Wednesday, November 28, 2018

Highly Intelligent Is Not Highly Creative – How to Be Truly Innovative

In research as well as in entrepreneurship, there are many characteristics distinguishing an exceptional researcher or entrepreneur from just a good one. In particular there is a key difference between highly intelligent and highly creative people, as these characteristics are not necessarily correlated. Intelligence is commonly defined as the ability to acquire and apply existing knowledge and skills, while creativity is the ability for something novel and valuable. New research and new enterprises can therefore be grouped into four categories:



Highly intelligent, incrementally creative researchers or entrepreneurs add an incremental twist to existing methods and knowledge. For instance, a researcher might add mathematical bells and whistles to widely accepted ideas and concepts - in Renaissance Europe they might have added an additional proof that Earth was indeed flat and that the Sun was circling around it. Most of the papers in Nature and Science fall into this category, cementing established ideas by repeating them using more complex algorithms and procedures or by writing the first overview paper categorizing trends in an emerging field (which has the nice side effect of boosting the author’s h-index as it will be widely cited). Among entrepreneurs, these are the people reaping the biggest rewards, Bill Gates falls into this category, as he is a master of recognizing emerging trends, which are then integrated in his products.

Highly creative, incrementally intelligent researchers and entrepreneurs, on the other hand, challenge existing wisdom, frequently upending established beliefs. However, as the execution of their ideas is frequently improvised and not presented in a polished way, they have a hard time gaining acceptance – when Galileo Galilei presented convincing proof that Earth was indeed circling around the Sun, and not the other way around, the Roman Inquisition in 1615 found this “foolish and absurd in philosophy, and formally heretical since it explicitly contradicts in many places the sense of Holy Scripture”. Highly creative researchers in more modern times still have a hard time challenging established wisdom, and more often than not fail in making their ideas stick. In particular, is it near impossible to get these ideas into Nature and Science, as today’s reviewers, just as the Roman Inquisition 400 years earlier, deem their ideas “foolish and absurd”.

Highly creative, highly intelligent researchers and entrepreneurs are the small group of people, who succeed in making their disruptive ideas stick. Other than Galileo Galilei, today’s Nobel Prize winners might get recognition of their disruptive ideas while still alive, although sometimes they have to wait 40 years for their ideas to be recognized by the scientific establishment, as happened to Barbara McClintock, who discovered genetic regulation mechanisms in maize in 1944, and got the Nobel for this insight only in 1983. In between she stopped publishing her research due to being ridiculed by her peers. I would also put entrepreneurs like Steve Jobs and Elon Musk into this category, as they are taking huge personal risks promoting and commercializing novel and untested technologies.

To convince one’s peers and society about novel thinking and products, researchers and entrepreneurs need other essential personality characteristics besides intelligence and creativity. Only by being highly persistent and highly empathic can one really succeed in getting others to accept new ideas.


In an excellent article, Albert-Laszlo Barabasi tells the story of Douglas Prasher, who pioneered a process leading to a Nobel Prize in Chemistry, but was unable to find funding for his research, and became a courtesy driver for a Toyota dealership, while Martin Chalfie and Roger Tsien, two of his more persistent collaborators shared the Nobel Prize. However, these two collaborators showed their empathy, by recognizing the contribution of Prasher in their Nobel acceptance speeches, and assisting him to subsequently return to science.

Sunday, November 25, 2018

Principles of Profiling Users with AI

Two days ago I read a Washington Post article about 3 Californian AI startups that profile users based on opaque AI algorithms. Their products calculate a quality score for people in different domains without explaining how it is calculated

  • Predictim.com calculates a “risk rating” of babysitters based on their social media activities.
  • HireVue analyzes tone, word choice, and facial movement of job candidates to predict their skill on the job.
  • Fama does employee screening on social media and internal HR data to prevent what they call “brand risk” such as sexual harassment, bullying, or insider threats of employees. 

The main problem with these and similar systems is that they use machine learning, in particular deep learning as a black box. Their algorithm gives back a score claiming high numerical accuracy without explaining how it has been calculated.

Our own Condor software is doing similar things, showing a bird’s eye view of the communication patterns of organizations based on their E-mail, or social media archives. There is one key difference though, we apply the “Google Maps Privacy Principle”: aggregated information is shown to all users, the individual information is only shown to the affected individual. The principle is derived from Google Maps, which becomes truly useful by aggregating the location information of Android users with location tracking turned on and iPhone users with Google Maps turned on through dynamically tracking their smartphone location. But the only individual who knows her/his own personal location is the owner of the phone. Google Maps therefore aggregates global information and returns individually useful information to the individual user.

This approach is what we are trying to pursue in our own work:
(1) Show aggregated information to the public, and individual information only to the affected individual.
There are however applications where the individual user has to be identified to others. These applications can be split into two categories.
(2) The application needs to identify the user, and the user gets a benefit from being identified, for example as a “rock star” employee, most collaborative employee, etc.
(3) The application needs to identify the user, and the user has a disadvantage from being identified, for example as a potential security risk, low performer, etc.

The applications from predictim, HireVue, and Fama are clearly in category (3). Users are convicted by a machine learning algorithm without knowing why. The algorithm operates as a black box. While arguments can be made for category (2) applications to run in such a mode – the user gets a pleasant surprise, even if s/he does not know why, this is clearly not acceptable for category (3) systems. At the very least does the user need to know why s/he has been convicted.
As I assume that more and more systems in category (3) will be built, for instance by law enforcement, I envision the need for an impartial authority, which can be public or private, to check and certify the accuracy of these AI-based prediction systems.

A second point which sets Condor apart from other AI-based prediction systems is the transparency of its algorithms. The scoring algorithms applying the "seven honest signals of collaboration" and a list of predefined "virtual tribes" is documented in great detail in over 150 academic papers and fivbooks. This is quite different from e.g. predictim's case studies, which predict the past, without disclosing how it is done. There is no guarantee that the training data of the past will still be valid to detect future criminals.

 It is still early days, so please tell me what you think, I would love to hear your opinion

Thursday, November 15, 2018

Why Donald Trump is No Leader

There are many ways to identify exemplary leaders. One of my preferred categorizations comes from here. It lists humility, curiosity, and empathy as the main criteria of successful leadership. I am afraid that on all three criteria Donald Trump scores zero:

Humility means that exemplary leaders treat everybody with respect, are not afraid of criticism and are willing to admit their own mistakes. They are willing to put their own ego into the background for the sake of others. Donald Trump stands for the opposite, as an egomaniac person who is obsessed with his own power and glory.

Curiosity means that a leader is constantly looking to further his own understanding, is willing to defer to the knowledge of others, and thirsty for new information. One of the first actions of Donald Trump was to cut funding in research, denying scientific facts about vaccination and climate change, showing zero scientific curiosity.

Empathy means that we treat others with compassion, try to understand what is going on inside their own minds, try not to hurt their feelings, and try to be not just nice, but also kind. Trump’s easy hiring and firing of allies, and not liking losers like John McCain who was tortured as a prisoner of war, sadly demonstrates utter lack of empathy.

So, sorry, Mr Trump, you are not a leader. But perhaps I am just living in an alternative reality and everything in reality (TV) is totally different!