One of the main concerns nowadays is the pace at which society can absorb all the technological changes happening.
According to Salim Ismail, Singularity University’s founding executive director and global ambassador in ‘What Happens If Society Is Too Slow to Absorb Technological Change?‘ “the true challenge with advancing technologies isn’t the threats they impose, but more that society is sluggish at absorbing and making use of the technology at its current pace.”
Honestly when one looks at the curves available on new technology adoption I don’t see so much of a difference on the latest technologies. In all cases adoption has been fairly rapid once the technology was there. The main difference might be how far reaching and simultaneously global the new technologies spread.
It does not seem to me that the rate of technology adoption is an issue. If it is a good and useful technology it gets adopted. Infrastructure will be modified to fit to it (sometimes with delays due to the investments required). The main issue is for us to learn how to deal with it, but honestly I can’t quite remember how work was before email as this change was so obviously great.
So let’s not believe that technology adoption is really a limiting factor. If a technology is useful and works, it spreads. Period.
A swivel chair is a behavioral magnifier. Hence use it when you interview someone!
This concept is for example developed in the book ‘Spy the Lie‘: “It’s worth mentioning here that when we interview someone, the last place we would want the interviewee to sit is in a straight-back chair with four legs. We want the person in a chair that has wheels, that rocks and swivels, that might even have moveable arm rests. That type of chair becomes a behavioral amplifier, magnifying those anchor-point movements and making them particularly easy to spot.”
Even in meetings it is always interesting to watch how participants relate to their chair and use all the various degrees of freedom available. It can be extremely useful during negotiations.
Train yourself to observe people on swivel chairs, and observe yourself when you are sitting on one too!
Enhancing lessons learnt and redistributing them to the entire ecosystem is a cornerstone of safety enhancement. It is much facilitated in the case of Artificial Intelligence (AI) thanks to the remote update possibility, as demonstrated successfully by Tesla.
Implementing a statistical approach instead of a deterministic one. Some statistical risk analysis approaches are already available for years in the form of fault trees to determine the statistical probability of a feared accident. However this only works in environments where statistical failure data of components is available, and with limited changes to the environment and the system. New statistical approaches will have to be developed based on specific testing of the entire AI-related system. These approaches need to be developed theoretically and empirically and remain the major challenge of the years to come.
Rules governing operability of the system in case of component failure will have to be strictly defined and enforced (with how many sensors out of order is it safe to drive autonomously?), because the degraded situations are the most difficult and cumbersome to regulate.
The problem of the new statistical approaches to safety demonstration is an exciting problem facing all regulators. I am looking for some science behind this, if any reader has useful links please share!
Our current approaches to the regulation of system risk management and prevention of deadly accidents remains very much deterministic. In the most critical applications such as in nuclear power plants or aircraft controls, regulatory authorities require a deterministic demonstration of the links between input and outputs. Superfluous code that is not used needs to be removed, just in case. Older processors are used which reactions are fully known.
With the advances of Artificial Intelligence, this won’t be possible any more. In particular because the devices become black boxes that have learned to behave in a certain manner most of the time when exposed to certain stimulus. However deterministic proof of the relationship between input and output is impossible and we don’t quite know how it really works inside. It can only be a statistical measure.
This situation is an extensive challenge for the regulatory authorities that will have to regulate safety-critical applications based on AI such as automatic driving. Most current regulatory approaches will become obsolete.
Some regulatory authorities have identified this challenge but most have not, although this will constitute a real revolution in regulation.
Change management is about developing autonomy as part of a conscious evolution.
Change management without autonomy development is an illusion. It corresponds to the old command and control approach. It can only create deviations as soon as control is removed.
The only sustainable change is created by developing the autonomy of those who have to develop and implement it. Only thus will change be adapted to the situation and only thus can it be really deployed throughout an entire organization.
The development of this autonomy needs to be a conscious decision and process. If the organization is not ready to develop autonomy it can only keep those working approaches of the past.
While the development of drones and robots that could take themselves the decision to engage targets becomes closer, the issue of whether to develop such system becomes a conundrum. It is important to be able to face such a possible threat, at the same time usage of this type of weapon will need to remain very much controlled. Mechanisms similar to control of nuclear proliferation or chemical weapons might need to be put in place – with the particular challenge that no huge and noticeable industrial complex will be needed to produce such weapons.
The Open Letter by concerned scientists on autonomous weapons is interesting to read. It states “If any major military power pushes ahead with AI weapon development, a global arms race is virtually inevitable, and the endpoint of this technological trajectory is obvious: autonomous weapons will become the Kalashnikovs of tomorrow.”
At the same time, military might contend with enhancing human capabilities by teaming humans with robots, in particular to be able to take decisions in uncertain situations. But the issue needs to be tackled quickly because the consequences of robots engaging without control could become a proliferation issue.
