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Today I am going to give a Webinar (in Italian) on the impact of the Digital Transformation on jobs and education to an audience of teachers from schools in Italy.
The crucial point we are facing is that on the one hand knowledge means potential revenue, on the other hand there is so much knowledge that it has become a commodity but at the same time I no longer have the capability to grasp it: it is just too much and it grows too rapidly.
As I pointed out in previous posts we area heading toward distributed knowledge mediated by artificial intelligence and companies like Unanimous AI are already starting to leverage what they call Swarm Knowledge.
A recent article on the Financial Times is making some interesting points on the progress of natural language understanding -NLU- and the growing capabilities of machines (that is of AI) to write in natural language. This is bound to create an explosion of reading material, and yet, I am still left with just two eyes (getting any day worse, by the way…).
We are starting to see AI flanking data analysts, be them working at biz intelligence agency (I have no visibility but I bet military intelligence is now a heavy user of AI based analyses) or in retail, like at Walmart. AI today is not necessarily better than humans but it is way faster and can process a huge amount of data. The big, undergoing, shift is the one from data analyses, where data are somehow structured, to NLU. There are tons of written (and spoken) texts in natural language, just think about the television and radio programs (according to a UN report there are 44,000 radios in the world transmitting 24/7 and over 20,000 television channels, you can get a comprehensive list on Wikipedia).
Reports of any type, blogs, tweets, newspapers … they are all written in natural language and they are all a potential source of raw data just waiting to be analysed. Clearly they exceed the capability of any human being.
It is not just about being capable of managing huge amount of text. Once a computer becomes available to understand natural language it can fit nicely into most enterprises’ processes. Today we talk and correspond, via mail, memos and reports, with our colleagues. If a machine can read and understand all of that clearly can also replace our correspondent, and if we look the other way around it can, as well, replace us!
A third point, just to close on a more positive note, is that we have been using natural language as THE means of communications from as long as we can look into the past and having a machine that could converse with us, understanding us as we communicate will create a sort of symbioses. Most likely, this symbioses is the way forward, augmenting ourselves through seamless machine interaction. If I can rely on a machine to read on my behalf and become, in a certain way, an integral part of myself than for the first time I will no longer need to rely solely on my eyes to navigate the Information Society, and by leveraging on a joint intelligence, my brain and AI, I can tackle the expanding Knowledge Society. This is something I will address in the webinar today. I am curious to hear the reactions.
