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Roles Responsibilities of Artificial Intelligence in Education
Technology has developed by leaps and bounds and it has penetrated into our daily lives in so many ways.
Despite this universal principal, children with learning disabilities (dyslexia, language processing disorder, auditory processing disorder, etc.) are shunned, discriminated, and often underestimated.
Though AI has not come up with concrete solutions, progress is taking place and hopefully, a decade or so from now, we will be able to see all kinds of children (with and without learning disabilities) sitting together in a classroom and learning together harmoniously.
The blind chase after grades and ranking has caused people to have such a parochial view on the purpose of education and in order for a change to take place, someone has to break this cycle.
It is essential for some sort of system to be put in place (perhaps a financial aid scheme) to establish equity and ensure everyone can benefit from AI despite socio-economic background.
[Related Article: How to Use Deep Learning to Write Shakespeare] Artificial intelligence is constantly pushing the envelope and opening new doors of opportunities for students and while all this sounds too good to be true, it is important to keep in mind that we should become complacent at any point in time;
The FLI 2018 Funding Round Project Summaries and Thoughts on Technical Abstracts
The Future of Life Institute (FLI) has appeared across various articles and areas within the field of artificial intelligence, at least where I have looked.
FLI has a mission: To catalyze and support research and initiatives for safeguarding life and developing optimistic visions of the future, including positive ways for humanity to steer its own course considering new technologies and challenges.
We will pursue four workstreams toward this aim, concerning the state of Chinese AI research and policy thought, evolving relationships between governments and AI research firms, the prospects for verifying agreements on AI use and development, and strategically relevant properties of AI systems that may guide states’ approaches to AI governance.
Our objectives are to: 1) develop techniques to imitate observed human behavior and interactions, 2) explicitly recover rewards that can explain complex strategic behaviors in multi-agent systems, enabling agents to reason about human behavior and safely co-exist, 3) develop interpretable techniques, and 4) deal with irrational agents to maximize safety.
For all to enjoy the benefits provided by a safe, ethical and trustworthy AI, it is crucial to enact appropriate incentive strategies that ensure mutually beneficial, normative behaviour and safety-compliance from all parties involved.
Using methods from Evolutionary Game Theory, this project will develop computational models (both analytic and simulated) that capture key factors of an AI race, revealing which strategic behaviours would likely emerge in different conditions and hypothetical scenarios of the race.
The project will thus provide foundations on which incentives will stimulate such outcomes, and how they need to be employed and deployed, within incentive boundaries suited to types of players, in order to achieve high level of compliance in a cooperative safety agreement and avoid AI disasters.” Main thread: The Anh Han is currently a Senior Lecturer in Computer Science at School of Computing, Media and the Arts, Teeside University.
These questions are analysed for paradigms such as reinforcement learning, inverse reinforcement learning, adversarial settings (Turing learning), oracles, cognition as a service, learning by demonstration, control or traces, teaching scenarios, curriculum and transfer learning, naturalised induction, cognitive architectures, brain-inspired AI, among others.” Main thread: José Hernández-Orallo is Professor of Information Systems and Computation at the Universitat Politècnica de València, Spain.
“Technical Abstract: The agent framework, the expected utility principle, sequential decision theory, and the information-theoretic foundations of inductive reasoning and machine learning have already brought significant order into the previously heterogeneous scattered field of artificial intelligence (AI).
This project will drive forward the theory of Universal AI to address what might be the 21st century’s most significant existential risk: solving the Control Problem, the unique principal-agent problem that arises with the creation of an artificial superintelligent agent.
Our focus is on the most essential properties that the theory of Universal AI lacks, namely a theory of agents embedded in the real world: it does not model itself reliably, it is constraint to a single agent, it does not explore safely, and it is not well-understood how to specify goals that are aligned with human values.” Main thread: Marcus Hutter is Professor in the Research School of Computer Science (RSCS) at the Australian National University (ANU) in Canberra.
His research at RSCS/ANU/NICTA/IDSIA is/was centered around Universal Artificial Intelligence, which is a mathematical top-down approach to AI, based on Kolmogorov complexity, algorithmic probability, universal Solomonoff induction, Occam’s razor, Levin search, sequential decision theory, dynamic programming, reinforcement learning, and rational agents.
This guide explores utility functions that might arise in an AGI but usually do not in economic research, such as those with instability, always increasing marginal utility, extremely high or low discount rates, those that can be self-modified, or those with preferences that violate one of the assumptions of the von Neumann-Morgenstern utility theorem.
von Neumann-Morgenstern utility theorem shows that, under certain axioms of rational behaviour, a decision-maker faced with risky (probabilistic) outcomes of different choices will behave as if he or she is maximizing the expected value of some function defined over the potential outcomes at some specified point in the future.
