Robots have not yet taken over the world, despite what the sci-fi pop culture of the late 20th century taught us. While all the claims made have not come true, machine learning is now present in almost all spheres of society. In many different industries, computers and AI systems are becoming proficient in a wide range of tasks — the seven machine learning applications we covered in this article are just the tip of the iceberg.
Among many applications, machine learning has proven beneficial in:
Finding and diagnosing diseases that are typically difficult to diagnose is one of the main applications of machine learning in healthcare. Typical examples include genetic diseases and a variety of cancers that are difficult to detect in their early stages.
For instance, the now-defunct IBM Watson Genomics project offered a very good example of how cognitive computing and genome-based tumor sequencing can result in a much quicker diagnosis. Similar to this, Berg, a well-known biopharmaceutical company, has already used machine learning to create treatments for a variety of conditions, including oncology.
The PreDicT program from P1Vital, which stands for Predicting Response to Depression Treatment, is another excellent illustration of machine learning applications in the healthcare industry.
Image recognition is a common practice. Have you noticed the automatic scene and facial recognition feature in the gallery app on your smartphone? Machine learning was used to accomplish that. The same is true for social media functions like auto-friend tagging.
Consider Facebook’s DeepFace technology, which makes recommendations for friends to be tagged in images. Modern secret surveillance systems used by some governments around the world rely on machine learning-assisted image recognition as well.
Machine learning-based image recognition has uses in the healthcare industry as well. Missing even a small detail during many treatment procedures could have disastrous effects. Image detection can be used in scans and X-ray images to find even the smallest differences between two points. This method is especially beneficial for precisely tracking the development of a virus infection or the spread of a tumor.
Regardless of the circumstance, AI is anticipated to outperform a human driver in autonomous vehicles. Machine learning algorithms are useful in this situation as they enable autonomous vehicles to make decisions instantly.
The onboard computer of self-driving cars like the Tesla Model S or Ford F-150 uses machine learning to process data input from a variety of sensors. This is to guarantee that the ADAS can interpret the environment around the vehicle safely and accurately. This data stream is used by the system to regulate the direction and speed of the vehicle. The data is also processed for other crucial tasks like object detection and tracking, perception, and forecasting.
Machine learning is becoming more and more effective at protecting our online transactions and spotting fraud. It works particularly well at spotting fraudulent activity involving the use of phishing, fake accounts, and IDs.
To suggest risk rules, machine learning algorithms are trained on historical data for fraud detection. These rules can be used to prevent users from taking specific actions that have been flagged as suspicious, such as unauthorized logins, potential identity theft, fraudulent transactions, and so forth.
Data scientists typically provide a massive amount of records of previous fraud and forgeries to the machine learning model. To minimize false positives, they also flag non-fraud cases. The rule suggestions are typically more accurate with more data to work with.
Prime examples of machine learning applications in business are product recommendations on e-commerce sites and entertainment platforms. The “recommender systems” that produce and distribute these recommendations use machine learning algorithms to divide up the customer base according to user information and behavioral trends. The browsing history, likes, shares, and other user actions are used to gather user information and behavioral patterns.
IVAs are computer-based systems that use machine learning to comprehend natural language, or human language, and respond appropriately. Each question is presented with a multiple-choice selection of responses by these clever AI assistants. They can decipher user intent from the free text as well.
IVAs, also known as Virtual Customer Assistants, are being used more and more in automated customer support. This enables users to express to the machine even very specific concerns in the same way they would to a human.
IVAs should be understood to be distinct from chatbots. They are much more intelligent and able to comprehend spoken language. In contrast, the majority of chatbots engage users by providing straightforward yes/no response options.
Now that no-code products are more prevalent, you can create IVAs in as little as 15 minutes.
The use of machine learning in trading stocks and cryptocurrencies is also expanding. The internal calculations and analyses are performed by sophisticated machine learning algorithms. The data produced aids traders in making wise choices.
More specifically, machine learning is useful for identifying signals in alternative and financial data. The development and backtesting of systematic strategies can then be done using these signals.
Applications of machine learning are becoming prevalent in many facets of our society. Much more than what we talked about above, sentiment analysis, data collection and classification in cosmology, marine life studies, engineering, military intelligence, and even digital art are some additional common applications.
Every day, as artificial intelligence expands, more technological advancements become possible. Given the importance and relevance of this technology, it makes sense for you to educate yourself about it — and make good use of ML for your businesses.
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