How AI can help us harness our ‘collective intelligence’


Edd Gent at the BBC: “…There are already promising examples of how AI can help us better pool our unique capabilities. San Francisco start-up Unanimous AI has built an online platform that helps guide group decisions. They’ve looked to an unlikely place to guide their AI: the way honeybees make collective decisions.

“We went back to basics and said, ‘How does nature amplify the intelligence of groups?’,” says CEO Louis Rosenberg. “What nature does is form real-time systems, where the groups are interacting all at once together with feedback loops. So, they’re pushing and pulling on each other as a system, and converging on the best possible combination of their knowledge, wisdom, insight and intuition.”

Their Swarm AI platform presents groups with a question and places potential answers in different corners of their screen. Users control a virtual magnet with their mouse and engage in a tug of war to drag an ice hockey puck to the answer they think is correct. The system’s algorithm analyses how each user interacts with the puck – for instance, how much conviction they drag it with or how quickly they waver when they’re in the minority – and  uses this information to determine where the puck moves. That creates feedback loops in which each user is influenced by the choice and conviction of the others allowing the puck to end up at the answer best reflecting the collective wisdom of the group.

Several academic papers and high-profile clients who use the product back up the effectiveness of the Swarm AI platform. In one recent study, a group of traders were asked to forecast the weekly movement of several key stock market indices by trying to drag the puck to one of four answers — up or down by more than 4%, or up and down by less than 4%. With the tool, they boosted their accuracy by 36%.

Credit Suisse has used the platform to help investors forecast the performance of Asian markets; Disney has used it to predict the success of TV shows; and Unanimous has even partnered with Stanford Medical School to boost doctors’ ability to diagnose pneumonia from chest X-rays by 33%….(More)”

See also: Where and when AI and CI meet: exploring the intersection of artificial and collective intelligence towards the goal of innovating how we govern and Identifying Citizens’ Needs by Combining Artificial Intelligence (AI) and Collective Intelligence (CI).

Are there laws of history?


Amanda Rees at AEON: “…If big data could enable us to turn big history into mathematics rather than narratives, would that make it easier to operationalise our past? Some scientists certainly think so.

In February 2010, Peter Turchin, an ecologist from the University of Connecticut, predicted that 2020 would see a sharp increase in political volatility for Western democracies. Turchin was responding critically to the optimistic speculations of scientific progress in the journal Nature: the United States, he said, was coming to the peak of another instability spike (regularly occurring every 50 years or so), while the world economy was reaching the point of a ‘Kondratiev wave’ dip, that is, a steep downturn in a growth-driven supercycle. Along with a number of ‘seemingly disparate’ social pointers, all indications were that serious problems were looming. In the decade since that prediction, the entrenched, often vicious, social, economic and political divisions that have increasingly characterised North American and European society, have made Turchin’s ‘quantitative historical analysis’ seem remarkably prophetic.

A couple of years earlier, in July 2008, Turchin had made a series of trenchant claims about the nature and future of history. Totting up in excess of ‘200 explanations’ proposed to account for the fall of the Roman empire, he was appalled that historians were unable to agree ‘which explanations are plausible and which should be rejected’. The situation, he maintained, was ‘as risible as if, in physics, phlogiston theory and thermodynamics coexisted on equal terms’. Why, Turchin wanted to know, were the efforts in medicine and environmental science to produce healthy bodies and ecologies not mirrored by interventions to create stable societies? Surely it was time ‘for history to become an analytical, and even a predictive, science’. Knowing that historians were themselves unlikely to adopt such analytical approaches to the past, he proposed a new discipline: ‘theoretical historical social science’ or ‘cliodynamics’ – the science of history.

