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Who does it?
Our Artificial Intelligence (AI) machine is trained to “De-Clutter Life” (AI to de-clutter life) by understanding a sizable corpus of documents. In this case, the machine is trained to identify mega-trends and provide insights about it.
How does it do it?
The machine is fed 1000s of articles on future and technology from various sources. The machine “reads” the documents, creates it’s own language model and uses that to convert every document into a vector. Then magic happens to identify the conversations and provide insights.
What did it find?
The top mega-trends are shown below (Figure 1)
Figure 1 — Mega Trends
The machine raw interpretation (derives 18 trends) is on the left and a quick glance will tell you what they stand for. I (human) have interpreted the same for you on the right-hand side for convenience.
This is a machine automatically deciphering by reading articles that the top trends humanity is seeing are AI, space travel, autonomous vehicles, renewable energy, cancer & brain research and so on. It even distinguishes between electric vehicles and autonomous cars. To reiterate the ability of the machine to distinguish see that it understands that bitcoin and block-chain are separate trends.
What’s the depth of understanding?
The machine converts every document to a vector and imagines it in a 2000 dimensional space. To simplify the understanding below (Figure 2) is a 2 dimensional (2D) chart where the multi-dimension is reduced to 2. The axes have no labels as this is a dimensional reduction chart.
Figure 2
In this figure the trend words (4 each) is plotted in the vector space. Each polygon represents a trend which is auto-generated by the machine connecting the words representing the trend (ex: energy wind solar power — the 4 words are connected to be a polygon)
The depth can be seen in the co-location of words. All aspects related to health like cancer, brain, stem cell etc are located together and on the left top corner while all aspects connected to energy like solar, wind etc are co-located.
As mentioned earlier it differentiates nuances like bitcoin and block-chain though correlated are separate trends and this can be seen in the above chart where they are co-located in the same space but are distinctly different points in the neighborhood.
This understanding allows the machine to provide a lot of interesting insights.
What insights?
This is something we speak about & are taught but now you can visualize. Our machine automatically identifies top trends and realizes that innovation is at the cross-section of disciplines (Automotive + Robotics = Self Driven Cars)
Every discipline is automatically understood and pulled together so polygons cutting across are traversing cross-engineering/ medical disciplines.
Figure 3 below shows this aspect.
Figure 3
It is interesting to observe that all proper nouns are grouped together. Aha… the machine is telling us in today’s world “people are trends”. I term this as an “uncommon insight” and this is where it is mind-blowing. Today Elon Musk is a trend, Mark Zuckerberg is a trend and if you think about it a trend is a momentum of people, research, investments, startups etc around a particular area. Elon Musk has opened up private space travel and electric cars. He is a trend and a key anchor point in the trend vector space.
The machine can now play in the vector space and understand more. What trends go together and what don’t? Below is a cross-tabulation of trends (Figure 4)
Figure 4
To see what each of the colored boxes mean let us pick a few examples. We as humans know that block-chain came out of original bitcoin concept and evolved to be a trend in itself. Does the machine know? See Figure 5 (right side) and how the machine views the closeness of the two trends.
Let us look at AI and Robotics which are many a time used interchangeably. They are different trends (Figure 5 left side) but closely related.
The machine understands the corollary that some trends don’t have much in common. See Figure 6 below where cancer research and bitcoin don’t seem to have much in common and use cases of quantum computing in electric vehicles don’t seem to be talked about much.
The question is are these product/ market opportunities?
What about the spaces that are empty in the vector space? What could be innovations needed there? What polygons are missing and can be built?
As you think let me offer you something mind-bending :-)
Take a trend polygon and deep dive recursively into that vector space. For example, autonomous vehicles can be on the ground (ex: autonomous cars) or in space (Ex: Flying taxis, drones). Drones can have a vector space of market and technology. How would that n’th level recursion look? Below is the drone technology vector-space based on patents. A vector space in a vector space in a vector space and so on… Inception.
Inception
Note: Companies interested in leveraging the above IP in their organization/ offerings can contact via Linkedin