Artificial Intelligence (AI) Presidency at ACADEMIC PARAGRAPH INTRODUCTION

ACADEMIC PARAGRAPH INTRODUCTION Artificial intelligence (AI) is the study of intelligent agents which are based on software system (Xia, Li and Shan, 2013). “We must proactively and thoughtfully reinvent the future of work and needs to be a whole society effort and finding long term solutions will require ideas.” This can be defined about the future and tell about the values of robots and they will help to sort out all problems according to needs of future. Both PwC and the OECD can suggest to

ACADEMIC
PARAGRAPH

INTRODUCTION
Artificial intelligence (AI) is the study of intelligent agents which are based on software
system (Xia, Li and Shan, 2013).
“We must proactively and thoughtfully reinvent the future of work and needs to be a
whole society effort and finding long term solutions will require ideas.” This can be defined
about the future and tell about the values of robots and they will help to sort out all problems
according to needs of future. The president said that 72% Americans are worried about the future
due to robots and computers regarding to jobs.
There is 50% chance to machines are performing as humans all tasks with in the age of
45 years. They are also predicting in future machines are better in translating languages by 2024,
writing high school essay by 2026, driving a truck by 2027 and many other activities in future.
Both PwC and the OECD can suggest to apply services of AI in different sectors for least
risk from automation. They are specialised in different tasks and people like as scientific and
technical work, education, accommodation and food services, information and communication. If
repetition of administrative tasks would badly affect on these sectors Retail, transport, public
administration and finance & insurance.
1

REFERENCES
Books and Journal
Xia, Y., Li, X. and Shan, Z., 2013. Parallelized fusion on multisensor transportation data: A case
study in cyberits. International Journal of Intelligent Systems. 28(6). pp.540-564.
Chen, J. and et. al, 2016. A hybrid intelligence-aided approach to affect-sensitive e-learning.
Computing. 98(1-2). pp.215-233.
2