This is the End…

After 11.5 years and 1785 posts I have decided to move on and to close out the Fourth Revolution blog.

From time to time it is good to review one’s activities and decide what to keep and what to stop, and I have decided that this marvelous blog was something I needed to move on from.

I will find alternate ways to publish references, views and opinions.

In the meantime do not hesitate to contact me at jeremie.averous – at –!

All the best to you in this incredible Fourth Revolution world


How Physical and Knowledge Work Are Not Independent

In this interesting post ‘The Ingredients for Making Something that Lasts’, I noted an important statement: “We forget that there’s physical work involved in knowledge work, too. That we learn with our whole bodies and not just with the head. And it works both ways.”

The prime mover is work, and work involved our entire being, not just our head or our body. It is important to remember because this needs to influence many aspects of our activities, be they mostly intellectual or physical.

The way we feel or behave physically will influence our creativity and intellectual production. The way we feel mentally will heavily influence our physical performance. As humans both dimensions are intertwined, something we need to grow to respect and build upon.

Physical work and knowledge work are intertwined and indisociable. Let’s remember this in everything we do.


How Sustainable Growth Also Has Physical Limits

This interesting and well remarked Scientific American article ‘The Delusion of Infinite Economic Growth‘ reminds us that there are physical limits to growth, whatever more “sustainable” technologies are implemented. Any technology that scales find its physical limits.

Every stage of the life cycle of any manufactured product exacts environmental costs: habitat destruction, biodiversity loss and pollution (including carbon emissions) from extraction of raw materials, manufacturing / construction, through to disposal. Thus, it is the increasing global material footprint that is fundamentally the reason for the twin climate and ecological crises.”

While “Technological innovation and efficiency improvements are often cited as pathways to decouple growth in material use from economic growth. While technology undoubtedly has a crucial role to play in the transition to a sustainable world, it is constrained by fundamental physical principles and pragmatic economic considerations.”

In addition, economic growth is exponential and not linear: “unfortunately, the situation is even more dire. Economic growth is required to be exponential; that is, the size of the economy must double in a fixed period.” Thus, “the inescapable inference is that it is essentially impossible to decouple material use from economic growth.” As a result, more is required today than to develop ‘sustainable’ solutions: solutions to the future raw material crises also need to be investigated.

Even sustainable growth will find its limits – as the economy and technologies scale, they require increasingly raw material and space, often in an exponential manner. But the world is finite, therefore a change of paradigm may be required.


How Challenging the Energy Transition Will Be

This excellent article in The Atlantic ‘Why the Energy Transition Will Be So Complicated‘ provides an important reminder and insight into how dependent we are on carbonated fuel, and how tough it will be to change: “The degree to which the world depends on oil and gas is not well understood“.


The article underlines how much we are dependent on oil&gas for a variety of materials in addition to energy, and how pervasive usage of oil can be in our societies. As a result, some warn “that going into overdrive on transitioning away from fossil fuels would lead to major economic shocks similar to the oil crises that rocked the global economy in the 1970s. “Policymakers,” [Jean Pisani-Ferry] wrote, “should get ready for tough choices.”

The term energy transition somehow sounds like it is a well-lubricated slide from one reality to another. In fact, it will be far more complex: Throughout history, energy transitions have been difficult, and this one is even more challenging than any previous shift.” In addition, it is supposed to happen much quicker than any other such transitions in the past, necessarily impacting the value of assets and making investment into anything related to energy more hazardous. Previously such energy transitions typically took more than a century to be established and to replace previous energy sources.

I am personally convinced that oil & gas will remain an important industry in the next 2 decades, while coal may start to whither. The solution may lie more in carbon capture than cutting too fact our dependency on oil & gas.

The current energy transition will be more challenging and complex that usually anticipated, in particular because it is supposed to be much quicker than any such historical transition. Let’s not forget this in our anticipations.


How AI Algorithms Evolution Approaches Provide Insight Into Natural Evolution

This interesting article ‘AI is now learning to evolve like earthly lifeforms‘ provides some insight about advances in AI algorithm development. Researches are trying to find the most effective way for algorithms to go through the process of natural evolution. And this provides interesting learning about our natural world.


The interest of this research is of course also to enlighten our understand of the principles of natural evolution, and how to keep its cost low (as it requires many trials for very few successful variants). “In their new work, the researchers at Stanford aim to bring AI research a step closer to the real evolutionary process while keeping the costs as low as possible. “Our goal is to elucidate some principles governing relations between environmental complexity, evolved morphology, and the learnability of intelligent control,” they write in their paper.

It involves the simulation of robotic agents in an environment, with some evolution algorithm for the AI algorithm driving the creatures.

Interesting results include: “validating the hypothesis that more complex environments will give rise to more intelligent agents“, and “in line with another hypothesis by DeepMind researchers that a complex environment, a suitable reward structure, and reinforcement learning can eventually lead to the emergence of all kinds of intelligent behaviors.”

