Jeremie Averous's Blog, page 26
July 11, 2020
How Company Ownership Can Be Extractive or Generative
In the very interesting (and recommended) book ‘The systems view of life‘, the work of Marjorie Kelly is mentioned about various ways of owning a company. She opposes extractive and generative ownership.
Her view of ownership design is that five essential patterns work together to achieve either an extractive or a generative design:
purpose,membership,governance,capitaland networks.
Extractive ownership has a mainly a financial purpose: maximizing profits. It is actually the classical business organisation that emerges from the classical theory. Generative ownership, however, has a living purpose: creating the conditions for life. It is closer to non-profit organisations and is generally more characteristic of smaller, more human-size organisations.
I find this analysis quite interesting as it creates a distinct framework to analyse the economy and the economic actors. And it is quite true that many smaller organisations correspond more to the generative type, as they contribute to develop living networks without focusing exclusively on profit.
Generative vs extractive ownership is quite a useful distinction.
July 9, 2020
How to Define Levels of Remote Working Organisational Proficiency
This Medium post ‘The Five Levels of Remote Work — and why you’re probably at Level 2‘ mentions how Matt Mullenweg, the founder of Automattic (managing amongst other products the WordPress platform) defines 5 levels of remote work.
Those 5 levels are (summarized with my interpretation):
level 1: majority office-based with some remote worklevel 2: recreating the office online (fixed hours, large meetings, office-type rituals)level 3: adapting to the remote working medium by changing rituals, minimizing meetings and maximizing asynchronous written informationlevel 4: fully asynchronous work, with no requirement for time coordination. This has also the benefit to avoid interruptions and allow longer focus timeslevel 5: nirvana. Not so clear for me. “Mullenweg equates this level with having more emphasis on ‘environment design’, insofar as the organisation’s culture, and the physical environment people work in is concerned“
The post mentions that “Companies that truly practice asynchronous communication have stepped out of the industrial revolution, and no longer conflate presence with productivity, or hours with output, as one might on the factory floor.” This is probably more like the Collaborative Age will look like.
Also it is worth mentioning that it is recommended to organise physical team bonding events on a periodic basis to support a majority of remote work time.
It is true that most of my corporate clients are stuck on level 2. What remote work level have you achieved so far?
July 7, 2020
How AI Algorithms Now Get Generated by Natural Selection
In this breathtaking post ‘Google Engineers ‘Mutate’ AI to Make It Evolve Systems Faster Than We Can Code Them‘, latest developments of AI algorithm generation is described.
It does not yet look like it really works for advanced algorithms, but there are possibilities that very quickly algorithms will evolve that will produce novel solutions to certain simple problems such as image recognition. This is quite an exciting – and troubling – development. It was due to arrive though with the development of ‘genetic algorithms’ that simulate natural selection.
Of course the novelty is now to apply it to AI algorithms which are by themselves heavier and more cumbersome to handle. Still it gives quite an interesting perspective of what we can expect in the short and medium term. Scary!
July 4, 2020
How to Ensure Data Is Not a New Toxic Waste
There are quite a number of discussions about the ambiguous status of data in the new Collaborative Age. On one side it is celebrated as the new oil (refer to our post How Data Really is the New Oil, and Better); on the other side some argue that it is rather a toxic waste as in this interesting column ‘Data – the new oil, or potential for a toxic oil spill?‘
The point of the article is linked to data security and the harm that can be done through data theft and possible advanced recombination with other data sources that would also have been stolen. With zillions of data generated everyday, the argument is that one day or the other, sensitive data will leak and produce toxic effects on the wider data landscape and digital environment.
Specifically, the article mentions “Re-identification of anonymized data-sets [which] is a hot research topic for computer science today” and the fact that the breaches are additive in nature, progressively weakening privacy and sensitive data.
Of course, unclean data (refer to our post on data hygiene) is also another issue of toxic waste that may influence the wider data ecosystem if it is used as a basis for AI algorithm teaching or other reference applications.
The large amounts of data available today are a great source of value and at the same time are fraught with risks – as any new technology. Which will win first? My optimistic self is rather confident that the benefits will outweigh the risks, but that does not detract from the need to reinforce security and privacy.
Let’s make sure data is the source of value and not a toxic waste.
July 2, 2020
How Data Hygiene Becomes Essential
In an AI conference lately I was struck by the mention of new jobs such as data hygienist and AI trainer. I did not realize how important data hygiene was – up to becoming a new profession!
Data hygiene is in reality quiet critical to AI development. Poor data hygiene is certain to create all sort of issues and false positive, and to lengthen dramatically the time it would take for an AI algorithm to learn its part.
Data hygiene is actually hard work because of the sheer size of the data bases to clean up, and the need to distinguish between rubbish and actual legitimate data points. It requires specific tools and particular attention, not to mention time. Hence it is a significant investment, but is found to be quite worthwhile apparently compared to the benefits.
Before we did not care so much about the quality of data in our databases – although there is still this old adage about garbage in, garbage out. Now we need a much higher quality level and apparently it is quite a challenge to achieve it.
