“NextNow Collaboratory is an interesting example of a new kind of collective intelligence: an Internet-enabled, portable social network, easily transferable from one social cause to another.”

A natural language provides its user with a ready-made structure of concepts that establishes a basic mental structure, and that allows relatively flexible, general-purpose concept structuring. Our concept of language as one of the basic means for augmenting the human intellect embraces all of the concept structuring which the human may make use of.

It has been jokingly suggested several times during the course of this study that what we are seeking is an "intelligence amplifier." (The term is attributed originally to W. Ross Ashby[2,3]. At first this term was rejected on the grounds that in our view one's only hope was to make a better match between existing human intelligence and the problems to be tackled, rather than in making man more intelligent. But deriving the concepts brought out in the preceding section has shown us that indeed this term does seem applicable to our objective. 2c2a Accepting the term "intelligence amplification" does not imply any attempt to increase native human intelligence. The term "intelligence amplification" seems applicable to our goal of augmenting the human intellect in that the entity to be produced will exhibit more of what can be called intelligence than an unaided human could; we will have amplified the intelligence of the human by organizing his intellectual capabilities into higher levels of synergistic structuring. What possesses the amplified intelligence is the resulting H-LAM/T system, in which the LAM/T augmentation means represent the amplifier of the human's intelligence.2c2b In amplifying our intelligence, we are applying the principle of synergistic structuring that was followed by natural evolution in developing the basic human capabilities. What we have done in the development of our augmentation means is to construct a superstructure that is a synthetic extension of the natural structure upon which it is built. In a very real sense, as represented by the steady evolution of our augmentation means, the development of "artificial intelligence" has been going on for centuries.

This is not a new idea. It is based on the vision expounded by Vannevar Bush in his 1945 essay “As We May Think,” which conjured up a “memex” machine that would remember and connect information for us mere mortals. The concept was refined in the early 1960s by the Internet pioneer J. C. R. Licklider, who wrote a paper titled “Man-Computer Symbiosis,” and the computer designer Douglas Engelbart, who wrote “Augmenting Human Intellect.” They often found themselves in opposition to their colleagues, like Marvin Minsky and John McCarthy, who stressed the goal of pursuing artificial intelligence machines that left humans out of the loop.

Thompson’s point is that “artificial intelligence” — defined as machines that can think on their own just like or better than humans — is not yet (and may never be) as powerful as “intelligence amplification,” the symbiotic smarts that occur when human cognition is augmented by a close interaction with computers.

lants speak in a chemical vocabulary we can’t directly perceive or comprehend. The first important discoveries in plant communication were made in the lab in the nineteen-eighties, by isolating plants and their chemical emissions in Plexiglas chambers, but Rick Karban, the U.C. Davis ecologist, and others have set themselves the messier task of studying how plants exchange chemical signals outdoors, in a natural setting.

Alexander Samuel reflects on tagging and its origins as a backbone to the social web. Along with RSS, tags allowed users to connect and collate content using such tools as feed readers. This all changed with the advent of social media and the algorithmically curated news feed.

What if you could use AI to control the content in your feed? Dialing up or down whatever is most useful to you. If I’m on a budget, maybe I don’t want to see photos of friends on extravagant vacations. Or, if I’m trying to pay more attention to my health, encourage me with lots of salads and exercise photos. If I recently broke up with somebody, happy couple photos probably aren’t going to help in the healing process. Why can’t I have control over it all, without having to unfollow anyone. Or, opening endless accounts to separate feeds by topic. And if I want to risk seeing everything, or spend a week replacing my usual feed with images from a different culture, country, or belief system, couldn’t I do that, too? 

Adult Learning in the Workplace:Emotion Work or Emotion Learning?

The idea of a “plant intelligence”—an intelligence that goes beyond adaptation and reaction and into the realm of active memory and decision-making—has been in the air since at least the early seventies.

“Trees do not have will or intention. They solve problems, but it’s all under hormonal control, and it all evolved through natural selection.”

so there won’t be a blinking bunny, at least not yet, let’s train our bunny to blink on command by mixing stimuli ( the tone and the air puff)

A notable by-product of a move of clinical as well as research data to the cloud would be the erosion of market power of EMR providers.

cloud computing is provided by a small number of large technology companies who have both significant market power and strong commercial interests outside of healthcare for which healthcare data might potentially be beneficial

it has long been argued that patients themselves should be the owners and guardians of their health data and subsequently consent to their data being used to develop AI solutions.

