A response to the fear of “AI could spell the end of the human race“

Artificial intelligence programs that predict, suggest and act extrapolating from our requests are already being used in everyday tools, and this technology cannot be stopped. I just changed my car and it’s incredible how many options are based in AI, providing a lot of functionalities to assist me, the driver, making it almost unthinkable that I could do without them.

At the same time we start hearing famous voices  such as the ones of Elon Musk, Steven Hawkings  and Bill Gates  warning us that “AI could spell the end of the human race“.

Their concerns of the potential issues that the rise of AI presents are real, and they will need to be addressed. This is why I liked this video from Stuart Russel, where he proposes 3 principles for creating a safer AI.

 

The King Midas Problem

Midas request was to be able to transform everything he touched into gold. His wish was granted but then he died because EVERYTHING he touched was transformed in gold, even his food. 

Current AIs are facing that same dilemma, they require from us (programmers) to be very specific and careful with the objectives we put into them. As Stuart Rusel says: “better be quite sure that the purpose put on the robots is what we want”.

He proposes to implement these following principles to make sure AI’s programs will be helpful to humans:

The laws for ‘Human compatible AI’

1. AI Goal Is To Maximize The Realization of Human Values

Robots should not have an objective per se,  with the exception of maximizing the realization of human values. This law will  overrule  Asimov’s self-preservation rule, making the AI truly altruisitic.

2. The Law of Humility

As our human values will not be completely defined, the AIs will need some humility to understand that they may not know what are the values they are trying to maximise. This will force them to observe and adapt the values to those observations. 

This law  is important because it avoids the problem of the mindless poursuit by eliminating the certainty of a single known objective to be maximised.

3. Human Behavior, The Information Source of Human Values

AIs  should try to understand the motivations behind our behavior, instead of copying our strict behavior. And they should be designed to satisfy the desires of everybody, not of one in particular.

But There Is Still Room For Improvement

Even following these rules, not everything may work as well as expected:

The GO master did not wanted to loose, he just couldn’t foresee the result of his move. But to understand this the AI should know the mental limitations of us Humans.

Or the emotional value of something may not be correctly weighted against an unfulfilled need, as in the example of making dinner of the cat..

We have huge incentives to get it right, because one bad example will make people mistrust AIs bringing a stop to their development.

 

 

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How our economy is shifting towards network-centric players

Managing our hub economy, HBR

I loved this article from the Harvard Business Review: Managing Our Hub Economy,by Marco Iansiti and Karim R. Lakhani, the authors explain in a very clear way what we are already experiencing in the last decade already at the macro-level economy.

The global economy is coalescing around a few digital superpowers. We see unmistakable evidence that a winner-take-all world is emerging in which a small number of “hub firms”—including Alibaba, Alphabet/Google, Amazon, Apple, Baidu, Facebook, Microsoft, and Tencent—occupy central positions. While creating real value for users, these companies are also capturing a disproportionate and expanding share of the value, and that’s shaping our collective economic future. The very same technologies that promised to democratize business are now threatening to make it more monopolistic.

Beyond dominating individual markets, hub firms create and control essential connections in the networks that pervade our economy. Google’s Android and related technologies form “competitive bottlenecks”; that is, they own access to billions of mobile consumers that other product and service providers want to reach. Google can not only exact a toll on transactions but also influence the flow of information and the data collected.

These big ‘Hub’ companies, as these authors call them, are companies that you cannot ignore when you want to do business in many markets today.  The interesting point of this article is that those same companies have a great competitive advantage over traditional companies in a lot of other markets. And each time they dominate in a different market, their competitive advantage grows to capture yet more easily the future next market they’ll wish to enter.

This is flipping, because one of the great advantages we all see on being connected through Internet and being heard by (almost) everybody is the democratisation of power, the opening of opportunities for everybody… and what is really happening is that the same companies that are offering the inter-connections are growing so much that they are not avoidable, so they monopolise the communications channels.

Hub firms don’t compete in a traditional fashion—vying with existing products or services, perhaps with improved features or lower cost. Rather, they take the network-based assets that have already reached scale in one setting and then use them to enter another industry and “re-architect” its competitive structure—transforming it from product-driven to network-driven. They plug adjacent industries into the same competitive bottlenecks they already control.

For example […] Google’s automotive strategy does not simply entail creating an improved car; it leverages technologies and data advantages (many already at scale from billions of mobile consumers and millions of advertisers) to change the structure of the auto industry itself.[…]

If current trends continue, the hub economy will spread across more industries, further concentrating data, value, and power in the hands of a small number of firms employing a tiny fraction of the workforce.[…]

To remain competitive, companies will need to use their assets and capabilities differently, transform their core businesses, develop new revenue opportunities, and identify areas that can be defended from encroaching hub firms and others rushing in from previously disconnected economic sectors. Some companies have started on this path—Comcast, with its new Xfinity platform, is a notable example—but the majority, especially those in traditional sectors, still need to master the implications of network competition.

