BMAI friends. The following ramble is my first cut at making sense of the grave role racial (and other) bias is playing in the world today. This was prompted by comments I see daily from my family and friends on social media. Thinking about the great lack of self- and group-awareness many of the commenters display, I turned my scope inward. How do my own innate, evolved biases slant me to take my group’s and my own privileges for granted and make invalid assumptions about those I perceive (subconsciously or explicitly) to be ‘the other’? I put this forward to start a discussion and hope you will contribute your own insights and references. Feel free to post comments or even insert questions, comments, or new text directly into my text. Of course, you can create your own new posts as well. Thanks.
Lakoff’s last article was published in this open access Ebook edited by Seana Coulson and Vicky T. Lai, published by Frontiers Media SA in Frontiers in Human Neuroscience (March, 2016). The blurb:
 Arbib, M. A. (1989). The metaphorical brain 2: Neural networks and beyond. John Wiley & Sons, Inc.
 Gibbs Jr, R. W. (Ed.). (2008). The Cambridge handbook of metaphor and thought. Cambridge University Press.
 Sweetser, Eve E. “Grammaticalization and semantic bleaching.” Annual Meeting of the Berkeley Linguistics Society. Vol. 14. 2011.
 Lakoff, G., & Johnson, M. (1999). Philosophy in the flesh: The embodied mind and its challenge to western thought.
 Coulson, S. (2008). Metaphor comprehension and the brain. The Cambridge handbook of metaphor and thought, 177-194.
 Winner, E., & Gardner, H. (1977). The comprehension of metaphor in brain-damaged patients. Brain, 100(4), 717-729.
 Coulson, S., & Van Petten, C. (2007). A special role for the right hemisphere in metaphor comprehension?: ERP evidence from hemifield presentation. Brain Research, 1146, 128-145.
 Lai, V. T., Curran, T., & Menn, L. (2009). Comprehending conventional and novel metaphors: An ERP study. Brain Research, 1284, 145-155.
 Schmidt, G. L., Kranjec, A., Cardillo, E. R., & Chatterjee, A. (2010). Beyond laterality: a critical assessment of research on the neural basis of metaphor. Journal of the International Neuropsychological Society, 16(01), 1-5.
 Desai, R. H., Binder, J. R., Conant, L. L., Mano, Q. R., & Seidenberg, M. S. (2011). The neural career of sensory-motor metaphors. Journal of Cognitive Neurosc., 23(9), 2376
By George Lakoff, Frontiers in Human Neureoscience, Hypothesis and Theory Article (link), 2014. Introduction: “An overview of the basics of metaphorical thought and language from the perspective of Neurocognition, the integrated interdisciplinary study of how conceptual thought and language work in the brain. The paper outlines a theory of metaphor circuitry and discusses how everyday reason makes use of embodied metaphor circuitry.” Also see the section on experimental results for the studies.
In this 4-minute clip Lakoff summarizes how philosophy is changed by cognitive science. Particular philosophies get attached to a root metaphor (or blend) that entails certain premises and conclude that it is reality in toto without going further to understand that other metaphors entail different premises with equally logical conclusions. The embodied thesis helps us understand how our body-minds work to correct many of philosophy’s metaphysical assumptions while providing a postmetaphysical frame for an empirical, embodied and multifarious philosophy.
Frontiers in Human Neuroscience, 2017; 11: 126. Some excerpts:
“In this article we suggest the idea that the processing of self-referential stimuli in cortical midline structures (CMS) may represent an important part of the conscious self, which may be supplemented by an unconscious part of the self that has been called an ’embodied mind’ (Varela et al., 1991), which relies on other brain structures.”
“When we describe the self as structure and organization we understand it as a system. But the concept of the embodied self states that the self or cognition is not an activity of the mind alone, but is distributed across the entire situation including mind, body, environment (e.g., Beer, 1995), thereby pointing to an embodied and situated self.”
“Furthermore, we argue that through embodiment the self is also embedded in the environment. This means that our self is not isolated but intrinsically social. […] Hence, the self should not be understood as an entity located somewhere in the brain, isolated from both the body and the environment. In contrast, the self can be seen as a brain-based neurosocial structure and organization, always linked to the environment (or the social sphere) via embodiment and embeddedness.”