To realize this vision and to realize the potential of AI across health systems, more fundamental issues have to be addressed: who owns health data, who is responsible for it, and who can use it? Cloud computing alone will not answer these questions—public discourse and policy intervention will be needed.

In spite of the widely touted benefits of “data liberation”,15 a sufficiently compelling use case has not been presented to overcome the vested interests maintaining the status quo and justify the significant upfront investment necessary to build data infrastructure.

However, clinician satisfaction with EMRs remains low, resulting in variable completeness and quality of data entry, and interoperability between different providers remains elusive.11

Second, most healthcare organizations lack the data infrastructure required to collect the data needed to optimally train algorithms to (a) “fit” the local population and/or the local practice patterns, a requirement prior to deployment that is rarely highlighted by current AI publications, and (b) interrogate them for bias to guarantee that the algorithms perform consistently across patient cohorts, especially those who may not have been adequately represented in the training cohort.9

Simply adding AI applications to a fragmented system will not create sustainable change.

Both artists, through annotation, have produced new forms of public dialogue in response to other people (like Harvey Weinstein), texts (The New York Times), and ideas (sexual assault and racial bias) that are of broad social and political consequence.

Security Issues, Dangers And Implications Of Smart Systems

1. Direction Over Speed

The term first appeared in 1984 as the topic of a public debate at the annual meeting of AAAI (then called the "American Association of Artificial Intelligence"). It is a chain reaction that begins with pessimism in the AI community, followed by pessimism in the press, followed by a severe cutback in funding, followed by the end of serious research.[2] At the meeting, Roger Schank and Marvin Minsky—two leading AI researchers who had survived the "winter" of the 1970s—warned the business community that enthusiasm for AI had spiraled out of control in the 1980s and that disappointment would certainly follow. Three years later, the billion-dollar AI industry began to collapse.

Deepmachinelearning,whichisusingalgorithmstoreplicatehumanthinking,ispredicatedonspecificvaluesfromspecifickindsofpeople—namely,themostpowerfulinstitutionsinsocietyandthosewhocontrolthem.

government investments

initiatives from the U.S., China, and Europ

Recent Government Initiatives

engagement in AI activities by academics, corporations, entrepreneurs, and the general public

Derivative Measures

AI Vibrancy Index

limited gender diversity in the classroom

improvement in natural language

the COCO leaderboard

patents

robot operating system downloads,

he GLUE metric

robot installations

AI conference attendance

the speed at which computers can be trained to detect objects

quality of question answering

changes in AI performance

Technical Performance

number of undergraduates studying AI

growth in venture capital funding of AI startups

percent of female applicants for AI jobs

Volume of Activity

increased participation in organizations like AI4ALL and Women in Machine Learning

producers of AI patents

ML teaching events

University course enrollment

83 percent of 2017 AI papers

Methodology The classic OSINT methodology you will find everywhere is strait-forward: Define requirements: What are you looking for? Retrieve data Analyze the information gathered Pivoting & Reporting: Either define new requirements by pivoting on data just gathered or end the investigation and write the report.

There’s a bug in the evolutionary code that makes up our brains.

If you do not like the price you’re being offered when you shop, do not take it personally: many of the prices we see online are being set by algorithms that respond to demand and may also try to guess your personal willingness to pay. What’s next? A logical next step is that computers will start conspiring against us. That may sound paranoid, but a new study by four economists at the University of Bologna shows how this can happen.

Worse still, even if we had the ability to take a snapshot of all of the brain’s 86 billion neurons and then to simulate the state of those neurons in a computer, that vast pattern would mean nothing outside the body of the brain that produced it. This is perhaps the most egregious way in which the IP metaphor has distorted our thinking about human functioning.

No ‘copy’ of the story is ever made

The IP perspective requires the player to formulate an estimate of various initial conditions of the ball’s flight

Walter Pitts was pivotal in establishing the revolutionary notion of the brain as a computer, which was seminal in the development of computer design, cybernetics, artificial intelligence, and theoretical neuroscience. He was also a participant in a large number of key advances in 20th-century science.