Like C P Snow 60 years before him, Turchin wanted to challenge the boundary between the sciences and humanities – even as periodic attempts to apply the theories of natural science to human behaviour (sociobiology, for example) or to subject natural sciences to the methodological scrutiny of the social sciences (science wars, anyone?) have frequently resulted in hostile turf wars. So what are the prospects for Turchin’s efforts to create a more desirable future society by developing a science of history?…

In 2010, Cliodynamics, the flagship journal for this new discipline, appeared, with its very first article (by the American sociologist Randall Collins) focusing on modelling victory and defeat in battle in relation to material resources and organisational morale. In a move that paralleled Comte’s earlier argument regarding the successive stages of scientific complexity (from physics, through chemistry and biology, to sociology), Turchin passionately rejected the idea that complexity made human societies unsuitable for quantitative analysis, arguing that it was precisely that complexity which made mathematics essential. Weather predictions were once considered unreliable because of the sheer complexity of managing the necessary data. But improvements in technology (satellites, computers) mean that it’s now possible to describe mathematically, and therefore to model, interactions between the system’s various parts – and therefore to know when it’s wise to carry an umbrella. With equal force, Turchin insisted that the cliodynamic approach was not deterministic. It would not predict the future, but instead lay out for governments and political leaders the likely consequences of competing policy choices.

Crucially, and again on the back of the abundantly available and cheap computer power, cliodynamics benefited from the surge in interest in the digital humanities. Existing archives were being digitised, uploaded and made searchable: every day, it seemed, more data were being presented in a format that encouraged quantification and enabled mathematical analysis – including the Old Bailey’s online database, of which Wolf had fallen foul. At the same time, cliodynamicists were repositioning themselves. Four years after its initial launch, the subtitle of their flagship journal was renamed, from The Journal of Theoretical and Mathematical History to The Journal of Quantitative History and Cultural Evolution. As Turchin’s editorial stated, this move was intended to position cliodynamics within a broader evolutionary analysis; paraphrasing the Russian-American geneticist Theodosius Dobzhansky, he claimed that ‘nothing in human history makes sense except in the light of cultural evolution’. Given Turchin’s ecological background, this evolutionary approach to history is unsurprising. But given the historical outcomes of making politics biological, it is potentially worrying….

Mathematical, data-driven, quantitative models of human experience that aim at detachment, objectivity and the capacity to develop and test hypotheses need to be balanced by explicitly fictional, qualitative and imaginary efforts to create and project a lived future that enable their audiences to empathically ground themselves in the hopes and fears of what might be to come. Both, after all, are unequivocally doing the same thing: using history and historical experience to anticipate the global future so that we might – should we so wish – avoid civilisation’s collapse. That said, the question of who ‘we’ are does, always, remain open….(More)”.

The Coronavirus Is Rewriting Our Imaginations


Kim Stanley Robinson at the New Yorker: “…We are individuals first, yes, just as bees are, but we exist in a larger social body. Society is not only real; it’s fundamental. We can’t live without it. And now we’re beginning to understand that this “we” includes many other creatures and societies in our biosphere and even in ourselves. Even as an individual, you are a biome, an ecosystem, much like a forest or a swamp or a coral reef. Your skin holds inside it all kinds of unlikely coöperations, and to survive you depend on any number of interspecies operations going on within you all at once. We are societies made of societies; there are nothing but societies. This is shocking news—it demands a whole new world view. And now, when those of us who are sheltering in place venture out and see everyone in masks, sharing looks with strangers is a different thing. It’s eye to eye, this knowledge that, although we are practicing social distancing as we need to, we want to be social—we not only want to be social, we’ve got to be social, if we are to survive. It’s a new feeling, this alienation and solidarity at once. It’s the reality of the social; it’s seeing the tangible existence of a society of strangers, all of whom depend on one another to survive. It’s as if the reality of citizenship has smacked us in the face.

As for government: it’s government that listens to science and responds by taking action to save us. Stop to ponder what is now obstructing the performance of that government. Who opposes it?…

There will be enormous pressure to forget this spring and go back to the old ways of experiencing life. And yet forgetting something this big never works. We’ll remember this even if we pretend not to. History is happening now, and it will have happened. So what will we do with that?

A structure of feeling is not a free-floating thing. It’s tightly coupled with its corresponding political economy. How we feel is shaped by what we value, and vice versa. Food, water, shelter, clothing, education, health care: maybe now we value these things more, along with the people whose work creates them. To survive the next century, we need to start valuing the planet more, too, since it’s our only home.

It will be hard to make these values durable. Valuing the right things and wanting to keep on valuing them—maybe that’s also part of our new structure of feeling. As is knowing how much work there is to be done. But the spring of 2020 is suggestive of how much, and how quickly, we can change. It’s like a bell ringing to start a race. Off we go—into a new time….(More)”.

