A study conducted by WECT before the pandemic found that only about 20 percent of colleges they surveyed charged less tuition and lower fees than they do to those who study in person. Counterintuitively, the study also revealed—to my surprise—that more than half of the colleges charged more tuition and higher fees to their remote students than to those studying on campus. The survey also uncovered another revelation: online fees added to tuition can be so large that they are greater than tuition alone.
A recent study by the National Bureau of Economic Research found that colleges with a greater-than-average share of remote students largely charge lower tuition than their on-campus counterparts. As prices rose at most post-secondary institutions over the last decades, tuition at these colleges fell.
Since then, MOOC degrees have mushroomed, now with more than 70 others available in partnership with about 30 first-class universities worldwide. Coursera, the biggest provider, offers nearly 30 virtual degrees in business, data science and public health, among other fields, most discounted at less than half of comparable on-campus programs
Narrow AI is what we see all around us in computers today — intelligent systems that have been taught or have learned how to carry out specific tasks without being explicitly programmed how to do so.
General AI
General AI is very different and is the type of adaptable intellect found in humans, a flexible form of intelligence capable of learning how to carry out vastly different tasks, anything from haircutting to building spreadsheets or reasoning about a wide variety of topics based on its accumulated experience.
What can Narrow AI do?
There are a vast number of emerging applications for narrow AI:
Interpreting video feeds from drones carrying out visual inspections of infrastructure such as oil pipelines.
Organizing personal and business calendars.
Responding to simple customer-service queries.
Coordinating with other intelligent systems to carry out tasks like booking a hotel at a suitable time and location.
Flagging inappropriate content online, detecting wear and tear in elevators from data gathered by IoT devices.
Generating a 3D model of the world from satellite imagery… the list goes on and on.
What can General AI do?
A survey conducted among four groups of experts in 2012/13 by AI researchers Vincent C Müller and philosopher Nick Bostrom reported a 50% chance that Artificial General Intelligence (AGI) would be developed between 2040 and 2050, rising to 90% by 2075.
What is machine learning?
What are neural networks?
What are other types of AI?
Another area of AI research is evolutionary computation.
What is fueling the resurgence in AI?
What are the elements of machine learning?
As mentioned, machine learning is a subset of AI and is generally split into two main categories: supervised and unsupervised learning.
Supervised learning
Unsupervised learning
Which are the leading firms in AI?
Which AI services are available?
All of the major cloud platforms — Amazon Web Services, Microsoft Azure and Google Cloud Platform — provide access to GPU arrays for training and running machine-learning models, with Google also gearing up to let users use its Tensor Processing Units — custom chips whose design is optimized for training and running machine-learning models.
Which countries are leading the way in AI?
It’d be a big mistake to think the US tech giants have the field of AI sewn up. Chinese firms Alibaba, Baidu, and Lenovo, invest heavily in AI in fields ranging from e-commerce to autonomous driving. As a country, China is pursuing a three-step plan to turn AI into a core industry for the country, one that will be worth 150 billion yuan ($22bn) by the end of 2020 to become the world’s leading AI power by 2030.
How can I get started with AI?
While you could buy a moderately powerful Nvidia GPU for your PC — somewhere around the Nvidia GeForce RTX 2060 or faster — and start training a machine-learning model, probably the easiest way to experiment with AI-related services is via the cloud.
Document-based questions have long been a staple of social studies classrooms
Since the human brain is essentially wired to recognize patterns, computational thinking—somewhat paradoxically—doesn’t necessarily require the use of computers at all.
In a 2006 paper for the Association for Computing Machinery, computer scientist Jeanette Wing wrote a definition of computational thinking that used terms native her field—even when she was citing everyday examples. Thus, a student preparing her backpack for the day is “prefetching and caching.” Finding the shortest line at the supermarket is “performance modeling.” And performing a cost-benefit analysis on whether it makes more sense to rent versus buy is running an “online algorithm.” “Computational thinking will have become ingrained in everyone’s lives when words like algorithm and precondition are part of everyone’s vocabulary,” she writes.
three main steps:
Looking at the data: Deciding what’s worth including in the final data set, and what should be left out. What are the different tools that can help manipulate this data—from GIS tools to pen and paper?