You were beginning to gather that there were other symbols mixed with the words that might be part of a sentence, and that the different parts of what made a full-thought statement (your feeling about what a sentence is) were not just laid out end to end as you expected.

we provide him as much help as possible in making a plan of action. Then we give him as much help as we can in carrying it out. But we also have to allow him to change his mind at almost any point, and to want to modify his plans.

In amplifying our intelligence, we are applying the principle of synergistic structuring that was followed by natural evolution in developing the basic human capabilities. What we have done in the development of our augmentation means is to construct a superstructure that is a synthetic extension of the natural structure upon which it is built. In a very real sense, as represented by the steady evolution of our augmentation means, the development of "artificial intelligence" has been going on for centuries.

Our culture has evolved means for us to organize the little things we can do with our basic capabilities so that we can derive comprehension from truly complex situations, and accomplish the processes of deriving and implementing problem solutions. The ways in which human capabilities are thus extended are here called augmentation means, and we define four basic classes of them: 2a4 Artifacts—physical objects designed to provide for human comfort, for the manipulation of things or materials, and for the manipulation of symbols.2a4a Language—the way in which the individual parcels out the picture of his world into the concepts that his mind uses to model that world, and the symbols that he attaches to those concepts and uses in consciously manipulating the concepts ("thinking"). 2a4b Methodology—the methods, procedures, strategies, etc., with which an individual organizes his goal-centered (problem-solving) activity. 2a4c Training—the conditioning needed by the human being to bring his skills in using Means 1, 2, and 3 to the point where they are operationally effective. 2a4d The system we want to improve can thus be visualized as a trained human being together with his artifacts, language, and methodology. The explicit new system we contemplate will involve as artifacts computers, and computer-controlled information-storage, information-handling, and information-display devices. The aspects of the conceptual framework that are discussed here are primarily those relating to the human being's ability to make significant use of such equipment in an integrated system.

Nearly half of FBI rap sheets failed to include information on the outcome of a case after an arrest—for example, whether a charge was dismissed or otherwise disposed of without a conviction, or if a record was expunged

Applicants also agree to have their fingerprints entered into DHS’ Automatic Biometric Identification System (IDENT) “for recurrent immigration, law enforcement, and intelligence checks, including checks against latent prints associated with unsolved crimes.

It cited research, including some authored by the FBI, indicating that “some of the biometrics at the core of NGI, like facial recognition, may misidentify African Americans, young people, and women at higher rates than whites, older people, and men, respectively.

machine intelligence

AI Robots will be replacing the White Collar Jobs by 6% until 2021

With approximately half of variance in cognitive task performance having non-g sources of variance, we believe that other traits may be important in explaining cognitive performance of both non-Western and Western groups.

Although we believe that this study establishes the presence of g in data from these non-Western cultures, this study says nothing about the relative level of general cognitive ability in various societies, nor can it be used to make cross-cultural comparisons. For this purpose, one must establish measurement invariance of a test across different cultural groups (e.g., Holding et al., 2018) to ensure that test items and tasks function in a similar way for each group.

Two peer reviewers raised the possibility that developmental differences across age groups could be a confounding variable because a g factor may be weaker in children than adults.

some of these data sets were collected by individuals who are skeptical of the existence or primacy of g in general or in non-Western cultures (e.g., Hashmi et al., 2010; Hashmi, Tirmizi, Shah, & Khan, 2011; O’Donnell et al., 2012; Pitchford & Outhwaite, 2016; Stemler et al., 2009; Sternberg et al., 2001, 2002). One would think that these investigators would be most likely to include variables in their data sets that would form an additional factor. Yet, with only three ambiguous exceptions (Grigorenko et al., 2006; Gurven et al., 2017), these researchers’ data still produced g.

the strongest first factor accounted for 86.3% of observed variable variance

The weakest first factor accounted for 18.3% of variance

The mean sample size of the remaining data sets was 539.6 (SD = 1,574.5). The large standard deviation in relationship to the mean is indicative of the noticeably positively skewed distribution of sample sizes, a finding supported by the much smaller median of 170 and skewness value of 6.297. There were 16,559 females (33.1%), 25,431 males (48.6%), and 10,350 individuals whose gender was unreported (19.8%). The majority of samples—62 of 97 samples (63.9%)—consisted entirely or predominantly of individuals below 18. Most of the remaining samples contained entirely or predominantly adults (32 data sets, 33.0%), and the remaining 3 datasets (3.1%) had an unknown age range or an unknown mix of adults and children). The samples span nearly the entire range of life span development, from age 2 to elderly individuals.

