Genetic Engineering holds a scientific symposium on the use of artificial intelligence in scientific research

The Institute of Genetic Engineering and Biotechnology for Postgraduate Studies/University of Baghdad held a scientific symposium entitled “Using Artificial Intelligence in Scientific Research” by the institute’s lecturer, Dr. Wissam Hazem Salo, and the lecturer, Dr. Rami Al-Tamimi, was the rapporteur of the symposium.
The symposium aimed to provide a comprehensive overview of how to integrate artificial intelligence (AI) into various fields of scientific research, as well as explain the different techniques and methodologies of artificial intelligence (such as machine learning, neural networks, deep learning) that are commonly used in scientific research.
The symposium presented specific case studies and examples where artificial intelligence has contributed significantly to the advancement of scientific knowledge, including areas such as biology, chemistry, physics and environmental sciences.
The symposium discussed the benefits of using artificial intelligence in scientific research, such as improving efficiency, accuracy, and the ability to deal with large data sets.
The symposium also revealed the current challenges, limitations and ethical considerations associated with the use of artificial intelligence in scientific research.
The symposium provided insights into the future possibilities and evolving role of artificial intelligence in advancing scientific research and innovation.
The symposium recommended using artificial intelligence in scientific research, as well as investing in teaching and training artificial intelligence, and encouraging cooperation between artificial intelligence experts and scientists from various fields.
The symposium also recommended developing a strong infrastructure for artificial intelligence, encouraging open data and participation, and promoting a culture of open data and participation among the scientific community. Access to large, diverse data sets is vital to train effective AI models and to enable reproducibility of research. Develop and adhere to ethical guidelines for the use of AI in research. Address issues such as data privacy, algorithmic bias, and transparency to maintain public trust and integrity of scientific endeavors and prioritize the development of AI models. Explainable provides clear and understandable insights into how conclusions are reached.