Is that a cancer cell? That is what a Pathologist does day in and day out, analysis of imagery to interpret the visual context of cells on a slide and classify the image into cancer, or a specific cancer.
Being able to replicate this operation with AI is important enough, but this specific Visual Understanding application can be applied with DNA analysis to produce the next-generation of cancer diagnostics. Take a look at the Graph Learning section below to learn more.
Just as language is foundational to thought, language understanding is foundational to artificial thought. Interpretation of words by AI allows for language translation, sentiment analysis, and processing the vast data available on the Internet. Language is the most common format of Enterprise data assets, and AIES/App's Language Understanding module leverages these assets for your business.
Some readers may think "hey this image is a visual classification or visual understanding application", and they would be correct - sort of. Attention OCR unifies language translation with visual classification neural networks. Put in simple terms, Attention OCR (one of the many algorithms available in AIES) is a translation between visual features and textual output.
We have worked with almost every format of data we can think of:
As IoT and AI come together, integration between AI systems and IoT is critical. You can't just bolt on AI to an IoT data aggregation engine. This is the specific situation AIES/DataFusion was built for.
Genetic and Epigenetic inference, causality, and correlation, is perhaps the most important use case for AI in today's world. The high-dimensionality of genetic data requires AI to analyze data at scale. AIES/App Graph Learning Engine is a unique system that builds computational graph structures from observation data, allowing inference to deduce correlation and causality of complex situations such as:
AIES/Graph Learning is a great integration system to sit above smaller and more focused AI prediction models.