Category Archives: cognition

‘Entangled’ consciousness app approaching release

The Global Consciousness Project, Institute of Noetic Sciences (IONS, for which I was once Hawaii state coordinator) and Princeton Engineering Anomalies Research (PEAR) are collaborating to release a smart phone app, Entangled, that aims to

  • Monitor your mind’s influence on your physical environment
  • Let you take part in large-scale consciousness experiments
  • Support ongoing development of a”consciousness technology” platform for developers and artists
  • Monitor global consciousness data in real-time

Before you think I’ve gone off the deep end, let me explain that I gently stepped away from IONS after nearly 20 years because I did not see enough focus on or progress toward their stated goal—scientifically researching consciousness. I fully enjoyed their practice-oriented emphases on intuitive, embodied, mindful living, but while they remained ‘entangled’ in New Age phenomenalism and esoteric speculations, true scientific programs at many universities and research organizations have made steady, sometimes frustratingly slow progress (which is how science typically works). So, please don’t take this post as a tacit endorsement of any of the sponsoring organizations. They each raise interesting questions and do some work of scientific merit or promise, but (in my view) if you’re interest is in verifiable, repeatable, causally intelligible phenomena, you must stay vigilant of the unscientific chaff.

That said, the spike in non-random streams in random number generators immediately prior to the 9-11 atrocity remains one of the very few well-documented phenomena that could be taken to imply a correlation between a specific objective event and human transpersonal consciousness. In the view of the Global Consciousness Project, by collecting large samples of the right sorts of data, they can test their hypothesis that “Coherent consciousness creates order in the world. Subtle interactions link us with each other and the Earth.” As I understand it, they are extrapolating to the transpersonal level how an individual brain achieves coherent, self-aware states. Also, they would say we’re aware of the apparent precognitive 9-11 phenomenon because someone was collecting the relevant data that could then be recognized as correlated. The Entanglement app aims to collect more of such data while also providing users real- or near-real-time feedback.

If truly well-designed scientific research programs can show significant evidence of direct, entanglement-like correlations between objectively observable phenomena and consciousness (shown in brain functioning), I’ll be excited to learn about it. I think this is a monumental challenge.

Embodied consciousness and the Flow Genome Project

In line with our July joint meeting with the NM Tech Council, I’m reading a fascinating book (Stealing Fire) on the variety of ways humans can experience states of flow (optimal states of consciousness and performance). The authors, Steven Kotler and Jamie Wheal, explain the significance of flow and introduce their Flow Dojo concept in the videos linked below. Applying methods for achieving flow is often categorized in the consciousness hacking movement, also called brain hacking.

What is Flow (6+ minutes)

The Flow Dojo (4+ minutes)

All Flow Genome Project videos

Long (1 hour) interview by Jason Silva follows:

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. 

Brain’s facial-recognition mechanism revealed

Caltech researchers have identified the brain mechanisms that enable primates to quickly identify specific faces. In a feat of efficiency, surprisingly few feature-recognition neurons are involved in a process that may be able to distinguish among billions of faces. Each neuron in the facial-recognition system specializes in noticing one feature, such as the width of the part in the observed person’s hair. If the person is bald or has no part, the part-width-recognizing neuron remains silent. A small number of such specialized-recognizer neurons feed their inputs to other layers (patches) that integrate a higher-level pattern (e.g., hair pattern), and these integrate at yet higher levels until there is a total face pattern. This process occurs nearly instantaneously and works regardless of the view angle (as long as some facial features are visible). Also, by cataloging which neurons perform which functions and then mapping these to a relatively small set of composite faces, researchers were able to tell which face a macaque (monkey) was looking at.

These findings seem to correlate closely with Ray Kurzweil’s (Google’s Chief Technology Officer) pattern-recognition theory of mind.

Scientific American article

BMCAI library file (site members only)

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

An article at 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.

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 essential 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.

How to Convince Someone When Facts Fail

Cognitive bias article of the day: How to Convince Someone When Facts Fail

A concise, timely look at how worldview-driven cognitive dissonance leads people to double down on their misbeliefs in the face of challenging evidence. It also recommends steps for having more meaningful conversations with others whose irrational positions differ from your own. 😉

Excellent article on the history and recent advances in AI

This NY Times article is worth your time, if you are interested in AI–especially if you are still under the impression AI has ossified or lost its way.