Cloud labs and remote research aren’t the future of science – they’re here

Curated on Posted on by Stefaan Verhulst

Article by Tom Ireland: “Cloud labs mean anybody, anywhere can conduct experiments by remote control, using nothing more than their web browser. Experiments are programmed through a subscription-based online interface – software then coordinates robots and automated scientific instruments to perform the experiment and process the data. Friday night is Emerald’s busiest time of the week, as scientists schedule experiments to run while they relax with their families over the weekend.

There are still some things robots can’t do, for example lifting giant carboys (containers for liquids) or unwrapping samples sent by mail, and there are a few instruments that just can’t be automated. Hence the people in blue coats, who look a little like pickers in an Amazon warehouse. It turns out that they are, in fact, mostly former Amazon employees.

Plugging an experiment into a browser forces researchers to translate the exact details of every step into unambiguous code

Emerald originally employed scientists and lab technicians to help the facility run smoothly, but they were creatively stifled with so little to do. Poaching Amazon employees has turned out to be an improvement. “We pay them twice what they were getting at Amazon to do something way more fulfilling than stuffing toilet paper into boxes,” says Frezza. “You’re keeping someone’s drug-discovery experiment running at full speed.”

Further south in the San Francisco Bay Area are two more cloud labs, run by the company Strateos. Racks of gleaming life science instruments – incubators, mixers, mass spectrometers, PCR machines – sit humming inside large Perspex boxes known as workcells. The setup is arguably even more futuristic than at Emerald. Here, reagents and samples whizz to the correct workcell on hi-tech magnetic conveyor belts and are gently loaded into place by dextrous robot arms. Researchers’ experiments are “delocalised”, as Strateos’s executive director of operations, Marc Siladi, puts it…(More)”.

The End of Real Social Networks

Curated on Posted on by Stefaan Verhulst

Essay by Daron Acemoglu: “Social media platforms are not only creating echo chambers, propagating falsehoods, and facilitating the circulation of extremist ideas. Previous media innovations, dating back at least to the printing press, did that, too, but none of them shook the very foundations of human communication and social interaction.

CAMBRIDGE – Not only are billions of people around the world glued to their mobile phones, but the information they consume has changed dramatically – and not for the better. On dominant social-media platforms like Facebook, researchers have documented that falsehoods spread faster and more widely than similar content that includes accurate information. Though users are not demanding misinformation, the algorithms that determine what people see tend to favor sensational, inaccurate, and misleading content, because that is what generates “engagement” and thus advertising revenue.

As the internet activist Eli Pariser noted in 2011, Facebook also creates filter bubbles, whereby individuals are more likely to be presented with content that reinforces their own ideological leanings and confirms their own biases. And more recent research has demonstrated that this process has a major influence on the type of information users see.

Even leaving aside Facebook’s algorithmic choices, the broader social-media ecosystem allows people to find subcommunities that align with their interests. This is not necessarily a bad thing. If you are the only person in your community with an interest in ornithology, you no longer have to be alone, because you can now connect with ornithology enthusiasts from around the world. But, of course, the same applies to the lone extremist who can now use the same platforms to access or propagate hate speech and conspiracy theories.

No one disputes that social-media platforms have been a major conduit for hate speech, disinformation, and propaganda. Reddit and YouTube are breeding grounds for right-wing extremism. The Oath Keepers used Facebook, especially, to organize their role in the January 6, 2021, attack on the United States Capitol. Former US President Donald Trump’s anti-Muslim tweets were found to have fueled violence against minorities in the US.

True, some find such observations alarmist, noting that large players like Facebook and YouTube (which is owned by Google/Alphabet) do much more to police hate speech and misinformation than their smaller rivals do, especially now that better moderation practices have been developed. Moreover, other researchers have challenged the finding that falsehoods spread faster on Facebook and Twitter, at least when compared to other media.

Still others argue that even if the current social-media environment is treacherous, the problem is transitory. After all, novel communication tools have always been misused. Martin Luther used the printing press to promote not just Protestantism but also virulent anti-Semitism. Radio proved to be a powerful tool in the hands of demagogues like Father Charles Coughlin in the US and the Nazis in Germany. Both print and broadcast outlets remain full of misinformation to this day, but society has adjusted to these media and managed to contain their negative effects…(More)”.