Our weird behavior during the pandemic is messing with AI models


Will Douglas Heaven at MIT Technology Review: “In the week of April 12-18, the top 10 search terms on Amazon.com were: toilet paper, face mask, hand sanitizer, paper towels, Lysol spray, Clorox wipes, mask, Lysol, masks for germ protection, and N95 mask. People weren’t just searching, they were buying too—and in bulk. The majority of people looking for masks ended up buying the new Amazon #1 Best Seller, “Face Mask, Pack of 50”.

When covid-19 hit, we started buying things we’d never bought before. The shift was sudden: the mainstays of Amazon’s top ten—phone cases, phone chargers, Lego—were knocked off the charts in just a few days. Nozzle, a London-based consultancy specializing in algorithmic advertising for Amazon sellers, captured the rapid change in this simple graph.

It took less than a week at the end of February for the top 10 Amazon search terms in multiple countries to fill up with products related to covid-19. You can track the spread of the pandemic by what we shopped for: the items peaked first in Italy, followed by Spain, France, Canada, and the US. The UK and Germany lag slightly behind. “It’s an incredible transition in the space of five days,” says Rael Cline, Nozzle’s CEO. The ripple effects have been seen across retail supply chains.

But they have also affected artificial intelligence, causing hiccups for the algorithms that run behind the scenes in inventory management, fraud detection, marketing, and more. Machine-learning models trained on normal human behavior are now finding that normal has changed, and some are no longer working as they should. 

How bad the situation is depends on whom you talk to. According to Pactera Edge, a global AI consultancy, “automation is in tailspin.” Others say they are keeping a cautious eye on automated systems that are just about holding up, stepping in with a manual correction when needed.

What’s clear is that the pandemic has revealed how intertwined our lives are with AI, exposing a delicate codependence in which changes to our behavior change how AI works, and changes to how AI works change our behavior. This is also a reminder that human involvement in automated systems remains key. “You can never sit and forget when you’re in such extraordinary circumstances,” says Cline….(More)”.

The Analog City and the Digital City


L. M. Sacasas at The New Atlantis: “…The challenges we are facing are not merely the bad actors, whether they be foreign agents, big tech companies, or political extremists. We are in the middle of a deep transformation of our political culture, as digital technology is reshaping the human experience at both an individual and a social level. The Internet is not simply a tool with which we do politics well or badly; it has created a new environment that yields a different set of assumptions, principles, and habits from those that ordered American politics in the pre-digital age.

We are caught between two ages, as it were, and we are experiencing all of the attendant confusion, frustration, and exhaustion that such a liminal state involves. To borrow a line from the Marxist thinker Antonio Gramsci, “The crisis consists precisely in the fact that the old is dying and the new cannot be born; in this interregnum a great variety of morbid symptoms appear.”

Although it’s not hard to see how the Internet, given its scope, ubiquity, and closeness to human life, radically reshapes human consciousness and social structures, that does not mean that the nature of that reshaping is altogether preordained or that it will unfold predictably and neatly. We must then avoid crassly deterministic just-so stories, and this essay is not an account of how digital media will necessarily change American politics irrespective of competing ideologies, economic forces, or already existing political and cultural realities. Rather, it is an account of how the ground on which these realities play out is shifting. Communication technologies are the material infrastructure on which so much of the work of human society is built. One cannot radically transform that infrastructure without radically altering the character of the culture built upon it. As Neil Postman once put it, “In the year 1500, fifty years after the printing press was invented, we did not have old Europe plus the printing press. We had a different Europe.” So, likewise, we may say that in the year 2020, fifty years after the Internet was invented, we do not have old America plus the Internet. We have a different America….(More)”.

Models v. Evidence


Jonathan Fuller at the Boston Review: “COVID-19 has revealed a contest between two competing philosophies of scientific knowledge. To manage the crisis, we must draw on both….The lasting icon of the COVID-19 pandemic will likely be the graphic associated with “flattening the curve.” The image is now familiar: a skewed bell curve measuring coronavirus cases that towers above a horizontal line—the health system’s capacity—only to be flattened by an invisible force representing “non-pharmaceutical interventions” such as school closures, social distancing, and full-on lockdowns.

