Artificial intelligence today helps to research in different fields of science and technology, forging cooperation in other disciplines.
According to forecasts on the website of Statista, global income from artificial intelligence will grow from 2018 to 2027. However, various studies show how much the worldwide market size will increase. IDC predicts that the global AI market will reach over half a million US dollars by 2024.
Machine learning is often becoming a vital tool for researchers in various fields. For example, machine learning techniques are used to:
- Use genomic data to predict protein structure.
- Understand the effects of climate change on cities and regions.
- Find patterns in astronomical data and more.
Some scientists and philosophers argue that AI and the emergence of generative network rivalry can also create a new scientific method, based not on the scientific method of Galileo but on productive approaches.
Today’s AI works from massive datasets. It develops strategies for solving a problem or predicting, rather than relying on people to program it to conclude explicitly. As a result, many innovative applications have emerged.
It is also interesting that researchers often need an ordinary … programmer to professionally use AI in their research. A productive strategy to obtain the highest results in the natural sciences can be born in such interaction. The collaboration of scientists and a dedicated development team should be a partnership to answer questions.
Artificial intelligence has unexpectedly blended into our lives. Computer science and engineering people have felt this before anyone else. It became clear that developers should strive to solve real-world problems. It means using modern technology to solve pressing problems. And who, if not natural science researchers, can help them with this? Today, many large laboratories and centers of artificial intelligence tackle real-world applied problems. In a collaboration between developers and researchers, clear goals must be pursued, and there must be a common understanding of the matter. Then the use of AI in natural sciences will become many times more valuable.
What are the challenges for AI in natural sciences?
There are tasks at several levels: at the data, algorithmic, and computation levels.
The new AI models require more data than traditional methods; they need more labeled data, which can be costly, especially when manual annotations are done. Forbes notes that AI, specifically the machine learning and deep learning techniques that show the most promise, require many calculations to be made very quickly. It means they use a lot of processing power.
In doing so, AI researchers are tackling more fundamental problems: making AI models explicable and interpretable and obtaining uncertainty estimates to alert users when a model is applied to the wrong dataset. Generalizability is also a limiting factor for the application of AI models.
Since many data mining problems in life science are less defined and lack underlying datasets, it becomes even more difficult for an AI expert to study them. At the application level, researchers applying artificial intelligence techniques in their research need to understand the requirements, advantages, and disadvantages of using artificial intelligence tools, and more importantly, the ability to validate the results obtained using artificial intelligence models.
What are the opportunities for this cooperation?
We can be sure that AI in natural sciences is a promising and demanded direction.
There are many opportunities for data-driven life science. The most important is that we are generating more and more data, new imaging and sequencing techniques are becoming more available. It is becoming much easier to create massive datasets. We can combine raw data and different methods to create labels, which further removes expensive manual annotations and makes the result more reproducible and less biased.
When trained on large amounts of data, AI models perform better and become more reliable and more versatile for invisible data. Research in unsupervised learning enables learning with fewer or no annotations, opening the door to applying AI to larger unlabeled datasets.
In general, the AI research community is open, and the vast majority of articles are published with source code, making it easy to reproduce and make further improvements. The community creates open-source software libraries, online courses, resources, and tools.
What can the collaboration between AI and science lead to?
Shortly, we will be able to create large-scale models of cells, tissues, and more complex biological systems from data. Ultimately, an AI can help scientists study the literature using NLP models, experimental guide design, generate ideas, and help make decisions during an experiment.
When dealing with sensitive data in the life sciences, techniques such as differentiated privacy and federated learning show promising avenues for further development.
We need to think about AI at the beginning of research planning. On the level when we modernize the infrastructure. Early planning is beneficial for designing experiments using AI models rather than collecting data first and analyzing the data later.
Author’s bio: Anastasiia Lastovetska is a technology writer at MLSDev, a software development company that builds web & mobile app solutions from scratch. She researches the area of technology to create great content about app development, UX/UI design, tech & business consulting.