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All ultimate explanations are bizarre

Book review – Life 3.0: Being Human in the Age of Artificial Intelligence, by Max Tegmark

Max Tegmark’s new book, Life 3.0: Being Human in the Age of Artificial Intelligence, introduces a framework for defining types of life based on the degree of design control that sensing, self-replicating entities have over their own ‘hardware’ (physical forms) and ‘software’ (“all the algorithms and knowledge that you use to process the information from your senses and decide what to do”).

It’s a relatively non-academic read and well worth the effort for anyone interested in the potential to design the next major forms of ‘Life’ to transcend many of the physical and cognitive constraints that have us now on the brink of self-destruction. Tegmark’s forecast is optimistic.

The President discusses technology in government

Once upon a time, the United States of America had a competent president who actually grasped complex issues and could intelligently explore governmental implications. Those were the days.

Giant neuron found encircling and intraconnecting mouse brain

A neuron that encircles the mouse brain emanates from the claustrum (an on/off switch for awareness) and has dense links with both brain hemispheres. Scientists including Francis Crick and Christoph Koch have speculated that the claustrum may play a role in enabling conscious thought.

https://www.sciencealert.com/a-giant-neuron-has-been-found-wrapped-around-the-entire-circumference-of-the-brain 

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1569501/ (Crick and Koch academic article)

We’ve frequently discussed how self-aware consciousness likely arises not from any single brain structure or signal, but from complex, recursive (reentrant), synchronized signaling among many structures organized into functional regions. (Did I get close to accurate there?) That a giant neuron provides another connection path among such regions can be taken to align with the reentrant signaling and coordination view of consciousness (ala Edelman and Tononi).

Computer metaphor not accurate for brain’s embodied cognition

It’s common for brain functions to be described in terms of digital computing, but this metaphor does not hold up in brain research. Unlike computers, in which hardware and software are separate, organic brains’ structures embody memories and brain functions. Form and function are entangled.

Rather than finding brains to work like computers, we are beginning to design computers–artificial intelligence systems–to work more like brains.

https://www.wired.com/story/tech-metaphors-are-holding-back-brain-research/

Should AI agents’ voice interactions be more like our own? What effects should we anticipate?

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. To make matters worse, she clicked on a string of underlined text in a document and was forcibly and instantly transported to a strange destination. Cries for help punctuated my hours. 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.

Prefrontal lesions correlated with religious fundamentalism

A study of Vietnam veterans with brain lesions suggests religious beliefs originate in evolutionarily recent brain areas. The study found that ability to modify beliefs in light of new evidence is lowered when there is damage to the ventromedial or dorsolateral prefrontal cortex. Such damage correlates with religious fundamentalism.

The study design had some notable limitations, but the findings suggest promising focus areas for further research.

Original report: Vietnam Head Injury Study

Liberals also susceptible to confirmation bias (of course)

Confirmation bias is a human problem. It afflicts throughout the range of political perspectives.

https://www.newscientist.com/article/2129319-liberals-are-no-strangers-to-confirmation-bias-after-all/ 

National Transportation Noise Map

This map of transportation noise in the U.S. should help us find our next place to be.

Mathematical field of topology reveals importance of ‘holes in brain’

New Scientist article: Applying the mathematical field of topology to brain science suggests gaps in densely connected brain regions serve important cognitive functions. Newly discovered densely connected neural groups are characterized by a gap in the center, with one edge of the ring (cycle) being very thin. It’s speculated that this architecture evolved to enable the brain to better time and sequence the integration of information from different functional areas into a coherent pattern.

Aspects of the findings appear to support Edelman’s and Tononi’s (2000, p. 83) theory of neuronal group selection (TNGS, aka neural Darwinism).


Edelman, G.M. and Tononi, G. (2000). A Universe of Consciousness: How Matter Becomes Imagination. Basic Books.

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