In this article, the authors encourage the ‘hub’ companies to realize the impact they have on society, the resentment that could rise if their power is not wisely used.

Most importantly, the very same hub firms that are transforming our economy must be part of the solution—and their leaders must step up. As Mark Zuckerberg articulated in his Harvard commencement address in May 2017, “we have a level of wealth inequality that hurts everyone.” Business as usual is not a good option. Witness the public concern about the roles that Facebook and Twitter played in the recent U.S. presidential election, Google’s challenges with global regulatory bodies, criticism of Uber’s culture and operating policies, and complaints that Airbnb’s rental practices are racially discriminatory and harmful to municipal housing stocks, rents, and pricing.

Thoughtful hub strategies will create effective ways to share economic value, manage collective risks, and sustain the networks and communities we all ultimately depend on. If carmakers, major retailers, or media companies continue to go out of business, massive economic and social dislocation will ensue. And with governments and public opinion increasingly attuned to this problem, hub strategies that foster a more stable economy and united society will drive differentiation among the hub firms themselves.[…]

A real opportunity exists for hub firms to truly lead our economy. This will require hubs to fully consider the long-term societal impact of their decisions and to prioritize their ethical responsibilities to the large economic ecosystems that increasingly revolve around them. At the same time, the rest of us—whether in established enterprises or start-ups, in institutions or communities—will need to serve as checks and balances, helping to shape the hub economy by providing critical, informed input and, as needed, pushback.

They explain that with the growing connectivity, we share information at near-zero marginal cost. Thus networks are creating value:

Metcalfe’s law states that a network’s value increases with the number of nodes (connection points) or users—the dynamic we think of as network effects. This means that digital technology is enabling significant growth in value across our economy, particularly as open-network connections allow for the recombination of business offerings[…]

But that value is not much distributed among players to begin with, moreover the bigger the network, the stronger effect of attraction that it will exert, thus exacerbating the differences:

But while value is being created for everyone, value capture is getting more skewed and concentrated. This is because in networks, traffic begets more traffic, and as certain nodes become more heavily used, they attract additional attachments, which further increases their importance. This brings us to the third principle, a lesser-known dynamic originally posited by the physicist Albert-László Barabási: the notion that digital-network formation naturally leads to the emergence of positive feedback loops that create increasingly important, highly connected hubs. As digital networks carry more and more economic transactions, the economic power of network hubs, which connect consumers, firms, and even industries to one another, expands. Once a hub is highly connected (and enjoying increasing returns to scale) in one sector of the economy (such as mobile telecommunications), it will enjoy a crucial advantage as it begins to connect in a new sector (automobiles, for example). This can, in turn, drive more and more markets to tip, and the many players competing in traditionally separate industries get winnowed down to just a few hub firms that capture a growing share of the overall economic value created—a kind of digital domino effect.

They give then some well-known examples of our near past:

Just a few years ago, cell phone manufacturers competed head-to-head for industry leadership in a traditional product market without appreciable network effects. [..] But with the introduction of iOS and Android, the industry began to tip away from its hardware centricity to network structures centered on these multisided platforms. The platforms connected smartphones to a large number of apps and services. Each new app makes the platform it sits on more valuable, creating a powerful network effect that in turn creates a more daunting barrier to entry for new players. Today Motorola, Nokia, BlackBerry, and Palm are out of the mobile phone business, and Google and Apple are extracting the lion’s share of the sector’s value. The value captured by the large majority of complementors—the app developers and third-party manufacturers—is generally modest at best.

The domino effect is now spreading to other sectors and picking up speed. Music has already tipped to Apple, Google, and Spotify. […] On-premise computer and software offerings are losing ground to the cloud services provided by Amazon, Microsoft, Google, and Alibaba. In financial services, the big players are Ant, Paytm, Ingenico, and the independent start-up Wealthfront; in home entertainment, Amazon, Apple, Google, and Netflix dominate.

Where are powerful hub firms likely to emerge next? Health care, industrial products, and agriculture are three contenders. But let’s examine how the digital domino effect could play out in another prime candidate, the automotive sector […].

The authors then describe their analysis of the transformation that is going on in the automotive sector:

As with many other products and services, cars are now connected to digital networks, essentially becoming rolling information and transaction nodes. This connectivity is reshaping the structure of the automotive industry. When cars were merely products, car sales were the main prize. But a new source of value is emerging: the connection to consumers in transit. […] If consumers embrace self-driving vehicles, that one hour of consumer access could be worth hundreds of billions of dollars in the U.S. alone.

Which companies will capitalize on the vast commercial potential of a new hour of free time for the world’s car commuters? Hub firms like Alphabet and Apple are first in line. They already have bottleneck assets like maps and advertising networks at scale, and both are ready to create super-relevant ads pinpointed to the car’s passengers and location. […]

The transformation will also upend a range of connected sectors—including insurance, automotive repairs and maintenance, road construction, law enforcement, and infrastructure—as the digital dominos continue to fall. […]

In conclusion :

To reach the scale required to be competitive, automotive companies that were once fierce rivals may need to join together. […]

Of course, successful collaboration depends on a common, strongly felt commitment. So as traditional enterprises position themselves for a fight, they must understand how the competitive dynamics in their industries have shifted.

