Future of Machine Learning
In this article, we will learn about the future of machine learning. Let’s start!!!
Future of Machine Learning
In today’s tech landscape, the term ‘machine learning’ has become such a buzzword that you’d be hard pressed to find someone who hasn’t heard of it. The topic of this article, i.e. what the future holds for machine learning and ML engineers is not likely to get you a straight answer.
If you ask an avid science fiction writer this question, you’d likely get an answer about AI plotting domination. A business major would tell you about the projected increase in funding and revenue. If you’re looking for thrilling stories of our robot overlords or to crunch some numbers, sorry to disappoint. This blog is about the future of machine learning from the point of view of an engineer.
Machine learning is a subset of artificial intelligence, central to which are algorithms that can improve dramatically through experience and through the data inputted.
The field has undeniably seen tremendous growth in the last decade and the future looks as exciting, if not more. Some of the more promising advances include:
1. Health Care:
Massive volumes of data are generated in the healthcare business. Machine learning approaches can make a big difference in terms of bettering forecasts and cures.
a. Prediction of disease:
Technology advancements have improved illness prediction and prevention rather than treatment after diagnosis. We take into account a restricted set of characteristics, such as height, size, age, and sex in the conventional approach to illness prediction. The machine learning technique allows for the study of a broader range of factors such as research studies, patient information, health data, and other data, perhaps leading to improved illness prediction findings.
b. Drug development:
This is a complex process. Drug discovery takes a long time and is expensive. Based on recent research, getting a new medication to market costs $985 million USD on average. Machine learning algorithms can predict the impact of a drug compound on human cell lines and genetic makeup and detect possible complications using datasets with the chemical composition of the drug molecule. Machine learning will shorten the time it takes to test drugs, speeding up the process of introducing a medicine to market.
c. Recordkeeping:
Electronic health records (EHRs) contain massive amounts of data in various formats and from various sources. Machine learning techniques like natural language processing and image processing can aid in the conversion of data into a standard format. EHRs powered by machine learning have the potential to simplify and improve the process of discovering clinical trends, resulting in improved prediction outcomes.
2. Quantum computing:
Quantum computing has the potential to improve machine learning capabilities. It enables the execution of several multi-state processes at the same time, resulting in speedier processing of data. In 2019, Google’s quantum processor completed a task in 200 seconds that would have taken 10,000 years for the world’s finest supercomputer.
Quantum machine learning can help with data analysis and provide more in-depth insights. This improved performance can assist organizations in achieving better results than standard machine learning approaches.
3. Education:
In many nations, machine learning is now playing an essential role. In many nations, it has revolutionized the face of education.
China is a shining example of AI-assisted learning. China employs a variety of methods to examine each pupil. It measures a student’s focus with headbands. It is worn by every student in the class. Each student’s brain is measured by the bands. We then send the data to the teachers. Teachers can tell which kids are paying attention. This information is also forwarded to the parents. China uses machine learning to improve student attentiveness in this way. This was one of them.
4. Banking:
In commerce, machine learning is becoming increasingly important. Stock market performance may be predicted using machine learning techniques. It has the ability to forecast GDP growth in the future. It can be beneficial to a variety of startups and enterprises. As a result of all of this, the future of Machine Learning in banking is looking brighter than ever. ML is assisting a variety of businesses in making money.
It can also succeed in financial advisory. It’s utilized to help people make better business and investing choices. It will result in low loss for the businesses in the area. In banking, ML will be a huge assistance.
5. Agriculture:
Agriculture’s potential for Machine Learning is an excellent platform for ML to prosper. Initiatives are being taken by major corporations such as Microsoft and Google. It covers artificial intelligence for Earth-related tasks. This guarantees that the crop is of the highest quality and that it is produced in a shorter amount of time.
The machine learning algorithms examine soil conditions and anticipate produce quality. It can save money on labour by performing all of the fieldwork itself.
Seeds, water, insecticides, and other items may be sprayed by AI-powered drones. This can contribute to a considerably higher grade of harvest. Several nations in North America and Europe make use of this. It’s steadily making its way throughout Asia and other continents.
6. Geology:
Machine Learning’s potential in geology sounds intriguing. If it is a success, it will undoubtedly have an impact on the field. However, it is actively being researched. Its only purpose is to map and analyze the Earth’s crust and subsurface.
Seismic research can benefit greatly from ML. It is impossible to anticipate the exact date and location of earthquakes. However, researchers are investigating this area. If this occurs, it can be extremely beneficial in saving lives. Researchers are working on discovering algorithms to make this feasible. Developing a high-level algorithm, on the other hand, will take some time.
7. Weather:
Weather forecasting is crucial for a variety of reasons. Changes in the weather may be disastrous. It can, for example, generate cyclones in the oceans. In some locations, it may result in drought or excessive rains. It has the potential to trigger a variety of additional alterations in a location.
Certain models in AI or machine learning track changes. We check the weather of a location using the Machine Learning method. It will keep the data for a certain amount of time. We can forecast the weather using that information as well as the scenery of the area. We can forecast the weather in the area for the next several days. This will serve as a forewarning of impending doom. This can be found in a variety of places.
