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AI in STEM – Expanding the Horizons of Physical Sciences

Over the last few weeks, we’ve started a series exploring the impact of artificial intelligence in STEM. To start off, we discussed the role of AI in scientific research, and then, in the second article, we focused on its application across the life sciences.

This time, we’ll look specifically into physical sciences, including physics, chemistry, astronomy, and earth science, and will discuss how artificial intelligence is being used to advance these fields.

While today we regularly use technology in our everyday lives, many people distrust its application in certain areas. As we’ve discussed in our previous articles and will again discuss in this one, many scientists still resist the application of artificial intelligence in their work, despite the benefits modern technological tools have already had in their fields and the promise AI holds.

Nevertheless, AI continues to evolve and expand across STEM, facilitating the work of professionals in these fields and enabling major breakthroughs in some cases.

For students and professionals in physical sciences today, exploring AI and its applications may quickly become an incredibly valuable asset.

To start, we need to understand how this technology is already being used and how it may be applied in the near future.

AI in Physics

Physics very clearly portrays the reasoning behind scientists’ resistance to the implementation of artificial intelligence in their work. This area focuses on understanding matter and the interactions between the fundamental elements of the universe. Physicists aren’t interested in merely knowing how the universe and all its components function and interact; rather they want to fully understand the processes behind these.

This is one of the main reasons that has kept many physicists from using AI in their research, given that algorithms typically analyze data and provide only the final answer to a problem, without an explanation of the process behind it.

Even so, AI is slowly making its way into this field and can already be applied across every step of research, from experimental design to data analysis.

AI can be employed in a way that facilitates scientists’ work, despite its inherent limitations; it simply depends on how it’s used. However, there’s another essential contributing factor to the growth of AI in physics – the advent of explainable AI.

Essentially, explainable AI consists of tools and frameworks that can help interpret the outputs of more advanced algorithms. As discussed by the authors of this article, this isn’t a silver bullet. While potentially very useful to interpret the results presented by AI algorithms, these tools aren’t always enough, and thus more progress needs to be made.

While there is still a lot more left to explore in this area, it’s already possible for physicists to successfully apply AI in a series of ways, taking advantage of its numerous strengths.

As an example, we can look into the several attempts made by physicists to use AI to rediscover physical laws and concepts. For example, AI algorithms have rediscovered mechanical equations of motion and Newton’s laws of gravity. 

To this end, researchers commonly take advantage of two distinct machine learning techniques – deep learning, which allows computers to learn independently by example using the data fed into it, and symbolic regression, a method of finding patterns in data by running through mathematical operations and variables until a representative equation is found.

The success seen so far through this approach opens up the possibility to discover new relationships in data that we have not yet been able to discover. This could ultimately allow physicists to find new laws and concepts at a faster rate, given AI’s capacity to speed up the process of trial and error and to find patterns even in very complex datasets.

While still in the early stages, this type of application of AI is already being explored. This paper is a great example of this type of research, describing the use of AI to understand how dark matter clusters.

The same symbolic regression algorithm, called PySR, has also been used to explore other topics, including particle collisions and the connection between galaxies and clusters of dark matter.

Nevertheless, there are many other ways to employ AI in physics aside from detecting relationships within datasets.

AI can be trained, for example, to identify specific features in data. In this case, the objective would not necessarily be to discover patterns we haven’t yet identified, but rather accelerate work by automating the detection of these.

In a paper published in 2021, physicists constructed a workflow using four distinct artificial intelligence models to process data from the Laser Interferometer Gravitational-Wave Observatory (LIGO).

As described on the LIGO website, “gravitational waves are ‘ripples’ in space-time caused by some of the most violent and energetic processes in the Universe” including colliding black holes or exploding stars (see the Gravitational Waves section to learn more). As such, their study could help scientists understand several physical phenomena.

The method presented in the paper mentioned enabled researchers to analyze a month of data gathered through the LIGO project in only seven minutes. Most importantly, it was able to correctly detect and identify four black hole mergers.