I think this analysis is highly accurate and we can expect similar developments in other industries.  They give a good advice to bare in mind when defining the best strategy for the long term.

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Embrace difficulties to stay mentally fit and happy!

I just came by this old article from Ian Leslie in The Economist magazine, it’s about a thought: embrace difficulties when they arise, they force us to be more creative and bring more satisfaction when we overcome them.

There are two ideas intertwined here: the first one is that when things come too easy, we don’t savor them enough. In French I would say « Il faut de la pluie pour faire le beau temps ».

This article brought up a memory of my childhood: we had the means to eat good meat every day. Yes, you can argue that having meat every day is not healthy, but having been brought up in Argentina, well, meat (of any kind) was mandatory at the menu! The thing is that I remember a period we ate beef tenderloin, that is a very tender cut of beef meat. Obviously, we appreciated that cut, and for a long period, every dish at home containing beef meat was done with that cut.  On the oven, as a steak, or in a wok, it was always tenderloin.

Believe me, you can get tired of it!  After a while, whenever I went for dinner to friends and they had another cut, I really savored it, even if it was not so tender.

What about not having money limitations? Yes, I’m sure I would go for a ravaging shopping for a while… until I’ll end up having more than what I need, more than what I could wear on a season! And what after that?  Shopping will not taste the same ?

It’s the same on other levels. At work, if there is no challenge, we’d lose interest, emotion.

But not only that, here is the second idea: challenges force us to think, guide our imagination and help us to come up with innovative solutions. And after the exercise, we end up with a sense of satisfaction of having solved the problem that we would not have experienced without the problem in the first place. This sense of satisfaction for having stretched our brain muscle is equivalent to the endorphin’s after a physical exercise!

Our brains respond better to difficulty than we imagine. In schools, teachers and pupils alike often assume that if a concept has been easy to learn, then the lesson has been successful. But numerous studies have now found that when classroom material is made harder to absorb, pupils retain more of it over the long term, and understand it on a deeper level. Robert Bjork, of the University of California, coined the phrase “desirable difficulties” to describe the counter-intuitive notion that learning should be made harder by, for instance, spacing sessions further apart so that students have to make more effort to recall what they learnt last time. Psychologists at Princeton found that students remembered reading material better when it was printed in an ugly font.

So remember next time you encounter a pebble on your way : embrace the opportunity of some brain gymnastic and enjoy life!

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Using The Past To Discover What The Customer Will Want Next

I loved the article What’s your best innovation bet? by Melissa Schilling in this summer issue of the Harvard Business Review, as it has always been very hard to guess the future:

Image from Magda Kochanowicz

Melissa Schilling says that “By mapping a technology’s past, you can predict what future customers will want.”  For that she explains her method:

  • 1 – Identify the key dimensions

What she means here is to examine/analyse/determine the different aspects in which the technology has evolved, like on processing speed or on precision just to mention some typical dimensions, and to relate them to the need of users: how much has the technology satisfied that need? She gives a clear example with the recording industry, where the basic dimension for many years was the audio fidelity:

By the mid-1990s, both industries were eager to introduce a next-generation audio format. In 1996 Toshiba, Hitachi, Time Warner, and others formed a consortium to back a new technology, called DVD-Audio, that offered superior fidelity and surround sound. They hoped to do an end run around Sony and Philips, which owned the compact disc standard and extracted a licensing fee for every CD and player sold.

Sony and Philips, however, were not going to go down without a fight. They counterattacked with a new format they had jointly developed, Super Audio CD. Those in the music industry gave a collective groan; manufacturers, distributors, and consumers all stood to lose big if they bet on the wrong format. Nonetheless, Sony launched the first Super Audio players in late 1999; DVD-Audio players hit the market in mid-2000. A costly format war seemed inevitable.

You may be scratching your head at this point, wondering why you’ve never heard about this format war. What happened? MP3 happened. While the consumer electronics giants were pursuing new heights in audio fidelity, an algorithm that slightly depressed fidelity in exchange for reduced audio file size was taking off. Soon after the file-sharing platform Napster launched in 1999, consumers were downloading free music files by the millions, and Napster-like services were sprouting up like weeds.

If you wonder: ”who could have predicted the disruptive arrival of MP3? How could the consumer electronics giants have known that a format on a trajectory of ever-increasing fidelity would be overtaken by a technology with less fidelity?” Well, that’s just the method she’s presenting in this article, which first step is identifying the different dimensions at play.

For example, computers became faster and smaller in tandem; speed was one dimension, size another. Developments in any dimension come with specific costs and benefits and have measurable and changing utility for customers. Identifying the key dimensions of a technology’s progression is the first step in predicting its future.

To determine these dimensions, trace the technology’s evolution to date, starting as far back as possible. Consider what need the technology originally fulfilled, and then for each major change in its form and function, think about what fundamental elements were affected.

