A recent study by a team of scientists at MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) revealed that improved algorithms may be more important for AI performance than faster hardware.
Indeed, the researchers analyzed data from 57 computer science textbooks and more than 1,110 research papers to determined where algorithms improved. Algorithms help software make sense of text, visual, and audio data so as to draw inferences from it. Hence, if an algorithm is efficient, the software has less work to do.
It was then found out that for large computing problems, 43% of algorithm families had year-on-year improvements that were equal to or larger than the gains from Moore’s law, the principle that the speed of computers doubles every two years. In 14% of problems, the performance improvements largely outpaced those that came from improved hardware, with the gains from better algorithms being especially meaningful for big data problems.
Moreover, the study also showed that the size of algorithms matters less than their architectural complexity. The researchers were able to report how many more tasks could be done using the same amount of computing power after an algorithm improved.
By doing so, this could help businesses and other organizations improve without the downside.