Similarly, physicists are also exploring the possibility of employing AI to analyze the data collected from the Large Hadron Collider (LHC), the largest particle accelerator in the world.

LHC (Source: Anna Pantelia/CERN)

As described by CERN, high-energy particle beams travel inside the accelerator, almost at the speed of light in opposite directions. Thousands of magnets are used to direct these beams, causing them to collide at specific points within the accelerator wherein particle detectors are located.

As protons collide, different particles are generated, some of which may have never been observed before. The particle detectors capture features that help identify these particles, including their speed, mass, and charge.

Given the volume of data created, physicists are now considering the use of AI to analyze this data more effectively. Particularly, considering that researchers are searching for the unknown, it can be difficult to distinguish between what is relevant and truly new and what is simply an error.

As such, AI could potentially enhance this task by picking up on patterns and correlations that researchers could otherwise not notice.

Beyond analyzing data, artificial intelligence is also incredibly useful to model data and through that help make predictions.

For example, as shown in this article, AI can be used to predict the occurrence and effect of extreme weather events such as cold and heat waves. In this case, AI was trained with surface temperature and large-scale atmospheric circulation patterns. As a result, it was able to predict cold and heat waves with 88% accuracy between 1 and 5 days before their occurrence.

With the drastic climate changes we’re already seeing today, this type of application is more valuable than ever.

Representation of the Lorenz attractor, which is behind the commonly known butterfly effect (source: Dschwen via Wikimedia Commons)

Artificial intelligence’s predictive capabilities are so strong that some physicists are starting to apply them to chaotic systems, which are considered unpredictable.

The butterfly effect is the idea that even the smallest perturbations can have a deep impact on complex systems like the weather. Overall, chaos theory shows that long-term prediction of chaotic systems is essentially impossible to make.

However, several physicists have started to use artificial intelligence to enhance the precision with which we can attempt to predict the evolution of complex systems. As discussed in this review of the topic, this type of application could be extended to areas such as astrophysics and the study of black-hole systems or even medicine.

In some cases, it may be useful to input our acquired knowledge of different physical processes and laws so that artificial intelligence has a framework through which to process data and create accurate models.

Nevertheless, in certain instances simply providing observational data and allowing the AI to freely analyze it and discover relationships in the data on its own can also be extremely beneficial.

AI can inclusively be trained only with images, breaking these down into simpler blocks which it then manipulates to understand how different components relate to and impact each other. In some instances, this can lead to more accurate models given that the algorithm is not bound by any previous assumptions.

The researchers in this study tried to understand whether this type of AI application could be used to answer complex questions in areas like astrophysics. To do this, they fed AI with artificial galaxy images so that it could create a model that allowed them to investigate the physical changes associated with the evolution of galaxies.

Researchers could then alter specific elements of the data and analyze the differences in the model created by AI. For instance, when galaxies are in a high-density environment, they tend to be redder, and their stars are more concentrated in the center. They hypothesized that this could be caused by higher levels of dust or a reduction in star formation.

Using AI, the scientists were able to test both hypotheses, altering each element individually and observing the changes caused in the model. They found that the color of the galaxies seemed to be influenced by star formation, showing the potential that this type of application could have in physics research.

As we mentioned previously, AI can also be used to help create simulations. In fact, as shown in this article, employing AI in this sense can give rise to more accurate simulations, given its capacity to learn from the data provided and extrapolate more information from its analysis. In this specific study, scientists used a deep neural network to create a complex 3D simulation of the structure of the universe.

Overall, the work that we’ve explored so far highlights the various avenues through which artificial intelligence can be applied in physics to assist us in our pursuit to understand the universe.


Photo by Stefan Widua on Unsplash

Looking specifically into the field of electromagnetics, artificial intelligence is already being applied in a multitude of ways, from research in this field to the design and optimization of related devices.