Teaching AI algorithms how to evolve provides interesting insights. The fact that more complex environments will give rise to more intelligent agents is definitely a key insight into life’s evolution.


How Most Data Problems Can Be Made Human-Size Before Going for AI

In this excellent post ‘What If You Can’t Afford AI?‘, Christopher C Penn explains how AI is only suited to very large problems and actually most people deal with human-size problems for which AI may not be very well suited.

AI is good at three things: processing data faster (and thus being able to handle a lot of it), processing data more accurately, and processing data in routine ways.”

But that presumes we have enough data to do all that labeling and processing. AI fails when we don’t have enough data. And therein lies the distinguishing factor, the real answer to the question. You need AI when you have machine-sized problems. You can use human solutions when you have human-sized problems.”

The point Christopher C Penn makes is that also a proper sampling of a small representative sample will also give quickly a proper estimate of trends and we don’t necessarily need a huge dataset and a trained AI to provide us guidance. In summary: be clever and don’t think AI will provide much more accurate responses than a clever sampling. “Find a way to reduce the data down to a human-sized problem and solve it with humans until you have enough resources – money, time, people – to work with the full-size dataset. Sampling data is a time-honored method to make big data smaller, and doesn’t require anything more sophisticated than a semester’s worth of statistics classes in university (assuming you did well in the class, of course). Make the data and the problem fit the resources you have to solve it as best as you can.”

Even if you are using AI, it is a good idea to check the robustness of the algorithm by some sampling anyway to check the main trends are adequately captured by the process

It is already possible to get good trends through proper data sampling and reduce problems to human-level problems before embarking on a complex and expensive AI solution. Don’t underestimate the power of this approach!


How Implementing AI Requires Organizational Transformation

This interesting article ‘Artificial intelligence: Everyone wants it, but not everyone is ready‘ takes an interesting angle on the spread of AI-driven systems throughout organizations: as for all new tools, success requires to change the way organizations work, and not all organizations are ready for that change.

While many AI and machine learning deployments fail, in most cases, it’s less of a problem with the actual technology and more about the environment around it,” says Harish Doddi, CEO of Datatron. Moving to AI “requires the right skills, resources,?and?systems.

While it’s arguably true that AI can add significant value to practically any department across any business, one of the biggest mistakes a business can make is to implement AI for the sake of implementing AI, without a clear understanding of the business value they hope to achieve“. In particular, understanding how data biases or poor data hygiene can affect AI algorithms, understanding those effects and how they influence performance appear to be an essential capability.

In addition, the organization processes and particularly the data production, gathering and structuring appears to be an essential area for review and upgrade when implementing AI-based tools.

Like any new powerful tool, AI has transformational impact on organizations and the way their data is gathered and managed. This should not be overseen when implementing those new capabilities.


How the Project Economy Has Finally Arrived

It has always be my conviction that economic activity would be increasingly driven as a multitude of temporary projects – thus my main activity around project management. This is finally recognized in this HBR piece ‘The Project Economy Has Arrived‘.

Quietly but powerfully, projects have displaced operations as the economic engine of our times. That shift has been a long time coming.” “In Germany, for example, projects have been rising steadily as a percentage of GDP since at least 2009, and in 2019 they accounted for as much as 41% of the total. Precise data is hard to come by for other countries, but similar percentages are likely to apply in most other Western economies. The percentages are probably even higher in China and other leading Asian economies, where project-based work has long been an important source of growth.”

This transformation to a project economy will have profound organizational and cultural consequences. The problem is, many leaders still don’t appreciate the value of projects and write them off as a waste of time.

The author has been very active in the Project Management Institute and can thus slightly partial to the subject. However the reality is here and many leaders do not necessarily understand the implications of this shift in terms of work organisation and leadership. The image in this post is one of a turbulent flow, which is how I see the organization of the future: a number of projects (the vortices) that appear and disappear in the flow like projects with a limited time span.

Leaders must now account for the fact that probably a majority of value-creating endeavors is project-based. This must lead to significant shifts in organization and competencies to deal with those projects effectively.


How Marketing Rules Have Changed Significantly In a Few Years with AI

Since the beginning of the Fourth Revolution there is a growing concern of the gap building between technology have and have nots. This has been quite alleviated since the arrival of the smart phone. However, there is still a growing issue when it comes to understand how algorithms work and take advantage of them. In this eye-opening piece ‘What’s On My Mind: What About the Gap?‘, Christopher Penn provides a compelling example about the difference it can make in marketing to understand AI-driven algorithms, because it drives directly what potential customers see or not.

In the era before popular, commercial use of machine learning, success in business was largely a combination of effort and luck. Effort encompasses the skill needed to make a good product and sell it well, and luck encompasses being in the right place at the right time, whether you’re the local burger joint or a multinational corporation.”

