Applications of AI in Niche and Emerging Areas
Applications of AI in Niche and Emerging Areas
AI is impacting many areas. In this post, we will discuss some niche applications of AI. Let's discuss such application in detail.
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There is no denying the fact that Artificial Intelligence is the breakthrough technology of recent times. Machines have come a long way from assisting humans in mechanical operations to performing smarter tasks using cognitive intelligence. Every day, we are coming across interesting applications of AI.
The ability of Deep Learning algorithms to learn and predict efficiently has opened the doors of possibilities. Nowadays, AI is impacting many other areas, as well. In this blog post, we will discuss some niche applications of AI. Let's discuss such application in detail.
Applications of AI in self-driving cars, image processing (Prisma, Google Photos), and chatbots are something you must be experiencing in your daily life or hearing about it a lot on the internet. However, there are many other niche areas where AI is creating a significant impact. These niche areas of applications don't get as much attention in the press but they are actively featured in AI research publications. With this blog, we aim to explore these niche domains and develop a deeper and well-rounded understanding of applications of AI.
Astronomers deal with a lot of data in the form of signals and images. Analyzing and categorizing this data is a humongous task. Astronomers are now leveraging the power of unsupervised Machine Learning to automate this task, which was previously done by thousands of volunteers.
Alex Hocking, a Master's student from the University of Hertfordshire, who led the work on one such system, commented:
“The important thing about our algorithm is that we have not told the machine what to look for in the images, but instead taught it how to 'see'."
Image: ML algorithm identifying the elliptical galaxies from an image. Credits: Royal Astronomical Society.
AI is helping astronomers make the blurry and noisy images better. Researchers have used neural networks to enhance the already existing data as well. The deployment of such Machine Learning algorithms have certainly reduced the human effort and, at times, have enhanced the images better than their original source. What did Alex Hocking say about teaching a machine to see?
The future of AI in astronomy looks brighter as more and more astronomers are shifting to advanced Machine Learning techniques to analyze the astronomical data. During this era of extensive space exploration, maybe AI can help identify a habitable planet by deeply analyzing the images and signals.
Construction and Planning
Machine Learning algorithms have been used to model the best building layout to minimize energy consumption. It is important to adapt to energy efficient construction practices. The machine learning powered energy models can provide the best solutions by combining the analysis from historical and current data.
Certain buildings in Manhattan, NY are already being constructed using Machine Learning powered energy modeling.
Talking about energy conservation, AI is being deployed in optimizing electricity grids for entire cities. Grid4c is one of the companies providing predictive analytics solutions for smart grids. Machine Learning algorithms can analyze the data from millions of smart meters. Clubbing this data to the consumer behavior gives a holistic solution for smart delivery of electricity. This method can learn from user behavior and appliance usage. This will help suppliers understand the peak usage time and the downtime at the granular level. Thus, optimizing the overall supply by the smart electricity grid.
AI is entering even one of the most traditional verticals, Agriculture. The idea is to produce more with same resources. This is where AI is helping farmers. The AI research is exploring various aspects of farming, most notable areas being automated intelligent irrigation and detecting crop diseases.
Intelligent machine learning algorithms are helping farmers automate and optimize irrigation. It reduces water wastage and optimizes water supply to the crops. AI-driven system can analyze soil, weather and crop type to deliver optimized water supply.
Input and output: Detecting bacterial disease using image processing in beans leaf.
An app-based startup Plantix is helping farmers detect plant diseases using image recognition technology. It leverages the power of crowdsourced data of images for identification, prevention, and treatment. Currently, it can identify 60 plant diseases with more than 90% accuracy. Both these numbers should increase with more crowdsourced data getting fed to the app.
Nowadays, players and coaches heavily analyze their performance. The analysis of competitors is another important area coaches and managers deal with. There is a lot of data available to capture, analyze, and predict the performance. This is where Machine Learning is making a mark in sports analytics.