As discussed in detail in this review, antenna technology is one of the areas in electromagnetics that has been significantly transformed in the last decades. From network systems to wireless devices, wearables, and even textile products, many of the products we use today rely on innovative antenna technology.

AI can be used to accelerate development in this area, helping to meet the demands for improved performance and the creation of increasingly complex and versatile designs.


Geophysics is a field dedicated to the study of the Earth, the atmosphere, and other planets, using physical principles and techniques.

Current applications of AI in this field help not only to handle the massive volume of data associated with the research conducted but also facilitate the entire process. In fact, artificial intelligence can be applied to every step, from data observation and processing to modeling and prediction.

Representation of a seismic survey for the study of the Earth’s subsurface (Source: “Rapid development of seismic imaging
applications using Symbolic mathematics

For instance, as shown in this study, AI can enhance data collection. In this case, machine learning helped to increase the accuracy of earthquake detection, which will in turn facilitate the study of the physical properties of the Earth’s subsurface.

Oftentimes, after collection, the data has to be classified and processed to improve its quality. AI can be applied to this process, as seen in this study, where it was used to enhance the quality of seismic data by separating signals from noise.

Artificial intelligence also shows great potential for improving the accuracy of data analysis and interpretation. In particular, researchers believe that it could be useful to localize specific attributes (such as faults and layers) in seismic interpretation.

Another essential part of geophysics research is modeling and simulation. As we’ve discussed previously, this is an area wherein AI is being applied with tremendous success.

In this field specifically, AI can be applied in the creation of models to help characterize geophysical phenomena, as well as in the development of predictions related to the Earth’s characteristics and its past and future states.

For instance, artificial intelligence can make predictions about sea currents, the weather, or even changes in the sea level.

Nuclear Physics 

The field of nuclear physics focuses on the study of atomic nuclei, their constituents, and the interactions between them. Artificial intelligence can be broadly applied across this field, contributing to both theoretical and practical work.

For one, AI can monitor experiments in real-time, providing feedback that may help optimize the research conducted.

Further, when analyzing the data obtained, physicists may sometimes overlook rare events that constitute discoveries given some preconceptions and biases they may have. AI can be a great tool in this case as it can not only detect patterns that would otherwise go undetected, but it’s also able to experiment with the data and test different hypotheses to fully understand what is or isn’t to be expected within a set of data.

Finally, not all data required to fully understand physical processes can be obtained experimentally. As such, AI can be utilized to analyze the data we currently possess and make more accurate predictions on the missing data through the creation of complex nuclear models.

Quantum Physics

Quantum physics studies matter and energy at the most fundamental level, providing a foundation to understand physics at every scale. This field is behind several pieces of technology that we use today, including lasers for example.

Artificial intelligence is starting to be explored as a tool in this field as several physicists believe that AI could help enhance and expedite discoveries in quantum physics.

A large focus in this field is the attempt to understand how electrons behave within molecules. If physicists were able to master this, they could essentially predict the behavior of everything. So far, the primary theory used to understand how electrons (and by extension molecules) act is the density functional theory (DFT). However, this can quickly become extremely complex given the interactions between them.

This study tried to overcome this issue using a neural network. Taking advantage of a massive volume of molecules with already known energies, the researchers trained a functional that could more accurately predict the behavior of electrons than previous methods. As such, as the authors suggest, AI could become a very useful tool when used in conjunction with DFT.

Neural networks can also be applied in the creation of simulations of these physical states, which can be essential when trying to understand not just how an individual particle behaves, but how an entire system is constructed and functions.

Furthermore, AI can also be employed to identify phases of matter and the transitions between these more accurately. This can be achieved in one of two ways – either by training the algorithm with data that has been labeled or by simply allowing it to find patterns in the datasets provided.

Representation of Schrödinger’s cat paradox (credit: Shutterstock)

For example, in a study, researchers were able to use an AI algorithm to identify paramagnetic and ferromagnetic phases as well as phase transitions.

