Benevolence means being well meaning and showing kindness. It is an older word that became a bit disused. The concept however, I believe, is becoming very important in today’s world.
We are always wondering what are the motives behind the actions of people. In particular in the daily office world, this question is always lurking behind in our minds. And this is becoming increasingly so.
However we know that some people, and some actions, are only motivated by goodwill and kindness that does not seek anything specific in return. We struggle sometimes to recognize this fact in particular in today’s fast paced environment full of unknown motives.
Showing and recognizing benevolence is an essential element of goodwill and credibility. It is important sometimes to do some actions without seeking any advantage, just for the pleasure of helping, supporting, creating stuff.
How much benevolence do you demonstrate on a day-to-day basis?
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!…
The reality is that 95%+ of our daily interactions with people remain at a too superficial level to figure out what it is they know we don’t know. The issue is then to figure out how to setup those conversations in a way to enrich our experience and their experience.
It all comes down to connecting in the right manner, demonstrating interest to the person, its interests and aspirations. It also come down to a benevolent attitude that does not seek immediate advantage or profit from the relationship.
Of course that takes time so we can’t do that for everyone we meet, but we can certainly do better.
Benevolence is important. I had written first the first sentence of this post “how to benefit from this knowledge”. But the point is not to benefit, but to share!
Let’s try to learn more about the world by connecting better with more people, learning exciting new stuff we did not even know existed and sharing our knowledge too!
“Everyone you Will Ever Meet Knows Something that You Don’t” is a quote by Bill Nye, and american science educator. It is a very powerful statement that I find by experience to be actually quite true. However we can only find out provided we take the time to establish the right connection to figure it out.
The reason for this situation is of course the variety of individual experience and interests.
We often tend to dismiss the knowledge that is available around us, while daily experience shows how fruitful it can be. For example on the workplace, leveraging on the interest and knowledge of the people constituting the team or the extended team is a very effective way to increase effectiveness. It is too often forgotten in particular with the race to efficiency.
Let’s never forget that anyone around us, however menial their occupation be, have something to teach us.
Independence and autonomy might seem quite similar but there is a substantial difference: autonomy does not preclude asking for support and help, while independence does.
Before proceeding further, let’s note that we apply those terms here in the personal sense and not in the diplomatic sense.
This distinction between the two concepts is essential because it shows that being independent is far more limiting than being autonomous. Autonomy implies being able to take one own’s decisions but at the same draw on help and support from others to reach one’s goals.
This is why we should strive personally for autonomy, not independence.
It has been 5 years since I left the employee status in a large corporation. I resigned in November 2011 and started my consultancy business on 1 Feb 2012. What an adventure with many ups and downs!
5 years and 5 companies founded on 2 continents later, with quite moderate but sustainable success, here are some experiences I would like to share from this journey:
Going on one’s own is not for the faint-hearted. It is tough, and requires a lot of dedication and effort. A lot of people I know do fail.
Family support is essential, as is caring for the comfort of the family.
Pay yourself fairly, while keeping enough in the company for growth. The money you own personally is your freedom.
Commercial skills are essential. They are worth a lot. I am still learning as it is not really my background. In reality I cover most of my companies’ commercial development. Develop these skills early if you can.
Do not start on your own. I made the mistake to start by myself alone. Now that I have experience having ventures with partners and working with teams of people I like and respect, I see how much more comfortable that is, even when it’s tough.
Highs will be high and downs will be very low. Brace for uncertainty and change. Keep reserves and be conservative in accounting.
Clients will not always be fair or follow previous agreements. Protect yourself with enough written stuff. Keep reserves for the unexpected (see 6.). And don’t follow the example of your clients: stay fair to your partners and contractors.
Small is beautiful. In today’s world it is possible to bring substantial change from a small structure. I am perfectly happy to keep my companies small but very ambitious when it comes to their impact to the world.
I am very proud of providing an opportunity to the people that work in the companies I am controlling, having been able to keep them as much as possible through the downs and this a great motive of satisfaction.
Having the freedom to experiment is an essential motivation. We don’t have the means to do a lot, but we can still experiment at the limit of technology some new services for our clients. It is a great motivator.
As you can see the positives far exceed the negatives. If you consider such a move, plan it well in advance. But do it, it is worth it.
Democracy is the political regime best adapted to complexity. The reason is that it allows bifurcations to happen at every election, i.e. depending on the country every 4 to 7 years. Those changes can be unexpected and worrying, but they happen more frequently and -one hopes- less abruptly than in other political regimes.
Elections are always creating surprises in particular in troubled times, and this has been demonstrated heavily in 2016 where in several western countries there has been a reaction against the establishment and from people who feel left aside from the world’s transformation (Brexit, Trump election).
It is a good property of a system setup to manage a complex world to be able to implement those important changes with this frequency.
Other political regimes will in fact only allow such changes much less frequently and therefore, they will be more abrupt and can even degenerate into civil wars.
We concur heavily with Churchill saying that “democracy is the worst form of government except all the others that have been tried“! And this conclusion on democracy should be kept in mind when we are not happy with election results.
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.