Alternatively, one could postulate that a general cognitive ability is a Western trait but not a universal trait among humans, but this would require an evolutionary model where this general ability evolved several times independently throughout the mammalian clade, including separately in the ancestors of Europeans after they migrated out of Africa and separated from other human groups. Such a model requires (a) a great deal of convergent evolution to occur across species occupying widely divergent environmental niches and (b) an incredibly rapid development of a general cognitive ability while the ancestors of Europeans were under extremely strong selection pressures that other humans did not experience (but other mammal species or their ancestors would have experienced at other times). We find the more parsimonious model of an evolutionary origin of the general cognitive ability in the early stages of mammalian development to be the more plausible one, and thus we believe that it is reasonable to expect a general cognitive ability to be a universal human trait.

Researchers studying the cognitive abilities of animals have identified a general factor in cognitive data from many mammal species

Investigating cultural beliefs about intelligence may be mildly interesting from an anthropological perspective, but it sheds little light on the nature of intelligence. One undiscussed methodological problem in many studies of cultural perspectives on intelligence is the reliance on surveys of laymen to determine what people in a given culture believe about intelligence. This methodology says little about the actual nature of intelligence.

Panga Munthu test of intelligence

Berry (1986)

Curiosity Is as Important as Intelligence

We believe that members of the public likely learn some inaccurate information about intelligence in their psychology courses. The good news about this implication is that reducing the public’s mistaken beliefs about intelligence will not take a massive public education campaign or public relations blitz. Instead, improving the public’s understanding about intelligence starts in psychology’s own backyard with improving the content of undergraduate courses and textbooks.

This means that it is actually easier to measure intelligence than many other psychological constructs. Indeed, some individuals trying to measure other constructs have inadvertently created intelligence tests

many psychologists simply accept an operational definition of intelligence by spelling out the procedures they use to measure it. . . . Thus, by selecting items for an intelligence test, a psychologist is saying in a direct way, “This is what I mean by intelligence.” A test that measures memory, reasoning, and verbal fluency offers a very different definition of intelligence than one that measures strength of grip, shoe size, hunting skills, or the person’s best Candy Crush mobile game score. (p. 290)

Minnesota Transracial Adoption Study

the Stanford-Binet intelligence test

Some readers will also be surprised to find that The Bell Curve is not as controversial as its reputation would lead one to believe (and most of the book is not about race at all).

Gardner’s multiple intelligences

Most frequently this appeared in the form of a tacit acknowledgment that IQ test scores correlate with academic success, followed by a quick denial that the scores are important for anything else in life

this study highlights the mismatch between scholarly consensus on intelligence and the beliefs of the general public

Judged solely by the number of factually inaccurate statements, the textbooks we examined were mostly accurate.

We found that 79.3% of textbooks contained inaccurate statements and 79.3% had logical fallacies in their sections about intelligence.

Gottfredson’s (1997a) mainstream statement on intelligence

CTP synthesizes critical reflection with technology production as a way of highlighting and altering unconsciously-held assumptions that are hindering progress in a technical field.

The vast majority of machine-learning applications rely on supervised learning.

most importantly, however, when the group has real synergy, it will by far exceed the best individual performance. Synergy is best thought of as members of the same team feeding off one another in positive ways; as result the "whole" becomes better than "the sum of the parts". Collaboration can actually raise the "group IQ" – i.e. the sum total of the best talents of each member on the team.

In effective collaboration, all people involved use their emotional intelligence well to balance emotional needs with their thinking, build authentic relationships and make good quality decisions on behalf of the organisation. Whether working with others one-to-one, in small groups or large teams, there is exemplary communication with empathy that engages hearts and minds.  This occurs at all levels of the organisation.

t turns out emotional intelligence in a group setting accelerates the group's development. Team members need EI on an individual level. And when we work together with EI, it's fascinating to see what happens. Studies are finding that collaboration among those with high emotional intelligence creates outcomes that exceed the sum of their individual talents. Shared emotional intelligence not only improves work processes, it improves the work product!

Entscheidend ist, dass sie Herren des Verfahrens bleiben - und eine Vision für das neue Maschinenzeitalter entwickeln.