Looking for patterns: Typically, this involves shifting to greater levels of abstraction—or conversely, getting more granular.
Decomposition: What’s a trend versus what’s an outlier to the trend? Where do things correlate, and where can you find causal inference?
Can We Develop Herd Immunity to Internet Propaganda?
Internet propaganda is becoming an industrialized commodity, warns Phil Howard, the director of the Oxford Internet Institute and author of many books on disinformation. In an interview, he calls for greater transparency and regulation of the industry.
Platforms like Parler, TheDonald, Breitbart and Anon are like petri dishes for testing out ideas, to see what sticks. If extremist influencers see that something gets traction, they ramp it up. In the language of disease, you would say these platforms act as a vector, like a germ that carries a disease into other, more public forums.
at some point a major influencer takes a new meme from one of these extremist forums and puts it out before a wider audience. It works like a vector-borne disease like malaria, where the mosquitoes do the transmission. So, maybe a Hollywood actor or an influencer who knows nothing about politics will take this idea and post it on the bigger, better known platform. From there, these memes escalate as they move from Parler to maybe Reddit and from there to Twitter, Facebook, Instagram and YouTube. We call this “cascades of misinformation.“
Sometimes the cascades of misinformation bounce from country to country between the U.S., Canada and the UK for example. So, it echoes back and forth.
Within Europe, two reservoirs for disinformation stick out: Poland and Hungary.
Our 2020 report shows that cyber troop activity continues to increase around the world. This year, we found evidence of 81 countries using social media to spread computational propaganda and disinformation about politics. This has increased from last years’ report, where we identified 70 countries with cyber troop activity.
identified 63 new instances of private firms working with governments or political parties to spread disinformation about elections or other important political issues. We identified 21 such cases in 2017-2018, yet only 15 in the period between 2009 and 2016.
Why would well-funded Russian agencies buy disinformation services from a newcomer like Nigeria?
(1) Russian actors have found a lab in Nigeria that can provide services at competitive prices. (2) But countries like China and Russia seem to be developing an interest in political influence in many African countries, so it is possible that there is a service industry for disinformation in Nigeria for that part of the world.
Each social media company should provide some kind of accounting statement about how it deals with misuse, with reporting hate speech, with fact checking and jury systems and so on. This system of transparency and accountability works for the stock markets, why shouldn’t it work in the social media realm?
We clearly need a digital civics curriculum. The 12 to 16 year olds are developing their media attitudes now, they will be voting soon. There is very good media education in Canada or the Netherlands for example, and that is an excellent long-term strategy.
Last year, Australia’s Chief Scientist Alan Finkel suggested that we in Australia should become “human custodians”. This would mean being leaders in technological development, ethics, and human rights.
A recent report from the Australian Council of Learned Academies (ACOLA) brought together experts from scientific and technical fields as well as the humanities, arts and social sciences to examine key issues arising from artificial intelligence.
A similar vision drives Stanford University’s Institute for Human-Centered Artificial Intelligence. The institute brings together researchers from the humanities, education, law, medicine, business and STEM to study and develop “human-centred” AI technologies.
Meanwhile, across the Atlantic, the Future of Humanity Institute at the University of Oxford similarly investigates “big-picture questions” to ensure “a long and flourishing future for humanity”.
The IT sector is also wrestling with the ethical issues raised by rapid technological advancement. Microsoft’s Brad Smith and Harry Shum wrote in their 2018 book The Future Computed that one of their “most important conclusions” was that the humanities and social sciences have a crucial role to play in confronting the challenges raised by AI
Without training in ethics, human rights and social justice, the people who develop the technologies that will shape our future could make poor decisions.
A growing body of evidence has found strong consumer interest in recent months in skills-based, online credentials that are clearly tied to careers, particularly among adult learners from diverse and lower-income backgrounds, whom four-year colleges often have struggled to attract and graduate.
For years the demographics of higher education have been shifting away from traditional-age, full-paying college students while online education has become more sophisticated and accepted.
That has amplified interest in recent months among employers, students, workers and policy makers in online certificates, industry certifications, apprenticeships, microcredentials, boot camps and even lower-cost online master’s degrees.
Moody’s, the credit ratings firm, on Wednesday said online and nondegree programs are growing at a rapid pace.
