Searching for "artificial intelligence"
Law is Code: Making Policy for Artificial Intelligence
Jules Polonetsky and Omer Tene January 16, 2019
Twenty years have passed since renowned Harvard Professor Larry Lessig coined the phrase “Code is Law”, suggesting that in the digital age, computer code regulates behavior much like legislative code traditionally did. These days, the computer code that powers artificial intelligence (AI) is a salient example of Lessig’s statement.
- Good AI requires sound data. One of the principles, some would say the organizing principle, of privacy and data protection frameworks is data minimization. Data protection laws require organizations to limit data collection to the extent strictly necessary and retain data only so long as it is needed for its stated goal.
- Preventing discrimination – intentional or not.
When is a distinction between groups permissible or even merited and when is it untoward? How should organizations address historically entrenched inequalities that are embedded in data? New mathematical theories such as “fairness through awareness” enable sophisticated modeling to guarantee statistical parity between groups.
- Assuring explainability – technological due process. In privacy and freedom of information frameworks alike, transparency has traditionally been a bulwark against unfairness and discrimination. As Justice Brandeis once wrote, “Sunlight is the best of disinfectants.”
- Deep learning means that iterative computer programs derive conclusions for reasons that may not be evident even after forensic inquiry.
Yet even with code as law and a rising need for law in code, policymakers do not need to become mathematicians, engineers and coders. Instead, institutions must develop and enhance their technical toolbox by hiring experts and consulting with top academics, industry researchers and civil society voices. Responsible AI requires access to not only lawyers, ethicists and philosophers but also to technical leaders and subject matter experts to ensure an appropriate balance between economic and scientific benefits to society on the one hand and individual rights and freedoms on the other hand.
more on AI in this IMS blog
EASI Free Webinar: Inclusive Design of Artificial Intelligence Thursday
Artificial Intelligence (AI) and accessibility: will it enhance or
impede accessibility for users with disabilities?
Artificial intelligence used to be all about the distance future, but it
has now become mainstream. It is already impacting us in ways we may not
recognize. It is impacting us today already. It is involved in search
engines. It is involved in the collecting of big data and analyzing it.
It is involved in all the arguments about the way social media is being
used to effect, or try to effect, our thinking and our politics. How
else might it play a role in the future of accessibility?
The webinar presenter: Jutta Treviranus at University of Toronto will
explore these questions in the webinar on Thursday, October 25 at 11
Pacific, noon Mountain, 1 central or 2 Eastern You can register now but
registration closes Wed. Oct. 24 at midnight Eastern.
Those who register should get directions for joining sent late wednesday
or Early on Thursday.
more on AI in this IMS blog
Limbic thought and artificial intelligence
September 5, 2018 Siddharth (Sid) Pai
An AI programme “catastrophically forgets” the learnings from its first set of data and would have to be retrained from scratch with new data. The website futurism.com says a completely new set of algorithms would have to be written for a programme that has mastered face recognition, if it is now also expected to recognize emotions. Data on emotions would have to be manually relabelled and then fed into this completely different algorithm for the altered programme to have any use. The original facial recognition programme would have “catastrophically forgotten” the things it learnt about facial recognition as it takes on new code for recognizing emotions. According to the website, this is because computer programmes cannot understand the underlying logic that they have been coded with.
Irina Higgins, a senior researcher at Google DeepMind, has recently announced that she and her team have begun to crack the code on “catastrophic forgetting”.
As far as I am concerned, this limbic thinking is “catastrophic thinking” which is the only true antipode to AI’s “catastrophic forgetting”. It will be eons before AI thinks with a limbic brain, let alone has consciousness.
Stephen Hawking warns artificial intelligence could end mankind
thank you Sarnath Ramnat (firstname.lastname@example.org) for the finding
more on AI in this IMS blog
A team of German researchers has used artificial intelligence to create a “self-aware” version of Super Mario who can respond to verbal commands and automatically play his own game.
Artificial Intelligence helps Mario play his own game
Students at the University of Tubingen have used Mario as part of their efforts to find out how the human brain works.
The cognitive modelling unit claim their project has generated “a fully functional program” and “an alive and somewhat intelligent artificial agent”.
Can Super Mario Save Artificial Intelligence?
The most popular approaches today focus on Big Data, or mimicking humansthat already know how to do some task. But sheer mimicry breaks down when one gives a machine new tasks, and, as I explained a few weeks ago, Big Data approaches tend to excel at finding correlations without necessarily being able to induce the rules of the game. If Big Data alone is not a powerful enough tool to induce a strategy in a complex but well-defined game like chess, then that’s a problem, since the real world is vastly more open-ended, and considerably more complicated.
Intelligence: a history
Intelligence has always been used as fig-leaf to justify domination and destruction. No wonder we fear super-smart robots
To say that someone is or is not intelligent has never been merely a comment on their mental faculties. It is always also a judgment on what they are permitted to do. Intelligence, in other words, is political.
