The Need for Human Learning

What is the role of the humanist in a world increasingly defined by data? What new skills are necessary to understand and influence the artificial? Is the human perspective still relevant in technological debate?

While these looming questions may encourage contemporary professionals in social and human sciences to pursue a focus on Machine Learning (ML) - we must not lose sight on the evolving needs of Human Learning (HL), in order to reach the full realm of our collective potential.

Earlier this year, I entertained a thought feared by many throughout their career: Am I still relevant? As a qualitative strategist and researcher, I am interested in deeply understanding the concrete needs and motivations of human beings. Thus, my unit of measure for strategic decision-making has historically been stories, not data. As a designer and storyteller, I've been trained to revel in the uncomfortable space between observation and insight, to turn my gaze outside the patterns, and look for what is unique or contradictory.

Though these traits still ring true, reality is changing. In a world where algorithms have become the force that guides the invisible hand - often with unparalleled efficacy - is a qualitative study able to surpass the accuracy of a recommendation engine? Will an acute observation ever overturn a data-driven decision? Is human ingenuity beat by just adding another badge to the training data?

With the looming sensation of professional oblivion, I turned to the web to look for conferences and events that would help actualize my knowledge, and slowly inch my way into the world of Artificial Intelligence and Machine Learning. After gazing into the programs of the most prominent events at the brim of popular technology, it seemed like there was no other choice but to surrender to our robot overlords, or even worse - learn how to code.

Right around that time, I learned about the House of Beautiful Business (HOBB), a gathering of diverse thinkers collectively addressing our biological and non-biological evolutions through an optimistic and human-centered lens. Not knowing much more about it, I packed my bags and left for Lisbon, Portugal to a week that would redefine and re-energize my role as a humanist. Today, I am convinced that, in parallel to Machine Learning, there is a vast opportunity to develop Human Learning on the age of machines. With that said, let's dive in.

Human Learning

While the current technological debate narrows on the applications, development and limitations of Machine Learning (ML), the scope and rate of this progress is dependent on our collective human preparedness to create, iterate and collaborate together in unexpected and complex ways. I'm calling this realm of abilities Human Learning (HL).

There are multiple ways in which technology and business are severely underdeveloped on Human Learning. Making progress here is not only a necessity, but arguably an obligation for these industries. Human Learning should be pursued and talked about with the same emphasis as Machine Learning. Though there are many to consider, I’ll address three areas of HL today: True Collaboration, Non-Binary Thinking, and Applied Mindfulness.

HL is True Collaboration and Inclusion

Collaboration is a cornerstone of contemporary organizations aiming to attract and retain the best talent. The ability to work well in teams and influence internal stakeholders ranks high as one of the most-demanded soft skills for individual contributors, as well as leaders and executives.

The products, services and algorithms we consume today are a result of some degree of collaboration between members of a team. Yet, this process loses power when it happens only within collaborators of similar backgrounds. Many studies show the vast imbalance between white males, women and other minorities participating in technology, both in school and in the workforce. Misrepresentation in the teams responsible for developing technology may lead to obvious blind-spots on their outcomes. As we continue to advance to more diverse workplaces, Human Learning will be necessary to facilitate and integrate emerging differences between team members with diverse points of view. Are we prepared to thrive in a world where technology is developed as a real representation of human diversity, or must we endure more biased examples of the contrary?

In a short Manifesto on gender in business, resulting from a meet up at the HOBB, ethnographer Jonathan Cook declares that "we need to find ways to keep business as fluid as the identities of the individuals with which it engages in commerce" Human Learning will be crucial to accomplishing this goal, with customers, associates and internal stakeholders alike.

Skills such as active listening, empathy, introspection and facilitation are necessary to guarantee a future where Machine Learning algorithms are not biased and myopic. We must learn to see each other as imperfect and complex human beings first, so we can collaborate to develop inclusive, multifaceted technologies second. To that end, we need people embedded in business and technology who deeply understand the intricate nature of human relationships, so that diverse perspectives can erupt in thriving collaboration and inclusive outcomes.

HL is Non-Binary Thinking and Play

One of the most groundbreaking ideas I learned about while at HOBB was Twain Liu's reflection on the current state of AI development, constricted by the binary and reductionist thought paradigms of the West. In Twain's thinking, the entire apparatus from which we are developing computational intelligence is based on a binary system of 0s and 1s. While this is a legacy method for reducing complex tasks to manageable units that we inherited from Western logic, it is simply out of scope to render a true image of our dynamic, and complex human nature.

