Searching for "Artificial Intelligence"
http://diyubook.com/2013/07/the-mooc-is-dead-long-live-open-learning/
We’re at a curious point in the hype cycle of educational innovation, where the hottest concept of the past year–Massive Open Online Courses, or MOOCs–is simultaneously being discovered by the mainstream media, even as the education-focused press is declaring them dead. “More Proof MOOCs are Hot,” and “MOOCs Embraced By Top Universities,” said the Wall Street Journal and USA Today last week upon the announcement that Coursera had received a $43 million round of funding to expand its offerings;
“Beyond MOOC Hype” was the nearly simultaneous headline in Inside Higher Ed.
Can MOOCs really be growing and dying at the same time?
The best way to resolve these contradictory signals is probably to accept that the MOOC, itself still an evolving innovation, is little more than a rhetorical catchall for a set of anxieties around teaching, learning, funding and connecting higher education to the digital world. This is a moment of cultural transition. Access to higher education is strained. The prices just keep rising. Questions about relevance are growing. The idea of millions of students from around the world learning from the worlds’ most famous professors at very small marginal cost, using the latest in artificial intelligence and high-bandwidth communications, is a captivating one that has drawn tens of millions in venture capital. Yet, partnerships between MOOC platforms and public institutions like SUNY and the University of California to create self-paced blended courses and multiple paths to degrees look like a sensible next step for the MOOC, but they are far from that revolutionary future. Separate ideas like blended learning and plain old online delivery seem to be blurring with and overtaking the MOOC–even Blackboard is using the term.
The time seems to be ripe for a reconsideration of the “Massive” impact of “Online” and “Open” learning. TheReclaim Open Learning initiative is a growing community of teachers, researchers and learners in higher education dedicated to this reconsideration. Supporters include the MIT Media Lab and the MacArthur Foundation-supported Digital Media and Learning Research Hub. I am honored to be associated with the project as a documentarian and beater of the drum.
Entries are currently open for our Innovation Contest, offering a $2000 incentive to either teachers or students who have projects to transform higher education in a direction that is connected and creative, is open as in open content and open as in open access, that is participatory, that takes advantage of some of the forms and practices that the MOOC also does but is not beholden to the narrow mainstream MOOC format (referring instead to some of the earlier iterations of student-created, distributed MOOCscreated by Dave Cormier, George Siemens, Stephen Downes and others.)
Current entries include a platform to facilitate peer to peer language learning, a Skype-based open-access seminar with guests from around the world, and a student-created course in educational technology. Go hereto add your entry! Deadline is August 2. Our judges include Cathy Davidson (HASTAC), Joi Ito (MIT), and Paul Kim (Stanford).
Reclaim Open Learning earlier sponsored a hackathon at the MIT Media Lab. This fall, September 27 and 28, our judges and contest winners will join us at a series of conversations and demo days to Reclaim Open Learning at the University of California, Irvine. If you’re interested in continuing the conversation, join us there or check us out online.
AI Technology in Education Will Grow 40% Annually Until 2027
Europe is expected to hold a significant market share with supportive government initiatives.
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more on AI in this IMS blog
https://blog.stcloudstate.edu/ims?s=artificial+intelligence
U.S. is ‘not prepared to defend or compete in the A.I. era,’ says expert group chaired by Eric Schmidt – In a report, it warned that AI systems will be used in the “pursuit of power” and that “AI will not stay in the domain of superpowers or the realm of science fiction.” from r/Futurology
https://www.cnbc.com/2021/03/02/us-not-prepared-to-defend-or-compete-in-ai-era-says-eric-schmidt-group.html
The National Security Commission on AI warned in a 756-page report on Monday that China could soon replace the U.S. as the world’s “AI superpower” and said there are serious military implications to consider.
The 15 members of the commission include technologists, national security professionals, business executives and academic leaders. Among them are Amazon’s next CEO, Andy Jassy, Oracle CEO Safra Catz, Microsoft Chief Scientific Officer Eric Horvitz and Google Cloud AI chief Andrew Moore.
China has stated that it wants to be a global leader in AI by 2030. The report’s authors have said it is vital that the U.S. does all it can to eliminate the chance of this happening.
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more on cybersecurity in this IMS blog
https://blog.stcloudstate.edu/ims?s=cybersecurity
DeepMind’s AI agent MuZero could turbocharge YouTube from r/technews
https://www.bbc.com/news/technology-55403473
MuZero follows in the footsteps of:
Most recently, DeepMind – which is owned by the same parent as Google’s – made a breakthrough in protein folding by adapting these techniques, which could pave the way to new drugs to fight disease.
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more on youtube in this IMS blog
https://blog.stcloudstate.edu/ims?s=youtube
more on AI in this iMS Blog
https://blog.stcloudstate.edu/ims?s=artificial+intelligence+
Elon Musk’s brain-computer startup is getting ready to blow your mind
Musk reckons his brain-computer interface could one day help humans merge with AI, record their memories, or download their consciousness. Could he be right?
https://www.zdnet.com/article/elon-musks-brain-computer-startup-is-getting-ready-to-blow-your-mind/
The idea is to solve these problems with an implantable digital device that can interpret, and possibly alter, the electrical signals made by neurons in the brain.
the latest iteration of the company’s hardware: a small, circular device that attaches to the surface of the brain, gathering data from the cortex and passing it on to external computing systems for analysis.
