Giong sgihltly off pstie tihs mtnoh - you’ve probably seen this experiment before. Where the letters at the end of words remain the same and middle is scrambled - this experiment has been around since at least 1976
and proves that psycholinguistics are easily interprated by humans for a number of reasons. Humans have perception, common sense and intuition, a strange concept based on environmental and implicit learning.
Unfortunately, much of technologically derived communication is predominantly text based, whether its code in a compiler, search terms, a scraper or the acquisition of encyclopedic knowledge from the web. Human communication on the other hand, or anthroposemiotics for the budding etimologists, is more multivariate - not only are we able to communicate by text but also by voice, visual and nonverbal. This not only allows us to communicate with ourselves but with multiple people and groups. This rich communication and coordination is the primary reason humans are top of the food chain - we can coordinate in groups, share and triangulate knowledge and strategy in a way that other primates cannot. Enough about monkeys.
Unfortunately, for our micro-chipped friends, the computer, this rich tapestry of communication and understanding is currently unavailable to them. Whilst, in the last few years, we’ve made incredible leaps in terms of machine learning within bounded problems (finite tasks, finite results) - we’ve yet to truly see machine learning applied to truly unbounded problems.
One of the key difficulties is that most machine learning algorithms are trained within narrow problem spaces.
Machine learning today is very good at understanding defined taxonomies, though struggles with input which has yet to be defined, i.e:
“what colour is the sky not?”
Whilst, you and I would understand this to be every colour other than blue, or grey if you live in the UK, a computer might struggle to answer this as no one has strictly defined which colours the sky isn’t (maybe unsurprisingly).