Tracing its [the recording industry] history reveals six dimensions that have been central to its development: desynchronization, cost, fidelity, music selection, portability, and customizability. Before the invention of the phonograph, people could hear music or a speech only when and where it was performed. When Thomas Edison and Alexander Graham Bell began working on their phonographs in the late 1800s, their primary objective was to desynchronize the time and place of a performance so that it could be heard anytime, anywhere. Edison’s device—a rotating cylinder covered in foil—was a remarkable achievement, but it was cumbersome, and making copies was difficult. Bell’s wax-covered cardboard cylinders, followed by Emile Berliner’s flat, disc-shaped records and, later, the development of magnetic tape, made it significantly easier to mass-produce recordings, lowering their cost while increasing the fidelity and selection of music available.

For decades, however, players were bulky and not particularly portable. It was not until the 1960s that eight-track tape cartridges dramatically increased the portability of recorded music, as players became common in automobiles. Cassette tapes rose to dominance in the 1970s, further enhancing portability but also offering, for the first time, customizability—the ability to create personalized playlists. Then, in 1982, Sony and Philips introduced the compact disc standard, which offered greater fidelity than cassette tapes and rapidly became the dominant format.

[…] I usually ask teams to agree on three to six key dimensions for their technology.

The recurring dimensions across industries are: ease of use, durability and cost.  To foresee the future, it is worth also to imagine new  dimensions worth exploring. A good tip to come up with those new aspects is to think big, no constraints, what could the customer want in an ideal world.

Folklore has it that Henry Ford once said, “If I had asked people what they wanted, they would have said faster horses.” If any car maker at the time had really probed people about exactly what their dream conveyance would provide, they probably would have said “instantaneous transportation.” Both consumer responses highlight that speed is a high-level dimension valued in transportation, but the latter helps us think more broadly about how it can be achieved. There are only limited ways to make horses go faster—but there are many ways to speed up transportation

  • 2 – Locate your position

For each dimension, examine the value consumers are receiving for actual technology

This will help reveal where the greatest opportunity for improvement lies.

[..] For example, the history of audio formats suggests that the selection of music available has a concave parabolic utility curve: Utility increases as selection expands, but at a decreasing rate, and not indefinitely. When there’s little music to choose from, even a small increase in selection significantly enhances utility. Consider that when the first phonographs appeared, there were few recordings to play on them. As more became available, customers eagerly bought them, and the appeal of owning a player grew. Increasing selection even a little had a powerful impact on utility. Over the ensuing decades, selection grew exponentially, and the utility curve ultimately began to flatten; people still valued new releases, but each new recording added less additional value. Today digital music services like iTunes, Amazon Prime Music, and Spotify offer tens of millions of songs. With this virtually unlimited selection, most customers’ appetites are sated—and we are probably approaching the top of the curve.

Many dimensions have S-shaped curves: Below some threshold of performance there is no utility, but utility increases quickly above that threshold and then maxes out somewhere beyond that.

  • 3 – Determine your focus

Once you know the dimensions along which your firm’s technology has (or can be) improved and where you are on the utility curves for those dimensions, it should be straightforward to identify where the most room for improvement exists. But it’s not enough to know that performance on a given dimension can be enhanced; you need to decide whether it should be. So first assess which of the dimensions you’ve identified are most important to customers. Then assess the cost and difficulty of addressing each dimension.

For example, of the four dimensions that have been central to automobile development—speed, cost, comfort, and safety—which do customers value most, and which are easiest or most cost-effective to address?

[..] Tata Motors’ experience with the Nano is instructive. The Nano was designed as an affordable car for drivers in India, so it needed to be cheap enough to compete with two-wheeled scooters. The manufacturer cut costs in several ways: The Nano had only a two-cylinder engine and few amenities—no radio, electric windows or locks, antilock brakes, power steering, or airbags. Its seats had a simple three-position recline, the windshield had a single wiper, and there was only one rearview mirror. In 2014, after the Nano received zero stars for safety in crash tests, analysts pointed out that adding airbags and making simple adjustments to the frame could significantly improve the car’s safety for less than $100 per vehicle. Tata took this under advisement—and placed its bets on comfort. All 2017 models include air-conditioning and power steering but not airbags.

Once you have identified the dimensions, the author suggests scoring these criteria to help you prioritize where to put the effort of innovation: how much users care about the dimension, room for improvement of the technology, and the cost involved in developing a new product on that dimension.  See this example for blood-sugar monitoring devices:

DIMENSION IMPORTANCE TO
CUSTOMERS (1–5 SCALE)
ROOM FOR
IMPROVEMENT (1–5 SCALE)
EASE OF
IMPROVEMENT (1–5 SCALE)
TOTAL
SCORE
RELIABILITY 5 1 1 7
COMFORT 4 4 3 11
COST 4 2 2 8
EASE OF USE 3 2 3 8

This matrix is very helpful to explicit the need to change a company’s traditional strategy:

It can also help overcome the bias and inertia that tend to keep an organization’s attention locked on technology dimensions that are less important to consumers than they once were.