How do the coronavirus models generating these hypothetical curves square with the evidence? What roles do models and evidence play in a pandemic? Answering these questions requires reconciling two competing philosophies in the science of COVID-19.

To some extent, public health epidemiology and clinical epidemiology are distinct traditions in health care, competing philosophies of scientific knowledge.

In one camp are infectious disease epidemiologists, who work very closely with institutions of public health. They have used a multitude of models to create virtual worlds in which sim viruses wash over sim populations—sometimes unabated, sometimes held back by a virtual dam of social interventions. This deluge of simulated outcomes played a significant role in leading government actors to shut borders as well as doors to schools and businesses. But the hypothetical curves are smooth, while real-world data are rough. Some detractors have questioned whether we have good evidence for the assumptions the models rely on, and even the necessity of the dramatic steps taken to curb the pandemic. Among this camp are several clinical epidemiologists, who typically provide guidance for clinical practice—regarding, for example, the effectiveness of medical interventions—rather than public health.

The latter camp has won significant media attention in recent weeks. Bill Gates—whose foundation funds the research behind the most visible outbreak model in the United States, developed by the Institute for Health Metrics and Evaluation (IHME) at the University of Washington—worries that COVID-19 might be a “once-in-a-century pandemic.” A notable detractor from this view is Stanford’s John Ioannidis, a clinical epidemiologist, meta-researcher, and reliable skeptic who has openly wondered whether the coronavirus pandemic might rather be a “once-in-a-century evidence fiasco.” He argues that better data are needed to justify the drastic measures undertaken to contain the pandemic in the United States and elsewhere.

Ioannidis claims, in particular, that our data about the pandemic are unreliable, leading to exaggerated estimates of risk. He also points to a systematic review published in 2011 of the evidence regarding physical interventions that aim to reduce the spread of respiratory viruses, worrying that the available evidence is nonrandomized and prone to bias. (A systematic review specific to COVID-19 has now been published; it concurs that the quality of evidence is “low” to “very low” but nonetheless supports the use of quarantine and other public health measures.) According to Ioannidis, the current steps we are taking are “non-evidence-based.”…(More)”.

Which Covid-19 Data Can You Trust?


Article by Satchit Balsari, Caroline Buckee and Tarun Khanna: “The Covid-19 pandemic has created a tidal wave of data. As countries and cities struggle to grab hold of the scope and scale of the problem, tech corporations and data aggregators have stepped up, filling the gap with dashboards scoring social distancing based on location data from mobile phone apps and cell towers, contact-tracing apps using geolocation services and Bluetooth, and modeling efforts to predict epidemic burden and hospital needs. In the face of uncertainty, these data can provide comfort — tangible facts in the face of many unknowns.

In a crisis situation like the one we are in, data can be an essential tool for crafting responses, allocating resources, measuring the effectiveness of interventions, such as social distancing, and telling us when we might reopen economies. However, incomplete or incorrect data can also muddy the waters, obscuring important nuances within communities, ignoring important factors such as socioeconomic realities, and creating false senses of panic or safety, not to mention other harms such as needlessly exposing private information. Right now, bad data could produce serious missteps with consequences for millions.

Unfortunately, many of these technological solutions — however well intended — do not provide the clear picture they purport to. In many cases, there is insufficient engagement with subject-matter experts, such as epidemiologists who specialize in modeling the spread of infectious diseases or front-line clinicians who can help prioritize needs. But because technology and telecom companies have greater access to mobile device data, enormous financial resources, and larger teams of data scientists, than academic researchers do, their data products are being rolled out at a higher volume than high quality studies.

Whether you’re a CEO, a consultant, a policymaker, or just someone who is trying to make sense of what’s going on, it’s essential to be able to sort the good data from the misleading — or even misguided.

Common Pitfalls

While you may not be qualified to evaluate the particulars of every dashboard, chart, and study you see, there are common red flags to let you know data might not be reliable. Here’s what to look out for:

Data products that are too broad, too specific, or lack context. Over-aggregated data — such as national metrics of physical distancing that some of our largest data aggregators in the world are putting out — obscure important local and regional variation, are not actionable, and mean little if used for inter-nation comparisons given the massive social, demographic, and economic disparities in the world….(More)”.

Can we escape from information overload?