By framing "genius" as something intrinsic rather than situational, we deny even the potential for achievement to a huge fraction of the population. As paleontologist Stephen Jay Gould wrote The Panda's Thumb, where he wrote, "I am, somehow, less interested in the weight and convolutions of Einstein’s brain than in the near certainty that people of equal talent have lived and died in cotton fields and sweatshops."

The problem is far worse when used to generalize about groups, such as gender and especially race. When combined with the cultural belief that only the "brainy" are worthy of science training, it becomes a self-reinforcing cycle: only certain white men are inherently "smart enough", as decided primarily by other white men. You'll hear (and I'll bet cash money that someone will argue in the comments) that African-American underrepresentation in science is because they're not "smart" or "motivated" enough, not that black-majority school districts are often underfunded, lacking teachers, supplies, and other necessities for STEM prep — not to mention daily challenges to their authority and intelligence for those who do earn STEM degrees.

To make matters worse, "intelligence" itself is weaponized by the status quo against people of color and white women. That's especially evident in the continuing battles over the interpretation of IQ test results.

Science writer Kat Arney delved into this issue in detail in a recent column for the (UK) Royal Society of Chemistry. As she points out, the problems with the "brainy scientist" stereotype are manifold: that science is a meritocracy, and that non-scientists are somehow less valuable.

No. It’s not you. You were different before. – I’m still the same person, Lin. – I wasn’t, when I was on it. I did things I would never do. – Those things saved your life. – But they weren’t me. – Yes, they were. No, the way it works… – I know how it works. I get it. I totally get it. You feel invincible.

Good has captured the essence of the runaway, but he does not pursue its most disturbing consequences. Any intelligent machine of the sort he describes would not be humankinds “tool” – any more than humans are the tools of rabbits, robins, or chimpanzees.

And its very likely that IA is a much easier road to the achievement of superhumanity than pure AI. In humans, the hardest development problems have already been solved. Building up from within ourselves ought to be easier than figuring out what we really are and then building machines that are all of that.

Into the Wormhole

Capacity can also affect crisis potential through staffing decisions that affect the diversity of acts that are available. Enactment is labour-intensive, which means understaffing has serious effects.

"... groups of individuals doing things collectively that seem intelligent.” [41]

hus it becomes possible to see how ques-tions around data use need to shift from asking what is in the data, to include discussions of how the data is structured, and how this structure codifies value systems and social practices, subject positions and forms of visibility and invisi-bility (and thus forms of surveillance), along with the very ideas of crisis, risk governance and preparedness. Practices around big data produce and perpetuate specific forms of social engagement as well as understandings of the areas affected and the people being served.

Big data is not just about knowing more. It could be – and should be – about knowing better or about changing what knowing means. It is an ethico- episteme-ontological- political matter. The ‘needle in the haystack’ metaphor conceals the fact that there is no such thing as one reality that can be revealed. But multiple, lived are made through mediations and human and technological assemblages. Refugees’ realities of intersecting intelligences are shaped by the ethico- episteme-ontological politics of big data.

Burns (2015) builds on this to investigate how within digital humanitarianism discourses, big data produce and perform subjects ‘in need’ (individuals or com-munities affected by crises) and a humanitarian ‘saviour’ community that, in turn, seeks answers through big data

Crises are often not a crisis of information. It is often not a lack of data or capacity to analyse it that prevents ‘us’ from pre-venting disasters or responding effectively. Risk management fails because there is a lack of a relational sense of responsibility. But this does not have to be the case. Technologies that are designed to support collaboration, such as what Jasanoff (2007) terms ‘technologies of humility’, can be better explored to find ways of framing data and correlations that elicit a greater sense of relational responsibility and commitment.

In some ways this constitutes the production of ‘liquid resilience’ – a deflection of risk to the individuals and communities affected which moves us from the idea of an all-powerful and knowing state to that of a ‘plethora of partial projects and initiatives that are seeking to harness ICTs in the service of better knowing and governing individuals and populations’ (Ruppert 2012: 118)

