News & Updates
Tradeoff: Global vs. Local Robustness?
Striking the Balance in AI Training Several factors contribute to the robustness of AI models during the training phase. These include the quality and diversity of the training data, the choice of architecture and hyperparameters, the regularization techniques used,...
Global vs. Local Robustness in AI Training
Games of Thought Artificial Intelligence (AI) has rapidly become an integral part of our lives. It is revolutionizing industries such as healthcare, aviation, transportation, smart homes and more. From personalized recommendations to communication or autonomous...
The Data Bottleneck and how we approach it
Diverse, complex, and never enough We have already learnt that neural network training requires a vast amount of data to effectively capture complex patterns and generalize well. This need for extensive data is expensive for companies due to the costs associated with...
Is Robust AI an Asset for YOUR Business?
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...
Discover Hidden Cost Savings with Spiki’s Robust AI Systems
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 fundamentals of cost-effective AI development
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...