With its Semantic Folding approach, Cortical.io demonstrates a massive 2,800x acceleration and 4,300x increase in energy efficiency compared to BERT and shows that operational costs can be reduced tremendously, making large-scale classification use cases commercially viable.
Cortical.io, a provider of AI-based solutions including SemanticPro, its Intelligent Document Processing solution, announced July 12 its breakthrough prototype for classifying high volumes of unstructured text.
The stunning benchmark results against BERT, a transformer-based machine learning technique for natural language processing (NLP), were achieved with Cortical.io’s Semantic Folding approach, the only approach that combines the computational efficiency of word vector models with the high accuracy of transformer models.
Francisco Webber, CEO at Cortical.io, explained: “Efficiency is the new precision in Artificial Intelligence. While large industries are determined to use less energy, the AI and ML industry is headed in the opposite direction: growing its carbon footprint exponentially. The future of green computing hangs by the thread of high efficiency AI capabilities.”
Full details of the innovative steps that led to the benchmark results can be found here.
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