Depending on your company’s situation (lack of cash, strong market position,..) you can weight some of the scoring to get your ‘personalised score. You can also use this method to analyse your competitors positioning and expected future products.  Knowing their actual market strength and their potential future directions will make you see the best ‘bet’ for your company in an ever evolving industry.

The technology assessment exercise can help companies anticipate competitors’ moves. Because competitors may differ in their capabilities (making particular technology dimensions harder or easier for them to address), or because they may focus on different segments (influencing which dimensions seem most important or have the most room for improvement), they are likely to come up with different rankings for a given set of dimensions.

The great insight of the method presented in this article is not on getting the innovation idea, but more at a strategic level, on where it will be better to put the effort for Your company considering its Actual circumstances at this Present market (evolution of the industry and existing competence).

Perhaps more valuable is the big-picture perspective it can give managers—shedding new light on market dynamics and the larger-scale or longer-term opportunities before them. Only then will they be able to lead innovation in their industries rather than scramble to respond to it.

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Big Data and Ethics

BIG Data and Ethics was held a few weeks ago in the new premises of the DigitYser, downtown Brussels.

It was a great Meetup, with interesting speakers and an interested public 😉 It’s always a pleasure when the public can contribute and presentations raise great discussions, and it is more important here on this gathering on ethics, as people still have to position themselves on the different aspects of this topic.

I was particularly surprised when Michael Ekstrand from Boise State University mentioned a use of the recommendations systems that I hadn’t think of: using it as a tool to tackle the intention behaviour gap: ‘I don’t do what I want to do’ (for example not eating while on a diet). Recommenders can be used to help you change your behaviour, giving you nudges as incentive.

Jochanan Eynikel also mentioned the use of technology as a morality enforcer.

Still, there are possible drawbacks:

Another area that was discussed was the ethical fact that Personalisation has a direct negative impact on Insurance as it goes against Risk mitigation (mutualising it among customers). There are sensible domains where a ‘human’ approach should be taken.
How to ensure ethical and moral concerns are taken into account? One approach is through participatory design, that is a framework to get users voices on the subject during the design phase. MIT is strongly pushing participatory design to tackle many basic dilemmas.

Solving and clarifying our human position on these kind of dilemmas is more than relevant when we are talking here about autonomous technology, that is when technology is teachings itself, as driving cars learning from users.
Can we imagine not having human supervision in all domains? How to introduce Ethics so that the system itself can choose the ‘good’ decision and discard the others?

Pierre-Nicolas Schwab presented us the General Data Protection Regulation as “the only thing that the EC can do to force companies to take data privacy into account: fine them if they don’t”:

At the end of the meeting, this question has been raised: “Do data scientist and programmers need an Hippocratic oath?” Like ACM that has a code of conduct, something like ‘don’t harm with your code’.
What’s your opinion on this?

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Kill your dragons to be creative

Walter Vandervelde did a presentation at Professional Women International on creativity this month. He taught us how to kill our internal dragons to be more creative 😉

  • NONO, the dragon of the criticism, prejudices and conservatism:
    Change your automatic reply from ‘yes but’ to a ‘yes and’. That will stop criticism and you’ll feel the energy rising as you build up collectively a solution and your ideas get wider and wilder.
  • HOHO, the dragon of fear of failure, lack of courage and uncertainty:
    There is a quick solution to this dragon: just do it! “Doing is the new Thinking”. To begin things rolling, use gamification -that is using techniques of games for serious stuff.  As example, Walter suggests to put 2 teams to compete, giving them basic instructions and restrictions to begin with, so that they are not stopped by uncertainty. Be sure to tell everybody that it’s ok to fail.
  • GOGO, the dragon of the stress, time constraints and lack of reflection:
    To kill this dragon do your working place more attractive, an enjoyable experience and less stressful.
  • DODO, the dragon of resignation, habit and lack of curiosity:
    To ovecome this, foster the creative thinking mind, the one that, in front of a question, tries to come up with many other questions instead of just a straight answer. In fact the creative thinker tries to find the best question to describe the problem.

When trying to come up with creative ideas, know how our mind works: after a while it becomes lazy and you cannot find more ideas, but if you allow it to rest just a few minutes and come back to your problem at hand, you ‘ll get more ideas and usually those will be the more creative ones, the first ones being the obvious ones. During the resting time your unconscious mind continues working, incubating your thoughts, finding new relations to the problem.

Some techniques Walter mentioned to open your mind is reverting a question or rephrasing it. You’ll be verbalising other ideas behind your problem : Ask “An examples of a car is…” and people will tend to name brands: “a Mercedes, a Ford, …”. Ask “A car is…” and you’ll get other definitions like the function ” a driving device”, the uses “a device for transportation” and other relations.
give examples of things, imagine new uses, different ways of doing the same thing

Thanks Walter  for an entertaining event.  I learned interesting tips and tricks to be creative, and even some swear words in different languages that I swear not to repeat 😉

 

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Visual tool for Systems Thinking


Thanks to @PascalMestdach for presenting the visual systems modelling method at the #LeanCampBxl Unconference.