The Low Threshold for Face Recognition in New Delhi

Curated on Posted on by Stefaan Verhulst

Article by Varsha Bansal: “Indian law enforcement is starting to place huge importance on facial recognition technology. Delhi police, looking into identifying people involved in civil unrest in northern India in the past few years, said that they would consider 80 percent accuracy and above as a “positive” match, according to documents obtained by the Internet Freedom Foundation through a public records request.

Facial recognition’s arrival in India’s capital region marks the expansion of Indian law enforcement officials using facial recognition data as evidence for potential prosecution, ringing alarm bells among privacy and civil liberties experts. There are also concerns about the 80 percent accuracy threshold, which critics say is arbitrary and far too low, given the potential consequences for those marked as a match. India’s lack of a comprehensive data protection law makes matters even more concerning.

The documents further state that even if a match is under 80 percent, it would be considered a “false positive” rather than a negative, which would make that individual “subject to due verification with other corroborative evidence.”

“This means that even though facial recognition is not giving them the result that they themselves have decided is the threshold, they will continue to investigate,” says Anushka Jain, associate policy counsel for surveillance and technology with the IFF, who filed for this information. “This could lead to harassment of the individual just because the technology is saying that they look similar to the person the police are looking for.” She added that this move by the Delhi Police could also result in harassment of people from communities that have been historically targeted by law enforcement officials…(More)”

Smart Streetlights are Casting a Long Shadow Over Our Cities

Curated on Posted on by Stefaan Verhulst

Article by Zhile Xie: “This is not a surveillance system—nobody is watching it 24 hours a day,” said Erik Caldwell, director of economic development in San Diego, in an interview where he was asked if the wide deployment of “smart” streetlights had turned San Diego into a surveillance city. Innocuous at first glance, this statement demonstrates the pernicious impact of artificial intelligence on new “smart” streetlight systems. As Caldwell suggests, a central human vision is important for the streetlight to function as a surveillance instrument. However, the lack of human supervision only suggests its enhanced capacity. Smart sensors are able to process and communicate environmental information that does not present itself in a visual format and does not rely on human interpretation. On the one hand, they reinforce streetlights’ function as a surveillance instrument, historically associated with light and visibility. On the other hand, in tandem with a wide range of sensors embedded in our everyday environment, they also enable for-profit data extraction on a vast scale,  under the auspices of a partnership between local governments and tech corporations. 

The streetlight was originally designed as a surveillance device and has been refined to that end ever since then. Its association with surveillance and security can be found as early as 400 BC. Citizens of Ancient Rome started to install an oil lamp in front of every villa to prevent tripping or thefts, and an enslaved person would be designated to watch the lamp—lighting was already paired with the notion of control through slavery. As Wolfgang Schivelbusch has detailed in his book Disenchanted Light, street lighting also emerged in medieval European cities alongside practices of policing. Only designated watchmen who carried a torch and a weapon were allowed to be out on the street. This ancient connection between security and visibility has been the basis of the wide deployment of streetlights in modern cities. Moreover, as Edwin Heathcote has explained in a recent article for the Architectural Review, gas streetlights were first introduced to Paris during Baron Haussmann’s restructuring of the city between 1853 and 1870, which was designed in part to prevent revolutionary uprisings. The invention of electric light bulbs in the late nineteenth century in Europe triggered new fears and imaginations around the use of streetlights for social control. For instance, in his 1894 dystopian novel The Land of the Changing Sun, W.N. Harben envisions an electric-optical device that makes possible 24-hour surveillance over the entire population of an isolated country, Alpha. The telescopic system is aided by an artificial “sun” that lights up the atmosphere all year round, along with networked observatories across the land that capture images of their surroundings, which are transmitted to a “throne room” for inspection by the king and police…(More)”.

Without appropriate metadata, data-sharing mandates are pointless

Curated on Posted on by Stefaan Verhulst

Article by Mark A. Musen: “Last month, the US government announced that research articles and most underlying data generated with federal funds should be made publicly available without cost, a policy to be implemented by the end of 2025. That’s atop other important moves. The European Union’s programme for science funding, Horizon Europe, already mandates that almost all data be FAIR (that is, findable, accessible, interoperable and reusable). The motivation behind such data-sharing policies is to make data more accessible so others can use them to both verify results and conduct further analyses.

But just getting those data sets online will not bring anticipated benefits: few data sets will really be FAIR, because most will be unfindable. What’s needed are policies and infrastructure to organize metadata.

