As long as the algorithm is in the right direction, it could keep getting better

Machine learning represents a significant shift from traditional product development. Traditionally, products require manual iteration and improvements based on user feedback and testing. However, machine learning algorithms operate differently. Once an algorithm is set in the right direction, it has the potential to continuously improve itself, minimizing the need for manual intervention. This self-improvement capability leads to a transformative impact, evident in innovations like ChatGPT. The process could move from mere quantitative changes to qualitative changes.

Big machine learning innovation will gradually be "productized"

The journey of machine learning innovations from cutting-edge technology to everyday applications is a fascinating one. Initially, technology like text-to-audio conversion was slow and used sparingly for crucial tasks. However, as these technologies developed, becoming faster and more accessible, their applications expanded into daily life. For example, I'm reading the IBM article right now with both the original text and audio generated by ML to improve my reading speed and focus. This transition illustrates how significant machine learning innovations gradually become "productized." As costs reduce and performance improves, people can discover new, practical use cases for the same technology, integrating it seamlessly into their everyday routines.

Maybe we could learn like machines?

Interestingly, while machine learning draws inspiration from human learning processes, it can also offer insights into how we learn. Machine learning algorithms make decisions, learn from mistakes, and adapt based on new information, mirroring how humans can approach learning. This reciprocal relationship suggests that educational systems could adopt a similar methodology. Effective education, much like ethical algorithm training, should focus not only on teaching knowledge but also on guiding the direction of learning.