This is a very easy management technique to help visualising a complex organisation, and reflect on the dynamics at work when aiming for a particular goal.  A full model of an organisation with all the different perspectives is very hard to do, and anyway, any model is only an approximation to reality and never perfect. But you can construct a model focusing on a particular sector or aspect of your business: it will help you grasp what’s going on, how things impact one another, and share that same information with your team.

A model allows you to reflect on it: you get a deeper understanding and that can provoke insight. You can see how external changes could influence your system, and you can use it to test new ideas, simulating the impact of a particular change.

Also, understanding how the system works and what is our role in it let us be more effective and proactive.

Here is how to begin: you will need many post-its, a whiteboard and a marker.

  1. Prepare beforehand some basic post-its, writing already on them an element at play for the part of the business you want to analyse. You can mention inputs, activities, events, stakeholders, processes, behaviours, business objectives, personal goals, external influences…
  2. Then, present the process to construct the model of your business system: the idea is to identify the relationships between the presented elements that are at work on your company.To begin with, focus on 2 types of relationships: either it is a positive one where one activity goes on the same direction of another one (the more defects in a product, the more time spent fixing defects), or a negative relationship where it goes the opposite way (e.g. the more defects in a product, the less happy is the customer).  Here is how to represent those links:
    You see there mentioned a third notation to indicate ‘Delay’. This reflects the dynamic aspect on the two types of relationships (positive or negative) that is the delayed effect on time of an action. When the effect of an action is almost immediate, we see the relationship (like if you hear a horn waiting at a red light, you know the person has lost patience, and he horned because of a long timing of the red light), but the longer the delay between cause and effect, the increased complexity of the model because it’s more difficult to see its influence (like the returns of a marketing campaign).
  3. Don’t forget to give indications on the mindset needed to accomplish this!

    The objective is to construct a model where participants agree that it reflects their reality. That will allow them to clearly understand the issues at play. Managers may be surprised to discover how some activities are perceived by the team, the knowledge (or lack of) of objectives or of parts of the process, and even by what motivates the different participants (if they label a relationship as positive or negative).As every organisation needs everybody to work together for the whole to function successfully, this gained insight is very valuable. New post-its can be added if the participants feel there is an element to be captured.
  4. Validating the Model: once there is a consensus on the resulting model, put it to test using the technique of an ‘Ideal world’.Imagine our goal is: ‘Customer satisfaction’, write then ‘100%’ on top of that post-it. Then propagate this value to any post-it that is linked with a positive link.  So all of them have 100%.  Then follow the opposite links, and instead of labeling them ‘100%’ you write ‘0%’.  Are there any conflicts? Could you propagate the values and keep the model consistent?  Well done then!If not, open a discussion on possible changes to make this model work ?

You may notice that if you maximise a particular goal, like for example ‘customer satisfaction’, you may end up minimizing another potential goal (like ‘employee satisfaction’, or ‘minimal investment’). Nothing is 100% or 0% in our real world, but this ‘Ideal world’ propagation strategy makes you see what are the important factors to achieve a particular goal, and where are its negative impacts.

With this new insight, you are better prepared to decide on how to realign your goals, and you also have a visual representation of your business to communicate the objectives to the team.

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Managing techniques to improve employee’s engagement: build a culture of trust

In a very interesting article in last Harvard Business Review “The neuroscience of trust” by Paul J. Zak. describes how trust works, and its relationship with employee engagement. Then presents eight management behaviors that create a culture of trust as the base to improve productivity through employee engagement.

Gallup’s meta-analysis […] shows that high engagement—defined largely as having a strong connection with one’s work and colleagues, feeling like a real contributor, and enjoying ample chances to learn—consistently leads to positive outcomes for both individuals and organizations. The rewards include higher productivity, better-quality products, and increased profitability.

[…]In my research I’ve found that building a culture of trust is what makes a meaningful difference. Employees in high-trust organizations are more productive, have more energy at work, collaborate better with their colleagues, and stay with their employers longer than people working at low-trust companies. They also suffer less chronic stress and are happier with their lives, and these factors fuel stronger performance.

Leaders understand the stakes[… but] they aren’t sure where to start. In this article I provide a science-based framework that will help them.

Paul Zak is a professor of economics, psychology  and management, and founder of the Center for Neuroeconomics Studies. He knew that a brain chemical called oxytocin was responsible in rodents to signal that another animal was safe to approach, and he wonder if it was the same for humans. He initiated a long term research to verify if the same neurological signal was also indicating us that we can trust someone. His  experiments proved that:

Oxytocin appeared to do just one thing—reduce the fear of trusting a stranger.