Finally, artificial intelligence can be used to solve problems that involve quantum entanglement, a phenomenon in which particles influence each other’s state even when separated by large distances. This issue is the basis for Schrödinger’s cat paradox.

In this case, given AI’s capacity to analyze complex systems, it can be used to produce more accurate representations of these systems, while accounting for quantum entanglement.

AI in Astronomy

Astronomy studies everything in the universe outside of our planet’s atmosphere. Having such a broad focal point, it’s only natural that the research conducted in this field involves massive volumes of data.

Helping to overcome the biggest challenges in the field of astronomy is artificial intelligence, which is already being applied in a variety of ways, from searching for habitable planets outside of the Solar System to assisting astronauts in space.


One of the primary objectives for astronomists is to discover and characterize celestial bodies, including exoplanets, that is, planets outside the Solar System. Typically, the discovery process would be carried out manually by studying transits.

Exoplanet phase curve (source: ESA)

Essentially, as planets pass in front of their parent star, they block some of the starlight, making themselves detectable to us. By observing this phenomenon during several of the planet’s orbits, astronomers could identify a series of the planet’s properties, including its size and the distance from its star.

However, this is a time-consuming process and, as more missions are launched to detect and characterize exoplanets, astronomers start struggling to keep up.

As such, scientists have started to explore the use of AI to complement this method. For example, in this study, researchers found that the AI algorithm used was able to identify the signals of exoplanets with an accuracy of 96%.

In some cases, astronomists are taking this one step further as they want to apply AI not only to help identify exoplanets but also to characterize them. The main goal today is not only to find new planets outside of the Solar System but to find planets that could be habitable.

Therefore, scientists are focused on detecting biosignatures, that is, features that indicate the presence of life in the present or past. If successful, AI could be used in observatories to help astronomers detect planets with water, for example.

Recently, two scientists published an article showing that AI could be very beneficial as a tool to help prioritize which exoplanets to target during future surveys. In fact, the AI used identified the presence of snow or clouds with more than 90% accuracy.  

Gravitational Waves

Spacetime Curvature (Credit: European Space Agency – ESA / C.Carreau)

In physics, spacetime is seen as a 4-dimensional system of coordinates that combines the 3 dimensions of space with time.

As predicted in Einstein’s general theory of relativity, spacetime exhibits a curvature that is caused by the presence of mass. Essentially, any volume of mass (from a planet, star, or any other body) has an impact on spacetime.

As objects move across space and time this curvature changes. In the case of accelerating objects, the changes made to the spacetime curvature are propagated in ripples called gravitational waves, transmitting information about the origin and nature of these objects.

The strongest type of gravitational waves is caused by large cosmic events like the collision of black holes or supernovae. We can then use the signals created to identify and study these events. For example, in June of 2021, astronomers detected two of these events in which a black hole and a neutron star collided.

As discussed in the “AI in Physics” section, a paper published in 2021 demonstrated the use of four distinct artificial intelligence models to process data from the Laser Interferometer Gravitational-Wave Observatory (LIGO).

Artificial intelligence has the potential to be applied in this area, helping to identify and analyze these cosmic events. In this case, researchers were able to analyze a month of data gathered through the LIGO project in only seven minutes, identifying four black hole mergers.

Artist’s impression of the merger of two supermassive black holes during a galaxy collision (credit: ESA)

Identifying Gravitational Lenses and Other Phenomena

With the advent of new technology, our ability to survey the universe and discover more about the celestial bodies within it and cosmic phenomena grows.

The Vera C. Rubin Observatory and the Nancy Grace Roman Space Telescope are two great representations of this progress. Set to be completed and launch during this decade, these already show great promise that they will help drive innovation in the field of astronomy.

In particular, the Rubin Observatory will count with the first 3.2-billion-pixel digital camera in the world and an integrated mirror system that will allow it to survey the entire Southern Hemisphere sky every three days.

It’s expected that the observatory will generate 20 terabytes of data per night. As a result, specialized AI algorithms will have to be applied to process this huge volume of data and detect particular phenomena of interest for further study.

