User:AayushmaPokhrel/Applications of artificial intelligence

Education

AI elevates teaching, focusing on significant issues like the knowledge nexus and educational equality. The evolution of AI in education and technology should be used to improve human capabilities in relationships where they do not replace humans. UNESCO recognizes the future of AI in education as an instrument to reach Sustainable Development Goal 4, called "Inclusive and Equitable Quality Education.”

The World Economic Forum also stresses AI's contribution to students' overall improvement and transforming teaching into a more enjoyable process.

Personalized Learning

AI driven tutoring systems, such as Khan Academy, Duo-lingo and Carnegie Learning are the forefoot of delivering personalized education.

These platforms leverage AI algorithms to analyze individual learning patterns, strengths, and weaknesses, enabling the customization of content to suit each student's pace and style of learning.

Administrative Efficiency

In educational institutions, AI is increasingly used to automate routine tasks like grading and attendance tracking, which allows educators to devote more time to interactive teaching and direct student engagement.

Furthermore, AI tools are employed to monitor student progress, analyze learning behaviors, and predict academic challenges, facilitating timely and proactive interventions for students who may be at risk of falling behind.

Ethical and Privacy Concerns

Despite the benefits, the integration of AI in education raises significant ethical and privacy concerns, particularly regarding the handling of sensitive student data.

It is imperative that AI systems in education are designed and operated with a strong emphasis on transparency, security, and respect for privacy to maintain trust and uphold the integrity of educational practices.

Finance

Anti-money laundering[edit]
AI software, such as LaundroGraph which uses contemporary suboptimal datasets, could be used for anti-money laundering (AML). AI can be used to "develop the AML pipeline into a robust, scalable solution with a reduced false positive rate and high adaptability". A study about deep learning for AML identified "key challenges for researchers" to have "access to recent real transaction data and scarcity of labelled training data; and data being highly imbalanced" and suggests future research should bring-out "explainability, graph deep learning using natural language processing (NLP), unsupervised and reinforcement learning to handle lack of labelled data; and joint research programs between the research community and industry to benefit from domain knowledge and controlled access to data".

Banks use machine learning (ML) to upgrade process monitoring and demonstrating the ability of responding efficiently to evolving techniques.

Through ML and other methods, financial organizations can detect laundering operations and run compliance in an automated and very fast mode.

Audit[edit]
AI makes continuous auditing possible. Potential benefits include reducing audit risk, increasing the level of assurance, and reducing audit duration.[quantify]

Continuous auditing with AI allows a real-time monitoring and reporting of financial activities and providing businesses with timely insights that can lead to quick decision making.

Audit task with AI reduces the labor intensive aspects and lead towards a substantial cost saving over time.

Architecture[edit]
This section is an excerpt from Artificial intelligence in architecture.[edit]

Artificial intelligence in architecture describes the use of artificial intelligence in automation, design and planning in the architectural process or in assisting human skills in the field of architecture. Artificial Intelligence is thought to potentially lead to and ensue major changes in Architecture. AI's potential in optimization of design, planning and productivity have been noted as accelerators in the field of architectural work. The ability of AI to potentially amplify an architect's design process has also been noted. Fears of the replacement of aspects or core processes of the architectural profession by Artificial Intelligence have also been raised, as well as the philosophical implications on the profession and creativity.

AI in architecture has created a way for architects to create things beyond human understanding.

AI implementation of machine learning text-to-render technologies, like DALL-E and stable Diffusion, gives power to visualization complex.

AI allows designers to demonstrate their creativity and even invent new ideas while designing. In future, AI will not replace architects; instead, it will improve the speed of translating ideas sketching.