Google will fund 100,000 need-based scholarships for the certificates, and said it will consider them the “equivalent of a four-year degree” for related roles.
Google isn’t alone in this push. IBM, Facebook, Salesforce and Microsoft are creating their own short-term, skills-based credentials. Several tech companies also are dropping degree requirements for some jobs, as is the federal government, while the White House, employers and some higher education groups have collaborated on an Ad Council campaign to tout alternatives to the college degree.
One of the most consistent findings in a nationally representative poll conducted by the Strada Education Network’s Center for Consumer Insights over the last five months has been a preference for nondegree and skills training options.
Despite growing skepticism about the value of a college degree, it remains the best ticket to a well-paying job and career. And data have shown that college degrees have been a cushion amid the pandemic and recession.
Experts had long speculated that employer interest in alternative credential pathways would wither when low employment rates went away,…. Yet some big employers, including Amazon, are paying to retrain workers for jobs outside the company as it restructures.
digital ethics, which I define simply as “doing the right thing at the intersection of technology innovation and accepted social values.”
Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy, written by Cathy O’Neil in early 2016, continues to be relevant and illuminating. O’Neil’s book revolves around her insight that “algorithms are opinions embedded in code,” in distinct contrast to the belief that algorithms are based on—and produce—indisputable facts.
Safiya Umoja Noble’s book Algorithms of Oppression: How Search Engines Reinforce Racism
The Age of Surveillance Capitalism: The Fight for a Human Future at the New Frontier of Power
Got a new open access article out on the ways AI is embedding in education research. Well-funded precision education experts and learning engineers aim to collect psychodata, brain data and biodata as evidence of the embodied substrates of learning. https://t.co/CbdHReXUiz
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.
Education scholars have already critiqued PISA as a valid global measure of education quality — but analysts also are skeptical about the selective participation of Chinese students from wealthier schools.
Second, Chinese students, on average, study 55 hours a week — also No. 1 among PISA-participating countries. This was about 20 hours more than students in Finland, the country that PISA declared to have the highest learning efficiency, or reading-test-score points per hour spent studying.
But PISA analysis also revealed that Chinese students are among the least satisfied with their lives.
Students look overseas for a more well-rounded education
Their top destination of choice, by far, is the United States. The 1.1 million or so foreign students in the United States in 2018 included 369,500 Chinese college students
hostility in U.S.-China relations could dampen the appeal of a U.S. education. Britain, in fact, recorded a 30 percent surge in Chinese applicants in 2019, challenging the U.S. global dominance in higher education.
Immigrant students, who made up 23 percent of all U.S. students taking PISA, performed significantly better compared to their native-born peers in the United States than they did on average throughout the OECD countries.
The survey found that 15-year-old students from Beijing, Shanghai, and the eastern provinces of Jiangsu and Zhejiang ranked top for all three core subjects, achieving the highest level 4 rating.
Students from the United States were ranked level 3 for reading and science, and level 2 for math, while teens from Britain scored a level 3 ranking in all three categories.
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Looking for Post-PISA Answers? Here’s What Our Obsession With Test Scores Overlooks
Andreas Schelicher, director of education and skills at the OECD—the Paris-based organization behind PISA wrote that “students who disagreed or strongly disagreed with the statement ‘Your intelligence is something about you that you can’t change very much’ scored 32 points higher in reading than students who agreed or strongly agreed.”
Those results are similar to recent findings published by Carol Dweck, a Stanford education professor who is often credited with making growth mindset a mainstream concept.
“Growth mindset is a very important thing that makes us active learners, and makes us invest in our personal education,” Schleicher states. “If learning isn’t based on effort and intelligence is predetermined, why would anyone bother?”
It’s “absolutely fascinating” to see the relationship between teachers’ enthusiasm, students’ social-emotional wellbeing and their learning outcomes, Schleicher notes. As one example, he noted in his summary report that “in most countries and economies, students scored higher in reading when they perceived their teachers as more enthusiastic, especially when they said their teachers were interested in the subject.”
In other words, happy teachers lead to better results. That’s hardly a surprising revelation, says Scheleicher. But professional development support is one thing that can sometimes be overlooked by policymakers when so much of the focus is on test scores.
