According to common knowledge it is difficult to entertain tight bonding with more than about 150 people at any one time. This is called the Dunbar’s number, a concept developed in the 1990s. However with the Fourth Revolution, I believe the rotation frequency of this tighter community might have increased.
My observation is that with social networks that allow us to maintain weak ties with a lot more people in a more intense manner than before, the composition of this elect group of stronger bonded people is much higher than before. Depending on exchanges, meetings and opportunities, we tend to renew this group much more frequently than before (around a much smaller stable core community). This has implications about the relative impermanence of stronger bonds which might be an issue, while the accelerated renewal rate is an opportunity for a richer life as well.
Is that your case also that your tighter community tends to rotate quicker than before?
In order to enhance significantly the commercial value of your profile (which they resell directly or indirectly to advertisers), they complete it by buying additional data not directly from your usage of the platform!
And the interesting aspect of it is that it appears to be practically impossible to opt out of all these data gathering sources.
Anyway, while I was quite sure that all those social networks would have had enough of all the data handed to them by us using their service, they seem to need more still. Scary, isn’t it? Maybe an area for regulation soon!
The Internet of Things is spreading, multiplying the number of clever devices and intruding deeper in our privacy. Those who will succeed in that market are those that will master the technologies that avoid fraud and excessive privacy intrusion.
The Internet of Things faces huge hacking risks. For two reasons:
IoT devices are relatively easier to hack because they do not usually include software upgrade and because they are based on standard chips with many more functionalities.
The consequences of hacking can also be much more visible, being devices that control the physical space.
At the start of internet, many services collapsed due to the issue of managing spam and fraud. For example, lots of paypal competitors died of this scourge, and paypal survived by having, from the start, implemented strong anti-fraud features.
The same will happen in the IoT: the survivors will be those that will develop and implement the technology that will avoid as much as possible hacking and subornation of their devices. This should be a key research angle for those that want to succeed in this field.
Data is at the core of the business model of the internet. Our private, personal data actually. And, as Cory Doctorow writes in one of his usual well-researched rants, ‘The Privacy Wars Are About to Get a Whole Lot Worse‘. The reason is the emergence of the Internet of Things.
The Internet of Things (IoT) started already with the smartphones. Because we will progressively be surrounded with sensors that will measure many things and even listen to what is being said or done, our privacy will be even more exposed than now.
“The returns from data-acquisition have been declining for years. […] Diminishing returns can be masked by more aggressive collection. If Facebook can’t figure out how to justify its ad ratecard based on the data it knows about you, it can just plot ways to find out a lot more about you and buoy up that price.”
We probably underestimate already the license we give to our smartphone and its apps to use various channels of data recovery. As Cory Doctorow underlines, no-one really bothers to read the long license agreements, and anyway what can you do if you disagree? We can’t go negotiate one particular section with Google or Facebook, can we?
Cory Doctorow’s point is that one day, on some particular issue, a judge may grant significant compensation because of the indirect usage of personal data. However this day is far away. AT the same time this privacy issue is currently slowing down the spread of IoT and its convenience. A solution must be found.
His analysis is mostly focused on the clear demarcation between rural and city votes and feel during the last US presidential elections. He interprets is as a geographical clustering of knowledge in cities and makes the point that power should be given back to the cities that would be the new driving political forces of the Collaborative Age.
I do not fully agree with this analysis of geographical clustering because it remains to be shown that knowledge concentrates geographically. There are strong diverging forces also at play, including internet access from anywhere and a certain trend at least in some countries for knowledge workers to work remotely and move back to the countryside.
At the same time, there is clearly clustering of knowledge among only a small part of the community, and at least in the virtual space. And this still does challenge our current institutions.
The Industrial Age has been built by the labor loop: pay workers more so that they can consume more factory products. This has led to unprecedented improvement of living conditions and overall wealth. This also worked well in an era of scarcity.
As we reach an era of abundance, where manufacturing can produce way more than we can or should consume, what will be the new model that will drive progress in the world? There is much talk about replacing it with a knowledge loop. An excellent post describes this transformation: ‘From the Job Loop to the Knowledge Loop (via Universal Basic Income)‘.
At the same time I find that the view of a knowledge loop supported by basic income where everybody would keenly participate to creating knowledge quite utopic. I am not sure everybody would indeed participate actively, and how the contributions of individuals could be valued.
Nevertheless, the crisis of the labor loop is upon us, as shown by the relative decrease of wages as part of wealth creation and we need to find an alternative model.
Irrespective of whether the amount of work available will change (it might remain quite stable), the number of ‘jobs’ (meant as being an employee) will certainly decrease.