My group then spent the next 10 years running additional experiments to identify the promoters and inhibitors of oxytocin. This research told us why trust varies across individuals and situations. For example, high stress is a potent oxytocin inhibitor. (Most people intuitively know this: When they are stressed out, they do not interact with others effectively.) We also discovered that oxytocin increases a person’s empathy, a useful trait for social creatures trying to work together. We were starting to develop insights that could be used to design high-trust cultures, but to confirm them, we had to get out of the lab.

So he developed safe experiments to measure oxytoxin and stress levels of employees, and also measured their productivity and creativity.

Through the experiments and the surveys, I identified eight management behaviors that foster trust. These behaviors are measurable and can be managed to improve performance.

  1. Recognize excellence.
    The neuroscience shows that recognition has the largest effect on trust when it occurs immediately after a goal has been met, when it comes from peers, and when it’s tangible, unexpected, personal, and public. Public recognition not only uses the power of the crowd to celebrate successes, but also inspires others to aim for excellence. And it gives top performers a forum for sharing best practices, so others can learn from them.
  2. Induce “challenge stress.”
    When a manager assigns a team a difficult but achievable job, the moderate stress of the task releases neurochemicals, including oxytocin and adrenocorticotropin, that intensify people’s focus and strengthen social connections. When team members need to work together to reach a goal, brain activity coordinates their behaviors efficiently. But this works only if challenges are attainable and have a concrete end point; vague or impossible goals cause people to give up before they even start. Leaders should check in frequently to assess progress and adjust goals that are too easy or out of reach.
    76% of people reported that their best days involved making progress toward goals.
  3. Give people discretion in how they do their work.
    Once employees have been trained, allow them, whenever possible, to manage people and execute projects in their own way. […]
    Autonomy also promotes innovation, because different people try different approaches. Oversight and risk management procedures can help minimize negative deviations while people experiment. And postproject debriefs allow teams to share how positive deviations came about so that others can build on their success.
    […]
  4. Enable job crafting.
    When companies trust employees to choose which projects they’ll work on, people focus their energies on what they care about most.
    […]
  5. Share information broadly.
    Only 40% of employees report that they are well informed about their company’s goals, strategies, and tactics. This uncertainty about the company’s direction leads to chronic stress, which inhibits the release of oxytocin and undermines teamwork. […] Ongoing communication is key: A 2015 study of 2.5 million manager-led teams in 195 countries found that workforce engagement improved when supervisors had some form of daily communication with direct reports.
    […]
  6. Intentionally build relationships.
    […] at work we often get the message that we should focus on completing tasks, not on making friends. Neuroscience experiments by my lab show that when people intentionally build social ties at work, their performance improves. A Google study similarly found that managers who “express interest in and concern for team members’ success and personal well-being” outperform others in the quality and quantity of their work.Yes, even engineers need to socialize. A study of software engineers in Silicon Valley found that those who connected with others and helped them with their projects not only earned the respect and trust of their peers but were also more productive themselves. You can help people build social connections by sponsoring lunches, after-work parties, and team-building activities. It may sound like forced fun, but when people care about one another, they perform better because they don’t want to let their teammates down.
    […]
  7. Facilitate whole-person growth.
    High-trust workplaces help people develop personally as well as professionally. Numerous studies show that acquiring new work skills isn’t enough; if you’re not growing as a human being, your performance will suffer. […]
    Investing in the whole person has a powerful effect on engagement and retention.
  8. Show vulnerability.
    Leaders in high-trust workplaces ask for help from colleagues instead of just telling them to do things. My research team has found that this stimulates oxytocin production in others, increasing their trust and cooperation. Asking for help is a sign of a secure leader—one who engages everyone to reach goals. Jim Whitehurst, CEO of open-source software maker Red Hat, has said, “I found that being very open about the things I did not know actually had the opposite effect than I would have thought. It helped me build credibility.” Asking for help is effective because it taps into the natural human impulse to cooperate with others.

The effect of trust on self-reported work performance was powerful. Respondents whose companies were in the top quartile indicated they had 106% more energy and were 76% more engaged at work than respondents whose firms were in the bottom quartile. They also reported being 50% more productive—which is consistent with our objective measures of productivity from studies we have done with employees at work. Trust had a major impact on employee loyalty as well: Compared with employees at low-trust companies, 50% more of those working at high-trust organizations planned to stay with their employer over the next year, and 88% more said they would recommend their company to family and friends as a place to work.

My team also found that those working in high-trust companies enjoyed their jobs 60% more, were 70% more aligned with their companies’ purpose, and felt 66% closer to their colleagues.

Looking at his conclusions, I see the same values that we foster on Agile/SCRUM management methodology.

[…] you cultivate trust by setting a clear direction, giving people what they need to see it through, and getting out of their way.

It’s not about being easy on your employees or expecting less from them. High-trust companies hold people accountable but without micromanaging them. They treat people like responsible adults.

His research proved that these techniques work, and also that a culture of trust accounts for more joy: “joy on the job comes from doing purpose-driven work with a trusted team”.

Embrace the movement for a happier society raising awareness on the benefits of stopping micro-management and  of trusting people to do their jobs.

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Elections warn about ethical issues in algorithms

I tweeted recently on this article about how Big Data has been used on the last American Presidential campaign.

