Databases that learn

In 1952, IBMer Arthur Samuel created the first implementation of a machine learning system in America — to play checkers.

At first, the system was beatable. Samuel continued to improve the learning capabilities of his checkers program, and in part trained the program by having it play thousands of games against itself.  By 1961, Samuel’s programs played the fourth-ranked checkers player in America and won. This demonstrated a level of play not yet achieved by a computer. The evolution of machine learning continued and the range of applicable domains expanded.  

By 1997, the IBM Deep Blue computer was able to rival the best chess prodigies of the world, beating World Chess Champion Gary Kasparov. In 2011, IBM Watson technology competed against legendary Jeopardy! champions Brad Rutter and Ken Jennings, winning the first place prize of $1 million.  Today, improvements to learning algorithms, combined with cheaper and more powerful computational capacity, as well as plentiful data, are making it possible for machine learning to go mainstream. The computeralgorithmsthat can perform analogous feats thatpowermachine learning are now automating the mundane and providing deep insight into the very complex without the need for explicit human programming of rules and heuristics. 

Machine learning is being used at the heart of next- generation methods for self-driving cars, facial recognition, fraud detection and much more.  At IBM, we’re applying machine learning methods to SQL processing so databases can literally learn from experience.

SQL is the industry standard language for accessing, querying and manipulating structured data. It is used by stock markets, investment banks, hospitals, logistics firms, insurance companies, pension funds, manufacturers, small companies and large ones. It is a data language that is at once rich in capabilities, elegant in its descriptive power and ubiquitous in use.  It’s everywhere. Improving SQL processing literally helps a vast cross section of industries.

SQL offers a fascinating opportunity for the application of machin elearning.