Finding a Model for Information Negotiations—Human or Machine?

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This week, a research group asked me, “For cognitive computing to take off, what advances do you think we need to make the in next five years?” I answered the question, listing the components of a cognitive system, and then discussing which ones were still fairly primitive. But the question continues to haunt me. The fact is that we’ve had most of the components for cognitive computing for a very long time. Language understanding, machine learning, categorization, voting algorithms, search, databases, reporting and visualization tools, genetic algorithms, inferencing, analytics, modeling, statistics, speech recognition, voice recognition, haptic interfaces, etc., etc. I was writing about all of these in the 1990’s. As hardware capacity and architectures have advanced, and our understanding of how to use these tools has evolved, we have finally been able to put all these pieces together. But the fact is, they have been growing up with us for decades, and we are still struggling to realize their value.

Here’s what we don’t have: an understanding of how people and systems can interact with each other comfortably. We need to understand and predict the process by which people interact: to question, to remove ambiguity, to discuss and decide. Then we need to translate that process into human-computer terms.

Even more, we need a change in attitude among developers and users. Today, we tend to think about the applications we develop in a vacuum. Consider the App. It’s a one trick pony. And we collect stables full of them. In today’s dominant application model, a human initiates a process and then stands back. The machine takes the query, the problem statement, and processes it, spitting out the answer at the end. Users, because of their expectations that machines will not be information partners, take the answer if it works and move on. If it doesn’t work, they throw it in the trash and try something else entirely.

That’s not the way a human information interaction happens. If two people exchange information, they first negotiate what it is they are going to discuss. They remove ambiguity and define scope. They refine, expand, or digress. This process certainly answers questions, but it does more: it generates a rich context for decisions, it builds trust and relationships, and it explores an information space rather than confining itself to the original question.

Here’s what we need to improve human-computer interactions: first, help in understanding the question. Then, we need better design to enable that question to evolve over time as we add more information, resolve some pieces and confront more puzzles. These interactions need to mirror the ancient human paradigm: the conversation. The app mentality today mirrors a different one: the transaction. Transactions have their place and can be highly practical. But conversations contribute a core of value in deep learning—they have to be the guide to developing interfaces for cognitive computing.


About the Author:

Sue Feldman is Co-founder and Managing Director at the Cognitive Computing Consortium. She also is PrAs VP for Content Technologies at IDC, Sue developed and led research on search, text analytics and unified access technologies and markets. Her most recent book, The Answer Machine was published in 2012. Her current research is on use cases and guidelines for adopting cognitive computing to solve real world problems.
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