Concordia Summit, New York 2016

“At Cambridge,” he said, “we were able to form a model to predict the personality of every single adult in the United States of America.” The hall is captivated. According to Nix, the success of Cambridge Analytica’s marketing is based on a combination of three elements: behavioral science using the OCEAN Model, Big Data analysis, and ad targeting. Ad targeting is personalized advertising, aligned as accurately as possible to the personality of an individual consumer.

Nix candidly explains how his company does this. First, Cambridge Analytica buys personal data from a range of different sources, like land registries, automotive data, shopping data, bonus cards, club memberships, what magazines you read, what churches you attend. Nix displays the logos of globally active data brokers like Acxiom and Experian—in the US, almost all personal data is for sale. […] Now Cambridge Analytica aggregates this data with the electoral rolls of the Republican party and online data and calculates a Big Five personality profile. Digital footprints suddenly become real people with fears, needs, interests, and residential addresses.
[…]

Nix shows how psychographically categorized voters can be differently addressed, based on the example of gun rights, the 2nd Amendment: “For a highly neurotic and conscientious audience the threat of a burglary—and the insurance policy of a gun.” An image on the left shows the hand of an intruder smashing a window. The right side shows a man and a child standing in a field at sunset, both holding guns, clearly shooting ducks: “Conversely, for a closed and agreeable audience. People who care about tradition, and habits, and family.”

Now I came across this other article by Peter Diamandis, featuring what we can expect in 4 year’s time for the next future elections’ campaign.

5 Big Tech Trends That Will Make This Election Look Tame

5 Big Tech Trends That Will Make This Election Look Tame

If you think this election is insane, wait until 2020.

I want you to imagine how, in four years’ time, technologies like AI, machine learning, sensors and networks will accelerate.

Political campaigns are about to get hyper-personalized thanks to advances in a few exponential technologies.

Imagine a candidate who now knows everything about you, who can reach you wherever you happen to be looking, and who can use info scraped from social media (and intuited by machine learning algorithms) to speak directly to you and your interests.

[…] For example, imagine I’m walking down the street to my local coffee shop and a photorealistic avatar of the presidential candidate on the bus stop advertisement I pass turns to me and says:

“Hi Peter, I’m running for president. I know you have two five-year-old boys going to kindergarten at XYZ school. Do you know that my policy means that we’ll be cutting tuition in half for you? That means you’ll immediately save $10,000 if you vote for me…”

If you pause and listen, the candidate’s avatar may continue: […] “I’d really appreciate your vote. Every vote and every dollar counts. Do you mind flicking me a $1 sticker to show your support?”

I know, this last article is from the SingularityHub, but even though they tend to be alarming, knowing how fast technology advances, the predictions they advance are not too exaggerated…

In any way, that reminds me how important it is to ACT on the ethical issues of algorithms. Please notice the capital letters to stress on the movement, which is to take action.  There are many issues that need to be identify, to be discussed, to raise awareness upon, to regulate, and on some of them we can already act on at company level.

I talked in May last year at the Data Innovation Summit about the biases that can be (and usually are) replicated by the new algorithms based on data.  Since then I began working on a training program to help identify and correct those bias when designing and using algorithms, and I’m reminded with the above mentioned articles that this cannot be delayed, it’s needed right now.

So if you are interested on getting your people and organization be aware of biases (human biases and digital ones), and be trained to fix these issues, contact me!

EmojiOne

We are creating our future, let’s don’t close our eyes, we can take control and assume our responsibility setting the railings that will guide the path to our future society.

 

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New year’s resolution: Apply the 8-Day Data Detox Kit

https://theglassroomnyc.org/data-detox/

theglassroomnyc.org/data-detox/

We are approaching the end of the year. For most of us this is the time to Last Year’s introspection and New Year’s big resolutions…(and if you don’t usually do it I recommend it to you: time flies (!) and taking the wheel of your life brings you a lovely sense of realisation 🙂

Have you given a thought about what you accomplished this year? How do that match your good intentions from the previous new year? Yes, I know, that’s a low blow… who can remember that far? And even if you do, we all tend to be so optimistic about our capabilities 😉

But if you don’t remember what you did this year, or what you were doing that didn’t allowed you to reach your goals…well, you can always check the web to remind you about that (or as we say nowadays: just google it!)

There was recently an exhibition in New York City called The glass room: Looking into your online life about our online data imprinting and the many tools that track our online behavior.

After checking it, you will be more convinced than ever to begin 2017 with the proposed Data Detox Plan.

So here I am proposing you to put, next to your diet to recover after the gastronomic excesses of New Year’s Eve, the 8-Day Data Detox Plan.  It will help you see how you look like to others online, and adjust the level of traces you leave behind, taking back control of your public image, of your ‘persona’.

Happy New Year 2017!  let me pass along a great message from my friend Marie-Noëlle (do I have to mention that she has a communication agency? ;-): ‘Welcome the 365 new opportunities to convert your goals into success

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