Tom Lamont at 1843 (Economist): “…Information overload was a term coined in the mid-1960s by Bertram Gross, an American social scientist. In 1970 a writer called Alvin Toffler, who was known at the time as a dependable futurist – someone who prognosticated for a living – popularised the idea of information overload as part of a set of bleak predictions about eventual human dependence on technology. (Good call, Alvin.) Information overload can occur in man or machine, wrote another set of academics in a 1977 study, “when the amount of input to a system exceeds its processing capacity”. Then came VHS, home computers, the internet, mobile phones, mobile-phones-with-the-internet – and waves of anxiety that we might be reaching the limits of our capacity.

A study in 2011 found that on a typical day Americans were taking in five times as much information as they had done 25 years earlier – and this was before most people had bought smartphones. In 2019 a study by academics in Germany, Ireland and Denmark identified that humans’ attention span is shrinking, probably because of digital intrusion, but was manifesting itself both “online and offline”.

By that time an organisation called the Information Overload Research Group had done a study which estimated that hundreds of billions of dollars were being shucked away from the American economy every year, in miscellaneous productivity costs, by an overload of data. The group had been co-founded in 2007 by a computer engineer-turned-consultant, Nathan Zeldes, who had once been asked by Intel, a computer-chip maker, to reduce the burden of email imposed on its workers. By the end of 2019 Zeldes was ready to sound a note of defeat. “I’d love to give you a magic potion that would restore your attention span to that of your grandparents,” he wrote in a blog, “but I can’t. After over a decade of smartphone use and social media, the harm is probably irreversible.” He advised people to take up a hobby.

In an age of overload it can feel as though technology has rather chanced its luck. Pushed too much, too far, bone-deep. Even before coronavirus spread across the world, parts of the culture had started to tack towards isolation and deprivation as desirable lifestyle signifiers, hot-this-year, as if some time spent alone and without a device was the new season’s outfit, the next Cronut, another twerk.

Before a pandemic limited the appeal of wallowing in someone else’s tepid water, flotation-tank centres were opening all over London. In the Czech Republic there are spas that sell clients a week in the dark in shuttered, serviced suites. “Social distancing is underrated,” Edward Snowden tweeted, deadpan, in March 2020: a corona-joke, but one that will have spoken to the tech bros of Silicon Valley, for whom retreats were the treat of choice.

Recently, I saw that a person called Celine in San Francisco had tweeted to her 2,500-odd followers about the difficulty of “trying to date SF guys in between their week-long meditation retreats, Tahoe weekends, month-long remote work sessions…” About 4,000 people tapped to endorse the sentiment, launching Celine onto an exponential number of strangers’ screens, including my own. The default sound for any new tweet is a whistle, somewhere between a neighbourly “yoo-hoo” and a dog-walker’s call to heel.

Hilda Burke, a British psychotherapist who has written about smartphone addiction, told me that part of the problem in this age of overload is the yoo-hooing insistence with which each new parcel of information seeks our attention. Speakers chime. Pixelated columns shuffle urgently or icons bounce, as if to signal that here is the fire. Our twitch response to urgency is triggered, in bad faith.

When Celine’s tweet whistled onto my phone one idle Friday I couldn’t understand why I found it mildly stressful to read. Was it that it made me feel old? That I already had enough to think about? Eventually I realised that, for me, every tweet is a bit stressful. Every trifling, whistling update that comes at us, Burke said, “is like a sheep dressed in wolf’s clothing. The body springs to attention, ready to run or fight, and for nothing that’s worth it. This is confusing.”…(More)”

The Machine Pauses: Will our means continue to dictate our ends?


Essay by Stuart Whatley: “It is now a familiar story. A civilization that measures itself by its technological achievements is confronted with the limits of its power. A new threat, a sudden shock, has shown its tools to be wanting, yet it is now more dependent on them than ever before. While the few in a position to wrest back a semblance of control busy themselves preparing new models and methods, the nonessential masses hurl themselves at luminescent screens, like so many moths to the flame.

It is precisely at such moments of technological dependency that one might consider interrogating one’s relationship with technology more broadly. Yes, “this too shall pass,” because technology always holds the key to our salvation. The question is whether it also played a role in our original sin.

