Readings

“Conspiracists Concur: Climate Change Is a Colossal Cover-Up” by Richard Martin

Article from MIT Tech Review covering a patchwork of articles on the theme.

“(…) That climate deniers are also conspiracy buffs might seem like one of those dog-bites-man findings for which social scientists are often ridiculed (“People in love do foolish things, study concludes”). But the background to this study is actually more interesting than its conclusion.

Published in the Journal of Social and Political Psychology, the new paper, “Recurrent Fury: Conspiratorial Discourse in the Blogosphere,” is based on an examination of blog comments in response to the authors’ previous paper, “Recursive Fury: Conspiracist Ideation in the Blogosphere”—itself a follow-up to their original study, “NASA Faked the Moon Landing—Therefore, (Climate) Science Is a Hoax: An Anatomy of the Motivated Rejection of Science,” published in Psychological Science in 2012. In other words, commenters responding (mostly angrily) to two studies of conspiratorial thought have accused the authors of being part of a massive conspiracy.

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The British newspaper The Telegraph has helpfully compiled a list of the most widely cited climate-change theories (…) a plot against the United States, a plot against Asia, and a plot against Africa. A vast right-wing conspiracy, or a dark plot from the left.(…) climate change was dreamed up by Margaret Thatcher as part of her campaign to break the U.K. coal unions.

(…) “Science literacy promoted polarization on climate, not consensus,” writes Achenbach from National Geographic. (…)  A well-designed experiment is no match for a Weltanschauung. This is most clearly understood by Thomas Pynchon, the greatest modern novelist of paranoia. “There is something comforting—religious, if you want—about paranoia,” Pynchon wrote in Gravity’s Rainbow. The alternative is “anti-paranoia, where nothing is connected to anything, a condition not many of us can bear for long.” read full article

“Ancestry Moves Further into Consumer Genetics” by Anna Nowogrodzki

article featured in MIT Tech covering new service by Ancestry.

“Ancestry entered the field of consumer DNA analysis in 2012 with the launch of AncestryDNA, a $99 spit test that will analyze your DNA – five years after 23andMe began to offer similar DNA-testing kits.

… Ancestry has an advantage over 23andMe in that it already has millions of users’ family trees. AncestryHealth capitalizes on this: the free service will import both family tree data from Ancestry and genetic data…

…family history is often the first thing doctors ask for to assess health risks, and AncestryHealth is betting that people would rather print out that history from a free website than dredge their memories for half-forgotten details in the five minutes before their doctor’s appointment.

And Ancestry is hoping to sell that data for medical research purposes. …

…“With the blessing of the FDA and regulators, we would like to communicate with that consumer, whether that is through a physician or a genetic counselor,” says Chahine.” read full article

Genomics as a Big Data science

in  “Big Data: Astronomical or Genomical?” researchers discuss the emergence of genomics as a big data science and it’s consequences in terms of techniques and methodology.

New technologies are required to meet the computational challenges.  Genomical progress challenges scientific community for concerted effort.

Their research compares genomics with Astronomy, Youtube, and Twitter – all major source from the tons of new data being added lately.

“The spiritual use of an orchard or garden of fruit trees” by Ralph Austen

Published first in 1653 as a companion to the book  A Treatise on Fruit-trees, showing the manner of grafting, setting, pruning, and ordering of them in all respects. this book is a testament to the fact that love for trees and the recognition of the benefits of human contact with trees is far from a XYZ generation fad.

Brain interconnected as an intranet

In “Building an organic computing device with multiple interconnected brains” researchers Miguel Pais-Vieira, Gabriela Chiuffa, Mikhail Lebedev, Amol Yadav, and Miguel A. L. Nicolelis introduces application of brain-to-brain interfaces.

Such interfaces are ways to receive from and send stimuli directly to animal’s brains.  In this papers experiments, rats.

Applications such as animal social behavior, sensorial phenomena and other insight into animal cognitive process are in the prospect of such – rather invasive – techniques.

Indirectly, it may be very intersting to use such neurological logs in reverse: how should our own, A.I. neural systems benefit from interconnectivity?

Are we fostering A.I. that will be compassionate of ourselves?

excerpts from “Friendly Artificial Intelligence: Parenthood and the Fear of Supplantation”  by Chase Uy, at Ethical Technology

“…Much of the discourse regarding the hypothetical creation of artificial intelligence often views AI as a tool for the betterment of humankind—a servant to man. (…) These papers often discuss creating an ethical being yet fail to acknowledge the ethical treatment of artificial intelligence at the hands of its creators. (…)

Superintelligence is inherently unpredictable (…) that does not mean ethicists and programmers today cannot do anything to bias the odds towards a human­friendly AI; like a child, we can teach it to behave more ethically than we do. Ben Goertzel and Joel Pitt discuss the topic of ethical AI development in their 2012 paper, “Nine Ways to Bias the Open­Source AGI Toward Friendliness.” (…)

Goertzel and Pitt propose that the AGI must have the same faculties, or modes of communication and memory types, that humans have in order to acquire ethical knowledge. These include episodic memory (the assessment of an ethical situation based on prior experience); sensorimotor memory (the understanding of another’s feelings by mirroring them); declarative memory (rational ethical judgement); procedural memory (learning to do what is right by imitation and reinforcement); attentional memory (understanding patterns in order to pay attention to ethical considerations at appropriate times); intentional memory (ethical management of one’s own goals and motivations) (Goertzel & Pitt 7).

The idea that an AI must have some form of sensory functions and an environment to interact with is also discussed by James Hughes in his 2011 book, Robot Ethics, in the chapter, “Compassionate AI and Selfless Robots: A Buddhist Approach”. (…) This method proposes that in order for a truly compassionate AI to exist, it must go through a state of suffering and, ultimately, self­transcendance.

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Isaac Asimov’s Three Laws of Robotics are often brought up in discussions about ways to constrain AI.(…). The problematic nature of these laws (…) allow for the abuse of robots, they are morally unacceptable (Anderson & Anderson 233). (…)  It does not make sense to have the goal of creating an ethically superior being while giving it less functional freedom than humans.

From an evolutionary perspective, nothing like the current ethical conundrum between human beings and AI has ever occurred. Never before has a species intentionally sought to create a superior being, let alone one which may result in the progenitor’s own demise. Yet, when viewed from a parental perspective, parents generally seek to provide their offspring with the capability to become better than they themselves are. Although the fear of supplantation has been prevalent throughout human history, it is quite obvious that acting on this fear merely delays the inevitable evolution of humanity. This is not a change to be feared, but instead to simply be accepted as inevitable. We can and should bias the odds towards friendliness in AI in order to create an ethically superior being. Regardless of whether or not the first superintelligent AI is friendly, it will drastically transform humanity as we know it.(…).”

Probabilistic Inference Techniques for Scalable Multiagent Decision Making

In a colaboration by Singapore, USA, and Germany based researchers  Akshat Kumar, Shlomo Zilberstein, and Marc Toussaint published “Probabilistic Inference Techniques for Scalable Multiagent Decision Making“.

This paper introduces a new class of algorithms for machine learning applied to multiagent planning.  Especifically, in scenarios of partial observation.  Application of bayesian inference not being unheard of, this paper advances in determining conditions for scalability.