WEST LAFAYETTE, Ind. – It is hard enough for humans to interpret the deeper meaning and context of social media and news articles. Asking computers to do it is a nearly impossible task. Even C-3PO, fluent in over 6 million forms of communication, misses the subtext much of the time. Natural language processing, the subfield of artificial intelligence connecting computers with human languages, uses statistical methods to analyze language, often without incorporating the real-world context needed for understanding the shifts and currents of human society. To do that, you have to translate online communication, and the context from which it emerges, into something the computers can parse and reason over.

It's been nearly two years since Tesla's first "Autonomy Day" event, at which CEO Elon Musk made numerous lofty predictions about the future of autonomous vehicles, including his infamous claim that the company would have "one million robotaxis on the road" by the end of 2020. This time, the event will be called "AI Day," and according to Musk, the "sole goal" is to persuade experts in the field of robotics and artificial intelligence to come work at Tesla. The company is known for its high rate of turnover, the latest being Jerome Guillen, a key executive who worked at Tesla for 10 years before recently stepping down. Attracting and retaining talent, especially top tier names, has proven to be a challenge for the company. The August 19th event is scheduled to start at 5PM PT / 8PM ET at Tesla's headquarters in Palo Alto, California.

The explosion of breakthroughs, investments, and entrepreneurial activity around artificial intelligence over the last decade has been driven exclusively by deep learning, a sophisticated statistical analysis technique for finding hidden patterns in large quantities of data. A term coined in 1955--artificial intelligence--was applied (or mis-applied) to deep learning, a more advanced version of an approach to training computers to perform certain tasks--machine learning--a term coined in 1959. The recent success of deep learning is the result of the increased availability of lots of data (big data) and the advent of Graphics Processing Units (GPUs), significantly increasing the breadth and depth of the data used for training computers and reducing the time required for training deep learning algorithms. The term "big data" first appeared in computer science literature in an October 1997 article by Michael Cox and David Ellsworth, "Application-controlled demand paging for out-of-core visualization," published in the Proceedings of the IEEE 8th conference on Visualization. They wrote that "Visualization provides an interesting challenge for computer systems: data sets are generally quite large, taxing the capacities of main memory, local disk, and even remote disk. We call this the problem of big data. When data sets do not fit in main memory (in core), or when they do not fit even on local disk, the most common solution is to acquire more resources."