Here are four short features from my micro-blog (Tumbld Thoughts) that creatively discuss the current and future state of Artificial Intelligence/Machine Learning research. Featured are: LIVE from Annoying Valley, Internet (I), Thinking more like a theorist...... (II), Trends in Future Research (III), and The path to machine consciousness will run through the executive network (IV).
I. LIVE from Annoying Valley, Internet
Here are a few readings [1] on how recommender systems and other intelligent agents go AWOL on a platform of creative destruction. Ads such as "enlarge the size of your portfolio using this one simple trick" [2] are an example of the "annoying valley", which is a version of the uncanny valley ubiquitous in human-robot interaction.
Thanks go to Calvin and Hobbes (Bill Watterson), and Thomas Hobbes and Joseph Schumpeter for the quotes (reworked from their original syntax).
II. Thinking more like a theorist......
Now, I think I'll get in touch with my inner theorist. Sheldon from "Big Bang Theory" summarizes the story of my academic career (at the 00:10 mark). What can we do Glenn Shafer's mathematical theory of evidence [3]? How about enabling complex data fusion and context-aware robotics (see figure below)? Next, Paul Krugman brings us his thoughts on the purported death of economic theory. Last but not least, a group of psychologists ask under which conditions theory can obstruct research progress [4]. This can be contrasted with the exclusive use of naive theories (e.g. common sense models) in AI research.
III. Trends in Future Research
Here is a Quora thread on the top problems in machine learning. Answers range from a bulleted list of hot topics to longer discussions. Major problems (as defined by the crowd) include: gesture recognition, learning from social networks and media, deep learning [5], newsfeed aggregation, and scalability. Interestingly, there is only moderate overlap between individual answers.
Also interesting is the related Quora thread on important problems in the field of Artificial Intelligence over the short term (5-10 years). It will be interesting to see how these predictions correspond with future breakthroughs and developments in the field [6].
IV. The path to machine consciousness will run through the executive network
"Attention (awareness) is a data-handling method used by neurons. It isn’t a substance and it doesn’t flow"
Interesting quote from a even more interesting story [7] by Michael Graziano, a Neuroscientist at Princeton. He makes the case for how and how not to study consciousness. While theories of consciousness are plentiful, he argues (along with Christof Koch [8] of Caltech/Allen Brain Institute) that consciousness is primarily of phenomenon of attention and mental reflection.
Returning to the quote. If consciousness is a form of awareness, and awareness is a model of attention, then they all seem to be a data handling procedure the brain uses to select and reflect on information from the environment. In that sense, it is a non-physical entity that does not operate like a physical intention [9]. The flow of physical intention can be distinguished from experiential flow involved with creativity [10] or the flow of information in attentional networks of the brain [11]. By contrast, the idea that consciousness flows into objects in the environment (e.g. a portrait or object) is pre-scientific superstition.
In addition, there is also evidence (highlighted in Graziano's article) that consciousness is largely an "after the facts" mental construction, which feeds back to subsequent attentional selection. What is the missing piece of science's understanding of consciousness? Graziano's answer might surprise you.
Images in IV: Debugger, xckd comics; human attentional network [11] with my own annotations; Superman fighting Zod from Superman II
NOTES:
[1] Read the following articles in succession:
Turley, J. Damn You, Autocorrect! EE Journal, August 21 (2013).
Moyer, B. The Annoying Valley. EE Journal, November 17 (2011).
[3] Notes on belief functions from the book: Shafer, G. A mathematical theory of evidence. Princeton University Press, Princeton, NJ (1976). A precursor to Dempster-Shafer theory.
[4] see the following reference, with an explanation grounded in the philosophy of science: Greenwald, A.G., Pratkanis, A.R., Leippe, M.R., and Baumgardner, M.H. Under what conditions does theory obstruct research progress? Psychological Review, 93(2), 216-229 (1986).
[5] For more on the promise of deep learning, please see: 10 Breakthrough Technologies 2013. MIT Technology Review, April 23 (2013).
[6] For more on the predictability of research trends, see the chart at bottom and the following reference: LeHong, H. and Fenn, J. Key Trends to Watch in Gartner 2012 Emerging Technologies Hype Cycle. Forbes Tech news, September 18 (2012).
[7] Graziano, M. How Consciousness Works. Aeon Magazine, August 23 (2013).
[8] Koch, C. Consciousness is Everywhere. HuffPo blog, August 15 (2012).
[9] Graziano contrasts consciousness (the awareness of stuff) with "extramission theory", a naive theory that posits human control over the natural world using visual cues.
[10] For more on this idea, please see: Csikszentmihalyi, M. Flow: the psychology of optimal experience. Harper and Row, New York (1990).
[11] Posner, M.I. and Patoine, B. How Arts Training Improves Attention and Cognition. DANA Foundation News, September 14 (2009).
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