The problem has taken an interesting 21st-century twist with the rise of Artificial Intelligence (AI).
The term ‘intelligence’ itself has never been popular with English-language philosophers. Nor does it have a direct translation into German or ancient Greek, two of the other great languages in the Western philosophical tradition. But that doesn’t mean philosophers weren’t interested in it. Indeed, they were obsessed with it, or more precisely a part of it: reason or rationality. The term ‘intelligence’ managed to eclipse its more old-fashioned relative in popular and political discourse only with the rise of the relatively new-fangled discipline of psychology, which claimed intelligence for itself.
Plato conclude, in The Republic, that the ideal ruler is ‘the philosopher king’, as only a philosopher can work out the proper order of things. This idea was revolutionary at the time. Athens had already experimented with democracy, the rule of the people – but to count as one of those ‘people’ you just had to be a male citizen, not necessarily intelligent. Elsewhere, the governing classes were made up of inherited elites (aristocracy), or by those who believed they had received divine instruction (theocracy), or simply by the strongest (tyranny).
Plato’s novel idea fell on the eager ears of the intellectuals, including those of his pupil Aristotle. Aristotle was always the more practical, taxonomic kind of thinker. He took the notion of the primacy of reason and used it to establish what he believed was a natural social hierarchy.
So at the dawn of Western philosophy, we have intelligence identified with the European, educated, male human. It becomes an argument for his right to dominate women, the lower classes, uncivilised peoples and non-human animals. While Plato argued for the supremacy of reason and placed it within a rather ungainly utopia, only one generation later, Aristotle presents the rule of the thinking man as obvious and natural.
The late Australian philosopher and conservationist Val Plumwood has argued that the giants of Greek philosophy set up a series of linked dualisms that continue to inform our thought. Opposing categories such as intelligent/stupid, rational/emotional and mind/body are linked, implicitly or explicitly, to others such as male/female, civilised/primitive, and human/animal. These dualisms aren’t value-neutral, but fall within a broader dualism, as Aristotle makes clear: that of dominant/subordinate or master/slave. Together, they make relationships of domination, such as patriarchy or slavery, appear to be part of the natural order of things.
Descartes rendered nature literally mindless, and so devoid of intrinsic value – which thereby legitimated the guilt-free oppression of other species.
For Kant, only reasoning creatures had moral standing. Rational beings were to be called ‘persons’ and were ‘ends in themselves’. Beings that were not rational, on the other hand, had ‘only a relative value as means, and are therefore called things’. We could do with them what we liked.
This line of thinking was extended to become a core part of the logic of colonialism. The argument ran like this: non-white peoples were less intelligent; they were therefore unqualified to rule over themselves and their lands. It was therefore perfectly legitimate – even a duty, ‘the white man’s burden’ – to destroy their cultures and take their territory.
The same logic was applied to women, who were considered too flighty and sentimental to enjoy the privileges afforded to the ‘rational man’.
Galton believe that intellectual ability was hereditary and could be enhanced through selective breeding. He decided to find a way to scientifically identify the most able members of society and encourage them to breed – prolifically, and with each other. The less intellectually capable should be discouraged from reproducing, or indeed prevented, for the sake of the species. Thus eugenics and the intelligence test were born together.
From David Hume to Friedrich Nietzsche, and Sigmund Freud through to postmodernism, there are plenty of philosophical traditions that challenge the notion that we’re as intelligent as we’d like to believe, and that intelligence is the highest virtue.
From 2001: A Space Odyssey to the Terminator films, writers have fantasised about machines rising up against us. Now we can see why. If we’re used to believing that the top spots in society should go to the brainiest, then of course we should expect to be made redundant by bigger-brained robots and sent to the bottom of the heap.
Natural stupidity, rather than artificial intelligence, remains the greatest risk.
