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Can a Machine Learn to Write for The New Yorker? | The New Yorker
The skin prickled on the back of my neck, an involuntary reaction to what roboticists call the “uncanny valley”—the space between flesh and blood and a too-human machine.
For several days, I had been trying to ignore the suggestions made by Smart Compose, a feature that Google introduced, in May, 2018, to the one and a half billion people who use Gmail—roughly a fifth of the human population.
guesses where your thoughts are likely to go and, to save you time, wraps up the sentence for you, appending the A.I.’s suggestion, in gray letters, to the words you’ve just produced.
Paul Lambert, who oversees Smart Compose for Google, told me that the idea for the product came in part from the writing of code—the language that software engineers use to program computers.
Google thought that a similar technology could reduce the time spent writing e-mails for business users of its G Suite software, although it made the product available to the general public, too.
And yet until now I’d always finished my thought by typing the sentence to a full stop, as though I were defending humanity’s exclusive right to writing, an ability unique to our species.
I will gladly let Google predict the fastest route from Brooklyn to Boston, but if I allowed its algorithms to navigate to the end of my sentences how long would it be before the machine started thinking for me?
On the far shore, I imagined, was a strange new land where machines do the writing, and people communicate in emojis, the modern version of the pictographs and hieroglyphs from which our writing system emerged, five thousand years ago.
(I didn’t really get it, but that choice wasn’t on the menu.) I felt a little guilty right afterward, as though I’d replied with a form letter, or, worse, a fake personal note.
Along with almost everyone else who texts or tweets, with the possible exception of the President of the United States, I have long relied on spell-checkers and auto-correctors, which are limited applications of predictive text.
Now that spell-checkers are ubiquitous in word-processing software, I’ve stopped even trying to spell anymore—I just get close enough to let the machine guess the word I’m struggling to form.
MIT Technology Review
The research: Researchers at the MIT-IBM Watson AI Lab have now developed a new technique for training video recognition models on a phone or other device with very limited processing capacity.
In testing, the researchers found that the new approach trained video recognition models three times faster than the state of the art.
It was also able to quickly classify hand gestures with a small computer and camera running only on enough energy to power a bike light.
Why it matters: The new technique could help reduce lag and computation costs in existing commercial applications of computer vision.
The technique could also unlock new applications that previously weren’t possible, such as by enabling phones to help diagnose patients or analyze medical images.
and 3) experts (those with new insights as well as recognized authorities) that continually learns from feedback to produce just-in-time knowledge for better decisions than these three elements acting alone.
Pierre Lévy defines collective intelligence as, 'It is a form of universally distributed intelligence, constantly enhanced, coordinated in real time, and resulting in the effective mobilization of skills.
I'll add the following indispensable characteristic to this definition: The basis and goal of collective intelligence is mutual recognition and enrichment of individuals rather than the cult of fetishized or hypostatized communities.'
According to researchers Pierre Lévy and Derrick de Kerckhove, it refers to capacity of networked ICTs (Information communication technologies) to enhance the collective pool of social knowledge by simultaneously expanding the extent of human interactions.
Raymond (1998) and JC Herz (2005), open source intelligence will eventually generate superior outcomes to knowledge generated by proprietary software developed within corporations (Flew 2008).
Both Pierre Lévy (2007) and Henry Jenkins (2008) support the claim that collective intelligence is important for democratization, as it is interlinked with knowledge-based culture and sustained by collective idea sharing, and thus contributes to a better understanding of diverse society.
Similar to the g factor (g) for general individual intelligence, a new scientific understanding of collective intelligence aims to extract a general collective intelligence factor c factor for groups indicating a group's ability to perform a wide range of tasks.
The concept (although not so named) originated in 1785 with the Marquis de Condorcet, whose 'jury theorem' states that if each member of a voting group is more likely than not to make a correct decision, the probability that the highest vote of the group is the correct decision increases with the number of members of the group (see Condorcet's jury theorem).
Many theorists have interpreted Aristotle's statement in the Politics that 'a feast to which many contribute is better than a dinner provided out of a single purse' to mean that just as many may bring different dishes to the table, so in a deliberation many may contribute different pieces of information to generate a better decision.
In a 1962 research report, Douglas Engelbart linked collective intelligence to organizational effectiveness, and predicted that pro-actively 'augmenting human intellect' would yield a multiplier effect in group problem solving: 'Three people working together in this augmented mode [would] seem to be more than three times as effective in solving a complex problem as is one augmented person working alone'.
