You may have heard about deep learning and felt like it was an area of data science that is incredibly intimidating. How could you possibly get machin ...

Watson’s strengths lie primarily in language and text based learning and reasoning which are extremely important to many aspects of human life, but far from the ultimate range or potential of deep ...

Right now, most examples of deep learning are neither deep enough nor widespread enough to make much of a difference. Deep learning must become the evolutionary force we so badly need at this ...

For example, Gartner says, “Deep learning, a variant of machine learning algorithms, uses multiple layers of algorithms to solve problems by extracting knowledge from raw data and transforming ...

For another example, in AR technology, deep learning-enabled AI is used in camera pose estimation, immersive rendering, real-world object detection and 3D object reconstruction, ...

If you take a simple problem, a deep neural network is not more accurate. Statistical modeling can only go so far. Humans can learn a whole bunch of things about objects and how they move when you ...

For example, you might train a deep learning algorithm to recognize cats on a photograph. You would do that by feeding it millions of images that either contains cats or not.

Most recently, of course, deep learning has led to the large language models that power garrulous and increasingly capable chatbots. Axiom, in theory, promises a more efficient approach to ...