This article presents an examination of how education research is being remade as an experimental data-intensive science. AI is combining with learning science in new ‘digital laboratories’ where ownership over data, and power and authority over educational knowledge production, are being redistributed to research assemblages of computational machines and scientific expertise.
Research across the sciences, humanities and social sciences is increasingly conducted through digital knowledge machines that are reconfiguring the ways knowledge is generated, circulated and used (Meyer and Schroeder, 2015).
Knowledge infrastructures, such as those of statistical institutes or research-intensive universities, have undergone significant digital transformation with the arrival of data-intensive technologies, with knowledge production now enacted in myriad settings, from academic laboratories and research institutes to commercial research and development studios, think tanks and consultancies. Datafied knowledge infrastructures have become hubs of command and control over the creation, analysis and exchange of data (Bigo et al., 2019).
The combination of AI and learning science into an AILSci research assemblage consists of particular forms of scientific expertise embodied by knowledge actors – individuals and organizations – identified by categories including science of learning, AIED, precision education and learning engineering.
Precision education overtly uses psychological, neurological and genomic data to tailor or personalize learning around the unique needs of the individual (Williamson, 2019). Precision education approaches include cognitive tracking, behavioural monitoring, brain imaging and DNA analysis.
Expert power is therefore claimed by those who can perform big data analyses, especially those able to translate and narrate the data for various audiences. Likewise, expert power in education is now claimed by those who can enact data-intensive science of learning, precision education and learning engineering research and development, and translate AILSci findings into knowledge for application in policy and practitioner settings.
the thinking of a thinking infrastructure is not merely a conscious human cognitive process, but relationally performed across humans and socio-material strata, wherein interconnected technical devices and other forms ‘organize thinking and thought and direct action’.
As an infrastructure for AILSci analyses, these technologies at least partly structure how experts think: they generate new understandings and knowledge about processes of education and learning that are only thinkable and knowable due to the computational machinery of the research enterprise.
Big data-based molecular genetics studies are part of a bioinformatics-led transformation of biomedical sciences based on analysing exceptional volumes of data (Parry and Greenhough, 2018), which has transformed the biological sciences to focus on structured and computable data rather than embodied evidence itself.
Isin and Ruppert (2019) have recently conceptualized an emergent form of power that they characterize as sensory power. Building on Foucault, they note how sovereign power gradually metamorphosed into disciplinary power and biopolitical forms of statistical regulation over bodies and populations.
Sensory power marks a shift to practices of data-intensive sensing, and to the quantified tracking, recording and representing of living pulses, movements and sentiments through devices such as wearable fitness monitors, online natural-language processing and behaviour-tracking apps. Davies (2019: 515–20) designates these as ‘techno-somatic real-time sensing’ technologies that capture the ‘rhythms’ and ‘metronomic vitality’ of human bodies, and bring about ‘new cyborg-type assemblages of bodies, codes, screens and machines’ in a ‘constant cybernetic loop of action, feedback and adaptation’.
Techno-somatic modes of neural sensing, using neurotechnologies for brain imaging and neural analysis, are the next frontier in AILSci. Real-time brainwave sensing is being developed and trialled in multiple expert settings.
more on AI in this IMS blog
https://nationalinterest.org/feature/why-chinas-race-ai-dominance-depends-math-163809Why China’s Race For AI Dominance Depends On Math | Forget about “AI” itself: it’s all about the math, and America is failing to train enough citizens in the right kinds of mathematics to remain dominant. from r/technology
more on CHina in this IMS blog
International Data Corporation says it expects the number of AI jobs globally to grow 16% this year.
a new report released Wednesday, IBM found the majority (85%) of AI professionals think the industry has become more diverse over recent years
3,200 people surveyed across North America, Europe and India, 86% said they are now confident in AI systems’ ability to make decisions without bias.
A plurality of men (46%) said they became interested in a tech career in high school or earlier, while a majority of women (53%) only considered it a possible path during their undergraduate degree or grad school.
more on immersive technologies in this IMS blog
The Secretive Company That Might End Privacy as We Know It: It’s taken 3 billion images from the internet to build a an AI driven database that allows US law enforcement agencies identify any stranger. from r/Futurology
Until now, technology that readily identifies everyone based on his or her face has been taboo because of its radical erosion of privacy. Tech companies capable of releasing such a tool have refrained from doing so; in 2011, Google’s chairman at the time said it was the one technology the company had held back because it could be used “in a very bad way.” Some large cities, including San Francisco, have barred police from using facial
But without public scrutiny, more than 600 law enforcement agencies have started using Clearview in the past year, according to the company, which declined to provide a list. recognition technology.
Facial recognition technology has always been controversial. It makes people nervous about Big Brother. It has a tendency to deliver false matches for certain groups, like people of color. And some facial recognition products used by the police — including Clearview’s — haven’t been vetted by independent experts.
Clearview deployed current and former Republican officials to approach police forces, offering free trials and annual licenses for as little as $2,000. Mr. Schwartz tapped his political connections to help make government officials aware of the tool, according to Mr. Ton-That.
