Category Archives: interface

Neurocapitalism: Technological Mediation and Vanishing Lines

Open access book by Giorgio Griziotti is here. Technical book for you techies. The blurb:

“Technological change is ridden with conflicts, bifurcations and unexpected developments. Neurocapitalism takes us on an extraordinarily original journey through the effects that cutting-edge technology has on cultural, anthropological, socio-economic and political dynamics. Today, neurocapitalism shapes the technological production of the commons, transforming them into tools for commercialization, automatic control, and crisis management. But all is not lost: in highlighting the growing role of General Intellect’s autonomous and cooperative production through the development of the commons and alternative and antagonistic uses of new technologies, Giorgio Griziotti proposes new ideas for the organization of the multitudes of the new millennium.”

Can children learn to read without explicit instruction from adults?

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An experiment in a remote Ethiopian village demonstrates the potential of mobile devices to enable children to learn and teach each other how to read without traditional schooling.

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See also: How Reading Rewires Your Brain for Empathy

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The Singularity is Near: When Humans Transcend Biology

Kurzweil builds and supports a persuasive vision of the emergence of a human-level engineered intelligence in the early-to-mid twenty-first century. In his own words,

With the reverse engineering of the human brain we will be able to apply the parallel, self-organizing, chaotic algorithms of  human intelligence to enormously powerful computational substrates. This intelligence will then be in a position to improve its own design, both hardware and software,  in a rapidly accelerating iterative process.

In Kurzweil's view, we must and will ensure we evade obsolescence by integrating emerging metabolic and cognitive technologies into our bodies and brains. Through self-augmentation with neurotechnological prostheses, the locus of human cognition and identity will gradually (but faster than we'll expect, due to exponential technological advancements) shift from the evolved substrate (the organic body) to the engineered substrate, ultimately freeing the human mind to develop along technology's exponential curve rather than evolution's much flatter trajectory.

The book is extensively noted and indexed, making the deep-diving reader's work a bit easier.

If you have read it, feel free to post your observations in the comments below. (We've had a problem with the comments section not appearing. It may require more troubleshooting.)

Prosthetic memory system successful in humans

“This is the first time scientists have been able to identify a patient’s own brain cell code or pattern for memory and, in essence, ‘write in’ that code to make existing memory work better, an important first step in potentially restoring memory loss”

We showed that we could tap into a patient’s own memory content, reinforce it and feed it back to the patient,” Hampson said. “Even when a person’s memory is impaired, it is possible to identify the neural firing patterns that indicate correct memory formation and separate them from the patterns that are incorrect. We can then feed in the correct patterns to assist the patient’s brain in accurately forming new memories, not as a replacement for innate memory function, but as a boost to it.”


Algorithm brings whole-brain simulation within reach

An improvement to the Neural Simulation Tool (NEST) algorithm, the primary tool of the Human Brain Project, expanded the scope of brain neural data management (for simulations) from the current 1% of discrete neurons (about the number in the cerebellum) to 10%. The NEST algorithm can scale to store 100% of BCI-derived or simulated neural data within near-term reach as supercomputing capacity increases. The algorithm achieves its massive efficiency boost by eliminating the need to explicitly store as much data about each neuron’s state.

Abstract of Extremely Scalable Spiking Neuronal Network Simulation Code: From Laptops to Exascale Computers

State-of-the-art software tools for neuronal network simulations scale to the largest computing systems available today and enable investigations of large-scale networks of up to 10 % of the human cortex at a resolution of individual neurons and synapses. Due to an upper limit on the number of incoming connections of a single neuron, network connectivity becomes extremely sparse at this scale. To manage computational costs, simulation software ultimately targeting the brain scale needs to fully exploit this sparsity. Here we present a two-tier connection infrastructure and a framework for directed communication among compute nodes accounting for the sparsity of brain-scale networks. We demonstrate the feasibility of this approach by implementing the technology in the NEST simulation code and we investigate its performance in different scaling scenarios of typical network simulations. Our results show that the new data structures and communication scheme prepare the simulation kernel for post-petascale high-performance computing facilities without sacrificing performance in smaller systems.


Recording data from one million neurons in real time

Given the human brain’s approximately 80 billion neurons, it would take tens of thousands of these devices to record a substantial volume of neuron-level activities. Still, this is a remarkable achievement.

The system would simultaneously acquire data from more than 1 million neurons in real time. It would convert the spike data (using bit encoding) and send it via an effective communication format for processing and storage on conventional computer systems. It would also provide feedback to a subject in under 25 milliseconds — stimulating up to 100,000 neurons.

Monitoring large areas of the brain in real time. Applications of this new design include basic research, clinical diagnosis, and treatment. It would be especially useful for future implantable, bidirectional BMIs and BCIs, which are used to communicate complex data between neurons and computers. This would include monitoring large areas of the brain in paralyzed patients, revealing an imminent epileptic seizure, and providing real-time feedback control to robotic arms used by quadriplegics and others.


Next discussion meeting Apr 2: Brain-Computer Interface, now and future

During our next discussion meeting, we’ll explore the status, future potential, and human implications of neuroprostheses–particularly brain-computer interfaces. If you are local to Albuquerque, check our Meetup announcement to join or RSVP. The announcement text follows.

Focal questions

What are neuroprostheses? How are they used now and what may the future hold for technology-enhanced sensation, motor control, communications, cognition, and other human processes?

Resources (please review before the meeting)

Primary resources
• New Brain-Computer Interface Technology (video, 18 m)
• Imagining the Future: The Transformation of Humanity (video, 19 m)
• The Berlin Brain-Computer Interface: Progress Beyond Communication and Control (research article, access with a free Frontiers account)
• The Elephant in the Mirror: Bridging the Brain’s Explanatory Gap of Consciousness (research article)

Other resources (recommend your own in the comments!)

• DARPA implant (planned) with up to 1 million neural connections (short article)

Extra Challenge: As you review the resources, think of possible implications from the perspectives of the other topics we’ve recently discussed:
• the dilemma of so much of human opinion and action deriving from non-conscious sources
• questions surrounding what it means to ‘be human’ and what values we place on our notions of humanness (e.g., individuality and social participation, privacy, ‘self-determination’ (or the illusion thereof), organic versus technologically enhanced cognition, etc.)

A dive into the black waters under the surface of persuasive design

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.

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.