Where robustness is needed in real life applications
After some well-founded theoretical observations about robustness in our latest articles, let us put them to the test. There are many real-world use cases where robustness is crucial for AI systems, and where implementing robustness techniques can lead to significant cost savings. Here are a few examples:
Autonomous vehicles: Self-driving cars rely heavily on computer vision algorithms to navigate roads, avoid obstacles, and make split-second decisions. However, these algorithms are only as good as the data they’ve been trained on, and they can be vulnerable to unexpected inputs like extreme weather conditions or unusual road configurations. By building robustness techniques into the training process, developers can help ensure that autonomous vehicles are able to handle a wide range of inputs and scenarios, which can improve safety and reduce the risk of accidents. Additionally, a robust system may be less likely to require retraining or modifications down the line, which can save significant time and money in the long run.
Aviation: Robustness techniques are highly relevant in the aviation industry because they help ensure the safety, reliability, and efficiency of aircraft operations. By employing these techniques, aviation systems can handle unforeseen circumstances, adversarial attacks, and data anomalies, which are crucial in such a safety-critical domain. AI models can, for example, be trained to detect and diagnose faults in aircraft systems. Robustness techniques enable these models to handle noisy sensor data, intermittent failures, and partial system malfunctions. By accurately identifying faults in real-time, maintenance crews can take immediate actions to prevent potential safety risks. Secondly, machine learning models in aviation often need to make decisions based on limited or uncertain data. Robustness techniques enable the models to quantify and propagate uncertainties, providing decision-makers with more reliable predictions. This is particularly crucial for tasks like weather forecasting, route planning, or aircraft trajectory optimization, where uncertainties can significantly impact safety and operational efficiency.
Medical diagnosis: AI systems are increasingly being used to assist with medical diagnosis, but the accuracy and reliability of these systems is critical. In medical settings, a misdiagnosis can have serious consequences for patients, and it’s essential that AI systems be able to handle unexpected inputs like rare diseases or unusual symptoms. By implementing robustness techniques during training, developers can help ensure that these systems are better able to generalize to new inputs and make more accurate predictions. This can lead to better patient outcomes, reduced healthcare costs, and potentially significant cost savings for hospitals and healthcare providers.
Fraud detection: Fraud is a major problem in many industries, and AI systems can be used to help detect and prevent fraudulent activity. However, fraudsters are often highly skilled at exploiting weaknesses in AI systems, and it’s essential that these systems be able to resist adversarial attacks. By building robustness techniques into the training process, developers can help ensure that fraud detection systems are more resilient to these attacks and better able to detect and prevent fraud. This can lead to significant cost savings for businesses and financial institutions, as fraudulent activity can be extremely costly and damaging.
Natural language processing: Natural language processing (NLP) is a rapidly growing field that has many applications, from chatbots to virtual assistants to language translation. However, NLP models are often highly sensitive to variations in language use, and they can struggle to handle unexpected inputs like misspellings or regional dialects. By implementing robustness techniques like data augmentation and regularization during training, developers can help ensure that NLP models are better able to handle these variations and make more accurate predictions. This can lead to more effective communication with customers and significant cost savings for businesses that rely on NLP-based systems.
Overall, there are many real-world applications where robustness is crucial for AI systems, and where implementing robustness techniques during training can lead to significant cost savings. By building robustness into AI systems from the start, developers can help ensure that these systems are better able to handle unexpected inputs, resist adversarial attacks, and make more accurate predictions. This can lead to improved safety, better patient outcomes, reduced fraud, and more effective communication with customers, all while potentially saving time and money.
Conclusion: leave the work to Spiki
In conclusion, robustness is a crucial component of neural network training that can improve performance and save time and money in the long run. By implementing robustness techniques, AI developers can create systems that are better able to handle unexpected inputs and resist adversarial attacks. The benefits can be substantial, especially in high-stakes applications like healthcare and finance. As AI continues to play an increasingly important role in our lives, robustness will become even more important.
Creating a robust neural network is both costly and time-consuming. 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. 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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