own challenges. For example, creating a large and diverse enough dataset can be time-consuming and costly. Additionally, the quality and realism of the 3D objects used can vary, leading to potential inconsistencies in the synthetic data.
Despite these challenges, the use of 3D objects to generate synthetic data is a promising avenue for improving the performance of AI models. As the technology continues to advance and the challenges are overcome, we can expect to see even more impressive results in the future.
Elaborating further:
The current state of synthetic data and its limitations: Synthetic data is currently used to train AI models when real-world data is not available or is difficult to obtain. However, synthetic data is often limited in its realism and diversity, which can make it less effective for training high-quality AI models.
Using 3D graphic design assets to create more realistic and diverse synthetic data sets: By using 3D graphic design assets, it is possible to create synthetic data sets that are more realistic and diverse, which could improve the performance of AI models. For example, 3D models of different objects and environments could be used to create synthetic data sets that accurately reflect the real world.
Challenges and potential pitfalls of using 3D graphic design assets to generate synthetic data: One potential challenge of using 3D graphic design assets to generate synthetic data is the high cost and complexity of creating and maintaining these assets. Additionally, there may be issues with ensuring the quality and consistency of the synthetic data sets generated from these assets.
Overcoming challenges in the future: In the future, advancements in AI and computer graphics technology could make it easier and more cost-effective to create and maintain 3D graphic design assets for generating synthetic data. Additionally, new techniques for evaluating and improving the quality and diversity of synthetic data sets could help to overcome some of the current limitations of using these assets.
Current industry challenges around training reliable models are as follows:
Training a forklift AI to perform mechanical operations is like teaching a student to perform a complex task. Just as a student needs to learn the necessary skills and knowledge through instruction and practice, the forklift AI needs to be trained on a dataset that reflects how humans perform the task. This dataset acts as a sort of "lesson plan" for the AI, providing it with examples of how the task should be done. As the AI processes this dataset, it gradually learns to recognize patterns and make decisions, just like a student would. Over time, with enough training and practice, the AI becomes adept at performing the mechanical operations, just as a student becomes proficient in the task they have been taught.
However, this comes with a bottleneck: lots of great data.
WHAT IS SYNTHETIC PROGRAMMING?
Synthetic data refers to data that is artificially generated, typically with the aim of simulating real-world data in order to test or train machine learning models. Synthetic intelligence, on the other hand, refers to the use of artificial intelligence algorithms to generate data or to perform tasks that would be difficult or impossible for humans to do.
Synthetic data is information that is generated artificially, based on real data from existing inputs. It can be structured or unstructured, but the goal is to produce data that resembles the original information without using it directly. This data can be produced directly from real data, or it can be indirectly produced from a model that does not have any direct links to identifiable data sets.
Synthetic data is used in a variety of settings, such as simulations and visualizations, to test and train artificial intelligence and business analytics models. It can help identify technical problems in fields such as engineering, finance, and healthcare. Synthetic data has potential applications in many industries, beyond the aforementioned few.
How to generate synthetic data
To build synthetic data, organizations often use powerful processing resources to develop complex and detailed data sets. This may involve multiple steps, depending on the desired outcome. If the goal is to build a model that is not tied to an identifiable source, the data will need to be trained to accurately simulate real-world results without using actual real-world data.
To generate large amounts of synthetic data, organizations often use cloud platforms such as Google Cloud, Microsoft Azure, and Amazon Web Services. These platforms can provide the necessary processing power to develop data for business intelligence and machine learning applications.
The use of graphics processing units (GPUs) is common in synthetic data generation, as they offer the parallel processing capabilities required for artificial intelligence (AI) applications.
There are various strategies for building synthetic data, including the use of physical simulations and APIs provided by startups. NVIDIA's Omniverse platform is one example of a tool for building realistic 3D simulations. The company also offers Omniverse Replicator (where Nyla works), which can generate in-depth neural networking data for these simulations. Omniverse Replicator is part of the Omniverse Cloud Service, which can integrate with other cloud platforms to generate and store data.
As AI technology continues to advance, so too will the methods used to generate synthetic data for training AI models.
Factoring in 3D Art Assets?
