A Guardian article last October brings the darker aspects of the attention economy, particularly the techniques and tools of neural hijacking, into sharp focus. The piece summarizes some interaction design principles and trends that signal a fundamental shift in means, deployment, and startling effectiveness of mass persuasion. The mechanisms reliably and efficiently leverage neural reward (dopamine) circuits to seize, hold, and direct attention toward whatever end the designer and content providers choose.
The organizer of a $1,700 per person event convened to show marketers and technicians “how to manipulate people into habitual use of their products,” put it baldly.
subtle psychological tricks … can be used to make people develop habits, such as varying the rewards people receive to create “a craving”, or exploiting negative emotions that can act as “triggers”. “Feelings of boredom, loneliness, frustration, confusion and indecisiveness often instigate a slight pain or irritation and prompt an almost instantaneous and often mindless action to quell the negative sensation”
Particularly telling of the growing ethical worry are the defections from social media among Silicon Valley insiders.
Pearlman, then a product manager at Facebook and on the team that created the Facebook “like”, … confirmed via email that she, too, has grown disaffected with Facebook “likes” and other addictive feedback loops. She has installed a web browser plug-in to eradicate her Facebook news feed, and hired a social media manager to monitor her Facebook page so that she doesn’t have to.
It is revealing that many of these younger technologists are weaning themselves off their own products, sending their children to elite Silicon Valley schools where iPhones, iPads and even laptops are banned. They appear to be abiding by a Biggie Smalls lyric from their own youth about the perils of dealing crack cocaine: never get high on your own supply.
If you read the article, please comment on any future meeting topics you detect. I find it a vibrant collection of concepts for further exploration.
In this 20-minute video Jeremy Lent gives a brief introduction into his system of liology, his response to substance dualism. Conventional science maintains this dualism, so it is up to the ecological science of dynamical systems theory to correct it. He finds a precursor of systems science in Chinese Neo-Confucianism, which seems a bit of romantic retro-fitting to me, given their own environmental degradation which he minimalizes in his book The Patterning Instinct. That aside, he’s right about the emerging paradigm of systems science as a necessary metaphoric shift if we are to have any chance of curtailing climate change and implementing a sustainable and humane future.
“A new picture is taking shape in which conscious experience is seen as deeply grounded in how brains and bodies work together to maintain physiological integrity – to stay alive.”
“The brain is locked inside a bony skull. All it receives are ambiguous and noisy sensory signals that are only indirectly related to objects in the world. Perception must therefore be a process of inference, in which indeterminate sensory signals are combined with prior expectations or ‘beliefs’ about the way the world is, to form the brain’s optimal hypotheses of the causes of these sensory signals.”
“A number of experiments are now indicating that consciousness depends more on perceptual predictions, than on prediction errors. […] We’ve found that people consciously see what they expect, rather than what violates their expectations.”
This article discusses a new paper in the European Journal of Social Psychology that shows our brain’s penchant for seeing patterns can go awry. Illusory pattern perception is displayed for example in climate science denial, 9/11 truthers, Pizzagate etc. This phenomenon correlates with irrational beliefs that connect dots that aren’t there. We all have this tendency to confirm our biases. However training in critical thinking can reduce the effects of this syndrome.
An MIT Technology Review article introduces the man responsible for the 30-year-old deep learning approach, explains what deep machine learning is, and questions whether deep learning may be the last significant innovation in the AI field. The article also touches on a potential way forward for developing AIs with qualities more analogous to the human brain’s functioning.
Should it surprise us that human biases find their way into human-designed AI algorithms trained using data sets of human artifacts?
Machine-learning software trained on the datasets didn’t just mirror those biases, it amplified them. If a photo set generally associated women with cooking, software trained by studying those photos and their labels created an even stronger association.
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.
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.
Google and others are developing neural networks that learn to recognize and imitate patterns present in works of art, including music. The path to autonomous creativity is unclear. Current systems can imitate existing artworks, but cannot generate truly original works. Human prompting and configuration are required.
Google’s Magenta project’s neural network learned from 4,500 pieces of music before creating the following simple tune (drum track overlaid by a human):
Click Play button to listen->
Is it conceivable that AI may one day be able to synthesize new made-to-order creations by blending features from a catalog of existing works and styles? Imagine being able to specify, “Write me a new musical composition reminiscent of Rhapsody in Blue, but in the style of Lynyrd Skynyrd.
There is already at least one human who could instantly play Rhapsody in Blue in Skynyrd style, but even he does not (to my knowledge) create entirely original pieces.