Common sense makes humans very efficient learners, so machine learning researchers have been working on ways to imbue machines with at least some ‘common sense’. In a previous blog we discussed using pictures to train natural language processing systems, in a sense giving the systems partial ‘knowledge’ of what words represent in the physical world. ML systems can get even closer to common sense with a little help from video ML models and human teachers.
In my latest iMerit blog I discuss an innovative deep learning architecture that applies the concept of attention, commonly used in sequence models for language processing, to analyze motion patterns in video using only 30 percent of the computations used in previous approaches.
Next I discuss training such a video analysis system to learn the basic language of movement. For this training the human teacher goes beyond typical training data annotation, drawing on knowledge of the physical world to improvise representative examples of the basic concepts of movement. It is hoped that this will give the ML system a bit of ‘common sense’, allowing it to more easily learn new video analysis tasks.
Natural language processing (NLP) machines have made great progress by learning to recognize complex statistical patterns in sentences and paragraphs. Work with modern deep learning models such as the transformer has shown that sufficiently large networks (hundreds of millions parameters) can do a good job processing language (e.g., translation), without having any information about what the words mean.
We humans make good use of meaning when we process language. We understand how the things, actions, and ideas described by language relate to each other. This gives us a big advantage over NLP machines – we don’t need the billions of examples these machines need to learn language.
NLP researchers have asked the question, “Is there some way to teach machines something about the meaning of words, and will that improve their performance?” This has led to the development of NLP systems that learn not just from samples of text, but also from digital images associated with the text, such as the one above from the COCO dataset. In my latest iMerit blog I describe such a system – the Vokenizer!
Language is a hallmark of human intelligence, and Natural Language Processing (NLP) has long been a goal of Artificial Intelligence. The ability of early computers to process rules and look up definitions made machine translation seem right around the corner. However language proved to be more complicated than rules and definitions.
The observation that humans use practical knowledge of the world to interpret language set off a quest to create vast databases of human knowledge to apply to NLP. But it wasn’t until deep learning became available that human-level NLP was achieved, using an approach quite unlike human language understanding.
In my latest iMerit blog I trace the path that led to modern NLP systems, which leave meaning to humans and let machines do what they are good at – finding patterns in data.