Gravitational lensing (source: ESA/ATG medialab)

A main focus of the observatory will be to study dark matter, particularly using gravitational lensing. This is an astronomical phenomenon that occurs as galaxies align in our line of sight in such a way that the one closest to Earth magnifies the other as a lens.

Through the observation of this phenomenon, astronomers can calculate the mass of a cluster of galaxies and, consequently, of the dark matter hiding within it. AI can help optimize this process by analyzing the images captured with perhaps even more accuracy than humans.

As highlighted in this article, AI has already been applied in a similar way by a scientist at Caltech to identify comets. In fact, the model created ended up identifying Comet C/2020 T2, the first comet discovered by AI.

Image Simulation

One of the main features of artificial intelligence is its capacity to learn from real-world data and create simulations from it.

As shown in this article, this can be utilized in the field of astronomy to create realistic simulations that mimic observations made of galaxies. These simulations can then be employed in a variety of ways from testing hypotheses, to filling in gaps in the information obtained during future surveys and improving the methods used to process this data.

Personal Assistant

A curious application of artificial intelligence in astronomy is the creation of personal AI assistants for astronauts.

In the same way that many of us have AI assistants in our homes today – from Alexa to Siri or Cortana – so too will astronauts be able to have their own version when they’re in the International Space Station (ISS).

CIMON (source: IBM)

CIMON was created by the German space agency DLR, Airbus, and the International Business Machines Corporation (IBM) and is the first AI assistant for humans in space.

As can be expected, this is a special AI assistant; CIMON is equipped with facial recognition software and presents basic facial expressions while conversing with the astronauts, acting as a companion while traveling in space.

It is designed to assist astronauts as they conduct experiments, providing information and even filming their work when necessary.

As highlighted by IBM, CIMON should be especially useful to astronauts during an emergency, as it can easily provide them with any necessary information without being affected by the pressure associated with space travel.

You can learn more information through IBM’s interactive page focused on CIMON.

AI in Chemistry

The primary goal in the field of chemistry is to understand the structure and functioning of molecules. Only by gaining this knowledge can scientists put these molecules to use (for example, as new therapeutic compounds), manipulate them as needed, and create new compounds.

Nowadays, scientists are able to simulate the structure of molecules with more accuracy, thanks to artificial intelligence. Overall, molecules can be represented in various ways, reflecting different configurations. Typically, research would focus on 1-dimensional or 2-dimensional representations; however, AI can be used to facilitate the creation of 3-D models that fully reflect the geometry of molecules.

Image by DCStudio on Freepik

This is very important, given that, in principle, the properties of molecules depend on the distribution and behavior of the electrons within them.

As we discussed in the quantum physics section, AI models can be trained to predict the behavior of electrons with more accuracy than previous methods. As such, AI could become a useful tool when used in conjunction with DFT, the primary theory used to understand how electrons (and by extension molecules) behave.

As reviewed in this article, DeepMind created an AI model that can predict the distribution of electrons within molecules. Consequently, it seems to be able to determine molecules’ properties with more accuracy than other techniques.

The information collected through these methods is then compiled in databases, which researchers can search through to find chemicals with specific characteristics. Nevertheless, there is still one major issue as scientists are limited to molecules already known and these represent only a fraction of the entire chemical space.

To overcome this, we can use generative models which, as the name indicates, are able to generate new molecules. AI models can be particularly useful, especially in comparison to other techniques, by ensuring that the molecules created are chemically valid and present the properties desired.

The molecules created can then be synthesized and tested and considering that AI already selected the most promising candidates for a given application, this process becomes less time-consuming and simpler.

Once we have our desired molecule, however, we still need to understand how to synthesize it. This is carried out through a process called retrosynthesis in which chemists work backward from the end product to discover the necessary starting materials and steps to follow to synthesize it.