Welcome to the world of data hygiene and data hygienists!
June 30, 2020
How We Should Worry About Who We Have Helped Become Better People
Clayton Christensen, the innovation scholar that wrote so many books about innovation, passed away recently. He wrote “Don’t worry about the level of individual prominence you have achieved; worry about the individuals you have helped become better people.”
This quote if of course inspiring, and coming from someone with the life experience of Clayton Christensen, quite interesting too – since he had certainly reached a very high level of prominence.
This probably explains his drive to be a university professor while he could have had a very successful career in consulting and private ventures.
Still its reminds us that what people will remember is how much we helped them become better people. How can we work to achieve this better?
June 27, 2020
How Easy It Is to Fool Artificial Intelligence
I love this funny post ‘Hackers stuck a 2-inch strip of tape on a 35mph speed sign and successfully tricked 2 Teslas into accelerating to 85mph’. The point here is not really about Tesla reliability, but how easy it is still to trick Artificial Intelligence recognition tools.
In this particularly funny example the researchers just changed slightly the speed limit sign and it was enough to trick the sign recognition algorithm that watches the road and determines what is the acceptable speed (see the image). This type of system is increasingly prevalent in cars generally just to update the actually applicable speed limit that is provided as a guidance to the driver.
What is really impressive here is obviously how easy it seems to fool an Artificial Intelligence-based recognition software. If that’s the case for something so obvious and mundane, then what are the consequences for more complex applications like face recognition? Are they also as easy to fool?
Artificial Intelligence does not seem to be quite completely robust yet. Some progress is still needed!
June 25, 2020
How Irrelevant Industrial Age Approaches Are Still Prevalent
I read recently this account ‘Exclusive: Barclays installs Big Brother-style spyware on employees’ computers‘ of how a bank had installed a productivity measuring tool on its employees computer, that issues warning when people pause doing stuff on their computer. And indeed after a quick search I realized there seem to be quite an offer of “productivity monitoring tools” on the market.
This is an impressive application of the Industrial Age mindset as we move into Collaborative Age. Monitoring my computer activity would have absolutely no meaning as to my productivity: my work is about creativity, facilitating, getting people to work together. How can you expect to measure that based on my active interaction with my computer?
The article does not detail what were the specific tasks of the targeted employees, but in most modern organisations people don’t spend their entire day in front of the screen just repeatedly doing tasks that can be measured for actual productivity. Only some specific administrative departments could possibly be considered for that to be relevant.
In any case installing some software is a serious breach of confidence with regard to the employees and says a lot about the workplace culture that must be prevalent there.
In the Collaborative Age, productivity measurement must be more comprehensive than just interaction with a computer; and in case, trust will ever be a more essential characteristic of healthy workplaces.
June 23, 2020
How Difficult It Becomes to Create Virtual Legends for Covert Operations
We already know ‘How Easy Modern Technology Makes It For Spies‘, it seems on the other hand that it makes it more complicated on some other aspects, like creating legends for secret agents. As explained in this interesting article ‘There Is No ‘Going Dark:’ Always-On Surveillance Posing Risks To US Covert Operations‘
In this fascinating account, US government seems to be quite worried about being able to maintain secrecy around its operations. Available data makes it relatively easy to know the background of individuals and what their real occupation is likely to be. Agents can easily be under constant surveillance as soon as they are in a foreign country. Biometric verification makes it much more difficult to change identities and travel under a different name.
“This is how spy craft works now. Everything is online, digitized, and likely to be accessed by agents of enemy states. There’s no flying under the digital radar. And if it’s true for government employees, it’s doubly true for US citizens who don’t have the ability to alter/remove collected data or a network of security professionals doing whatever they can to protect them (and their data) from outsiders.”
Even better, some recent reports show how critical it is not allow soldiers to carry their hand phone in battle, making it too easy to geo-localise them or to identify the source of their phone signal.
Our privacy is gone, and one consequence is that it is much more difficult for covert operations to be setup. It becomes increasingly difficult to make believe we are someone else than what really are.
June 20, 2020
How Long Term Work Motivation is Related to Alignment with Purpose
Following up from the previous post ‘How Sexy Startups Can Also Sometimes Be Toxic Workplaces‘, one important aspect is that poor alignment with purpose generally don’t make it a sustainable venture. Toxic workplace cultures can’t be sustainable. Because if we want to remain individually motivated, our work needs to align with some purpose – and not just external motivators like compensation. This is developed quite well in Steve Pavlina’s post ‘Numbers vs Alignment‘
I find the point well written “If the numbers in your work (like sales and profits) matter more than the alignment of your work (like fulfillment, purpose, and appreciation), then even if you succeed on those terms, you may end up with bigger numbers but with lower alignment, which can strangle your motivation.”
And the corollary “People so often underestimate how much motivation matters – and especially how sensitive it is to alignment. It’s so easy to make misaligned decisions that eventually drag down motivation and lead to a place of stagnation, where it’s possible to be stuck for years.”
How aligned is your work with your purpose and what fulfills you? Is your motivation thus sustainable and not just temporary?
