Robustness makes AI perform reliably and is a prerequisite for safety-critical applications. Making a neural network locally robust sets AI apart from current state-of-the-art AI. In our last article we highlighted the benefits of implementing robustness already into the neural network training instead of just checking for robustness a posteriori. This is a more cost-effective way of building robust neural networks.
One of the lesser-known benefits of robustness is its potential to save time and money prior to AI development AND in the long run.
- By reducing the need for expensive data collection and labeling, robustness can significantly reduce the cost of developing AI systems. For example, in the healthcare industry, collecting and labeling medical images can be time-consuming and expensive. Especially in safety-critical applications, data requirements are infinite! By implementing robustness techniques, AI developers can reduce the amount of labeled data required to achieve high performance, which can save time and money. Spiki offers a unique way to limit and specify the number and characteristics of data going into your specific neural network. Clients are guided through the data collection or measuring process to make it as simple and effective as possible.
- Robustness can also reduce the need for complex pre- and post-processing steps. Take natural language processing as an example: Clients need to define a metric range, for example a specific signal to noise ratio in order to be robust against background noise. The network is then fed with predefined data and trained against these specific metrics. By implementing robustness techniques, AI developers can reduce the need for pre-processing and achieve higher accuracy with less effort. The need for data augmentation or further adversarial training is reduced. Spiki can source the data needed or tell you exactly which measurements to take to ensure locally robust training with various types of input data (images, sounds, voice recordings, continuous sensor data etc.).
- Finally, robustness can reduce the need for model retraining. In many real-world applications, the data distribution can change over time. If an AI system is not robust to these changes, it may require retraining or even a complete overhaul. By implementing robustness techniques, AI developers can make their systems more adaptable and reduce the need for frequent retraining. Spiki’s robust training outperforms other state of the art neural networks also in this respect.
Quantifying the potential cost savings from robustness is difficult, as it depends on the specific industry and application. However, some studies have estimated that implementing robustness techniques can reduce the amount of labeled data required by up to 90%, which can lead to significant cost savings in the long run. So, what are you waiting for?
Outsource data collection and training to Spiki
It becomes clear that creating a robust neural network can be both costly and time-consuming since every step requires expertise, fine-tuning and calibration. Collecting and processing the input data needed, and training, testing and retraining the model are huge challenges for companies not specialised in this field. So why not leave those tasks to Spiki?
We have developed a unique approach to limit the amount of data needed for our neural network training and either source the data ourselves, or help you take the correct measurements and samples in a predefined and clearly specified manner. Thus we can considerably limit time and efforts needed from your side. Our clients get a fully trained neural network model, which can be deployed via microcontroller, FPGA, as a cloud service or even ASIC as a future step in our product development.
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