Indeed, as Chandler (2015: 9) also argues, crowdsourcing of big data does not equate to a democratisation of risk assessment or risk governance:

ut Burns finds that humanitarian staff often describe the local communities and ‘crowds’ as the ‘eyes, ears and sensors’ of UN staff, which does not index a genuine collaborative relationship. He states: ‘In all these cases, the discourse talks of putting local people “in the driving seat” when in reality the direction of the journey has already been decided’ (Burns 2015: 48). Burns (2015: 42) also notes that this leads to a transformation of social responsibility into individual responsibility.Neoliberalism’s promotion of free market norms is therefore much more than the simple ideology of free market economics. It is a specific form of social rule that institutionalises a rationality of competition, enterprise indi-vidualised responsibility. Although the state ‘steps back’ and encourages the free conduct of individuals, this is achieved through active intervention into civil society and the opening up of new areas to the logic of private enter-prise and individual initiative. This is the logic behind the rise of resilience

The UNHCR has even called for the refugees themselves to also develop their own data solutions and ideas (see Palmer 2014) as a way to help build their ideologies into the data infrastructures and thus bring their prisms into view. This could create a richer situational awareness and a better ability to understand and deal with unfolding and future crises by supporting resilient communities through giving them the means of data producing and sharing

Doing so switches the discourse from vulnerability, where there is a need for external protection mobilised from above to come in and rescue the refugees, to one of resilience, where self- sufficiency and autonomy are part of the equation (Meier 2013).

Yet, at the same time as power is exercised by both the state and corporations, power is gathering from the bottom up in new ways. In disaster response, a dynamic interplay between publics and experts is captured by the concept of social collective intelligence (Büscher et al. 2014); a disruptive innovative force that is challenging the social, economic, political and organisational practices that shape disaster response.

Since the data is already being collected on a regular basis by ubiquitous private firms, it is thought to contain information that will increase opportunities for intelligence gathering and thereby security. This marks a shift from surveillance to ‘dataveillance’ (van Dijck 2014), where the impetus for data processing is no longer motivated by specific purposes or suspicions, but opportunistic discovery of anomalies that can be investigated. For crisis management this could mean benefits such as richer situation awareness, increased capacity for risk assess-ment, anticipation and prediction, as well as more agile response

lthough Snowden’s revelations shocked the world and prompted calls for a public debate on issues of privacy and transparency

While seemingly avoiding the traps of exerting top- down power over people the state does not yet have formal control over, and simultaneously providing support for self- determination and choice to empower individuals for self- sufficiency rather than defining them as vulnerable and passive recipients of top- down protection (Meier 2013), tying individual aid to mobile tracking puts refugees in a situation where their security is dependent upon individual choice and the private sector. Apart from disrupting traditional dynamics of responsibility for aid and protection, public–private sharing of intel-ligence brings new forms of dataveillance

However, with these big data collections, the focus becomes not the individu-al’s behaviour but social and economic insecurities, vulnerabilities and resilience in relation to the movement of such people. The shift acknowledges that what is surveilled is more complex than an individual person’s movements, communica-tions and actions over time.

Andrejevic and Gates (2014: 190) suggest that ‘the target becomes the hidden patterns in the data, rather than particular individuals or events’. National and local authorities are not seeking to monitor individuals and discipline their behaviour but to see how many people will reach the country and when, so that they can accommodate them, secure borders, and identify long- term social out-looks such as education, civil services, and impacts upon the host community (Pham et al. 2015).

as boyd and Crawford argue, ‘without taking into account the sample of a data set, the size of the data set is meaningless’ (2012: 669). Furthermore, many tech-niques used by the state and corporations in big data analysis are based on probabilistic prediction which, some experts argue, is alien to, and even incom-prehensible for, human reasoning (Heaven 2013). As Mayer-Schönberger stresses, we should be ‘less worried about privacy and more worried about the abuse of probabilistic prediction’ as these processes confront us with ‘profound ethical dilemmas’ (in Heaven 2013: 35).

Second, this tracking and tracing of refugees has become a deeply ambiguous process in a world riven by political conflict, where ‘migration’ increasingly comes to be discussed in co- location with terrorism.

Surveillance studies have tracked a shift from discipline to control (Deleuze 1992; Haggerty and Ericson 2000; Lyon 2014) exemplified by the shift from monitoring confined populations (through technologies such as the panopticon) to using new technologies to keep track of mobile populations.

