Beulr is a bot that attends Zoom class on your behalf. Beulr will join your Zoom meetings through a web browser on the cloud, displaying your information. You can schedule weeks in advance, and tell the bot exactly when to arrive and when to leave.
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.
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.
They then worked with a deepfake artist who used an open-source algorithm to swap in Putin’s and Kim’s faces. A post-production crew cleaned up the leftover artifacts of the algorithm to make the video look more realistic. All in all the process took only 10 days. Attempting the equivalent with CGI likely would have taken months, the team says. It also could have been prohibitively expensive.
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.
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.
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.