Today, data science, machine learning, and AI have thrown a bit of a wrinkle into this. So much of our lives are intermediated by machines and machine learning. What products we see, what ads we see, what news we see, what friends we see in the digital realm – which is the primary realm now for so many of these tasks ever since the smartphone became our external brains – are all controlled by machines and algorithms.”

Christopher Penn then continues to provide the example of what he could achieve easily given his background in data science for a florist shop friend, substantially increasing ranking and visibility on the internet through clever understanding of data analytics.

For a while, the Internet presented a level playing field where a small business could appear larger than it was, where relevance and not budget could win the day. That 20-year golden era of Internet marketing – 1997-2017 – has been supplanted by the AI-powered marketing era, and this is an era in which whoever has the technical resources to win will do so.

To be clear, having great products, good prices, and phenomenal service will still be fundamental to succeeding at business. No amount of AI will change a crap product, prices that aren’t competitive, or abusive service and get people to buy, long-term, who would not have bought before. But becoming visible, being seen, will be harder for those without skillful use of AI.”

Certainly a very useful warning. AI and data analytics knowledge is now the key to being visible and we all need to understand that the game has changed only a couple a years. Marketing is now different, rules are different and thus the game changed.


How Telling a Story Makes Start-Up Pitches Successful

This article ‘I Boiled Down Hundreds of Successful VC Pitches to One Winning Formula‘ provides some advice as to how to build start-up pitches for them to be successful in funding.

My answer is always the same: tell a story. Humans have responded to storytelling for all our evolutionary history — we’ve been passing down oral history and painting tales on cave walls for literally thousands of years. When you want to nail your pitch deck, the best way is to lean on that common love of stories we all have — and the fact that stories are far more memorable than facts, figures, data, numbers, bits and bytes.”

According to the author, this includes a vision of where the company will be when successful, how to resolves the pain of the future customer, how your product will slain the villain pain, and how the world will be better ever after.

From my experience it is certainly an excellent advice because it will create an emotional connection with the audience, even more so if it can relate to the story somehow; and we are all longing for a story where everything ends well.

For successful pitches, try to tell a compelling story providing at the same time a comforting vision of a bright future.


How Electromagnetic Weapons May Be Decisive in Future Wars

This excellent article ‘‘Revolution in warfare’: Israel has new ‘invisible’ defense system‘ provides useful insight into electromagnetic weapons that aim to destroy the enemy’s electronic systems, which are now so important in all weaponry.

The weapon, which reportedly can halt electronic capabilities of an enemy, is part of a new suite of electromagnetic warfare called Scorpius. The Scorpius “missiles” send narrowly targeted beams of energy that disrupt enemy electronic sensors, navigation, radar or other electronic activity.” Also, this new weapon is supposedly much more discriminating as “the new Scorpius weapons have an advantage over older forms of electromagnetic warfare because they can send targeted beams without interfering with unintended targets.”

We can also observe this type of weapons to be deployed in the form of drone killing devices. It may be difficult to protect electronics from such weapons if the electromagnetic energy sent is very dense. This has not yet be deployed in major conflicts between technological armies, but could certainly be a game changer in terms of requiring a new generation of reinforced electronics in all weapons to survive electromagnetic aggressions.

Electromagnetic weapons are now operational and will have a significant impact on how future wars may develop, not to mention their potentiel effect on unprotected civilian infrastructure. This is certainly a significant change ahead!


How Brains Predict Perception to Save Energy

This exciting article ‘To Be Energy-Efficient, Brains Predict Their Perceptions‘ discusses how “results from neural networks support the idea that brains are “prediction machines” — and that they work that way to conserve energy“. This has wide ranging consequences in perception and how we may be deceived by this predictive trait.

Many neuroscientists are pivoting to a view of the brain as a “prediction machine.” Through predictive processing, the brain uses its prior knowledge of the world to make inferences or generate hypotheses about the causes of incoming sensory information. Those hypotheses — and not the sensory inputs themselves — give rise to perceptions in our mind’s eye. The more ambiguous the input, the greater the reliance on prior knowledge.”

This results in the development of many models that approximate brain behavior when it comes to perception. Much work has been done on developing neural network models mimicking the brain, and analyzing their energy consumption. “The takeaway is that a neural network that minimizes energy usage will end up implementing some sort of predictive processing — making a case that biological brains are probably doing the same.”

This also explains many effects of visual illusions where the brain unconsciously infers an explanation to the image which may oversee another or be plainly wrong. This well-known phenomenon is also used in psychology to uncover our unconscious by studying what interpretations we spontaneously provide in those situations.

It is not surprising that evolution has found a way to minimise the brain energy consumption, which is already draining a lot of energy for itself. The balance between prediction and actual observation it has found may have made sense in the past, but does it make sense now? How can we exploit it or overcome it depending on the circumstances?

In any case the fact that neural-networks models have been developed of the brain that allow to explain some of its behaviors is a great step forward in understanding at least the perceptual part of the brain.