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Why The Future Of Artificial Intelligence Requires More Of Diversity

Diversity is about accepting differences and not forcing men, women, NLP engineers, data artists, decision scientists to fit into the same mold

A recent survey by a major publication in Singapore sparked a discussion about the value of art and artists in society. The survey found that over 70 per cent of the respondents picked artists as non-essential jobs.

It was later highlighted that the survey responses were closely tied to the ongoing COVID-19 pandemic where essential needs such as health and food were arguably top of mind. However, the debate over the value of art and education in the arts persisted.

As a former scholar of an unusual combination of applied maths, engineering, and studio art, I am keen to reflect on what this will mean for the future of STEM, particularly in the field of data and AI.

There is plenty of discussion about diversity but acceptance of diversity is a larger economic, political, socio-economic question. Diversity is about accepting differences and not forcing men, women, NLP engineers, data artists, decision scientists to fit into the same mold.

In AI, this is especially true. As we advance towards a data-driven future, AI will require not just data and engineering skills but increasingly, and some argue, more importantly, there will be a need to emphasise judgment, decision-making, and people skills.

I have spent over 10 years in technology, moving from science-based health projects to pure technology across three countries. However, I didn’t choose a career path in tech. I knew from very early on that I wanted a people-focused career and tech was just the medium. My real passion was and remains mathematical storytelling.

By choosing to study the different areas that I did, I was able to combine both my analytical and creative talents and get involved in game-changing innovation like building robotic arms for smart prosthetics and then moving across the world to delve into the world of insights for large technology companies.

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In my current role at GitLab, what I love most is making tech work for customers around the world through new innovation. We now have the capability to solve things that we couldn’t before through the lens of AI, but we can do this effectively only when we embrace the diversity in passions and talent.

As humans, we find comfort in certainty and reproducibility. For employers, to hire a good data scientist, they would fall back on a checklist of the robotic skills (python, stats, presentation). However, to build a good AI model, one not only needs a mathematician but also poets, storytellers, linguistic specialists, among others.

Instead of viewing analytics and soft skills as two distinct skill sets, they should be considered as part of the same genre of human problem-solving skills. How we use tools is the craft but how we apply these tools to creatively solve a human problem is an art. Analytics is, therefore, a subset of soft skills and vice versa.

In our day-to-day lives as STEM professionals, we have to be active listeners to understand the needs of customers, their problems, and their desires. Only with that understanding can we creatively craft the analytics solution to solve the need and articulate how the solution fits in the holistic journey of the customers.

We have reached the point in time where humanity and technology co-exist and our lives get more intertwined with technology in one way or another. While there is no denying that enhancing our technical skills is paramount, I believe that skills such as critical thinking, communication, and decision-making are equally important.

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For example, Pure Math is a craft but Applied Math and how we use it to solve problems is art. Similarly in AI, we have data, tools, fast computing engines, fast mathematical solutions such as tensor flow, DevOps frameworks extended to Machine Learning (ML)Ops, AIOps and DataOps, but how we apply all these tools and concepts to solve a human problem is a work of art.

We need all sorts of minds in harmony orchestrating every gender of different myelinated fibre strength, not just in STEM but also in art to create the magic of AI. Diversity in AI is having a platform where passion and individuality are embraced and creatively used in unified machine prediction and storytelling, embracing the personalisation of strengths and complementing each other’s weaknesses, finding freedom through problem-solving in the harmony of different backgrounds, age, sex, mindsets without altering each other.

At GitLab, the phrase “Diversity, Inclusion & Belonging” (or DIB) refers to the terminology for the initiative to create a diverse workforce and an environment where everyone can be their full selves.

The approach will help us not only in creating better AI models but fundamentally change the way we interact with computers, to make human interaction and society more efficient and ultimately enable a digitised ecosystem to solve critical problems and barriers to our evolution.

In order to achieve the true potential of an AI-driven world, we need to support young people in genuinely choosing their passion without any discrimination, whether they be science, technology or art, philosophy and international relations.

Source: e27

 

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