Nyla Worker prvovided a phenomenal delivery of how you can create synthetic datasets to train machine learning models using 3D art assets:
First, you will need to gather a collection of 3D art assets that you want to use in your dataset. These could include 3D models of objects, scenes, textures, and any other relevant assets.
Next, you will need to decide on the specific tasks that your machine learning models will be trained on. This will help you to determine the type and amount of data that you need to generate.
Once you have determined the tasks and the data you need, you can use a 3D rendering software to generate the synthetic data. This will involve setting up the 3D art assets in the desired configurations and then rendering them to create the synthetic data.
After generating the synthetic data, you can then use it to train your machine learning models. This will typically involve splitting the data into training and validation sets, and then using the training set to train the model, and the validation set to evaluate its performance.
Finally, you can continue to iterate and improve your models by generating additional synthetic data and fine-tuning the model's parameters.
Overall, creating synthetic datasets to train machine learning models using 3D art assets can be a powerful way to improve the performance and accuracy of your models. By generating a large and diverse set of data, you can provide your models with the necessary data to learn effectively and make accurate predictions.
Synthetic data is used in place of real data to train and test machine learning models, allowing for faster and more efficient model development. This approach, known as “bootstrapping,” involves creating a model using synthetic data, then fine-tuning it with real data. Synthetic data can also be used to augment real data, filling in gaps or simulating conditions that are difficult or impossible to capture in the real world.
Drawbacks of Synthetic Data
There are a few limitations to using synthetic data to train machine learning models, including the following:
Synthetic data is not always completely realistic. This can be a problem because machine learning models often perform best when they are trained on data that closely resembles the real world. In some cases, models trained on synthetic data may not perform as well on real-world data as those trained on real data.
Generating synthetic data can be time-consuming and computationally intensive. Depending on the complexity of the 3D art assets and the amount of data that needs to be generated, this process can take a significant amount of time and resources.
Synthetic data is often not as diverse as real-world data. This can be a problem because machine learning models often perform better when they are trained on a diverse and representative dataset. In some cases, models trained on synthetic data may not be able to generalize well to real-world data.
While, synthetic data techniques can be reasonably viewed as cost-effective and even offer privacy benefits, they also have significant risks and limitations. The quality of synthetic data depends on the quality of the model and dataset used to generate it. Verification steps, such as comparing model results to human-annotated real-world data, are necessary to ensure the accuracy of synthetic data.
Synthetic data may be misleading and can lead to inferior results, and it may not be completely foolproof when it comes to protecting privacy. The use of synthetic data may also face skepticism from users who perceive it as "fake" or "inferior" data. Additionally, as synthetic data becomes more widely adopted, there may be concerns about the openness and transparency of the data generation process.
Overall, while synthetic data can be a useful tool for training machine learning models, it is important to carefully consider its limitations and use it in combination with real-world data to achieve the best results.
Maximizing the Potential of Synthetic Data: A Vision for the Future
Nyla gives a nod to the rising role of “Technical Artist” as a growing demand to be filled in the immediate future for synthetic data.
The role of a technical artist is becoming increasingly important in the field of synthetic data, especially as the use of this type of data continues to grow. Technical artists use a combination of programming and art skills to integrate visual content created by artists and animators into video games and other digital media. They may design systems that allow artists and animators to quickly and easily create things like characters, environments, and animations. In the future, technical artists will likely play an essential role in the creation and use of synthetic data, as they will be responsible for designing and implementing the tools and systems needed to generate and use this type of data.
Further Readings and Links
The Open Synthetic Data Community is a great resource for those looking to learn more about synthetic data, including datasets, papers, code, and experts in the field. The Synthetic Data Community aims to break down barriers for data science teams, researchers, and beginner learners to unlock the power of synthetic data. This article claims synthetic data to be a multi-billion dollar this decade, citing Gartner making a prediction in 2021 that 60% of AI will be trained by it in 2024.
Synthetic data, while still in the early stages of growth, has the potential to accelerate the development of machine learning and AI-based products. While there are technical challenges and limitations to overcome, and standards and tools are not yet standardized, the use of synthetic data will likely continue to grow and become an important part of many industries and sectors.
