Category Archives: data analysis

Partisan Innumeracy

In his memoir, China in Ten Words, writer Yu Hua recalled an event following the end of the cultural revolution. Literature had been banned for many years but the memory of its joys had lingered in much of the population. Hua’s formative years had been during the intellectually desiccated period. Emerging from a time when being seen with any book other than officially sanctioned volumes of or about Chairman Mao could have grave consequences, he and many others craved stories featuring relatable human characters and situations.

Eventually China loosened its intellectual restrictions and the word that bookstores would be opening spread like a prairie fire. Hua tells of a local bookstore that announced it would distribute coupons for two books each to the first few customers to come to the shop’s opening. Hua rose before dawn and was dismayed to find several hundred people already in line, many of whom had been in line all night.

To occupy themselves those waiting speculated on how many coupons the shop owner would hand out. The speculations fell into three general groups. Those who’d gotten in line the day prior were sure their proaction would be rewarded with coupons. Those who arrived later and comprised the middle of the line agreed among themselves there would be more coupons for such obviously high-quality people who’d been wise enough to arrive before the deadbeats behind them. Those like Hua, farther back in the line, reached a consensus there would be enough coupons for them. After all, all of them were clearly quality people for having the foresight and diligence to arrive so early. Hilariously, each person’s assessment derived not from any objective assessment, such as estimating the number of books visible through the shop window, but rather on an emotional assessment of how deserving they were for making the effort to be in the line and, tellingly, how undeserving those farther back in the line were, considering they were comparative slackers. When the shop door opened and the actual number of coupons—50—was announced, persons one through 50 smugly congratulated themselves while the remainders moaned and cursed. Person 51 felt auspiciously unlucky and the number ’51’ became shorthand for unlucky throughout the town.

It’s entertaining to poke fun at the sort of irrational innumeracy the Chinese villagers displayed but it’s a universal flaw in human thinking. Our estimates and judgments of others’ estimates and judgments skew sharply in keeping with our sense of identity. Logically, our sense of who we are, which involves ongoing comparisons of ourselves with others, is a psychological matter that has no bearing on quantifiable facts about the world.

Take the results of a recent Ipsos/Axios survey in which American’s belief in whether the reported number of COVID-19 deaths was accurate, under-reported, or excessive. Beliefs closely aligned with political perspectives (which are primarily sociological and psychological, not fact oriented). This does not mean objective facts cannot align with a group’s position on a matter. It means such alignment often has less to do with a group’s objectivity or rationality than with how convenient the facts are to a group’s claims about what is real or important.

As for a more facts-based approach to assessing COVID-19 related death counts, the article linked in the preceding paragraph provides objective reasons for why an undercounting scenario is likely:

  • Insufficient testing obscures many COVID-19 deaths.
  • Several states’ death rates are significantly higher than statistics for other causes would suggest, and are still increasing.

What’s important to most people, however, is not recognizing accurate facts or more valid reasoning but feeling they are true to their in-groups’ values and self-definition.

COVID-19 (Average) vs Other Causes of Death (Actual) in the U.S. – Animated Data Graph

Source: Covid vs. US Daily Average Cause of Death, Robert Martin on 8 Apr 2020

For those still saying influenza is a much bigger killer than COVID-19 (SARS-COV-2), the numbers don’t support that argument, especially considering there are many deaths that strongly appear to be due to COVID-19 that are not reported as such because the deceased are not tested. The animation conveys the speed with which an exponentially increasing infection rate overtakes other, relatively linear rates of expansion.

History of complexity science

Here’s an interesting infographic of the main concepts and thinkers in complexity science across time. Notice S. Kauffman is slated in the 1980s column, suggesting the graphic depicts when influential thinkers first make their marks. 

https://www.art-sciencefactory.com/complexity-map_feb09.html

Applying artificial intelligence for social good

This McKinsey article is an excellent overview of this more extensive article (3 MB PDF) enumerating the ways in which varieties of deep learning can improve existence. Worth a look.

The articles cover the following:

  • Mapping AI use cases to domains of social good
  • AI capabilities that can be used for social good
  • Overcoming bottlenecks, especially around data and talent
  • Risks to be managed
  • Scaling up the use of AI for social good

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.

Source: http://www.kurzweilai.net/new-algorithm-will-allow-for-simulating-neural-connections-of-entire-brain-on-future-exascale-supercomputers

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.

Source: http://www.kurzweilai.net/recording-data-from-one-million-neurons-in-real-time?utm_source=KurzweilAI+Weekly+Newsletter&utm_campaign=ef0a349adb-UA-946742-1&utm_medium=email&utm_term=0_147a5a48c1-ef0a349adb-282174293

Cambridge Analytica pilfered Facebook data to influence election

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Sophisticated, sometimes AI-enabled data analytics tools allow construction of individual personality profiles accurate enough to support targeted manipulation of individuals’ perceptions and actions. 

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Last night Facebook announced bans against Cambridge Analytica, its parent company and several individuals for allegedly sharing and keeping data that they had promised to delete. This data reportedly included information siphoned from hundreds of thousands of Amazon Mechanical Turkers who were paid to use a “personality prediction app” that collected data from them and also anyone they were friends with — about 50 million accounts. That data reportedly turned into information used by the likes of Robert Mercer, Steve Bannon and the Donald Trump campaign for social media messaging and “micro-targeting” individuals based on shared characteristics.

 

https://www.engadget.com/2018/03/17/facebook-cambridge-analytica-data-analysis-chris-wylie/?sr_source=Facebook 

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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)
https://www.youtube.com/watch?v=CgFzmE2fGXA
• Imagining the Future: The Transformation of Humanity (video, 19 m)
https://www.youtube.com/watch?v=7XrbzlR9QmI
• The Berlin Brain-Computer Interface: Progress Beyond Communication and Control (research article, access with a free Frontiers account)
https://www.frontiersin.org/articles/10.3389/fnins.2016.00530/full
• The Elephant in the Mirror: Bridging the Brain’s Explanatory Gap of Consciousness (research article)
https://www.frontiersin.org/articles/10.3389/fnsys.2016.00108/full

Other resources (recommend your own in the comments!)

• DARPA implant (planned) with up to 1 million neural connections (short article)
https://www.darpa.mil/news-events/2015-01-19

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.

Neural responses to media a strong predictor of friendship

“The findings revealed that  similarity was strongest among friends, and this pattern appeared to manifest across brain regions involved in emotional responding, directing one’s attention and high-level reasoning. Even when the researchers controlled for variables, including left-handed- or right-handedness, age, gender, ethnicity, and nationality, the similarity in neural activity among friends was still evident. The team also found that fMRI response similarities could be used to predict not only if a pair were friends but also the social distance between the two.”

https://medicalxpress.com/news/2018-01-brain-reveals-friends-similar-neural.html