What once started as early analysis of singular data sources has now evolved into far more robust ways of analyzing information and the relationships between different fields and information sources. Data discovery is another area where machine learning (ML) is beginning to make inroads. Twenty years ago, data discovery was a term used to define the early analytics needed to better understand data. For instance, Evoke Software was a company that analyzed large volumes of customer data. It both used metadata to understand field content to find trends and exceptions, and also looked at raw data and used algorithms to identify field boundaries in older or less documented data sources.
The human orientation system is a complex system in which the brain merges information from a variety of sensors to help maintain a coherent interpretation of body position and movement. I designed a model of this system based on the observer theory model (OTM), which was developed by Merfeld (1990) for the orientation system of the squirrel monkey. Under this scheme, the central nervous system has an internal representation of the sensor organs and tries to minimize the error between its estimate of the sensory afferent signals and the actual afferent signals. It works iteratively until the results of the proposed experiment can be modeled.
When AI isn't busy taking our jobs, it's making brand new scientific discoveries that our clunky human brains somehow overlooked. Researchers from Lawrence Berkeley National Laboratory trained an AI called Word2Vec on scientific papers to see if there was any "latent knowledge" that humans weren't able to grock on first pass. The study, published in Nature on July 3, reveals that the algorithm found predictions for potential thermoelectric materials which can convert heat into energy for various heating and cooling applications. The algorithm didn't know the definition of thermoelectric, though. It received no training in materials science.