8. Oceans:
Machine Learning aids in the measurement of a variety of things in our seas. The ML algorithm can aid in pollution measurement. It has the ability to investigate the movement and behaviour of numerous animals.
ML has the potential to help clean up the oceans. It’s employed in the field of oceanography. The study of marine topography is aided by machine learning. It is capable of determining habitat dispersion. ML may aid in the investigation of tectonic plates. It also aids in the monitoring of a variety of animals. We can use machine learning to find and comprehend species behavior. It will undoubtedly be successful in the future. It has the potential to assist us in conserving our seas.
9. Food Processing:
Machine Learning can be beneficial in the food processing business.
Many dairy farms in Europe use ML based tests. This determines the quality of the milk. It can also tell you about the cow’s health. It is capable of determining if it is healthy or not.
The ML model distinguishes between low and high grade meals in this case. This may work nicely in a factory setting. A shipment of veggies arrives to be packaged. The sensors can tell the difference between rotting and excellent veggies. This keeps the entire batch from becoming contaminated.
Singapore is experimenting with machine learning in a novel approach. They employ machine learning to assess people’s health. It may tell what the individual is missing based on that reading. It will then make a jelly that has certain nutrients. But it’s still there.
10. Landscape recreation:
A decaying landscape may be recreated using machine learning.
Assume you have a forest that has been burned or cleared. ML can still be used to plant trees. This may be accomplished in a very effective manner. Scientists have a fantastic idea for this.
A drone driven by machine learning would scan the area. This would fire seed-filled bullets into the earth. We can plant millions of trees in a matter of days if we use this method. It’s a fantastic technique to resurrect a long-forgotten environment. It also saves you time and effort. The drones may then look for evidence of development in the region. It’s a fantastic approach that can lead to rapid growth. It has a bright future ahead of it.
11. Self-driving vehicles:
Tesla, Waymo, and Honda are among the manufacturers looking into the idea of self-driving vehicles. While several companies have previously shown automobiles with some level of automation, completely independent vehicles are still in the works. One of the primary techniques that can assist in making these goals a reality is machine learning.
Route modeling, scene categorization, and object and person identification are all examples of how deep learning, a type of machine learning technique, might aid enhanced vision and routing in automated car manufacture.
12. Manufacturing:
Machine learning techniques may be utilized in a variety of ways in the manufacturing sector, such as monitoring efficiency of equipment and state, projecting quality of the product, and estimating power usage. We may expect more machines in industrial facilities in the coming years, thanks to continued advances in the field of machine learning.
Machine learning in manufacturing may cut costs, increase quality control, and improve supply chain management, among other things.
13. Digital Marketing:
Around the world, digital marketing agencies use machine learning. It enables more precise customization. As a result, businesses can engage and connect with customers.
Segmentation focuses on the right customer at the right time. Furthermore, with the appropriate message, companies have data that algorithms use to learn about their behavior.
Companies use this information to provide product suggestions to their potential leads, such as customized emails and texts. Clients’ preferences and dislikes may be better understood using machine learning techniques, which retains customers interested in your solutions and products.
14. Search engines:
While one may not be conscious of it while scrolling through Google in pursuit of content, the classification and ordering of those results is done intentionally. Machine learning algorithms have recently had a significant influence on search engine results. Over the next several years, search engines will dramatically improve both consumer and server experience.
Future search engines will be considerably better at generating replies and views that are substantially relevant to online researchers and visitors, thanks to continued neural network growth and progress combined with expanding deep learning methodologies.
15. Robotics:
Robotics is a discipline that has piqued the curiosity of both scholars and the general public. George Devol created the first computerized automaton in 1954, which he dubbed Unimate. The first AI-robot, Sophia, was constructed by Hanson Robotics in the twenty-first century. Machine Learning and Artificial Intelligence made these inventions feasible.
Robots that imitate the brain are continuously being developed by scientists all around the globe. In this study, they use neural networks, AI, machine learning, computer vision, and a variety of other techniques. In the long term, we might encounter robots adept at doing duties comparable to those performed by humans.
16. Cognitive Services
Agriculture’s potential for Machine Learning is an excellent platform for ML to prosper. Initiatives are being taken by major corporations such as Microsoft and Google. It covers artificial intelligence for Earth-related tasks. This guarantees that the produce is of the highest quality and that it is produced in a shorter amount of time.
The machine learning algorithms examine ground conditions and anticipate produce quality. It can save money on labor by performing all of the work autonomously.
Seeds, water, insecticides, and other items may be sprayed by AI-powered drones. This can contribute to a considerably higher grade of harvest. Several nations in North America and Europe use this. It’s steadily making its way throughout Asia and other continents.
17. Automated machine learning:
Automated ML allows laymen to apply ML models to real world problems without having to dive deep into the technicalities of it. It does so by automating the selection, composition and parameterization of models, all time-consuming iterative tasks. This allows developers and businesses to simply apply the models to their data without having to focus on the nitty-gritty parts of it.
Conclusion
Advances in machine learning continue to take the world by storm, one epoch at a time. These advances bring with them new challenges to solve and new opportunities for engineers everywhere. The question remains: are you ready to face these opportunities head-on?