Credits: Chris Pearson
The huge amount of data sets likes fitness, health, strength, speed, ability, skills, match-day conditions, etc. makes a Machine Learning algorithm an ideal fit. The algorithms can help stakeholders predict the performance metrics of their players and competitors at an individual level. This is getting achieved by learning from historical data and clubbing it with real-time analysis of the game which was not possible by mere human cognitive abilities.
Iceberg is one such company which is helping ice hockey teams analyze data by leveraging AI. They claim to give real-time insights accurate to 0.1 seconds and 500 player stats. In the future, AI might help us predict outcomes and results of a match. It will be interesting to see how deeply can AI go in analyzing and predicting stats for a sports team/athlete.
On June 9, 2016, a short movie named Sunspring was released. It was a movie written by Benjamin, an intelligent automatic screenwriter. Benjamin was trained using an LSTM RNN. Apart from screenwriting, AI is being used in direction, cinematography, and other aspects of movie production. For example, check out this trailer of the AI thriller movie named Morgan, created by IBM's Watson. Notably, AI boiled down the time taken to complete the overall process of trailer creation from approx 30 days to 24 hours.
The applications of AI in entertainment industries are not limited to movies. Art and music verticals are also getting inspired by AI. 0music, is a music album completely composed by Melomics109, a computer cluster at Universidad de Málaga, Spain.
With computational and cognitive abilities of machine learning algorithms increasing day by day, we will see much more intervention of AI in entertainment industries. Maybe in upcoming future years from now, AI could replace a musician or cameraman with average skills, but it would still be no match for the best artists in the business. After all, generating original thoughts in a subjective domain is not something AI has been proven to achieve.
Educational data mining is among the new and emerging applications of AI in education. Generally, schools opt a single approach to teaching students. This approach has its downsides as not every student has similar learning capabilities. Also, it is difficult for teachers to carefully assess the performance of every individual student.
Nowadays, algorithms can assess and analyze the performance of students at an individual level. Students can learn through visual content and every activity can be tracked to analyze the learning performance of an individual student. Carnegie Learning and Thinkster Math Learning are two such companies who are using advanced analytics in education.
Education can also be benefited by advanced machine algorithm by taking learning out of the limitations of classrooms. AI can assist learners with self-direction, self-assessment, teamwork and more. Also, AI driven collaborative learning platforms are coming up. Brainly, a collaborative learning platform, is exploring the power of AI to enhance social learning.
AI-powered robots are detecting pollution levels and tracking changes in temperature and pH of the oceans much better than humans. These autonomous robots can collect a huge amount of data from multiple geographies. This data is analyzed and predictions are made. Action plans to curb such changes in the ecosystem can then be executed more accurately.
- The COTSBot: AI driven autonomous bot used to eliminate Crown-of-thorn Starfish.
Another important application of AI in protecting oceans is to control invasive marine animals. AI is helping robots become more precise, effective, and constant by using Machine Learning and Computer Vision to track invasive species and eliminate them. Notably, such operations are already taking place to eliminate Lionfish from Atlantic ocean and Crown-of-thorns Starfish from Great Barrier Reef. Invasive species are capable of causing extinctions of native plants and animals, reducing biodiversity, competing with native organisms for limited resources, and altering habitats. When Lionfish expanded to Atlantic from their native Indo-pacific region, they flourished due to the lack of a natural predator. Thus causing harm to local Atlantic species.
There are more emerging applications of AI in areas never explored before. Healthcare is getting better diagnosis and prevention solutions powered by AI. Also, logistics industries are eyeing fully automated warehouses using intelligent robots. There are tons of industries that can leverage the power of AI.
People pit AI's impact on industries to be similar to what electricity did when it was invented in the early 20th century. Many tech veterans have said that Machine Intelligence is going to be the biggest disruptive force in the future, but we will leave you with a piece of advice given by our favorite visionary, Bill Gates, to the young college crowd:
AI, energy, and biosciences are promising fields where you can make a huge impact. It's what I would do if starting out today.
Published at DZone with permission of Parth Shrivastava . See the original article here.
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