In 1909, following a watershed era of technological progress, but preceding the industrialized massacres of the Somme and Verdun, E.M. Forster imagined, in “The Machine Stops,” a future society in which the entirety of lived experience is administered by a kind of mechanical demiurge. The story is the perfect allegory for the moment, owing not least to its account of a society-wide sudden stop and its eerily prescient description of isolated lives experienced wholly through screens.

The denizens (for they are not citizens) of Forster’s world wile away their days in single-occupancy hexagonal underground rooms, where all of their basic needs are made available on demand. “The Machine…feeds us and clothes us and houses us,” they exclaim, “through it we speak to one another, through it we see one another, in it we have our being.” As such, one’s only duty is to abide by the “spirit of the age.” Whereas in the past that may have entailed sacrifices, always to ensure “that the Machine may progress, that the Machine may progress eternally,” most inhabitants now lead lives of leisure, “eating, or sleeping, or producing ideas.” 

Yet despite all of their comforts and free time, they are a harried leisure class, because they have absorbed the values of the Machine itself. They are obsessed with efficiency, an impulse that they discharge by trying to render order (“ideas”) from the unmanageable glut of information that the machine spits out. One character, Vashti, is a fully initiated member of the cult of efficiency. She does not bother trying to acquire a bed to fit her smaller stature more comfortably, for she accepts that “to have an alternative size would have involved vast alterations in the Machine.” Nor does she have any interest in traveling, because she generates “no ideas in an air-ship.” To her mind, any habit that “was unproductive of ideas…had no connexion with the habits that really mattered.” Everyone simply accepts that although the machine’s video feeds do not convey the nuances of one’s facial expressions, they’re “good enough for all practical purposes.”

Chief among Vashti’s distractions is her son, Kuno, a Cassandra-like figure who dares to point out that, “The Machine develops—but not on our lines. The Machine proceeds—but not to our goal.” When the mechanical system eventually begins to break down (starting with the music-streaming service, then the beds), the people have no choice but to take further recourse in the Machine. Complaints are lodged with the Committee of the Mending Apparatus, but the Mending Apparatus itself turns out to be broken. Rather than protest further, the people pray and pine for the Machine’s quick recovery. By that “latter day,” Forster explains, they “had become so subservient that they readily adapted themselves to every caprice of the Machine.”…(More)”.

Distantiated Communities: A Social History of Social Distancing


Article by Lily Scherlis: “The term “social distancing” trickled into the US news at the end of January, and by mid-March had become the governing creed of interpersonal relations for the time being. It surfaced in the midst of early doubts about the efficacy and ethics of the quarantine in China. The media began to recite it, wrapping it in scare quotes. The omnipresent quotation marks created the impression that reporters were holding the term at bay and contemplating it. By mid-March—after the flood of guidelines from the Center for Disease Control (CDC) and subsequent executive orders—social distancing had become sufficiently imperative for the term to be folded directly into sentences, shedding its quotation marks once and for all. But the initial presence of the quotes reflects the early mass fascination with the unfamiliar term. It materialized as if from nowhere: a scientific coinage, a spontaneous naming of a systematized set of behaviors miraculously devised by presumed experts.

“Social distancing” has actually lived several lives. It and its precursor, “social distance,” had long been used in a variety of colloquial and academic contexts, both as prescriptions and descriptions, before being taken up by epidemiologists in this century. In the nineteenth century, “social distance” was a polite euphemism used by the British to talk about class and by Americans to talk about race. It was then formally adopted in the 1920s by sociologists as a term to facilitate the quantitative codification that was then being introduced into the nascent study of race relations. In the second half of the twentieth century, psychiatry, anthropology, and zoology all adapted it for various purposes. And it was used in the 1990s in the United States to analyze what happened to the gay community when faced with straight fears of contagion. It was only in 2004 in a CDC publication on controlling the recent SARS outbreak that the term “social distance” was finally deployed for the first time by the medical community.

The history I trace here doesn’t presume that the doctors who appropriated it to control disease knew about its legacy, or that these links are relationships of causation. But there was something in the air in 2004 that encouraged the practices we now know as social distancing to be christened in this way—as if its past meanings had coalesced into a semantic atmosphere ripe for the emergence of this new use. Which is why if you think the term is weird, you’re right….(More)”.