To take on the challenge of writing a real likeness of the human condition, practitioners must first un-learn some of the most fundamental ways in which the world is understood and categorized. Snapping out of binary constructs such as right and wrong or true and false can be quite uncomfortable and unnerving for those trained under the logical principles outlined by Twain. To that end, we must bring Human Learning to the root institutions where problem-solving is taught and practiced. CS students should have core curriculum on human emotion and self-awareness. Workers should be encouraged to develop scenarios, rather than answers, and encouraged to play, diverting from traditional methods of thinking.

How else could we mimic the power of a beautiful contradiction, the wrenching tension between gut and thought, or the elusive light of an insight? How could we design Machine Learning algorithms for human beings if we oversimplify and reduce our own existence? Human Learning is needed at the core of this transformation, so that the collective output of our work is truthfully dynamic, adaptable and human.

HL is Applied Mindfulness and Ethics

The rise of Machine Learning and Artificial Intelligence in the collective domain has been steadfast. In the last few years, terms only known within research communities have crossed the threshold to popular culture, bringing along a massive wave of unanswered questions, exaggerated predictions, and unfounded fears. This impressive growth has taken a protagonist role in media, industry, and public debate.

There is another trend pervasive in business that has been growing at an unprecedented rate but hasn't yet garnered the same amount of headline attention. Last year, the World Health Organization (WHO) declared Anxiety and Depression to be the #1 source of global disability, affecting 300 million people worldwide. The estimated cost of this epidemic is US$1 trillion per year in lost productivity. One quarter of workers in the US reported they felt more anxious in the workplace compared to last year, signaling an alarming climb that begs not to be dismissed.

At the HOBB, I had the chance to meet Vasco Gaspar, a brilliant humanist working on advancing Human Learning within the world's largest organizations. The logic here is simple; if the aim is to develop Artificial Intelligence that is capable of high human traits such as empathy and good judgement, we need to be able to cultivate those traits in ourselves as well. Vasco works with the Search Inside Yourself Leadership Institute, an initiative originated and refined at Google, that intersects business leadership, psychology and neuroscience to apply practices of mindfulness, emotional intelligence and self-awareness inside corporations. Program participants reported a dramatic drop in anxiety and increased workplace performance, expanding the reach of the program within Google, and beyond.

Other traditionally tech-minded institutions such as The Massachusetts Institute of Technology (MIT) have brought forth impressive initiatives focusing on Human Learning such as the Dalai Lama Center for Ethics and Transformative Values, which offers programs to help students as well as business and technology leaders align their work with their deepest personal values. When taking a systemic view at these influences, it's simple to see how leaders who apply mindfulness and Human Learning would develop products and algorithms with less room for ethical misinterpretations and loopholes.

While in Lisbon, John C. Havens further solidified the need for Human Learning at the heart of technological development by outlining the vision of The Council on Extended Intelligence at the MIT Media Lab - an initiative he directs. The Council believes that growth for humanity's future should be defined by "the holistic evolution of our species in positive alignment with the environmental and other systems comprising the modern algorithmic world." Such alignment could be reinforced by practicing awareness, applying mindfulness and tapping into Human Learning.

Humanists, where art thou

Upon my return from the House of Beautiful Business, the question of relevance seemed less like a threat, and more like an unparalleled opportunity. In a world at the verge of technological reinvention, humanists are a fundamental piece of the puzzle to build a beautiful future. The unwavering attention and care for the complexities of our existence are not a hindrance but a necessary companion to the power of data. It is only through the combination of Human and Machine learning that we will become prepared to create exponentially better days that end in a state of collective afterglow, not isolated hangovers.

The challenge for us is not relevance, it is community. Many have zig-zagged their way through diverse career paths, assuming the role of the wild card or the generalist. Feeling like outsiders, humanists often endure a lonely path, struggling to fit into any conventional shape. The House of Beautiful Business, and its two valiant organizers Tim Leberecht and Till Grusche recognized the need for this tribe to connect, share and celebrate each other. We have come back emboldened to keep the momentum going, knowing that there are others like us who carry the flag of the human perspective. In doing so, interlocking our individual and collective developments increases the potential for relevance, influence and change.

In an effort to continue coalescing with humanists across the board, my friend Pablo Criado and I have created a series of gatherings named The New Boheme, initially out of New York City. Our aim is to curate experiences where conversation, inspiration and play help us revel in the collision of interesting ideas. More importantly, we aim to address the question of relevance by providing a forum where newfound ideas of identity, responsibility and impact can emerge.

At last, I address you with an invitation to continue reflecting on Human Learning. We've only scratched the surface of this topic and there are many other angles to unpack. As we traverse into the age of Machine Intelligence this will be a much needed area to develop and practice, so we continue tracing a course towards a beautiful, integrative evolution.