Imagine having to search for publications on some topic — say, methods for carbon reclamation — but you could use only the article titles (no keywords, abstracts or search terms). That’s essentially the situation for finding data sets. If I wanted to identify all the deposited data related to carbon reclamation, the task would be futile. Current metadata often contain only administrative and organizational information, such as the name of the investigator and the date when the data were acquired.

What’s more, for scientific data to be useful to other researchers, metadata must sensibly and consistently communicate essentials of the experiments — what was measured, and under what conditions. As an investigator who builds technology to assist with data annotation, it’s frustrating that, in the majority of fields, the metadata standards needed to make data FAIR don’t even exist.

Metadata about data sets typically lack experiment-specific descriptors. If present, they’re sparse and idiosyncratic. An investigator searching the Gene Expression Omnibus (GEO), for example, might seek genomic data sets containing information on how a disease or condition manifests itself in young animals or humans. Performing such a search requires knowledge of how the age of individuals is represented — which in the GEO repository, could be age, AGE, age (after birth), age (years), Age (yr-old) or dozens of other possibilities. (Often, such information is missing from data sets altogether.) Because the metadata are so ad hoc, automated searches fail, and investigators waste enormous amounts of time manually sifting through records to locate relevant data sets, with no guarantee that most (or any) can be found…(More)”.

Spirals of Delusion: How AI Distorts Decision-Making and Makes Dictators More Dangerous

Curated on Posted on by Stefaan Verhulst

Essay by Henry Farrell, Abraham Newman, and Jeremy Wallace: “In policy circles, discussions about artificial intelligence invariably pit China against the United States in a race for technological supremacy. If the key resource is data, then China, with its billion-plus citizens and lax protections against state surveillance, seems destined to win. Kai-Fu Lee, a famous computer scientist, has claimed that data is the new oil, and China the new OPEC. If superior technology is what provides the edge, however, then the United States, with its world class university system and talented workforce, still has a chance to come out ahead. For either country, pundits assume that superiority in AI will lead naturally to broader economic and military superiority.

But thinking about AI in terms of a race for dominance misses the more fundamental ways in which AI is transforming global politics. AI will not transform the rivalry between powers so much as it will transform the rivals themselves. The United States is a democracy, whereas China is an authoritarian regime, and machine learning challenges each political system in its own way. The challenges to democracies such as the United States are all too visible. Machine learning may increase polarization—reengineering the online world to promote political division. It will certainly increase disinformation in the future, generating convincing fake speech at scale. The challenges to autocracies are more subtle but possibly more corrosive. Just as machine learning reflects and reinforces the divisions of democracy, it may confound autocracies, creating a false appearance of consensus and concealing underlying societal fissures until it is too late.

Early pioneers of AI, including the political scientist Herbert Simon, realized that AI technology has more in common with markets, bureaucracies, and political institutions than with simple engineering applications. Another pioneer of artificial intelligence, Norbert Wiener, described AI as a “cybernetic” system—one that can respond and adapt to feedback. Neither Simon nor Wiener anticipated how machine learning would dominate AI, but its evolution fits with their way of thinking. Facebook and Google use machine learning as the analytic engine of a self-correcting system, which continually updates its understanding of the data depending on whether its predictions succeed or fail. It is this loop between statistical analysis and feedback from the environment that has made machine learning such a formidable force…(More)”

Belfast to launch ‘Citizen Office of Digital Innovation’

Curated on Posted on by Stefaan Verhulst

Article by Sarah Wray: The City of Belfast in Northern Ireland has launched a tender to develop and pilot a Citizen Office of Digital Innovation (CODI) – a capacity-building programme to boost resident engagement around data and technology.

The council says the pilot will support a ‘digital citizenship skillset’, enabling citizens to better understand and shape how technology is used in Belfast. It could also lead to the creation of tools that can be used and adapted by other cities under a creative commons licence.

The tender is seeking creative and interactive methods to explore topics such as co-design, citizen science, the Internet of Things, artificial intelligence and data science, and privacy. It cites examples of citizen-centric programmes elsewhere including Dublin’s Academy of the Near Future and the DTPR standard for visual icons to explain sensors and cameras that are deployed in public spaces…(More)”

Nudging Consumers to Purchase More Sustainably

Curated on Posted on by Stefaan Verhulst

Article by Erez Yoeli: “Most consumers still don’t choose sustainable products when the option is available. Americans may claim to be willing to pay more for green energy, but while green energy is available in the majority of states — 35 out of 50 states or roughly 80% of American households as of 2018, at least — only 14% of households were even aware of the green option, and less than half of these households purchased it. Hybrids and electric vehicles are available nationwide, but still amount to just 10% of sales — 6.6% and 3.4%, respectively, according to S&P Global’s subscription services.

