Top Machine Learning Technologies List (Real World Examples)
Top Machine Learning Technologies List (Real World Examples). First of all, Machine learning seems to be an esoteric concept for many people who are not in the IT industry. Yet, technologies associated with artificial intelligence and machine learning are beginning to have discernible real world consequences (mostly good). The world is beginning to see this technology implemented in various industries. If you are an internet or smartphone user, you have most likely interacted with machine learning algorithms in some capacity. Nevertheless, these technologies tend to run in the background – unseen and thus many people do not actually feel their impact. The following guide explores and discusses the top machine learning technologies and provides you with real world examples of how people are benefiting from them.
Shall we start this article blog Top Machine Learning Technologies List (Real World Examples).
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What is Machine Learning
Truly, the machine learning is a subset of artificial intelligence. All in all, artificial intelligence is a complex concept and problem. The best way to tackle difficult problems is by breaking them down into simpler pieces. Therefore, machine learning was introduced to further the evolution and progress of artificial intelligence by giving computer systems the ability to learn. There are four types of machine learning (although some sources may only cite three):
- Supervised Machine Learning: Where labelled datasets are used to teach (train) machine learning algorithms how to identify and categorize information.
- Unsupervised Machine Learning: Where machine learning algorithms are fed unlabelled data that they must cluster and analyse. This is done without operator supervision or intervention. The machine learning algorithm searches for hidden patterns and anomalies in the data set.
- Semi Supervised Machine Learning: A hybrid of supervised and unsupervised machine language where a small number of labelled data sets are provided to the model against a large volume of unlabelled data sets. The machine learning algorithm uses this data to make predictions.
- Reinforcement in Machine Learning: Where machine learning algorithms or agents learn through experience or environment. The machine essentially trains itself. In most cases, a machine using reinforcement learning is used to determine the most efficient plan of action to achieve a particular goal.
Importantly, understanding the above concepts is important as different machine learning technologies fall under different categories. The rest of this guide is dedicated to listing some of the best top machine learning technologies.
Top Machine Learning Technologies List (with Examples)
1. Healthcare
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The healthcare industry is one of the best instances of an area that has been positively impacted by the implementation of machine learning. For example, data scientists and medical professionals leveraged machine and deep learning to perform epidemiological analyses of the spread of the Covid-19 pandemic. The coronavirus was (still is) fast spreading and evolving. Thus, tracking and making predictions on how it affects different communities and people was a challenge for epidemiologists.
Well, Experts found Dynamic Gaussian Processes Regression (GPR) models particularly useful in the prediction of mortality rates. Altogether, this model can be applied to all four types of machine learning problems (supervised, unsupervised, semi supervised, and reinforcement). Besides, machine learning was also used to track and identify Covid-19 hotspots.
Oncology is another example where machine learning has been effective in the real world. IBM’s Watson assists healthcare professionals in making more accurate diagnoses and treatment plans by comparing a patient’s medical history against the records of millions of patients.
Certainly, keeping track of the latest advancements in medical science is a daunting task especially if you consider how rapidly medical technology is changing. Consequently, medical professionals may be unaware of the latest treatment options for patients. Chiefly, Machine and deep learning solutions and models such as Watson scans through data on the internet and medical records to find the latest and most effective treatments.
2. Social Media
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Although Meta owns social media sites such as Facebook, Instagram, and WhatsApp, Meta isn’t just a social media or communications business. Meta contributions to software development and computer science have been understated. Whether it’s their heavy investment in open-source resources or their initiative to build the fastest AI supercomputer, Meta has become an important player in the advancement of machine learning and all of its associated technologies.
Thanks to their Applied Machine Learning (AML) and Meta AI, the company has managed to create some of the most advanced algorithms in the world. For instance, Meta’s ML models transcribes or captions videos on Facebook. Its Multilingual Machine Translation model was the first AI/machine learning model that could translate over 100 languages. Today, it’s used to translate Facebook posts and stories to help localize content for international audiences.
