Artificial Intelligence Research

There has been rapid development in artificial intelligence (AI) research, particularly the subfield of AI known as machine learning, and in machine vision. Both of these fields are pillars of OpenSource Research projects. Fundamental artificial intelligence research is carried out across a number of departments, including computer science, engineering, mathematics, statistics and the Internet research.

The application of artificial intelligence research has grown tremendously with a focus on automation of research techniques from generating a hypothesis to conducting experiments.

In fact, researchers are now being able to address complex problems in biomedical sciences, drug combinations, and predicting diseases using artificial intelligence research.

Artificial intelligence research aims to provide the abilities of perception, cognition, and decision-making for machines.

At present, new research and applications in information science are emerging at an unprecedented rate, which is inseparable from the support by the artificial intelligence research infrastructure.

AI composed of many layers.

  • The AI infrastructure layer includes data, storage and computing power, ML algorithms, and the AI framework.
  • The perception layer enables machines have the basic ability of vision, hearing, etc. For instance, CV enables machines to “see” and identify objects, while speech recognition and synthesis helps machines to “hear” and recognize speech elements.
  • The cognitive layer provides higher ability levels of induction, reasoning, and acquiring knowledge with the help of NLP, knowledge graphs, and continual learning.
  • In the decision-making layer, AI is capable of making optimal decisions, such as automatic planning, expert systems, and decision-supporting systems.

Numerous applications of artificial intelligence research have had a profound impact on fundamental sciences, industrial manufacturing, human life, social governance, and cyberspace.

There is a great trend for AI technology to grow more and more significant in daily operations, including medical fields.

With the growing needs of healthcare for patients, hospital needs are evolving from information networking to the Internet Hospital and eventually to the Smart Hospital.

At the same time, AI tools and hardware performance are also growing rapidly with each passing day. Eventually, common AI algorithms, such as Computer vision (CV), Natural Language Processing (NLP), and data mining, are increasingly being embedded in the medical equipment market.

The history of artificial intelligence research

The birth of AI goes back to the 1950s when John McCarthy organised a two-month workshop at Dartmouth College in the USA. In the workshop proposal, McCarthy used the term artificial intelligence for the first time in 1956. The study of artificial intelligence is to proceed on the basis of the conjecture that every aspect of learning or any other feature of intelligence can in principle be so precisely described that a machine can be made to simulate it.

An attempt will be made to find how to make machines use language, form abstractions and concepts, solve kinds of problems which were reserved for humans, and improve themselves.

Baker and Smith provide a broad definition of AI: “Computers which perform cognitive tasks, usually associated with human minds, particularly learning and problem-solving”. They explain that artificial intelligence research does not describe a single technology. It is an umbrella term to describe a range of technologies and methods, such as machine learning, natural language processing, data mining, neural networks or an algorithm.

Machine learning is a method of AI for supervised and unsupervised classification and profiling, for example to predict the likelihood of a student to drop out from a course or being admitted to a program, or to identify topics in written assignments.

Popenici and Kerr define machine learning “as a subfield of artificial intelligence that includes software able to recognise patterns, make predictions, and apply newly discovered patterns to situations that were not included or covered by their initial design”.

Artificial intelligence research is rapidly growing all over the world with huge investments especially form world leading economies such as USA, China, Japan and EU. In 2008 China spent a third as much as America did on research and development (R&D) and about half as much as Europe, after adjusting for differences in the cost of living. By 2014 it had surpassed Europe.

By 2020 its spending was 85% of America’s. The fruits of this investment in artificial intelligence research are becoming apparent: in August a Japanese research institute calculated that China now produces more of the world’s most highly cited academic research than America does.

The result of all this is a global boom in investment in innovation.

In OpenSourceResearch we are working to develop skills of researchers in emerging markets to conduct artificial intelligence research. Through intensive educational activities OSRC encourages multidisciplinary teams to form in these countries and use open-source artificial intelligence research products.


Yongjun Xu et al. Artificial intelligence: A powerful paradigm for scientific research. The Innovation. Volume 2, Issue 4, 28 November 2021, 100179

Zawacki-Richter, O. et al. Systematic review of research on artificial intelligence applications in higher education – where are the educators. Int J Educ Technol High Educ 16, 39 (2019).

The Economist 15th October 2022