Now it may be that this virtue thinking-doing gap will eventually close. I hope so. But it will certainly need help, because in these situations there’s often an insidious behavioral dynamic at work that often stops stated good intentions from turning into actual good deeds…

Allow me to illustrate what I mean by “the plausible deniability effect” with an example from a now-classic behavioral economics study. Every year, around the holidays, Salvation Army volunteers collect donations for the needy outside supermarkets and other retail outlets. Researchers Justin Rao, Jim Andreoni, and Hanna Trachtmann teamed up with a Boston chapter of the Salvation Army to test ways of increasing donations.

Taking a supermarket that had two exit/entry points, the team randomly divided the volunteers into two groups. In one group, just one volunteer was assigned to stand in front of one door. For the other group, volunteers were stationed at both doors…(More)”.

China May Be Chasing Impossible Dream by Trying to Harness Internet Algorithms

Curated on Posted on by Stefaan Verhulst

Article by Karen Hao: “China’s powerful cyberspace regulator has taken the first step in a pioneering—and uncertain—government effort to rein in the automated systems that shape the internet.

Earlier this month, the Cyberspace Administration of China published summaries of 30 core algorithms belonging to two dozen of the country’s most influential internet companies, including TikTok owner ByteDance Ltd., e-commerce behemoth Alibaba Group Holding Ltd. and Tencent Holdings Ltd., owner of China’s ubiquitous WeChat super app.

The milestone marks the first systematic effort by a regulator to compel internet companies to reveal information about the technologies powering their platforms, which have shown the capacity to radically alter everything from pop culture to politics. It also puts Beijing on a path that some technology experts say few governments, if any, are equipped to handle….

One important question the effort raises, algorithm experts say, is whether direct government regulation of algorithms is practically possible.

The majority of today’s internet platform algorithms are based on a technology called machine learning, which automates decisions such as ad-targeting by learning to predict user behaviors from vast repositories of data. Unlike traditional algorithms that contain explicit rules coded by engineers, most machine-learning systems are black boxes, making it hard to decipher their logic or anticipate the consequences of their use.

Beijing’s interest in regulating algorithms started in 2020, after TikTok sought an American buyer to avoid being banned in the U.S., according to people familiar with the government’s thinking. When several bidders for the short-video platform lost interest after Chinese regulators announced new export controls on information-recommendation technology, it tipped off Beijing to the importance of algorithms, the people said…(More)”.

New Theory for Increasingly Tangled Banks

Curated on Posted on by Stefaan Verhulst

Essay by Saran Twombly: “Decades before the COVID-19 pandemic demonstrated how rapidly infectious diseases could emerge and spread, the world faced the AIDS epidemic. Initial efforts to halt the contagion were slow as researchers focused on understanding the epidemiology of the virus. It was only by integrating epidemiological theory with behavioral theory that successful interventions began to control the spread of HIV. 

As the current pandemic persists, it is clear that similar applications of interdisciplinary theory are needed to inform decisions, interventions, and policy. Continued infections and the emergence of new variants are the result of complex interactions among evolution, human behavior, and shifting policies across space and over time. Due to this complexity, predictions about the pandemic based on data and statistical models alone—in the absence of any broader conceptual framework—have proven inadequate. Classical epidemiological theory has helped, but alone it has also led to limited success in anticipating surges in COVID-19 infections. Integrating evolutionary theory with data and other theories has revealed more about how and under what conditions new variants arise, improving such predictions.  

AIDS and COVID-19 are examples of complex challenges requiring coordination across families of scientific theories and perspectives. They are, in this sense, typical of many issues facing science and society today—climate change, biodiversity decline, and environmental degradation, to name a few. Such problems occupy interdisciplinary space and arise from no-analog conditions (i.e., situations to which there are no current equivalents), as what were previously only local perturbations trigger global instabilities. As with the pandemic crises, they involve interdependencies and new sources of uncertainty, cross levels of governance, span national boundaries, and include interactions at different temporal and spatial scales. 

Such problems, while impossible to solve from a single perspective, may be successfully addressed by integrating multiple theories. …(More)”.