News & Updates
Spiki’s robustness approach
MAXIMUM MACHINE LEARNING COVERAGE WITH LIMITED DATA Both global and local robustness play essential roles in designing AI systems with superior performance and adaptability. The key is striking a balance between the two, depending on the specific application...
Robustness means: Never enough data.
Unless... We have already learnt that a neural network architecture is based on specifications and measured datapoints for training. The more parameters and dimensional axes, the more complex and harder to robustly train the network. Need an example? Local...
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...