For years, computer scientists have been using neural networks as image classifiers, applying supervised-learning with massive amounts of manually labeled data for training.
In contrast, animal and human brains discover and comprehend generic representations of the external world without a necessary supervised learning process or vast training datasets.
These organic brains have a different objective which dictates a separate set of priorities in the design of the neural networks. This ultimately leads to dramatically improved performance when it comes to understanding images and other natural signals in real world scenarios.
Similar to the human brain, our technology operates on a flat architecture to create maximum efficiency and infinite scalability. Deep Learning systems on the other hand require multiple layers that limit the potential for scalability.
Our technology can work with noisy, unstructured data, requiring no prior organization. Just like a child's mind, it can be exposed to completely new things and gain an understanding on its own.
We’ve created algorithms that are capable of self-learning, meaning they do not require computer scientists to train them. Other systems however, rely on supervised processes in which individuals must train the system and point out incorrect answers until the AI learns. It’s a lengthy and resource-consuming process.
By visually indexing the world we apply our technology across many industries to improve lives. Imagine more accurate medical analyses, safer roads, and better experiences with your photo memories. See how our technology can have an impact.