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Human Questions About Artificial Intelligence at TEDxBoston

Human Questions About Artificial Intelligence at TEDxBoston

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Collective Next
October 20th, 2016

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Below is the second installment in our series, AIML: Big Changes, Big Conversations. Taking AIML (Artificial Intelligence and Machine Learning) as our example, we will explore methods for accelerating and amplifying conversations around major changes in ways that enable us, as individuals and as a collective, to understand and direct their impact.

“…Try to love the questions themselves, like locked rooms and
like books that are now written in a very foreign tongue. ”

Rainer Maria Rilke

Artificial Intelligence and Machine Learning (AIML) are complex topics that appear daunting—even threatening—to most of us. So how do we begin to build our collective understanding of this field? And once we do, once we move from haze and hype to information and insight, what are the actual opportunities and threats we will find ourselves grappling with?

When faced with change of this magnitude, we will need to be comfortable with the fact that asking the right questions rather than jumping to solutions is the necessary first step.

As people dedicated to facilitating meaningful change, we believe in the importance of making space for questions. Questions are the essential first step in setting an intention. We cannot positively and meaningfully direct transformation without first engaging freely and deeply in this phase of discovery.

At TedX Boston 2016, Collective Next facilitated a process of knowledge gathering and reflection about AIML. The afternoon was filled with ideas from AIML experts Suchi Saria, Hugo Larochelle,  Ben Vigoda, Andrew Ng, and Ryan Adams. Following these presentations, Collective Next hosted a discussion focused on surfacing the big, future-focused questions for those in attendance. And they were BIG questions. While AIML is about non-human intelligence, the questions it raises are profoundly human. This isn’t because we face sci-fi movie scenarios of robot uprisings, but because AIML forces us to cede to the non-human what we once thought was unique to our species.

The following is a condensed version of the important questions raised by members of Boston’s community in response to the presentations they heard. We offer them here as a way to get the conversation going in the most expansive terms possible.

Promoting Awareness

  • How do we de-mystify artificial intelligence and machine learning so the public no longer thinks of it in terms of scary robots or science fiction?
  • What is the baseline understanding of AIML that all of our organizational leaders need to have? How do we keep them up to speed?

Human Value

  • If  the pace of automation continues and machines do the work that our employees once did, how do we retrain employees while still bolstering their sense of self-worth?
  • How do we ensure that if machines do displace jobs, that people are able to shift to more enriching or creative work?
  • How do we cope with the fact that the rate of technological change is collapsing the available window for occupational retraining?
  • Will intelligence augmentation, human decision making aided by an artificial system, result in a leveling of human intelligence? If so, do we need to shift to a collective understanding of intelligence?

Access and Power

  • How will we ensure that access to big data will be democratic in nature rather than accessible only to a few elite companies?
  • Will AIML actually democratize knowledge by scaling access to information that is currently restricted to those able to pay premium prices (e.g., investment advice)?
  • How will the existence of highly intelligent machines impact how we think about privacy and data?

Social and Organizational Structures

  • What structural changes within organizations and policy measures are required to support the efficient and effective use of AI (e.g. predicting medical risks by using machine learning to make thorough use of electronic health record data posssible)?
  • Will we be able to train enough programmers to work in artificial intelligence and machine learning?

We invite you to use the questions above as a jumping off point to engage in dialogue with your colleagues and friends and to generate more questions. We also welcome you to continue this conversation with us here on our blog.

[Photo: TEDxBoston 2016 - Flickr]

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AIML: Big Changes, Big Conversations