Typically, this process would be done manually through theorization and trial-and-error, making it very time-consuming. However, some studies already show that AI algorithms can be used to accelerate and facilitate retrosynthesis. To do this, researchers have to train the algorithms using a large volume of data on chemical reactions. While limited in some ways, this approach can help generate complete pathways for retrosynthesis in mere seconds.

An area that greatly benefits from these strategies is drug discovery and design. In fact, even though it is estimated that there are between 1023 and 1060 molecules with therapeutic potential, we have only been able to synthesize 108 compounds.

Even the data that we have obtained so far relating to molecules and their effects on living systems has increased so much over the past decades that it has become too difficult to analyze all of it and, consequently, generate new knowledge.  

AI algorithms constitute one possible solution for this problem as they can help to mine the data available. As we explored in one of our previous articles (“AI in STEM – Enhancing Scientific Research”), AI can analyze the data available on molecules and compounds available as well as known disease targets, helping to find new therapeutic possibilities.

If you haven’t done so already, read that article to find out more about the use of AI in research, including the creation of the first drug fully engineered by AI!

In addition, generative models can be used to design new therapeutic compounds to target specific conditions. Finally, considering existing drugs, AI can be used to predict their chemical characteristics.

Another prominent area of chemistry wherein artificial intelligence is increasingly being applied in is materials discovery. This trend results from the growing pressure to create new, more sustainable materials as part of the effort to tackle climate change.

With increased demand, the area now required the use of innovative tools and techniques to speed up and enhance work, including AI, robotics, and hybrid cloud approaches.

For one, AI can be used to more efficiently analyze the relevant data, which is often scattered across a giant volume of unstructured papers and reports created over the years. Further, it can also help fill in the blanks where information is missing.

As discussed in detail in this review, AI can be used to predict the properties of materials and to assess which ones would be more appropriate for specific applications.

After selecting candidate materials, the next step is to synthesize and test these compounds. AI can be applied in many ways to facilitate chemical synthesis, from the prediction of the reactions needed to the automation of simple tasks.

A series of studies have explored the potential of AI for predicting the outcome of reactions and the conditions necessary to reach desired results.

A very interesting example of this type of application is this study, which looked specifically into the use of AI to help solve arson cases.

Photo by Arny Mogensen on Unsplash

As the authors explain, gasoline has a characteristic composition fingerprint as a result of the refining process. This can then be used during forensic investigations to identify the gasoline used by the arsonist.

However, the fire causes changes to the composition of the gasoline and so weathered gasoline samples can’t be used as a direct comparison with a sample of the original gasoline used. This is made even more complicated as we consider other factors that affect the weathering process, such as the fire extinguishing water.

The researchers defended the use of AI to predict the original composition of the gasoline using the composition of the weathered gasoline sample, even in more complex cases.

AI in Earth Science

Earth Science is a field dedicated to the study of Earth, including its structure and properties, and the interactions between its components, not only in the present but also throughout time.

Currently, scientists in this field are faced with a series of very complex challenges, particularly as climate change continues to escalate. Trying to understand these problems requires us to analyze a very significant volume of data from a variety of sources, from the ocean to the atmosphere.

Photo by Ian Turnell

With the growing pressure to find solutions to overcome these challenges, scientists are turning to innovative tools and techniques to facilitate and enhance their work, including artificial intelligence.

On one hand, AI can be employed to more efficiently analyze the observational data collected not only in regard to each of the Earth’s components but also human activity. As we explored in detail in our last article, this allows us to monitor the effects of climate change and understand how our actions are contributing to this issue.

On the other hand, AI can be used in the creation of detailed simulations, that not only help us quantify the state of the environment, but also predict future changes and catastrophes.  

For example, an AI system created by Google is able to predict when floods will occur in certain locations, as well as the effect they will likely have. This information is then publicly shared so that people in the areas at risk can prepare ahead of floods.

After these major weather events occur, AI can also provide information to help authorities identify priority areas for disaster relief.


Archeology is an Earth Science that focuses on the study of ancient materials and sites to understand human activity throughout history. AI is already being used by archeologists in a number of ways, from analyzing materials to creating 3D models of ancient sites.