As Coyle and Meier (2009) argue, disasters are often seen as crises of information where it is vital to make sure that people know where to find potable water, how to ask for help, where their relatives are, or if their home is at risk; as well as providing emergency response and human-itarian agencies with information about affected populations. Such a quest for information for ‘security’, in turn, provides fertile ground for a quest for technological solutions, such as big data, which open up opportunities for the extended surveillance of everyday life. The assumption is that if only enough information could be gathered and exchanged, preparedness, resilience and control would follow. This is particularly pertinent with regard to mobile pop-ulations (Adey and Kirby 2016)

hird, at this juncture, control is being equated with visibility and visibility with personal security. But how these individuals are made visible matters for both privacy and security, let alone the politics of conflating refugees, migration and terrorism. Indeed, working with specific data framing mechanisms affects how the causes and effects of disasters are identified and what elements and people are considered (Frickel 2008

First, there is a double dynamic to the generation of data in the refugee crisis.

Datafication refers to the fact that ‘we can now capture and calculate at a much more comprehensive scale the physical and intangible aspects of existence and act on them’ (Mayer- Schönberger and Cukier 2013: 97

There is an uneasy coming together of diverse computational and human intelligences in these intersections, and the ambiguous nature of intelligence – understood, on the one hand, as a capacity for perceiving, learning and under-standing and, on the other, as information obtained for strategic purposes – marks complex relationships between ‘good’ and ‘dark’ aspects of big data, surveil-lance and crisis management.

Peer production successfully elicits contributions from diverse individu-als with diverse motivations – a quality that continues to distinguish it fromsimilar forms of collective intelligence

Although peer production is central to social scientific and legal researchon collective intelligence, not all examples of collective intelligence created inonline systems are peer production. First, (1) collective intelligence can in-volve centralized control over goal-setting and execution of tasks.

Consistent with this exam-ple, foundational social scientific research relevant to understanding collec-tive intelligence has focused on three central concerns: (1) explaining the or-ganization and governance of decentralized projects, (2) understanding themotivation of contributors in the absence of financial incentives or coerciveobligations, and (3) evaluating the quality of the products generated throughcollective intelligence systems.

Historically,researchers in diverse fields such as communication, sociology, law, and eco-nomics have argued that effective human systems organize people through acombination of hierarchical structures (e.g., bureaucracies), completely dis-tributed coordination mechanisms (e.g., markets), and social institutions ofvarious kinds (e.g., cultural norms). However, the rise of networked systemsand online platforms for collective intelligence has upended many of the as-sumptions and findings from this earlier research.

Leading thinkers in China argue that putting government in charge of technology has one big advantage: the state can distribute the fruits of AI, which would otherwise go to the owners of algorithms.

In “Getting Real,” Barad proposes that “reality is sedimented out of the process ofmaking the world intelligible through certain practices and not others ...” (1998: 105). If,as Barad and other feminist researchers suggest, we are responsible for what exists, what isthe reality that current discourses and practices regarding new technologies makeintelligible, and what is excluded? To answer this question Barad argues that we need asimultaneous account of the relations of humans and nonhumansandof their asymmetriesand differences. This requires remembering that boundaries between humans and machinesare not naturally given but constructed, in particular historical ways and with particularsocial and material consequences. As Barad points out, boundaries are necessary for thecreation of meaning, and, for that very reason, are never innocent. Because the cuts impliedin boundary making are always agentially positioned rather than naturally occurring, andbecause boundaries have real consequences, she argues, “accountability is mandatory”(187). :We are responsible for the world in which we live not because it is an arbitraryconstruction of our choosing, but because it is sedimented out of particular practicesthat we have a role in shaping (1998: 102).The accountability involved is not, however, a matter of identifying authorship in anysimple sense, but rather a problem of understanding the effects of particular assemblages,and assessing the distributions, for better and worse, that they engender.

Finally, the ‘smart’ machine's presentation of itself asthe always obliging, 'labor-saving device' erases any evidence of the labor involved in itsoperation "from bank personnel to software programmers to the third-world workers whoso often make the chips" (75).

Chasin poses the question (which I return to below) of how a change in our view ofobjects from passiveand outside the social could help to undo the subject/object binaryand all of its attendant orderings, including for example male/female, or mental/manua

Figured as servants,she points out, technologies reinscribe the difference between ‘us’ and those who serve us,while eliding the difference between the latter and machines: "The servanttroubles thedistinction between we-human-subjects-inventors with a lot to do (on the onehand) andthem-object-things that make it easier for us (on the other)" (1995: 73)

"When tasks require high coordination because the work is highly interdependent, having more contributors can increase process losses, reducing the effectiveness of the group below what individual members could optimally accomplish". Having a team too large the overall effectiveness may suffer even when the extra contributors increase the resources. In the end the overall costs from coordination might overwhelm other costs.

