by Andrea | Jul 6, 2023 | Allgemein
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.
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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?
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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.
Excited?
Get in touch and learn how to unlock the full potential of your business with Spiki’s AI you can trust.
by Andrea | Jun 21, 2023 | Allgemein
In this article we will pick up what we have learnt about the concept of robustness and its advantages. We will learn more about how robustness can contribute to more efficient and cost-effective AI development and how it can be implemented in neural network training – during and after the training process.
Methods for Implementing Robustness During Training
There are several methods for implementing robustness during neural network training. One of the most common approaches is data augmentation. Data augmentation involves adding synthetic examples to the training data, such as rotating or flipping images or adding noise to audio signals. This can help the network learn to recognize variations in the data, and improve its ability to generalize to new examples.
Another approach is adversarial training. Adversarial training involves adding adversarial examples to the training data, which are designed to fool the network. By training on these examples, the network learns to recognize and resist adversarial attacks. However, this technique can be computationally expensive and may require a large amount of labeled data.
Dropout and regularization are other techniques for improving robustness during training. Dropout randomly drops out neurons during training, which helps prevent overfitting and improves generalization. Regularization adds a penalty term to the loss function, which encourages the network to learn simpler and more robust representations.
Finally, ensemble methods can also improve robustness. Ensemble methods involve training multiple networks and combining their outputs to make predictions. This can improve robustness by reducing the impact of individual network errors and improving generalization.
Methods for Implementing Robustness After Training
Even after a network has been trained, there are still ways to improve its robustness. One approach is adversarial training on pre-trained models. This involves generating adversarial examples from the pre-trained model and retraining the network on these examples. This can help the network learn to recognize and resist adversarial attacks without requiring additional labeled data.
Another approach is fine-tuning with adversarial examples. Fine-tuning involves taking a pre-trained network and retraining it on a smaller set of labeled data. By fine-tuning on adversarial examples, the network can learn to recognize and resist these attacks more effectively.
Post-processing techniques can also improve robustness after training. This can be achieved with input preprocessing, which involves applying transformations to the input data before it is fed into the network. These transformations can help make the data more robust to variations and noise. Another option is output post-processing, which involves modifying the network’s outputs to make them more robust to errors and uncertainty.
Spiki’s mission: robust AI to save you time and money
Implementing robustness during neural network training can be more cost-effective than checking robustness a posteriori for a number of reasons. One advantage of implementing robustness during training is that it can lead to more efficient use of resources. By building a robust AI system from the start, developers can potentially save significant amounts of time, money, and compute resources that would otherwise be spent on post-hoc testing and retraining. This is because building a robust system from the ground up can help ensure that the system performs well under a wide range of conditions, which reduces the likelihood that it will need to be retrained or modified at a later stage.
Another advantage of implementing robustness during training is that it can lead to more accurate models overall. When robustness techniques are built into the training process, they can help the network learn to generalize better and make more accurate predictions on new, unseen data. This is because robustness techniques like regularization and data augmentation can help prevent overfitting, which is when a model performs well on training data but poorly on new data. By reducing overfitting, robustness techniques can help ensure that a model’s performance is more representative of its true ability to generalize.
From a client perspective, there are several advantages to using AI systems that have been trained with robustness techniques. For one, a robust system is likely to perform better on new, unseen data, which can lead to more accurate predictions and better decision-making. This is particularly important in high-stakes applications like mobility, healthcare or finance, where accuracy can have a significant impact on outcomes. Additionally, using a robust system can potentially save clients time and money in the long run by reducing the need for retraining or modification down the line. A robust system is less likely to need to be updated or tweaked as data distributions change or new use cases emerge.
Overall, implementing robustness during neural network training can be a cost-effective way to build accurate, reliable AI systems that are better able to handle unexpected inputs and resist adversarial attacks. By building robustness techniques into the training process, developers can potentially save time, money, and compute resources while also improving the accuracy and generalization of their models. From a client perspective, using a robust AI system can lead to more accurate predictions, better decision-making, and potentially significant cost savings over time.
Robust neural network training: rely on Spiki
Rely on Spiki to provide you with robust neural network training fit for your purpose and tailored for your needs. 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 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.
Excited?
