TTI Vanguard heightens intellectual thought about technological possibilities through a method they call 'future-scan'. Future-scan refers to a focus on unanticipated sources for change and evaluating their transformative promise through highly interactive sessions and debate, stimulating breakthrough ideas.
The June 2018 conference invited members into discussions on Intelligence, Natural and Artificial. Dialogue was opened and enhanced by speakers heralded as thought-leaders in their industry. Additionally, the conference provided a private tour of NYU's Tandon School of Engineering and the Center of Urban Science + Progress (CUSP).
Intelligence, Natural and Artificial
Technology has always been in the service of augmenting human capabilities. AI is our way of doing for mental work what the wheel, the plow, and the steam engine did for physical labor. Yet machine learning has the capability of going its own way, whether it's Facebook bots inventing their own language or warms of drones making collective decisions that can't be attributed to any on node. What is the current state of the art, and how can we best harness its growing capabilities?
KEY TAKEAWAYS:
Compensating for Cognitive Brittleness
The obstacles to solving important problems - for us as individuals, as organizations, as nations, and even as a species - stem from limitations baked into our genetics. True, over the past thousand of years, we've invented a handful of ways to help us think better: language; writing; the scientific method; the Internet and its 'appsphere.' Nature has done her part too, empowering us in several ways, notably with a cerebral cortex and differently-abled brain hemispheres. Yet some of our capabilities are double-edged, such as the Faustian bargain we make by slipping into a paradigm or ideology: The tremendous power it gives us comes at a tremendous cost, blinding us to other ways of perceiving the world and dealing with it.
How Can We Trust a Robot?
Trust is essential for the successful functioning of society. Trust is necessary for cooperation, which produces the resources society needs. Morality, ethics, and other social norms encourage individuals to act in trustworthy ways, avoiding selfish decisions that exploit vulnerability, violate trust, and discourage cooperation. As we contemplate robots and other AIs that perceive the world and select actions to pursue their goals in that world, we must design them to follow the social norms of our society.
A Handmade Approach to Social Robotics
Most social robots nowadays feature white shiny plastic with metal or black accents, glass screens and smooth rounded lines and edges. The design of the social robot Blossom rejects almost all common wisdom of robot design. Blossom is perhaps the first robot to be soft both inside and outside, using a compliant internal structure to enable movements that give the robot a somewhat imperfect personality. Interestingly, outside of robotics, we buy more and more shiny plastic and glass devices - an opposite trend. From craft beer to craft light bulbs, we see a new appreciation of the slow, inefficient, and one-of-a-kind process of traditional crafts. This also makes a social robot more deeply personal, and potentially more deeply loved, as people cherished ragdolls in an earlier era.
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Deep Learning: A Critical Appraisal
Although deep learning has historical roots going back decades, neither the term 'deep learning' or the approach was popular as recently as five years ago. Considerable progress has been made in areas such as speech recognition, image recognition, game playing, and considerable enthusiasm in the popular press. Gary Marcus Professor of Psychology and Neural Science, New York University, discussed ten concerns around for deep learning.
- Deep learning is data hungry
- Deep learning is shallow and limited capacity for transfer
- Deep learning has no natural way to deal with hierarchical structure
- Deep learning has struggled with open-ended inference
- Deep learning is not sufficiently transparent
- Deep learning has not been well integrated with prior knowledge
- Deep learning cannot inherently distinguish causation from correlation
- Deep learning presumes a largely stable world, in ways that may be problematic
- Deep learning works well as an approximation, but its answers often cannot be fully trusted
- Deep learning is difficult to engineer with
Bottom Line
The purpose of SANDSTONE'S membership with TTI Vanguard is to stay ahead of the curve. The March conference focused on both disruptive technologies with social implications. We networked with thought-leaders while tracking, researching and developing speculation on the next big players of robotics and deep learning technologies.
Automation continues to be one of SANDSTONES key themes for investment.