While artificial intelligence and machine learning are solving a lot of real world problems, a complete comprehension of a lot of the “unsolved” problems in these fields is hindered due to fundamental limitations that are yet to be resolved with finality. There are various domains in the field of machine learning that developers dive deep into and come up with small incremental improvements. However, challenges to further advancement in these fields persist.
A recent discussion on Reddit brought in several developers of the AI/ML landscape to talk about some of these “important” and “unsolved” problems which, when solved, are likely to pave the way for significant improvements in these fields.
Arguably, the most important aspect of creating a machine learning model is gathering information from reliable and abundant sources. Beginners in the field of machine learning, who formerly worked as computer scientists, face the difficulty of working with imperfect or incomplete information—which is inevitable in the field.
“Given that many computer scientists and software engineers work in a relatively clean and certain environment, it can be surprising that machine learning makes heavy use of probability theory,” said Andyk Maulana in his book series—‘Adaptive Computation and Machine learning’.