Why Bridging the AI Gap Matters

I recently re-watched Simon Sinek's popular TED talk about the power of starting with why. It inspired me to delve deeper into why I believe great good can come from bridging the current gap between smart people and smart machines.

My career has focused on building AI tools for knowledge workers that uncover trends in data and learn from experience and feedback. I have also helped scientists, lawyers, and executives adopt these tools and watched them thrive. During my journey, I have had two insights: people accomplish exponentially more with the right AI tools, but we are still far from fully realizing this vision.

I am amazed at the progress I have seen and experienced in my own research. When an R&D chemist is paired with a drug discovery tool, she can narrow in on leads and begin research in hours rather than months. When a lawyer is paired with the right search tools and data, she can hone in on research needed to secure her client's freedom in a fraction of the time. And recently, I worked with others to inform global health policy decisions by identifying commonality in countries.

Yet for all this progress, there is so much more we can collectively accomplish. Examples like those above are still relatively uncommon. Many organizations still use outdated tools and approaches for mining information. This is a chicken and egg problem: individuals are reluctant to embrace technology that does not fit with their mental model of the world, and tools on the market today are generally not intuitive or tailored for interactive data discovery. So we stand at a stalemate while opportunities to make a profound impact in the world pass us by.

How do we fix this problem? I believe AI software needs to be dramatically more user-centric and insanely simple to use. Doing anything less is a cop-out. Poor design because of limited budget is an excuse. Thankfully, recent successes of elegant design in the consumer space, like Apple, have us demanding better.

One way to better outcomes is through purposeful design, inviting designers and artists into the conversation from the beginning. Another way is to ensure our machines are not only book-smart but people-smart. If we can teach software to analyze data and learn complex patterns, we can apply that learning to the user experience. We already see examples of this in e-commerce and social media. In the near future we will see more of this in all aspects of software design.

So back to the question of why. So many of humanity's most profound issues are solvable. Hunger, disease, war, crime, environmental damage. In each case, we have examples where a crisis was averted. How? What makes one country a success story while another spirals into a humanitarian disaster? With the right people using the right tools, we will be that much closer to solving these problems. And that is a cause worth fighting for.

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