more on intelligence in this IMS blog
what i find most important:
Future IT Workforce: Deploying a broad array of modern recruitment, retention, and employment practices to develop a resilient IT talent pipeline for the institution
Digital Integrations: Ensuring system interoperability, scalability, and extensibility, as well as data integrity, security, standards, and governance, across multiple applications and platforms
Engaged Learning: Incorporating technologies that enable students to create content and engage in active learning in course curricula
Student Retention and Completion: Developing the capabilities and systems to incorporate artificial intelligence into student services to provide personalized, timely support
Administrative Simplification: Applying user-centered design, process improvement, and system reengineering to reduce redundant or unnecessary efforts and improve end-user experiences
Improved Enrollment: Using technology, data, and analytics to develop an inclusive and financially sustainable enrollment strategy to serve more and new learners by personalizing recruitment, enrollment, and learning experiences
Workforce of the Future: Using technology to develop curriculum, content, and learning experiences that prepare students for the evolving workforce
Holistic Student Success: Applying technology and data, including artificial intelligence, to understand and address the numerous contributors to student success, from finances to health and wellness to academic performance and degree planning (my note: this is what Christine Waisner, Mark Gill and Plamen Miltenoff are trying to do with their VR research)
Improved Teaching: Strengthening engagement among faculty, technologists, and researchers to achieve the true and expanding potential of technology to improve teaching
Student-Centric Higher Education: Creating a student-services ecosystem to support the entire student life cycle, from prospecting to enrollment, learning, job placement, alumni engagement, and continuing education
Online Learning’s ‘Greatest Hits’
Robert Ubell (Columnist) Feb 20, 2019
dean of web-based distance learning
Learning Management Systems
Neck and neck for the top spot in the LMS academic vendor race are Blackboard—the early entry and once-dominant player—and coming-up quickly from behind, the relatively new contender, Canvas, each serving about 6.5 million students . The LMS market today is valued at $9.2 billion.
Digital Authoring Systems
Faced with increasingly complex communication technologies—voice, video, multimedia, animation—university faculty, expert in their own disciplines, find themselves technically perplexed, largely unprepared to build digital courses.
instructional designers, long employed by industry, joined online academic teams, working closely with faculty to upload and integrate interactive and engaging content.
nstructional designers, as part of their skillset, turned to digital authoring systems, software introduced to stimulate engagement, encouraging virtual students to interface actively with digital materials, often by tapping at a keyboard or touching the screen as in a video game. Most authoring software also integrates assessment tools, testing learning outcomes.
With authoring software, instructional designers can steer online students through a mixtape of digital content—videos, graphs, weblinks, PDFs, drag-and-drop activities, PowerPoint slides, quizzes, survey tools and so on. Some of the systems also offer video editing, recording and screen downloading options
As with a pinwheel set in motion, insights from many disciplines—artificial intelligence, cognitive science, linguistics, educational psychology and data analytics—have come together to form a relatively new field known as learning science, propelling advances in a new personalized practice—adaptive learning.
Of the top providers, Coursera, the Wall Street-financed company that grew out of the Stanford breakthrough, is the champion with 37 million learners, followed by edX, an MIT-Harvard joint venture, with 18 million. Launched in 2013, XuetangX, the Chinese platform in third place, claims 18 million.
Former Yale President Rick Levin, who served as Coursera’s CEO for a few years, speaking by phone last week, was optimistic about the role MOOCs will play in the digital economy. “The biggest surprise,” Levin argued, “is how strongly MOOCs have been accepted in the corporate world to up-skill employees, especially as the workforce is being transformed by job displacement. It’s the right time for MOOCs to play a major role.”
In virtual education, pedagogy, not technology, drives the metamorphosis from absence to presence, illusion into reality. Skilled online instruction that introduces peer-to-peer learning, virtual teamwork and other pedagogical innovations stimulate active learning. Online learning is not just another edtech product, but an innovative teaching practice. It’s a mistake to think of digital education merely as a device you switch on and off like a garage door.
more on online learning in this IMS blog
third Library 2.019 mini-conference: “Emerging Technology,” which will be held online (and for free) on Wednesday, October 30th, from 12:00 – 3:00 pm US-Pacific Daylight Time (click for your own time zone).
Tomorrow’s technologies are shaping our world today, revolutionizing the way we live and learn. Virtual Reality, Augmented Reality, Artificial Intelligence, Machine Learning, Blockchain, Internet of Things, Drones, Personalization, the Quantified Self. Libraries can and should be the epicenter of exploring, building and promoting these emerging techs, assuring the better futures and opportunities they offer are accessible to everyone. Learn what libraries are doing right now with these cutting-edge technologies, what they’re planning next and how you can implement these ideas in your own organization.
This is a free event, being held live online and also recorded.
a two-day conference about artificial intelligence in education organized by a company called Squirrel AI.
he believes that having AI-driven tutors or instructors will help them each get the individual approach they need.
the Chinese government has declared a national goal of surpassing the U.S. in AI technology by the year 2030, so there is almost a Sputnik-like push for the tech going on right now in China.
more on AI in education in this IMS blog
Fraunhofer-Gesellschaft June 3, 2019
Researchers at the Fraunhofer Institute for Microelectronic Circuits and Systems IMS have developed AIfES, an artificial intelligence (AI) concept for microcontrollers and sensors that contains a completely configurable artificial neural network. AIfES is a platform-independent machine learning library which can be used to realize self-learning microelectronics requiring no connection to a cloud or to high-performance computers. The sensor-related AI system recognizes handwriting and gestures, enabling for example gesture control of input when the library is running on a wearable.
a machine learning library programmed in C that can run on microcontrollers, but also on other platforms such as PCs, Raspberry PI and Android.
more about machine learning in this IMS blog