“We have no data to suggest this tool is accurate,” said Clare Garvie, a researcher at Georgetown University’s Center on Privacy and Technology, who has studied the government’s use of facial recognition. “The larger the database, the larger the risk of misidentification because of the doppelgänger effect. They’re talking about a massive database of random people they’ve found on the internet.”
Law enforcement is using a facial recognition app with huge privacy issues Clearview AI’s software can find matches in billions of internet images. from r/technology
Part of the problem stems from a lack of oversight. There has been no real public input into adoption of Clearview’s software, and the company’s ability to safeguard data hasn’t been tested in practice. Clearview itself remained highly secretive until late 2019.
The software also appears to explicitly violate policies at Facebook and elsewhere against collecting users’ images en masse.
while there’s underlying code that could theoretically be used for augmented reality glasses that could identify people on the street, Ton-That said there were no plans for such a design.
Banning Facial Recognition Isn’t Enough from r/technology
In May of last year, San Francisco banned facial recognition; the neighboring city of Oakland soon followed, as did Somerville and Brookline in Massachusetts (a statewide ban may follow). In December, San Diego suspended a facial recognition program in advance of a new statewide law, which declared it illegal, coming into effect. Forty major music festivals pledged not to use the technology, and activists are calling for a nationwide ban. Many Democratic presidential candidates support at least a partial ban on the technology.
facial recognition bans are the wrong way to fight against modern surveillance. Focusing on one particular identification method misconstrues the nature of the surveillance society we’re in the process of building. Ubiquitous mass surveillance is increasingly the norm. In countries like China, a surveillance infrastructure is being built by the government for social control. In countries like the United States, it’s being built by corporations in order to influence our buying behavior, and is incidentally used by the government.
People can be identified at a distance by their heart beat or by their gait, using a laser-based system. Cameras are so good that they can read fingerprints and iris patterns from meters away. And even without any of these technologies, we can always be identified because our smartphones broadcast unique numbers called MAC addresses.
China, for example, uses multiple identification technologies to support its surveillance state.
There is a huge — and almost entirely unregulated — data broker industry in the United States that trades on our information.
This is why many companies buy license plate data from states. It’s also why companies like Google are buying health records, and part of the reason Google bought the company Fitbit, along with all of its data.
The data broker industry is almost entirely unregulated; there’s only one law — passed in Vermont in 2018 — that requires data brokers to register and explain in broad terms what kind of data they collect.
The Secretive Company That Might End Privacy as We Know It from r/technews
Until now, technology that readily identifies everyone based on his or her face has been taboo because of its radical erosion of privacy. Tech companies capable of releasing such a tool have refrained from doing so; in 2011, Google’s chairman at the time said it was the one technology the company had held back because it could be used “in a very bad way.” Some large cities, including San Francisco, have barred police from using facial recognition technology.
on social credit system in this IMS blog
AI computing involves two phases: training and inference. Training requires computers that can process enormous amounts of data. For example, getting an AI system to recognize what’s in photographs requires a computer to sort through billions of labeled photos to create a model. That model is used in the second step to infer, or identify, what’s in a specific photo.
Intel already sells its Nervana chips for training and inference to data centers packed with servers, computing infrastructure that often powers services at AI-heavy companies such as Google and Facebook. Intel is now shipping its larger, more expensive and power-hungry Nervana NNP-T chips for training and its smaller NNP-I chips for inference, the chipmaker announced.
“Social media had changed not just the message, but the dynamics of conflict. How information was being accessed, manipulated, and spread had taken on new power. Who was involved in the fight, where they were located, and even how they achieved victory had been twisted and transformed. Indeed, if what was online could swing the course of a battle — or eliminate the need for battle entirely — what, exactly, could be considered ‘war’ at all?“
Even American gang members are entering the fray as super-empowered individuals, leveraging social media to instigate killing s via “Facebook drilling” in Chicago or “wallbanging” in Los Angeles.
Instagram announced a new anti-bullying feature called Restrict.
Fifty-nine percent of American teens have been bullied or harassed online, according to a 2018 survey by the Pew Research Center. Instagram is one of the most popular social media networks among teenagers and a likely place for teens to be bullied.
In a recent study, conducted by the investment bank Piper Jaffray, Instagram is the second most popular social media platform among teenagers. Thirty-five percent of teens surveyed said that Instagram is their favorite social media platform, compared with 41% who preferred Snapchat.
more on cyberbullying in this IMS blog
AI and Mixed Reality Drive Educational Gaming into ‘Boom Phase’
By Dian Schaffhauser 09/16/19
Artificial intelligence and mixed reality have driven demand in learning games around the world, according to a new report by Metaari. A five-year forecast has predicted that educational gaming will reach $24 billion by 2024, with a compound annual growth rate of 33 percent and a quadrupling of revenues. Metaari is an analyst firm that tracks advanced learning technology.