Of course, all social media platforms use algorithms for newsfeed and content personalization which ties into marketing and advertising. Consequently, social media platforms analyse what you post, comment on, share, react to, etc. They use this information for target marketing and other content. Reddit famous for its use of bots also uses machine learning for its recommendations and discovery features.
Of course, bloggers and site owners can use the same algorithms for their websites. If you own an eCommerce store, you track what visitors query or shop for and ensure that your marketing campaigns and advertisements are effective. SAS Customer Intelligence is one such tool that companies can implement.
3. Navigation
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Equally, navigation and web mapping services such as Google Maps and Waze use machine learning to enhance the accuracy of their features. For instance, machine and deep learning is used to accrue data that determines the best route based on traffic. Furthermore, it helps to scour the internet for information related to traffic jams and other impediments.
Traditionally, static data and information were used to inform the routes and directions of navigation apps. However, we live in a world that’s constantly changing – from traffic to road works. Thus, navigation software was also able to account for this. Global navigation satellite systems (GNSS) have done a spectacular job providing us with geo tracking and geolocation features. Paired with the right sensors, these technologies can be remarkably accurate. However, machine learning adds more dynamism as it sifts through information regarding the surroundings of a GNSS/GPS target.
This is particularly important for the navigation systems of self driving cars that use machine learning algorithms to make predictions and act accordingly. In fact, it has been purported that machine learning algorithms can completely replace computer vision (AI) systems. Google’s Timeseries allows you to stream real time data and add anomaly detection to your software. Of course, this is used in navigation and traffic forecasting.
It is also used to extract information about the layout of buildings. This in turn helps people to navigate through large shopping complexes, malls, and parking buildings.
4. Agriculture
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Both, farming and agriculture are crucial industries as we depend on them for food security. Furthermore, a large portion of a country’s exports and ultimately, its GDP consists of farmed goods. As such, it’s important to find ways to optimize it.
Omdena, a software engineering firm that specializes in artificial intelligence, developed an application that predicts crop yields. It uses a combination of satellite imagery and machine learning. We also apply machine learning and artificial intelligence to track crop damage from pests. This can also be used to test the efficacy of certain pesticides and preventative measures.
Evidently, machine learning can also be used in winemaking. Most people underestimate what is required to produce good vintage wine. The quality of the wine depends on factors such as the quality of the soil the grapevine grows out of, the climate/environment, and ultimately how the wine is made.
Sensory devices paired with machine learning helps you to determine the quality of the wine more objectively and with more accuracy than the nose or tongue of a sommelier. It is also used to determine the variables for growing and producing a bottle of great wine.
5. Facial Detection and Recognition
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One of the most well known use cases for machine learning technologies is in image and facial recognition software. This guide previously mentioned how machine learning algorithms have revolutionized social media. Social media uses facial recognition software powered by AI and machine learning to determine the subject(s) of a picture. Thus, you easily tag people in a group photograph.
However, old methods of facial recognition are limited. They can be inaccurate when they are tasked with discerning faces in visually noisy photographs. Machine learning has been implemented to address this shortcoming. It helps in facial recognition and surveillance software detect and track faces and people more accurately.
One way that machine learning for facial and body recognition has to be accessible and helpful to people is through a project called Child Growth Monitor (GCM). It helps parents track the growth and health of their children through an easy-to-use app. GCM can enable parents to detect any deficiencies, disorders, and diseases early.
Thank you for reading Top Machine Learning Technologies List (Real World Examples). We shall conclude this article now.
Top Machine Learning Technologies List (Real World Examples) - Conclusion
Nonetheless, top machine learning Technologies List (Real World Examples) and its high level subtype, Deep Learning are particularly useful for sorting and interpreting massive volumes of data. Any problem that requires a tool that processes and sorts data quickly can utilize machine learning technology. The above guide provides you with five top examples. Other areas where machine learning has been beneficial include personal assistants, governance, fitness tracking, and cyber security. How has machine learning benefited you? If you have any questions or queries, please leave a comment down below. As always, thank you for reading.
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