Epistemic democratic theories refer to the capacity of the populace, either through deliberation or aggregation of knowledge, to track the truth and relies on mechanisms to synthesize and apply collective intelligence.
Later he showed how the collective intelligences of competing bacterial colonies and human societies can be explained in terms of computer-generated 'complex adaptive systems' and the 'genetic algorithms', concepts pioneered by John Holland.
Atlee feels that collective intelligence can be encouraged 'to overcome 'groupthink' and individual cognitive bias in order to allow a collective to cooperate on one process – while achieving enhanced intellectual performance.'
George Pór defined the collective intelligence phenomenon as 'the capacity of human communities to evolve towards higher order complexity and harmony, through such innovation mechanisms as differentiation and integration, competition and collaboration.'
The features of composition that lead to increased levels of collective intelligence in groups include criteria such as higher numbers of women in the group as well as increased diversity of the group.
Atlee and Pór suggest that the field of collective intelligence should primarily be seen as a human enterprise in which mind-sets, a willingness to share and an openness to the value of distributed intelligence for the common good are paramount, though group theory and artificial intelligence have something to offer.
Robert David Steele Vivas in The New Craft of Intelligence portrayed all citizens as 'intelligence minutemen,' drawing only on legal and ethical sources of information, able to create a 'public intelligence' that keeps public officials and corporate managers honest, turning the concept of 'national intelligence' (previously concerned about spies and secrecy) on its head.
Hereby, an individual's performance on a given set of cognitive tasks is used to measure general cognitive ability indicated by the general intelligence factor g extracted via factor analysis.
In the same vein as g serves to display between-individual performance differences on cognitive tasks, collective intelligence research aims to find a parallel intelligence factor for groups 'c factor'
this measurement of collective intelligence can also be seen as an intelligence indicator or quotient respectively for a group (Group-IQ) parallel to an individual's intelligence quotient (IQ) even though the score is not a quotient per se.
Analogously, collective intelligence research aims to explore reasons why certain groups perform more intelligent than other groups given that c is just moderately correlated with the intelligence of individual group members.
However, they claim that three factors were found as significant correlates: the variance in the number of speaking turns, group members' average social sensitivity and the proportion of females.
which refers to the ability to attribute mental states, such as beliefs, desires or intents, to other people and in how far people understand that others have beliefs, desires, intentions or perspectives different from their own ones.
In modern times, mass communication, mass media, and networking technologies have enabled collective intelligence to span massive groups, distributed across continents and time-zones.
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.
Based on natural process of Swarm Intelligence, these artificial swarms of networked humans enable participants to work together in parallel to answer questions and make predictions as an emergent collective intelligence.
The value of parallel collective intelligence was demonstrated in medical applications by researchers at Stanford University School of Medicine and Unanimous AI in a set of published studies wherein groups of human doctors were connected by real-time swarming algorithms and tasked with diagnosing chest x-rays for the presence of pneumonia.
the originators of this scientific understanding of collective intelligence, found a single statistical factor for collective intelligence in their research across 192 groups with people randomly recruited from the public.
Both studies showed support for a general collective intelligence factor c underlying differences in group performance with an initial eigenvalue accounting for 43% (44% in study 2) of the variance, whereas the next factor accounted for only 18% (20%).
That fits the range normally found in research regarding a general individual intelligence factor g typically accounting for 40% to 50% percent of between-individual performance differences on cognitive tests.
Afterwards, a more complex criterion task was absolved by each group measuring whether the extracted c factor had predictive power for performance outside the original task batteries.
In a regression analysis using both individual intelligence of group members and c to predict performance on the criterion tasks, c had a significant effect, but average and maximum individual intelligence had not.
According to Woolley et al., this supports the existence of a collective intelligence factor c, because it demonstrates an effect over and beyond group members' individual intelligence and thus that c is more than just the aggregation of the individual IQs or the influence of the group member with the highest IQ.
(2014) replicated Woolley et al.'s findings applying an accelerated battery of tasks with a first factor in the factor analysis explaining 49% of the between-group variance in performance with the following factors explaining less than half of this amount.
Moreover, they found a similar result for groups working together online communicating only via text and confirmed the role of female proportion and social sensitivity in causing collective intelligence in both cases.
Similarly, demand for further research on possible connections of individual and collective intelligence exists within plenty of other potentially transferable logics of individual intelligence, such as, for instance, the development over time
(2001) showed in a meta-analysis that mean cognitive ability predicts team performance in laboratory settings (.37) as well as field settings (.14) – note that this is only a small effect.
