The AI Problem Nobody’s Talking About: The Interface

When IBM’s Deep Blue defeated Chess Grandmaster Garry Kasparov on May 11, 1997, Newsweek painted it as a historical loss for humanity.  They called Deep Blue “a supercomputer especially outfitted to whack the human race down a notch.”

Now almost a decade later, Kasparov narrates a more hopeful reflection in “Learning to Love Intelligent Machines,” (WSJ paywall):

Machines that replace physical labor have allowed us to focus more on what makes us human: our minds. Intelligent machines will continue that process, taking over the more menial aspects of cognition and elevating our mental lives toward creativity, curiosity, beauty and joy. 

In contrast to bleaker prognostications about mass job destruction, I think Garry’s got it right.

Machine intelligence will complement how humans work — freeing us from menial, repetitive work — and allow humans to focus on the types of cognition we’re best at: creativity and social interactions.

However, while many entrepreneurs and investors are pouring resources into solving the data & algorithmic challenges of machine intelligence, most seem to be overlooking an even more critical problem:

How do we optimally pair machine & human intelligence?  

In a less famous but similarly significant freestyle chess match in 2005, two amateurs with three off-the-shelf HP laptops defeated grandmasters aided by supercomputers.  It was their ability to effectively interface with the machine intelligence that allowed this darkhorse team to defeat more capable humans allied with supercomputers.  As Kasparov concluded in a 2010 piece:

Their skill at manipulating and “coaching” their computers to look very deeply into positions effectively counteracted the superior chess understanding of their grandmaster opponents and the greater computational power of other participants…

Weak human + machine + better process was superior to a strong computer alone and, more remarkably, superior to a strong human + machine + inferior process.

You can have the most talented humans allied with the most powerful machine intelligence, but if they aren’t working well together – it doesn’t matter at all.

For machine intelligence: The process and interface matter more than human expertise or processing power

I find this challenge fascinating, because it requires the combination of three disciplines: operations research, psychology, and computer science. In 2012, my senior thesis at Princeton tackled exactly this problem.  I reviewed the published literature in each of these disciplines and defined principles that should guide the design of productivity applications.

I discovered psychology research on the science of attention, motivation, memory, and even visual perception that should influence how we design software experiences.  I applied these principles to build a simple todo list application.

After graduation, I became a member of Insight Venture Partners’ investment team.  While I became engrossed in the world of software investing, I couldn’t give up the interest I had in this human-machine interface challenge.  On nights and weekends I built a suite of browser-based technology to simplify my own daily workflow, looking for new investment opportunities.  The tools I had built focused on improving process and automating what was best done by the computer.

This project became so critical to the firm that when I left for HBS and stopped maintaining it, a team was hired to recreate parts of it that they could figure out how to.   Further to my surprise, friends and colleagues who had left and gone to other firms were so impacted by the project that a number asked if I could build solutions for their new firms.  It suggested that this type of software — focused on process, guidance, and automation for knowledge work — had a clear market opportunity.

The first thing I looked for in my investments at Insight was massive TAM, and venture capital CRM software certainly didn’t meet that hurdle.  But taking on a similar problem at a much larger scale — for all knowledge work — clearly does.  And that became the charter for RocketVisor.

We’re Patiently Setting Up Our Chess Board

While we are solving critical process and user experience issues, we’re also setting up our chessboard to make moves when the underlying AI technology matures.  We’ll be building the UX layer that spans all SaaS applications, establishing deep customer relationships, understanding their business processes, and collecting a massive data training set.

The key for us will be knowing how and when to integrate components of intelligence.  As much as I want to believe in AI, I’m still disappointed in the so-called “intelligent bots” being built today.  The back-end technology just isn’t ready yet, and I really don’t see conversational UI panning out as the panacea for modern UX.  It’s just too early to make that move.

Bent Larsen, a Danish grandmaster renowned for his unusual style of chess, famously remarked:

Lack of patience is probably the most common reason for losing a game, or drawing games that should have been won. 

Image Source: Shyam Sankar (TED)