How Artificial Intelligence is Transforming Scientific Research & Development

As artificial intelligence (AI) grows more sophisticated, its developers are identifying new applications in
consumer, academic, and business environments, among others. Scientific research & development is
perhaps one of the most exciting fields where AI is delivering results. According to Brookings, AI is ideal
for a “data-friendly ecosystem with unified standards and cross-platform sharing,” which makes the
research community an ideal environment for AI applications.

 

Even so, questions remain about the practicality, usability, and ethics of using AI in research &
development, especially in sensitive fields like pharmaceuticals and defense. Even in the research
community, “the way AI systems are developed needs to be better understood due to the major
implications these technologies will have for society as a whole,” Brookings describes. This article
discusses some of the use cases and potential outcomes of AI in scientific research & development, with
examples and concerns from multiple scientific fields.

 

Applications of AI in Scientific Research & Development

 

The first time artificial intelligence was used in scientific research & development was in the 1940s when
AI pioneer Alan Turing developed a computer program to help crack the Enigma code during World War
II. Ever since then, AI has been gradually making its way into more and more scientific fields.

 

One of the biggest benefits of using AI in scientific research & development is that it can help scientists
make more accurate predictions and faster breakthroughs—just as it did in Turing’s case. AI can also
help scientists process large amounts of data more quickly and efficiently. Additionally, AI can help
scientists automate certain tasks that would otherwise be time consuming or difficult to do manually.

 

Today, AI is being used several scientific fields. The following are some of the top fields where AI is being
tested or utilized on a limited or semi-autonomous basis.

 

Pharmaceuticals

 

AI is being used in the pharmaceutical industry to develop new drugs faster and with more accuracy. AI
can analyze data from clinical trials to help identify which drugs are most likely to be effective for a
particular patient population. AI can also predict how a drug will interact with other medications that a
patient is taking, helping reduce the potential for adverse effects.

 

Renewable Energy

 

Energy scientists are using AI to develop new renewable energy sources. AI can help identify the most
promising locations for wind and solar farms, as well as predict how much energy a particular location
will produce. AI can also be used to improve the efficiency of turbines and solar panels.

 

Computer Science

 

AI is also playing an important role in computer science research. AI algorithms are being used to design
new computer chips and systems that are faster and more energy efficient. AI is also being used to
create new methods for data compression, which will be important for the development of 5G wireless
networks.

 

Military & Defense

 

AI has been used in defense and military research & development for years. AI is often used to help with
tasks such as target recognition, image recognition, and data analysis. Additionally, AI can be used to
develop new weapons systems and improve existing ones. AI has also been used to create algorithms
that can predict enemy movements or intentions.

 

The potential applications of AI in scientific research & development are vast and only beginning to be
explored. With every successful use case, AI is helping scientists make faster and better discoveries,
which will have a broad and positive impact on multiple aspects of society.

 

Addressing Ethical Concerns About AI

 

As with any new technology, there are ethical concerns around AI in scientific research & development.
Some of the most common ethical concerns include the use of AI to create weapons, using AI to make
decisions about life and death, and AI bias seeping into scientific methodologies.

 

These matters grow more concerning as AI takes on more decision-making roles. “AI not only replicates
human biases—it confers on these biases a kind of scientific credibility,” said Michael Sandel, political
philosopher and Anne T. and Robert M. Bass Professor of Government in The Harvard Gazette. “It makes
it seem that these predictions and judgments have an objective status” when there is proven evidence
of bias in some AI-driven decisions.

 

What’s more, there are aspects of how AI solutions operate that are hidden even to their developers.
“These systems are black boxes—it’s not clear how they use input data to arrive at outputs like actions
or decisions,” Harvard Business Review describes. This can obfuscate some key processes and may make
some elements of AI vulnerable to bad actors.

 

Optimizing AI Tools for Research Communities

 

Although AI has a lot of potential to help scientists in their research, many researchers are still hesitant
to use AI—not only because of ethical concerns, but also because they are not familiar with it or they
don’t know how to incorporate it into their work. Additionally, some researchers may be concerned that
AI will replace them in their core capacities, effectively ‘putting them out of a job.’

 

Fortunately, AI developers can address these concerns in targeted ways so that scientists can use AI
more effectively and optimize how they use their own professional time. That includes delivering results
scientists can easily test and prove themselves, even without the cognitive labor required to produce
those outcomes. They can develop platforms and tools that make it easier for scientists to access AI
capabilities and understand AI processes as they are underway as well.

 

Upon developing AI tools and optimizing them for scientific research & development, companies must
educate scientists about them before they can use them in their work. That means creating educational
content about AI so that scientists can learn how to use it effectively. As AI continues to evolve and
become more user-friendly, more researchers will understand it and find their own applications.

 

How Software Companies Can Get Started

 

Not all AI tools and algorithms are suitable for scientific research & development. Digital technology
companies who hope to develop these solutions can begin by identifying use cases for AI in scientific
research & development. That means achieving a deeper understanding of the needs of scientists,
especially through consultation with relevant scientists while developing their tools.

 

Although the specifics vary, general needs among scientists to consider include:

 

-Shorting phases associated with data analysis
– Improving the accuracy and efficiency of testing
– Relieving scientists of repetitive manual processes
– Quickly identifying flaws or errors that may skew results

 

Human-AI Collaboration is the Future

 

Despite both practical and ethical concerns, the change that AI will drive in scientific research &
development will be both real and profound. AI and human beings will work side by side to drive the
innovations of the future. There may always be concerns about relinquishing thinking to machines—but
when educated, empathetic human beings have the ‘final say,’ we can be optimistic about the results.

 

Partner with Uvation as You Consider Robotics in Your Industry

 

The consultants at Uvation can help you understand the implications of AI in your industry; or, help you
as you develop AI tools for unique markets yourself. Book an online session with an AI expert begin.