To start off, researchers can take advantage of AI to analyze geospatial data (obtained, for example, through satellite and aerial imaging), in search for new archaeological sites. As described in this article, AI can detect specific characteristics and patterns in these images that indicate that certain sites could be appropriate targets for excavation.

Once ancient artifacts are recovered, AI can also assist in their analysis, including the identification, description, and classification of objects, and the determination of characteristics such as their origin and timeframe.

For example, AI can more efficiently describe the structural elements of ancient monuments or even analyze artistic elements in rocks or help identify remains.

Further, when ancient documents are found, oftentimes they’re in an ancient language and may even be incomplete due to damage over time. Due to its language processing capacity, artificial intelligence can help translate ancient languages, in some instances, even automatically. In cases where there is missing text on an artifact, AI can also be useful in reconstructing the text and analyzing its contents to extract the most important information.

Finally, AI can also be used in the creation of 3D digital models of ancient landmarks. This is particularly important when it is considered that a given site is at high risk of being damaged due to threats like war and natural catastrophes.

For example, a company from Paris called Iconem uses drones to capture images of historical sites, which are then used by AI to create 3D models.

Visualization of a 3D model of the Umayyad Mosque in Damascus, created by Iconem


The final area that we’ll be exploring in this article is Geology. Focused on the study of the Earth’s structure and composition as well as how these have evolved throughout history, this field is also starting to see the benefits of the application of AI.

Overall, artificial intelligence can analyze geological data more accurately and quickly, helping to summarize the information available on geological characteristics and phenomena.

One of the ways in which AI can be used in the field of Geology is in identifying and classifying geological samples not only more efficiently but also with very low error.

Erzberg mine, the largest iron ore reserve in Austria (photo by Chris on Unsplash)

For instance, as explored in depth in this review article, artificial intelligence models have been shown to be very useful in the identification of rocks and minerals across several studies. As such, its application in practice could be revolutionizing, not only saving geologists time but also overcoming the need for specialized equipment to analyze these samples.

Another application of AI that has been receiving more attention recently is its use in geological mapping. In particular, the focus has been on finding mineral deposits, a task that is becoming increasingly more difficult.

AI can help not only detect the occurrence of specific types of mineralization but also in predicting other areas in which this likely has occurred.

Additionally, as explored in this review, artificial intelligence has been applied in anomaly identification and the analysis of deposit formation.

Finally, as we’ve touched on previously, AI is already being used in some cases to predict natural disasters and provide information to the people in the locations affected before and after these events.

Geologically induced disasters, such as earthquakes and landslides, are particularly hard to predict. The application of AI in seismic data analysis can greatly facilitate seismic monitoring and seismic exploration operations.

STEAM in AI Research and Build Program

At the DataEthics4All Foundation, one of our main objectives is to help the next generation enter and succeed in STEAM, Data, and AI fields. That is why we started the STEAM in AI Movement, which encompasses a series of initiatives aimed at young people interested in these areas.

One of the main components of this movement is the STEAM in AI Research and Build Program. Aimed specifically at high school students, this program offers an opportunity to develop a unique project while being mentored by PhD students and college graduates working in STEM.

Our goal is to enable students to learn more about the field they’re interested in, including potential career paths they could pursue, and receive important guidance to help them throughout their journey. Further, by conducting an original project, students can gain a realistic understanding of what working in their area of interest is like.

In particular, we want students to be able to learn more about the development of AI and its application across all STEM areas. As we’ve explored throughout this series, AI is starting to revolutionize all areas of STEM and has the potential to become an indispensable tool in professionals’ arsenal.

We believe that young people entering STEM should gain some experience working with AI, as this will likely become extremely valuable for future professionals.

To learn more, visit our website or book a consultation with us.

Content Writer

Content Writer Intern for DataEthics4All Foundation | Freelance Science Writer | Creator of The Unfiltered Scientist | BSc (Hons) Biology

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