The importance of investing in intuitive AI technologies
Meet Keras. Keras is a high-level neural networks library, written in Python.
Since the rise of neural networks in production environments–those with practical real life applications–the field has been primarily a privilege for scientists specializing in machine learning and complex math theories. I like to compare the state of AI by drawing a line back to 50’s, when the same statement applied to early days of computer science. Earlier, to write a program, a scientist had to implement a decent amount of math and then to translate it into machine codes, which ran on a particular hardware.
It was true until Grace Hopper wrote the first high-level compiler in 1952. Ever since compilers became even more abstract. A few years later the invention of C programming language started a golden age of computation and enabled thousands of people to join computer science.
We live in the same era of AI. It still requires a significant amount of effort and specialized knowledge to develop machine learning models for practical applications–a «high-level compiler for AI» is yet to be created. However, the technologies like Bonsai.ai, Keras, TensorFlow and others are closing this gap.
The increasing demand for engineers capable of writing AI-driven programs and the lack of more intuitive technologies create a surge on the job market. Companies like Google, Facebook, Intel and other leaders in the field are paying millions of dollars for AI talents. But the talent pool is relatively limited compared to the expanding set of practical AI applications: medicine, education, agriculture, transport and many other fields.
Investing in building more intuitive and simpler technologies that enable everyone to code AI-driven programs is one of the greatest ways to advance the entire industry.