Get in touch and learn how to unlock the full potential of your business with Spiki’s AI you can trust.
by Andrea | Jun 7, 2023 | Allgemein
ONLY WITH ROBUSTNESS!
Neural networks have emerged as powerful tools for solving complex problems in various domains, including computer vision, natural language processing, and robotics. However, deploying neural networks in real-world applications requires addressing several challenges, such as handling out-of-distribution examples, dealing with adversarial attacks, and reducing input data and processing costs. In this series of articles, we’ll explore the concept of robustness in neural network training, its advantages and consequences, and the methods for implementing robustness during and after the neural network training. We’ll also discuss how robustness can lead to cost savings in different industries.
Robustness in a nutshell
Neural networks have revolutionized many industries, from self-driving cars to personalized healthcare. However, these models often fail in unexpected ways when deployed in real-world scenarios. For instance, a self-driving car may not recognize a pedestrian wearing a dark hoodie, leading to a potentially dangerous situation. Similarly, a chatbot may give inappropriate responses to sensitive topics due to biases in the training data. To address these challenges, researchers have focused on improving the robustness of neural networks, which refers to their ability to perform well on input data that is different from the training data.
In other words, a robust neural network can handle noisy, corrupted, or adversarial examples that are not present in the training set. Robustness is crucial for ensuring the reliability, safety, and fairness of neural network-based systems.
Advantages and challenges of Robustness
Robustness brings several advantages to neural network training, including:
Improved performance on out-of-distribution examples: In many real-world scenarios, the input data may not match the distribution of the training data. For instance, a medical imaging system may encounter rare diseases that are not present in the training set. A robust neural network can handle such out-of-distribution examples by generalizing well to unseen data.
Reduced sensitivity to adversarial attacks: Adversarial attacks refer to the deliberate manipulation of input data to fool the neural network. For instance, adding imperceptible noise to an image can make the network misclassify it with high confidence. A robust neural network can detect and resist such attacks, which is crucial for security-critical applications like defense, finance, and healthcare.
However, there are also some trade-offs to consider when implementing robustness. For example, a more robust AI system may sacrifice some accuracy on clean data, or it may require more computational resources to train and run. Implementing robustness often requires additional layers, modules, or training steps in the neural network pipeline, which can increase its complexity and computational cost. This can make the training process slower and more resource-intensive, especially for large-scale models. Additionally, some robustness techniques may not be applicable to all types of data or tasks.
This is why it is best to leave this job to the experts at Spiki, where we build robust neural networks with limited data requirements to help you save time and money.
In the following article we will tell you more about why Robustness is the Key to Cost-Effective AI Development.
Excited?
Get in touch and learn how to unlock the full potential of your business with Spiki’s AI you can trust.
by Andrea | May 24, 2023 | Allgemein
Status quo: unlimited high-quality data needed
Robust neural network training involves ensuring that the network is resistant to noise, variations in input data, and other forms of perturbation. This is important for real-world applications, where the input data may be subject to variability or noise.
To train a neural network robustly, a sufficient amount of diverse and high-quality data is needed. The exact amount and type of data required depend on the specific problem that the neural network is being trained to solve, as well as the complexity of the network architecture.
In general, the more data that is available for training, the better the performance of the neural network is likely to be. However, the quality of the data is also crucial. It is important that the data be representative of the problem domain and include examples of all possible input and output configurations that the network may encounter in practice.
Let’s say you are working on a project to develop an autonomous car that can detect and avoid obstacles on the road. To train the neural network that will control the car, you need to provide it with a large and diverse set of data that includes images of different types of roads, weather conditions, and obstacles. The neural network needs to learn how to recognize various objects on the road such as cars, pedestrians, traffic lights, and road signs.
If you only provide the neural network with a limited amount of data, it may not be able to generalize well to new and unseen situations. For example, if the network has only been trained on images of roads during daylight, it may not be able to detect obstacles in low-light or nighttime conditions.
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…correctly labelled and annotated by domain experts
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Additionally, it is important that the data be labeled correctly, as this is necessary for the network to learn the correct associations between inputs and outputs. The labeling process may require domain expertise or human annotation, which can be time-consuming and costly.
Let’s say you are working on a project to develop a spam filter for an e-mail service. To train the neural network, you need to provide it with a large dataset of e-mails that are labeled as either spam or non-spam. The labeling process involves marking each e-mail in the dataset as spam or non-spam based on its content.