The Fermi paradox: implies that we should seek scientific data based on astronomical observations not accessible to civilizations that lived in the distant past, and that we should create machines to flood our galaxy with radio signals conditional on our civilization’s collapse.
In these tasks, it is desirable for robots to build predictive and robust models of humans’ behaviors and preferences: a robot manipulator collaborating with a human needs to predict her future trajectories, or humans sitting in self-driving cars might have preferences for how cautiously the car should drive.
Our goal in this project is to actively learn a mixture of reward functions by eliciting comparisons from a mixed set of humans, and further analyze the generalizability and robustness of such models for safe and seamless interaction with AI agents.” Main thread: Dorsa Singh is an Assistant Professor in the Computer Science Department and Electrical Engineering Department at Stanford University.
You can see the full video here from which the image is taken: “Technical Abstract: As technology develops, it is only a matter of time before agents will be capable of long term (general purpose) autonomy, i.e., will need to choose their actions by themselves for a long period of time.
As a result, the “ad hoc teamwork” problem, in which teammates must work together to obtain a common goal without any prior agreement regarding how to do so, has emerged as a recent area of study in the AI literature.
Unfortunately, Not Everyone Will be Able to Climb the Skillset Ladder
New AI systems have beyond-human cognitive abilities, which many of us fear could potentially dehumanize the future of work.
However, by automating these skills, AI will push human professionals up the skillset ladder into uniquely human skills such as creativity, social abilities, empathy, and sense-making, which machines cannot automate.
Robotic dystopias, where robots replace humans or even rise up against humans, are a recurrent topic in popular culture and innumerable science fiction movies.
In fact, many of us can easily imagine a dystopian workplace in the near future where robots are taking control of most activities or decisions, and where human interactions or feelings do not matter much.
Apart from traditional manufacturing automation, new and more capable artificially intelligent systems are appearing in fields ranging from self-driving cars to automated supermarket check-outs and customer service bots.
But will AI make really the workplace a colder place ruled by brute intelligence without empathy or interpersonal skills, and eventually lead to net job destruction?
For example, a software developer requires multiple skills ranging from coding and testing software to understanding clients’ needs and supervising junior developers.
We can group human skills in three broad categories depending on their readiness for automation: AI will impact each one of these three categories of skills in a different degree.
Machines are much better than humans in deterministic tasks involving process-oriented and quantitative reasoning skills, but humans are far better in more ambiguous cross-functional reasoning skills tasks.
For example, as supermarkets and stores are introducing self-checkout systems, human cashiers are becoming checkout assistants, who answer customers’ questions and troubleshoot or supervise the check-out machines.
This human-machine collaboration will create a large number of new jobs which leverage mainly quantitative reasoning skills as well as knowledge of specific digital and AI technologies.
These are some of the possible new jobs: By automating process-oriented and quantitative skills, AI will push us up the skillset ladder into the cross-functional reasoning skills, including creativity and social abilities, which make us uniquely human.
By freeing employees from these other tasks, AI will allow them to be more empathetic, to focus on creativity, customer experience, employee engagement, workplace culture, social skills, and emotional intelligence.
Higher employee satisfaction, more creativity, more free time, reduced employee churn, and increased customer satisfaction will be some of the positive consequences of AI in the workplace.
number of jobs focused on cross-functional reasoning skills are already emerging: By automating process-oriented and quantitative skills, AI will push us up the skillset ladder into the cross-functional reasoning skills, which make us uniquely human.
These include, for example, tax preparers, radiologists, lawyers, translators, loan underwriters, insurance adjusters, financial analysts, and even journalists or software-engineers.
Executive marketing professional with over 15 years of global experience in data-driven marketing, digital businesses, analytics, and AI, spanning across technology, telecom, and financial services.
Gifted in creating and coaching high-performing teams of marketing specialists, data scientists and digital developers across multiple countries and cultures.
Parker-Stanford, May 2018 “No-collar workforce: Humans and machines in one loop — collaborating in roles and new talent models”, Jeff Schwartz and Sharon Chand, Deloitte, Dec 2017 “AI will make us more human, shattering the glass ceiling of productivity”, Dr. Chris Brauer, IpSoft, Jan 2017 “Blue or white collar?
- On Thursday, June 4, 2020
The History of AI (What Is Artificial Intelligence)
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