More than programming? The impact of AI on work and skills
- URL: http://arxiv.org/abs/2306.05669v1
- Date: Fri, 9 Jun 2023 04:51:44 GMT
- Title: More than programming? The impact of AI on work and skills
- Authors: Toby Walsh
- Abstract summary: This chapter explores the ways in which organisational readiness and scientific advances in Artificial Intelligence have been affecting the demand for skills and their training in Australia and other nations leading in the promotion, use or development of AI.
The consensus appears that having adequate numbers of qualified data scientists and machine learning experts is critical for meeting the challenges ahead.
The chapter asks what this may mean for Australia's education and training system, what needs to be taught and learned, and whether technical skills are all that matter.
- Score: 17.68987003293372
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: This chapter explores the ways in which organisational readiness and
scientific advances in Artificial Intelligence have been affecting the demand
for skills and their training in Australia and other nations leading in the
promotion, use or development of AI. The consensus appears that having adequate
numbers of qualified data scientists and machine learning experts is critical
for meeting the challenges ahead. The chapter asks what this may mean for
Australia's education and training system, what needs to be taught and learned,
and whether technical skills are all that matter.
Related papers
- Curious, Critical Thinker, Empathetic, and Ethically Responsible: Essential Soft Skills for Data Scientists in Software Engineering [0.0]
Data scientists face challenges related to managing large volumes of data and addressing the societal impacts of AI algorithms.
This study aims to identify the key soft skills that data scientists need when working on AI-powered projects.
arXiv Detail & Related papers (2025-01-03T20:27:14Z) - Generative AI Literacy: Twelve Defining Competencies [48.90506360377104]
This paper introduces a competency-based model for generative artificial intelligence (AI) literacy covering essential skills and knowledge areas necessary to interact with generative AI.
The competencies range from foundational AI literacy to prompt engineering and programming skills, including ethical and legal considerations.
These twelve competencies offer a framework for individuals, policymakers, government officials, and educators looking to navigate and take advantage of the potential of generative AI responsibly.
arXiv Detail & Related papers (2024-11-29T14:55:15Z) - Imagining and building wise machines: The centrality of AI metacognition [78.76893632793497]
We argue that shortcomings stem from one overarching failure: AI systems lack wisdom.
While AI research has focused on task-level strategies, metacognition is underdeveloped in AI systems.
We propose that integrating metacognitive capabilities into AI systems is crucial for enhancing their robustness, explainability, cooperation, and safety.
arXiv Detail & Related papers (2024-11-04T18:10:10Z) - Understanding the Skills Gap between Higher Education and Industry in the UK in Artificial Intelligence Sector [1.5484595752241124]
This paper investigates how well universities in United Kingdom offering courses in AI, prepare students for jobs in the real world.
By using custom data scraping tools to gather information from job advertisements and university curricula, this study will show exactly what skills industry is looking for.
The study showed that the university curriculum in the AI domain is well balanced in most technical skills, but have a gap in Data Science and Maths and Statistics skill categories.
arXiv Detail & Related papers (2024-08-20T12:28:58Z) - Lifelong learning challenges in the era of artificial intelligence: a computational thinking perspective [0.0]
The rapid advancement of artificial intelligence (AI) has brought significant challenges to the education and workforce skills required to take advantage of AI for human-AI collaboration in the workplace.
This paper provides a review of the challenges of lifelong learning in the era of AI from a computational thinking perspective.
arXiv Detail & Related papers (2024-05-30T08:46:11Z) - Leveraging AI to Advance Science and Computing Education across Africa: Challenges, Progress and Opportunities [1.2691047660244332]
We describe our work developing and deploying AI in Education tools in Africa for science and computing education.
SuaCode is an AI-powered app that enables Africans to learn to code using their smartphones.
AutoGrad is an automated grading, and feedback tool for graphical and interactive coding assignments.
Kwame for Science is a web-based AI teaching assistant that provides instant answers to students' science questions.
arXiv Detail & Related papers (2024-02-12T04:10:09Z) - What Students Can Learn About Artificial Intelligence -- Recommendations
for K-12 Computing Education [0.0]
Technological advances in the context of digital transformation are the basis for rapid developments in the field of artificial intelligence (AI)
An increasing number of computer science curricula are being extended to include the topic of AI.
This paper presents a curriculum of learning objectives that addresses digital literacy and the societal perspective in particular.
arXiv Detail & Related papers (2023-05-10T20:39:43Z) - Selected Trends in Artificial Intelligence for Space Applications [69.3474006357492]
This chapter focuses on differentiable intelligence and on-board machine learning.
We discuss a few selected projects originating from the European Space Agency's (ESA) Advanced Concepts Team (ACT)
arXiv Detail & Related papers (2022-12-10T07:49:50Z) - An Experience Report of Executive-Level Artificial Intelligence
Education in the United Arab Emirates [53.04281982845422]
We present an experience report of teaching an AI course to business executives in the United Arab Emirates (UAE)
Rather than focusing only on theoretical and technical aspects, we developed a course that teaches AI with a view to enabling students to understand how to incorporate it into existing business processes.
arXiv Detail & Related papers (2022-02-02T20:59:53Z) - The MineRL BASALT Competition on Learning from Human Feedback [58.17897225617566]
The MineRL BASALT competition aims to spur forward research on this important class of techniques.
We design a suite of four tasks in Minecraft for which we expect it will be hard to write down hardcoded reward functions.
We provide a dataset of human demonstrations on each of the four tasks, as well as an imitation learning baseline.
arXiv Detail & Related papers (2021-07-05T12:18:17Z) - Building Bridges: Generative Artworks to Explore AI Ethics [56.058588908294446]
In recent years, there has been an increased emphasis on understanding and mitigating adverse impacts of artificial intelligence (AI) technologies on society.
A significant challenge in the design of ethical AI systems is that there are multiple stakeholders in the AI pipeline, each with their own set of constraints and interests.
This position paper outlines some potential ways in which generative artworks can play this role by serving as accessible and powerful educational tools.
arXiv Detail & Related papers (2021-06-25T22:31:55Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.