Self-employment is already dramatically on the rise in many countries for a while and it is I believe a trend that will remain.
Platforms that serve to link self-employed people and clients are also on the rise.
Self-employment is not always a choice: it is sometimes the only way to keep some activity and a lot of self-employed people work are forced to work part-time and at weird times too.
At the same time, self employment means more freedom and flexibility, and the possibility to have several concurring activities.
It is strange how the administrative organization of most developed countries are so tweaked to considering people as being employee of some organization. This creates all sorts of complications for the self-employed, or requires to create a company to become an employee of sorts.
The trend to self-employment is here to stay. Our institutions should change to cater for this situation and better protect those who work under this model.
Will there be more or less jobs in the Collaborative Age? This is a decisive political question at the core of many discussions and votes.
One one hand, there is an impression that robots will take over jobs, in particular those that are do not require high qualifications. For example, truck drivers represent up to 2.5% of the total workforce in the US and could be soon replaced by robots (to which other types of drivers like taxi etc need to be added, bringing possibly the total to 3.0 or 3.5%). This is a huge, huge number and the shift may happen soon. How will those people redeploy their talent?
Others like Tim O’Reilly in this video titled ‘why we’ll never run out of jobs’ take the stand that the Collaborative Age will provide new opportunities and that work will not be a problem: according to him we’ll never run out of jobs, because:
we will never run out of problems
there will always be the need for new, attractive products
That may be true once the transition has been performed.
One thing for sure, jobs in the Collaborative Age will be different than today’s. The skills and talents they will require will be different too.
The conversion of the current generation to the new situation may be painful and this may help explain the tidal waves of conservative fear of change that express themselves at each election.
One of the key transformations from the Industrial Age to the Collaborative Age is related to the function of Business Control.
In Industrial Age organizations, the control function acts as the police that checks that cost is minimized and employees and resources are used at their maximum productivity. It also covers all sorts of fraud prevention. It is by necessity a function kept independent of operational and line managers, reporting to senior management. Traditionally it is a role that concentrates a large part of the data gathering and analysis capability of the organization.
In Collaborative Age organization, a large part of the control function is evolving into a function that is embedded in the business and supports management decision-making on a day-to-day basis. This is the case for example in project management: project control is embedded in the project and its main role is to support the project manager pilot the project to its objectives. That role is not so much control as organizing the gathering of data, checking for its accuracy, analyzing it and devising appropriate forecasts as to the direction taken by the business.
However, the use of the confusing terminology of ‘project control’ is sometimes misinterpreted. It is not the traditional business control role and must actually be kept separate.
While there will still remain some part of actual business control in the older sense, most of the analytical resources of companies are now devoted to support decision-making, through Business Intelligence and other tools. This evolution will be reinforced into the Collaborative Age. And it is important we don’t keep the terminology ‘control’ to describe that function.
Now this has become quite more systematic and oriented towards teaching AI how to recognize certain patterns and things. It is quite a sad paradox that these tasks by humans are designed for machine learning and to replace in the very near future those same humans. In addition the opportunities they provide are quite limited and constraining on those who participate. Yet they do provide opportunities for people in developing countries to benefit from new technologies.
In the Quartz paper ‘Zappos is struggling with Holacracy because humans aren’t designed to operate like software‘, the demise of the method and the negative outcomes at Zappos are described quite dramatically. The reason quoted is that the human element was excessively removed in the rigid holacracy method: “Ironically, as it seeks efficiency and attempts to eliminate human emotion, Holacracy imposes layers of bureaucracy and adds unnecessary psychological weight on to employees.”
Holacracy is too rigid and bureaucratic. It is not designed to address the challenge of complexity, which requires agility and scalability. This view is developed in the excellent post ‘Holacracy Is Fundamentally Broken‘ on Forbes.
Let’s never forget that organizations and projects are first of all a human adventure!
After search-centric companies, and then mobile-centric companies, here come AI-centric companies! Following the trend such as at IBM, The new strategic impetus at Google is the inclusion of Artificial Intelligence in all its services, with dramatic quality improvements.
This interesting NYTimes article ‘the Great AI awakening‘ is worth reading. It hightlights in particular the work of a particular division at Google called “Google Brain” with a focus on the usage of neural networks for deep machine learning and outcome quality improvements. According to the paper, in particular for the ‘Translate’ application, “the AI system has demonstrated overnight improvements roughly equal to the total gains the old one had accrued over its entire lifetime” (i.e. since 2006).
The paper also interestingly gives an account of the historical moves that have made machine learning based on neural networks mainstream in the past few years.
Let’s brace for similar improvements in a bunch of similar services that we are increasingly using in our daily life!…