Games such as The Sims Series, and Second Life are designed to be non-linear and to depend on collective intelligence for expansion. This way of sharing is gradually evolving and influencing the mindset of the current and future generations.[117] For them, collective intelligence has become a norm.

The UNU open platform for "human swarming" (or "social swarming") establishes real-time closed-loop systems around groups of networked users molded after biological swarms, enabling human participants to behave as a unified collective intelligence.[140][141] When connected to UNU, groups of distributed users collectively answer questions and make predictions in real-time.[142] Early testing shows that human swarms can out-predict individuals.[140] In 2016, an UNU swarm was challenged by a reporter to predict the winners of the Kentucky Derby, and successfully picked the first four horses, in order, beating 540 to 1 odds.

Research performed by Tapscott and Williams has provided a few examples of the benefits of collective intelligence to business:[38] Talent utilization At the rate technology is changing, no firm can fully keep up in the innovations needed to compete. Instead, smart firms are drawing on the power of mass collaboration to involve participation of the people they could not employ. This also helps generate continual interest in the firm in the form of those drawn to new idea creation as well as investment opportunities.[38] Demand creation Firms can create a new market for complementary goods by engaging in open source community. Firms also are able to expand into new fields that they previously would not have been able to without the addition of resources and collaboration from the community. This creates, as mentioned before, a new market for complementary goods for the products in said new fields.[38] Costs reduction Mass collaboration can help to reduce costs dramatically. Firms can release a specific software or product to be evaluated or debugged by online communities. The results will be more personal, robust and error-free products created in a short amount of time and costs. New ideas can also be generated and explored by collaboration of online communities creating opportunities for free R&D outside the confines of the company.[38]

In one high-profile example, a human swarm challenge by CBS Interactive to predict the Kentucky Derby. The swarm correctly predicted the first four horses, in order, defying 542–1 odds and turning a $20 bet into $10,800.

To address the problems of serialized aggregation of input among large-scale groups, recent advancements collective intelligence have worked to replace serialized votes, polls, and markets, with parallel systems such as "human swarms" modeled after synchronous swarms in nature.

While modern systems benefit from larger group size, the serialized process has been found to introduce substantial noise that distorts the collective output of the group. In one significant study of serialized collective intelligence, it was found that the first vote contributed to a serialized voting system can distort the final result by 34%

To accommodate this shift in scale, collective intelligence in large-scale groups been dominated by serialized polling processes such as aggregating up-votes, likes, and ratings over time

The idea of collective intelligence also forms the framework for contemporary democratic theories often referred to as epistemic democracy.

The basis and goal of collective intelligence is mutual recognition and enrichment of individuals rather than the cult of fetishized or hypostatized communities."

Collective intelligence (CI) is shared or group intelligence that emerges from the collaboration, collective efforts, and competition of many individuals and appears in consensus decision making.

The alternative, of a regulatory patchwork, would make it harder for the West to amass a shared stock of AI training data to rival China’s.

Our principal claim is that a valid EI concept can bedistinguished from other approaches. This valid conceptionof EI includes the ability to engage in sophisticated infor-mation processing about one’s own and others’ emotionsand the ability to use this information as a guide to thinkingand behavior.

the termemotional intelligenceis now employedto cover too many things—too many different traits, toomany different concepts (Landy, 2005; Murphy & Side-man, 2006; Zeidner, Roberts, & Matthews, 2004). “Thesemodels,” wrote Daus and Ashkanasy (2003, pp. 69–70),“have done more harm than good regarding establishingemotional intelligence as a legitimate, empirical constructwith incremental validity potential.

The original definition of EI conceptualized it as a setof interrelated abilities (Mayer & Salovey, 1997; Salovey& Mayer, 1990). Yet other investigators have described EIas an eclectic mix of traits, many dispositional, such ashappiness, self-esteem, optimism, and self-management,rather than as ability based

one commentator recently argued that EI is an invalidconcept in part because it is defined in too many ways(Locke, 2005, p. 425)

. The orig-inal idea was that some individuals possess the ability toreason about and use emotions to enhance thought moreeffectively than others

Why there’s no such thing as a gifted child

The Semantic Sphere 1

A genome-wide analysis of putative functional and exonic variation associated with extremely high intelligence