DIGITAL TRENDSPOTTING 2019:2A — The total breakthrough for AI on every single front
And so it is time to go through the world’s largest revolution in deep tech, the AI — what that has meant this year, and what consequences it will have for our world in general and for people in particular. Does that represent a threat or an opportunity? Is it a fad or have we only seen the beginning? The consequences are so dramatic that in the trendspotting for this year we deep dive and find out the answers once and for all.
In previous sections, we saw how in 2018 we risked a generally good trend for humanity throughout the years, through politics in which Asia increasingly gained more global control, the US making one mistake after another and EMEA increasingly swaying. This has led to resolved conflicts quickly being replaced by conflicts that have been reinforced, while environmental efforts are moving in a reasonably good direction for Europe and India but still on a crazy low level for the US, Saudi Arabia and Iran.
As far as science is concerned, Asia is progressing as strongly, both in biotech and infotech, to some extent in competition with Sweden about 5G and with the US and Europe about the quantum computer. On the other hand, digitally, Asia keeps taking over, with mobiles, apps and social media as the main companion in growth. Economically, everything has resulted in a lousy stock market year for traditional companies, a much better development for digital growth companies, which together made for a mediocre year for GDP — again with especially Asia as the primary locomotive and America and Europe as the sink.
All in all, 2018 spells Asia, Asia and Asia, mixed with a large dose of advanced technology development with exploding growth enterprise and a digital life that is about to take over completely.
We now come to what impact all this, in turn, has had on what constitutes the very core of trendspotting 2019, i.e. the digital development and the beginning of the completely senseless monster of deep tech explosion that this year delivered in general as well as its consequences for us as individuals and society in particular.
The fourth industrial revolution
Over the past 2–3 years, more and more of us share the respected Klaus Schwab’s, the chairman of the WEF, division of the “industrial revolution” into four different parts.
Everything where we for generations before us had the first “revolution” in the early 1800s with the steam engine that basically started it all, while during the early 20th century we had the breakthroughs with all the possibilities with oil and electricity.
Then we come to what the present generation experienced, where the third revolution is about how we at the twilight of the 20th century saw how some of the most important electrical machines, digital technology such as the computer and mobile in combination with the Internet, pushed for a total efficiency increase of our previous way of thinking and acting in the world. In the end, we come to the fourth revolution we are now standing on the verge of, i.e. the deep tech that in many ways transcends humans’ own way of thinking and acting.
For us who we live in modern times, it is, of course, the transition from the third to the fourth revolution that we are concerned with at present, and it is precisely the consequences of the three main digital building blocks within this transition that we will deal with in our digital trendspotting 2019.
Data as the new oil and processing as the new oil drill
We start the analysis of this transition with the digital building block that in both media and public interest has held the most interest for the longest time, and which this time has had so much more substance in its uprising, i.e. AI.
Before we come to the actual technology development, and its consequences for society, businesses and individual, we first do a review of the foundation behind everything, i.e. the data. For just about everything in AI is about precisely that, i.e. new oil, i.e. data — or rather, everything takes its point of departure in the processing of data.
I myself belonged to one of the few who, at dawn of the millennium — long before the term “data scientist” was even invented (= today’s most sought-after role in relation to supply) — was captured by the brutal force of this. I then wrote a thesis in mathematical statistics, more specifically something then as unsexy as “multivariate analysis methods.” As soon as we had too many girls chasing after us (yes, they were too many :D), it was pretty easy to scare people off with talk about “multicollinearity” and “heteroscedasticity” so to speak 😉.
But in the same way that today the “nerd” itself is no longer as unsexy, and no longer has any problems attracting partners, data and statistics still have just as an unsexy tone — something representatives for Google strongly agree with:
“The sexy job in the next 10 years will be statisticians. People think I’m, joking, but who would’ve guessed that computer engineers would’ve been the sexy job of the 1990s?”
At university I myself studied not only the processing and analysis of data, but also economics, computer science as well as researching far before my time, the other perhaps most “sexy” that exist in the world right now, i.e. processes of change in the introduction of digital technology in large organizations.
But in the name of honesty, there was nothing so practical (if we refer to my other subjects as social psychology and philosophy as more intellectually or “theoretically” rewarding so to speak) as, in fact, these studies in “data.”
And this was not just about the statistical processing in my research (where multivariate analysis methods were obviously used in my thesis). Two decades later I am these days accused of being internationally known as a digital strategist, where I published five books on the subject, have developed digital strategies for over 100 businesses, have founded IAB, and have been a digital advisor for 600 businesses — all both nationally and internationally, with assignments on all continents in the world.
During the process I have been spoiled by the fact that people praise me as an awesome strategist at world level (which, of course, I am — it is “humility” that I may not be as awesome at :-D). But the thing that not everyone really understands is that I have almost invariably been an “awesome digital strategist” because I have a really good grasp on all the data behind this. I “never believe” anything, I “know” (at least I have over many years been able to brag about being right four times out of five ).
And “the secret sauce” behind all this is just the data, or rather, “processing” and analysis of this data.
And that is exactly from where “artificial” intelligence takes its point of departure. Behind everything is always a desire to try to identify patterns and ensure relationships between different events in the world in general and different people’s behaviors in particular. All in order to arrive at as statistically significant a model as possible on the truth of the past, so that with as high a probability as possible will be able to predict what will happen in different types of situations in the future.
Often with the purpose of manipulating the situation so that the processes are optimized and the result is in an even more desirable direction.
Offline you do it precisely through multivariate methods such as multiple regression analysis, other sexy things like factor analysis and analysis of principal components, etc. But, and here it comes, the more data we on this side the millennium have received in the digital world, the more impossible it become to analyze all this data manually.
Because while us humans since the dawn of civilization and until 2003 managed to create 0.005 Zettabyte data in the world (1 ZB = 10²¹ bytes = 1 trillion GB), today that same amount of data is created in 26 hours. All to the point that in 2012 we already had over 1 Zettabyte digital data in the world, where just global IP traffic 2018 grew to almost 2 ZB/year
It was when this data growth total escalated, completely magic techniques to process, analyze, predict, and act upon it were invented, which, overall, has been called AI. And that was when our multivariate analysis methods went on steroids for application, with an effect on us humans that will never be surpassed. Stay tuned to find out how :)
- Published Sections So Far: Foreword, Section 1A, Section 1B, Section 1C, Section 1D, Section 1E, Section 1F, Section 2A, Section 2B.
- Digital Trendspotting 2019: This Trendspotting is released as a serial for a number of weeks at the dawn of the New Year. It will be delivered both in Swedish here on LinkedIn (https://www.linkedin.com/in/onlinestrategy/), in English at Medium (https://medium.com/@blockchainboss), and sometimes with some video footage abstracts on our block chain journey (https://www.instagram.com/rufus.lidman.blockchainjourney/).
 Which is also a trick of course, as such an outcome also comes from not speaking when people ask something you just do not “know”: D.