It occurred to me that memes are a lot like frames as Lakoff describes them. Lakoff has done extensive cognitive scientific work on schemas, metaphors and frames. Check out this lengthy article in Frontiers in Human Neuroscience, 2014; 8: 958, “Mapping the brain’s metaphor circuitry.” Even though they don’t relate this to the concept of memes, there are some striking similarities. E.g.:
“Reddy had found that the abstract concepts of communication and ideas are understood via a conceptual metaphor: Ideas Are Objects; Language Is a Container for Idea-Objects; Communication Is Sending Idea-Objects in Language-Containers.”
In a few previous posts I posted articles on new scientific research questioning some of Piaget’s original premises. This Wikipedia article discusses those neo-Piagetians who have taken into account the more recent science. Also see this article that discusses some of the neo-Piagetians but then focuses on Kurt Fischer’s work.
An article at Wired.com considers the pros and cons of making the voice interactions of AI assistants more humanlike.
The assumption that more human-like speech from AIs is naturally better may prove as incorrect as the belief that the desktop metaphor was the best way to make humans more proficient in using computers. When designing the interfaces between humans and machines, should we minimize the demands placed on users to learn more about the system they’re interacting with? That seems to have been Alan Kay’s assumption when he designed the first desktop interface back in 1970.
Problems arise when the interaction metaphor diverges too far from the reality of how the underlying system is organized and works. In a personal example, someone dear to me grew up helping her mother–an office manager for several businesses. Dear one was thoroughly familiar with physical desktops, paper documents and forms, file folders, and filing cabinets. As I explained how to create, save, and retrieve information on a 1990 Mac, she quickly overcame her initial fear. “Oh, it’s just like in the real world!” (Chalk one for Alan Kay? Not so fast.) I knew better than to tell her the truth at that point. Dear one’s Mac honeymoon crashed a few days later when, to her horror and confusion, she discovered a file cabinet inside a folder. A few years later, there was another metaphor collapse when she clicked on a string of underlined text in a document and was forcibly and instantly transported to a strange destination.
Having come to terms with computers through the command-line interface, I found the desktop metaphor annoying and unnecessary. Hyperlinking, however–that’s another matter altogether–an innovation that multiplied the value I found in computing.
On the other end of the complexity spectrum would be machine-level code. There would be no general computing today if we all had to speak to computers in their own fundamental language of ones and zeros. That hasn’t stopped some hard-core computer geeks from advocating extreme positions on appropriate interaction modes, as reflected in this quote from a 1984 edition of InfoWorld:
“There isn’t any software! Only different internal states of hardware. It’s all hardware! It’s a shame programmers don’t grok that better.”
Interaction designers operate on the metaphor end of the spectrum by necessity. The human brain organizes concepts by semantic association. But sometimes a different metaphor makes all the difference. And sometimes, to be truly proficient when interacting with automation systems, we have to invest the effort to understand less simplistic metaphors.
The article referenced in the beginning of this post mentions that humans are manually coding “speech synthesis markup tags” to cause synthesized voices of AI systems to sound more natural. (Note that this creates an appearance that the AI understands the user’s intent and emotional state, though this more natural intelligence is illusory.) Intuitively, this sounds appropriate. The down side, as the article points out, is that colloquial AI speech limits human-machine interactions to the sort of vagueness inherent in informal speech. It also trains humans to be less articulate. The result may be interactions that fail to clearly communicate what either party actually means.
I suspect a colloquial mode could be more effective in certain kinds of interactions: when attempting to deceive a human into thinking she’s speaking with another human; virtual talk therapy; when translating from one language to another in situations where idioms, inflections, pauses, tonality, and other linguistic nuances affect meaning and emotion; etc.
In conclusion, operating systems, applications, and AIs are not humans. To improve our effectiveness in using more complex automation systems, we will have to meet them farther along the complexity continuum–still far from machine code, but at points of complexity that require much more of us as users.