Suggesting a strong dependence on the relevant tasks, other scholars showed that tasks requiring a high degree of communication and cooperation are found to be most influenced by the team member with the lowest cognitive ability.
results do not show any influence of group satisfaction, group cohesiveness, or motivation, they, at least implicitly, challenge these concepts regarding the importance for group performance in general and thus contrast meta-analytically proven evidence concerning the positive effects of group cohesion,
This theory allows simple formal definition of collective intelligence as the property of social structure and seems to be working well for a wide spectrum of beings, from bacterial colonies up to human social structures.
For this model of collective intelligence, the formal definition of IQS (IQ Social) was proposed and was defined as 'the probability function over the time and domain of N-element inferences which are reflecting inference activity of the social structure'.
– thus making it possible to determine the marginal intelligence added by each new individual participating in the collective action, thus using metrics to avoid the hazards of group think and stupidity.
Because of the Internet's ability to rapidly convey large amounts of information throughout the world, the use of collective intelligence to predict stock prices and stock price direction has become increasingly viable.
Websites aggregate stock market information that is as current as possible so professional or amateur stock analysts can publish their viewpoints, enabling amateur investors to submit their financial opinions and create an aggregate opinion.
The opinion of all investor can be weighed equally so that a pivotal premise of the effective application of collective intelligence can be applied: the masses, including a broad spectrum of stock market expertise, can be utilized to more accurately predict the behavior of financial markets.
On the basis of such evidence index funds became popular investment vehicles using the collective intelligence of the market, rather than the judgement of professional fund managers, as an investment strategy.
Knowledge focusing through various voting methods allows perspectives to converge through the assumption that uninformed voting is to some degree random and can be filtered from the decision process leaving only a residue of informed consensus.
Critics point out that often bad ideas, misunderstandings, and misconceptions are widely held, and that structuring of the decision process must favor experts who are presumably less prone to random or misinformed voting in a given context.
He criticizes contemporary education for failing to incorporate online trends of collective problem solving into the classroom, stating 'whereas a collective intelligence community encourages ownership of work as a group, schools grade individuals'.
Jenkins argues that interaction within a knowledge community builds vital skills for young people, and teamwork through collective intelligence communities contribute to the development of such skills.
Another art project using collective intelligence to produce artistic work is Curatron, where a large group of artists together decides on a smaller group that they think would make a good collaborative group.
The resulting information structure can be seen as reflecting the collective knowledge (or collective intelligence) of a community of users and is commonly called a 'Folksonomy', and the process can be captured by models of collaborative tagging.
Although there is no central controlled vocabulary to constrain the actions of individual users, the distributions of tags that describe different resources has been shown to converge over time to a stable power law distributions.
Once such stable distributions form, examining the correlations between different tags can be used to construct simple folksonomy graphs, which can be efficiently partitioned to obtained a form of community or shared vocabularies.
He argued that its commercial success was fundamentally dependent upon 'the formation and growth of an active and vibrant online fan community that would both actively promote the product and create content- extensions and additions to the game software'.
Military, trade unions, and corporations satisfy some definitions of CI – the most rigorous definition would require a capacity to respond to very arbitrary conditions without orders or guidance from 'law' or 'customers' to constrain actions.
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.
In learner-generated context a group of users marshal resources to create an ecology that meets their needs often (but not only) in relation to the co-configuration, co-creation and co-design of a particular learning space that allows learners to create their own context.
Such resources could combine into a form of collective intelligence accountable only to the current participants yet with some strong moral or linguistic guidance from generations of contributors – or even take on a more obviously democratic form to advance shared goal.
In such an integrated framework proposed by Ebner et al., idea competitions and virtual communities are combined to better realize the potential of the collective intelligence of the participants, particularly in open-source R&D.
In the article written by Kittur, Lee and Kraut the writers introduce a problem in cooperation: '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'.
Skeptics, especially those critical of artificial intelligence and more inclined to believe that risk of bodily harm and bodily action are the basis of all unity between people, are more likely to emphasize the capacity of a group to take action and withstand harm as one fluid mass mobilization, shrugging off harms the way a body shrugs off the loss of a few cells.
These theorists are more likely to refer to ecological and collective wisdom and to the role of consensus process in making ontological distinctions than to any form of 'intelligence' as such, which they often argue does not exist, or is mere 'cleverness'.
- On 14. april 2021
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