If the labeling is incorrect, the neural network will learn the wrong associations between inputs (the content of the e-mail) and outputs (whether the e-mail is spam or not). For example, if an e-mail that should be labeled as spam is labeled as non-spam, the network may not be able to identify similar spam e-mails in the future. This can result in a poor performance of the spam filter and frustration for users who still receive unwanted e-mails.
Labeling a large dataset of e-mails can be a time-consuming and costly process, especially if domain expertise or human annotation is required. Domain expertise may be needed to correctly identify certain types of spam e-mails, such as those that use sophisticated techniques to avoid detection. Human annotation may be needed to review and correct the labeling done by automated tools, to ensure that it is accurate and consistent across the dataset. This may require significant effort and expertise, but it is essential for achieving the desired performance of the system.
The specific amount of data required varies widely depending on the problem and network architecture. Deep neural networks, for example, may require hundreds of thousands or even millions of examples for effective training, while smaller networks may require fewer examples.
In summary, robust neural network training requires sufficient and high-quality data that is representative of the problem domain and correctly labeled. The specific amount of data required depends on the complexity of the problem and the network architecture, and can vary widely.
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Data collection and annotation is costly
Data collection for neural network training can be costly because it usually requires a team of skilled individuals, including:
- Subject matter experts who can identify and collect relevant data.
- Data scientists who can design data collection protocols and manage the data pipeline.
- Data annotators or labelers who can manually annotate or label data as needed.
- Quality assurance personnel who can ensure the accuracy and quality of the collected data.
- Legal and ethical experts who can ensure that the data collection process is compliant with relevant regulations and ethical considerations.
In some cases, it may be possible to outsource certain aspects of the data collection process, such as annotation or labeling, to third-party providers. However, this can also introduce additional costs and challenges related to quality control and data ownership.
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Spiki has your back: robust training made cost-efficient
Spiki offers a unique neural network training framework which clearly specifies which, where and how data points need to be measured or go into the training. We guide our customers through the data collection process to ensure robust performance. At the same time we are able to limit the amount of data needed and thus make training your AI as effective and efficient as possible.
Spiki offers the developed robust neural network training workflow as a SaaS in the form of robust software (SW) or hardware (HW) IP licenses usable for safety critical applications in fields such as intelligent control, autonomous driving, robotics, aeronautics and other safety-critical domains.
Excited? Get in touch and learn how to unlock the full potential of your business with Spiki’s AI you can trust.
by Andrea | May 10, 2023 | Allgemein
Why not outsource expertise, data collection and development?
The cost and time required for robust AI training can vary widely depending on several factors, including the complexity of the task, the amount and quality of data available for training, and the expertise and resources of the team involved.
In general, building a robust AI model requires a significant investment of time, effort, and resources. Some estimates suggest that developing a state-of-the-art deep learning model can take months or even years of work by a team of skilled researchers and engineers. The cost of such a project can also be significant, ranging from hundreds of thousands to millions of Euros, depending on the scope and complexity of the project.
Factors that can contribute to the cost and time required for robust AI training include:
- Data collection and preparation: Gathering high-quality data for AI training can be a time-consuming and costly process, especially for complex tasks that require large and diverse datasets. Data cleaning, formatting, and preprocessing can also add significant time and cost to the project.
- Hardware and infrastructure: Training deep learning models requires significant computing resources, including powerful GPUs, memory, and storage. The cost of these resources can be substantial, and setting up and maintaining the necessary infrastructure can also require specialized expertise.
- Expertise and personnel: Building robust AI models requires a team of experts with a range of skills, including data science, machine learning, software engineering, and domain expertise.
- Iterative development and testing: Developing a robust AI model often requires an iterative process of training, testing, and refining the model. Each iteration can require significant time and resources, especially if the team needs to collect new data or make significant changes to the model architecture.
In summary, building a robust AI model can be a significant investment of time, effort, and resources, with costs ranging from hundreds of thousands to millions of euros. The exact cost and time required will depend on the specifics of the project and the expertise and resources of the team involved.
Outsource data collection and training to Spiki
It becomes clear that 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 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.
Excited? Get in touch and learn how to unlock the full potential of your business with Spiki’s AI you can trust.