Several different types of working brain-computer interfaces already exist, gathering data on electrical signals from the user’s brain and translating them into data that can be interpreted by machines.
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If we put computers in our brains, strange things might happen to our minds
Using a brain-computer interface can fundamentally change our grey matter, a view of ourselves and even how fast our brains can change the world.
https://www.zdnet.com/article/if-we-put-computers-in-our-brains-strange-things-might-happen-to-our-minds/
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more on AI in this IMS blog
https://blog.stcloudstate.edu/ims?s=artificial+intelligence
https://www.scienceopen.com/document/read?vid=992eaf61-35dd-454e-aa17-f9f8216b381b
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.
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more on AI in this IMS blog
https://blog.stcloudstate.edu/ims?s=artificial+intelligence
https://www.cnbc.com/2020/05/06/women-in-tech-jobs-in-artificial-intelligence-grow-amid-coronavirus.html
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.
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more on immersive technologies in this IMS blog
https://blog.stcloudstate.edu/ims?s=immersive+technologies
Our Bodies Encoded: Algorithmic Test Proctoring in Higher Education
https://hybridpedagogy.org/our-bodies-encoded-algorithmic-test-proctoring-in-higher-education/
While in-person test proctoring has been used to combat test-based cheating, this can be difficult to translate to online courses. Ed-tech companies have sought to address this concern by offering to watch students take online tests, in real time, through their webcams.
Some of the more prominent companies offering these services include Proctorio, Respondus, ProctorU, HonorLock, Kryterion Global Testing Solutions, and Examity.
Algorithmic test proctoring’s settings have discriminatory consequences across multiple identities and serious privacy implications.
While racist technology calibrated for white skin isn’t new (everything from photography to soap dispensers do this), we see it deployed through face detection and facial recognition used by algorithmic proctoring systems.
While some test proctoring companies develop their own facial recognition software, most purchase software developed by other companies, but these technologies generally function similarly and have shown a consistent inability to identify people with darker skin or even tell the difference between Chinese people. Facial recognition literally encodes the invisibility of Black people and the racist stereotype that all Asian people look the same.
As Os Keyes has demonstrated, facial recognition has a terrible history with gender. This means that a software asking students to verify their identity is compromising for students who identify as trans, non-binary, or express their gender in ways counter to cis/heteronormativity.
These features and settings create a system of asymmetric surveillance and lack of accountability, things which have always created a risk for abuse and sexual harassment. Technologies like these have a long history of being abused, largely by heterosexual men at the expense of women’s bodies, privacy, and dignity.
Their promotional messaging functions similarly to dog whistle politics which is commonly used in anti-immigration rhetoric. It’s also not a coincidence that these technologies are being used to exclude people not wanted by an institution; biometrics and facial recognition have been connected to anti-immigration policies, supported by both Republican and Democratic administrations, going back to the 1990’s.
Borrowing from Henry A. Giroux, Kevin Seeber describes the pedagogy of punishment and some of its consequences in regards to higher education’s approach to plagiarism in his book chapter “The Failed Pedagogy of Punishment: Moving Discussions of Plagiarism beyond Detection and Discipline.”
my note: I am repeating this for years
Sean Michael Morris and Jesse Stommel’s ongoing critique of Turnitin, a plagiarism detection software, outlines exactly how this logic operates in ed-tech and higher education: 1) don’t trust students, 2) surveil them, 3) ignore the complexity of writing and citation, and 4) monetize the data.
Technological Solutionism
Cheating is not a technological problem, but a social and pedagogical problem.
Our habit of believing that technology will solve pedagogical problems is endemic to narratives produced by the ed-tech community and, as Audrey Watters writes, is tied to the Silicon Valley culture that often funds it. Scholars have been dismantling the narrative of technological solutionism and neutrality for some time now. In her book “Algorithms of Oppression,” Safiya Umoja Noble demonstrates how the algorithms that are responsible for Google Search amplify and “reinforce oppressive social relationships and enact new modes of racial profiling.”
Anna Lauren Hoffmann, who coined the term “data violence” to describe the impact harmful technological systems have on people and how these systems retain the appearance of objectivity despite the disproportionate harm they inflict on marginalized communities.
This system of measuring bodies and behaviors, associating certain bodies and behaviors with desirability and others with inferiority, engages in what Lennard J. Davis calls the Eugenic Gaze.
Higher education is deeply complicit in the eugenics movement. Nazism borrowed many of its ideas about racial purity from the American school of eugenics, and universities were instrumental in supporting eugenics research by publishing copious literature on it, establishing endowed professorships, institutes, and scholarly societies that spearheaded eugenic research and propaganda.
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more on privacy in this IMS blog
https://blog.stcloudstate.edu/ims?s=privacy
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more on AI in this IMS blog
https://blog.stcloudstate.edu/ims?s=Artificial+Intelligencehttps://blog.stcloudstate.edu/ims?s=Artificial+Intelligence