The dominance of video over the internet is privatizing it: to get the video flowing, major operators like Netflix, Google etc have been obliged to invest heavily in infrastructure and this changes fundamentally the topology of internet. The internet has flattened and gotten more and more privatized. This is extensively explained in the Quartz post ‘The internet has been quietly rewired, and video is the reason why‘.
This does not go without creating an issue: “As Big Tech gobbles up more infrastructure and accounts for more internet traffic, it poses questions for the future of the network’s openness, says Farrell, the former ICANN executive. “It means the internet is evolving from being a peer-to-peer open standards network to being a proprietary set of, effectively, VPNs [virtual private networks],” she says. “Which users are not quite aware of—they think they’re on the open internet and they’re not.””
This evolution will have to be watched in particular when it comes to developing the infrastructure for those that have poor access to internet. The recent backlash against Facebook in India is just an example of things to come.
There are numerous definitions of leadership. Seen from the complexity view, a leader is someone that is able to create locally, more or less broadly, some alignment inside a complex organization.
In a complex system it is certainly difficult to create any sort of alignment. Contributors all have their own interest and are very inter-dependently linked and related to other contributors. However when one is able to create a dynamic movement and bring along the necessary contributors, astonishing things can happen. That’s probably what leadership in a complex world means.
This may be a new definition of leadership. At the same time I believe it is a useful approach to this issue. Seen from that perspective, a number of leadership practices become clearer and more founded in actual science.
As a leader, impress movement in complexity. It is will be even more powerful than what you believe.
After reinventing itself as a consulting company in the 1980s (after being a hardware company), IBM is reinventing itself again, this time around Artificial Intelligence, as described in length in this excellent NY Times article ‘IBM is counting on its bet on Watson, and paying big money for it‘. Whether that will effectively replace the struggling consulting activities remains to be seen, but this time this seems to be a major strategic move.
One of the interesting aspects from the ability to analyse large amounts of data is the possibility to help human decision. In the example quoted in the article, while in 99% of the cases of cancer diagnostics the machine arrived to the same conclusion as the experts (doctors) it also proposed in 30% of the cases alternative treatments, due to the fact it had digested the 160,000 cancer research papers published yearly.
This move away from consulting (which was very successful in the 1990s and corresponded certainly to a real need) is also another confirmation that the economic future probably lies in developing AI applications instead of IT systems consulting. Food for thought for many IT consulting companies!
I can’t quite believe it but according to the site statistics, this is our 1000th post!
What a journey from humble beginnings on October 11, 2010! And I am so pleased and proud to share with you, at the rythm of 3 posts per week.
There have been ups and downs along the way but as I find it useful even personally to sit down from time to time to develop my thoughts on what happens to the world I will certainly countinue for a long time sharing them with you!
I certainly hope we will continue together to explore the Fourth Revolution and what it implies for our daily lives.
Fourth Revolution is not just thoughts and a blog. It is also a company in Singapore with the ambition to actively change things. We are doing that on our scale through our investments and activities. Because in the Fourth Revolution, we also need to experiment and act. And we learn better thus.
Complex and chaotic systems can be described by mathematical equations that are in fact an extension and generalization of Quantum Mechanics equation. That’s what Ilya Prigogine (Nobel-price winner in 1977) explains in his excellent book “the laws of chaos” (apparently not available in English unfortunately).
We have argued numerous times that one of the precursors of the Fourth Revolution is the emergence of Quantum Mechanics, or at least the limits found to Newtonian Mechanics which founded the Industrial Age. The science of complexity and chaos is even newer. By finding that an extension and generalization of the maths of Quantum Mechanics is needed to describe it, we are indeed confirmed in our observation that it constitutes a further step towards the underlying paradigm of the Collaborative Age.
Complexity is still vastly misunderstood because it creates a rupture with the comfortable deterministic view of the world which we entertained during centuries. Its probabilistic nature, the fact that mere observation changes the observed world (like in Quantum Mechanics) makes it even more fascinating.
Welcome to the world beyond Quantum Mechanics and the Uncertainty Principle.
As mentioned in the paper, “Research has indicated that people who are better at detecting their heart rates perform better in laboratory studies of risky decision-making. When people were asked to gamble in laboratory settings, rapid and subtle bodily responses appeared to guide them away from unprofitable trades and toward profitable ones.”
The interesting aspect is that this research has been conducted on market traders, i.e. people that are used to take decisions in a complex environment under tremendous pressure. From the Nature abstract: “traders are better able to perceive their own heartbeats than matched controls from the non-trading population. Moreover, the interoceptive ability of traders predicted their relative profitability, and strikingly, how long they survived in the financial markets.”
To survive, learn to perceive your heartbeat better!