What is Machine Learning?

On Mar 15, 2020 at 1:56 pm UTC by Osaemezu Ogwu · 12 min read
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Photo: Depositphotos
Photo: Depositphotos

Machine learning has become an important aspect of our lives. This modern technology has gained use cases in several industries due to the immense benefits it offers. Find all the key information about machine learning in this guide.

Machine learning is the use of artificial intelligence (AI) that automatically enables machines to learn and improve based on experience. As such, these machines do not have to be explicitly programmed nor do they require human assistance or intervention to learn. Instead, they obtain data to learn and make necessary adjustments.

The learning process, on the other hand, centers on observing large amounts of data including images, numbers, instructions, examples, clicks, etc. The machine learns using the patterns in the data and is able to make better decisions in the future. Generally, it can be said that the machine learning process involves discovering the pattern in data and then applying the pattern.

Machine Learning Offers Advanced Applications

Machine-learning algorithms have brought about advancements in the applications available today. It also backs popular services including search engines like Google and Baidu, entertainment platforms like Netflix, Spotify and YouTube, and social media platforms like Facebook and Twitter.

Each of these platforms or services uses machine learning to predict what the user might do. Let’s take the case of Facebook, for instance, the platform collects data of the links you click, statuses you react to, type of content you view, etc. This information can be used to predict your behavior in the short term and offer you services based on it.

Methods of Learning

There are different methods of machine learning algorithms. These methods are supervised, unsupervised, semi-supervised, and reinforcement learning.

Supervised Machine Learning Algorithm

This method of machine learning algorithm is the most prevalent. In this method, data is labeled to instruct the machine on the exact patterns to find. A real-life scenario is watching a show on Netflix which instructs the algorithm to find related shows to the one you’ve accessed. It can be said that what was learned in the past is applied to new data to predict future events

Over and above that, a supervised machine learning algorithm begins with the analysis of a known training dataset. The learning algorithm then creates an inferred function to predict the output values. The system can provide targets for new inputs after gaining enough training. Also, the algorithm can compare its output with the correct intended output while discovering errors in the process and making modifications to the model

Unsupervised Machine Learning Algorithm

Unsupervised learning is less popular than supervised learning since they have low applications. This method of machine learning does not classify or label data that is used to train, hence, it allows the machine to look for patterns itself. The data the machine finds can then be arranged into groups based on similar data. Unsupervised learning may have less real-life applications, but it is used in cybersecurity.

Furthermore, unsupervised learning takes a study of how the system can infer a function that will describe the hidden structure from unlabeled data. The system does not know the right output, instead, it explores the data and makes an inference from datasets in order to describe the hidden structures from unlabeled data.

Semi-supervised Machine Learning Algorithm

These algorithms are classified between supervised and unsupervised learning due to their use of labeled and unlabeled data for training. Here, a small amount of labeled data is used with large amounts of unlabeled data.

Furthermore, this method of the machine learning algorithm is often chosen when the acquired labeled data needs skilled and relevant resources. These resources would enable it to be trained or learn from it. Nonetheless getting more data does not need additional resources. What’s more, this method may be employed in improving learning accuracy.

Reinforcement Learning Algorithm

Reinforcement learning is also an area of machine learning. It involves learning by trial and error to take suitable action or achieve a clear objective. It can be said that the trial and error search is the most relevant characteristics of this method. Here, the machine attempts different things and could be rewarded or penalized based on how its behavior helps it to achieve or retard its goal.

This method also interacts with its environment in a bid to generate actions and discover rewards or errors. Machine and software agents can automatically determine the behavior within a context in a bid to improve performance. The reward feedback helps the agent to learn the best action, and this is known as the reinforcement signal. Google’s AlphaGo is an example of reinforcement learning and the program defeats even the best players at the Go game.

Challenges and Limitations

Some of the challenges and limitations of machine learning include:

Ethics

It is still uncertain what the abilities and restrictions of artificial intelligence are. There are sentiments that humans are advancing into a stage where we trust calculations and information even more than our own sense of judgment and rationale. And that may come with a repercussion.

For instance, there have been reports of individuals crashing into the lake after trusting the GPS rather than their sense of judgment. The same can be said about self-driving cars that also employ machine learning. In the case of the latter, it is still uncertain how the machine will be taught to respond in case an accident occurs.

Data

Adequate data has to be fed to a model so that it can produce helpful outcomes. This is because several AI calculations rely on large amounts of data to provide the required outcome. And the bigger the design of neural systems, the more information required. Therefore, more information has to be provided instead of reusing the same information. The absence of good information can also pose a problem.

That aside, it may seem like data should not be a problem since it’s now inexpensive to store and process petabytes of information. However, sufficient time is needed to gather the big datasets that will be used to train the machine learning model. And if a person chooses to buy ready sets of data, it may be expensive. It could also be a complicated process to prepare data for algorithm training.

Scalability Issues

Machine learning relies on sets of data to create patterns and learn. However, having access to a large set of data may sometimes pose problems. Feeding more data into the machine has often helped in the success of machine and deep learning algorithms. Despite this, more data can lead to scalability issues where there is more data but less time to the data.

Oftentimes, machine learning beginners may test training data successfully and may be led to believe that the test was a success. Nonetheless, it is recommended that some of the data set should be separate when testing models. The set-aside data can then be used to test a chosen model and then learning on the whole data set.

Overfitting and Dimensionality

Overfitting is one of the major challenges of machine learning. In this case, the model may show bias towards the training data. As such, it may not generalize to new data or create a variance. Dimensionality is another limitation of machine learning. Here, algorithms that have more features operate in multiple or higher dimensions. For this reason, it may be more difficult to understand the data.

Developer Bias

One cannot totally write off the possibility for machine-learning algorithm developers to create algorithms that will benefit them more than the society. While these algorithms may be tailored for good, there is the potential for them to be focused on profit more than societal good.

For example, there may be medical algorithms recommending expensive treatments instead of treatments that would’ve offered the best patient outcomes. Furthermore, priority has to be made between traffic speed and reducing car accident death rate. If autonomous cars drive at most 15 mph, it would reduce road fatalities significantly, but at the expense of speed.

Bad Intentions

In 2017, Google began developing software for Project Maven, a U.S. Department of Defense artificial intelligence (AI) project. The project involved the use of drones to help the military in combat and could lead to the creation of complete autonomous weapon systems. Nonetheless, 12 Google employees who did not agree with the search engine giant resigned. About 3,000 others signed a petition asking Google to abandon the project with the military

Popular scientists in fields including AI, ethics, and IT also wrote an open letter to the company, requesting that the project be stopped. It was also required that an international agreement be made to prevent the creation of an autonomous weapon. The fears of these individuals were linked to the ease at which nations will start wars using automated weapons.

Use Cases of Machine Learning

Machine learning use cases are spread across the banking, healthcare, telecom, manufacturing, marketing, retail, amongst other industries. Here’s a detailed outline of each.

Smartphones

Smartphones have been revamped with facial recognition thanks to machine learning. The facial recognition can be used to unlock phones easily and speedily. Also, Facebook uses facial recognition to identify people in photos even when these individuals have not been tagged. The government also uses machine learning to identify and catch criminals.

What’s more, we can now take better pictures with clearer details as a result of phone cameras that have been optimized using machine learning algorithms. The latter analyzes every pixel in a picture and detects objects in the image. This makes it easy to enhance subjects, add or remove details, and even blur the background

Voice Assistant

Voice assistants enable us to issue voice commands to our phones and even smart home devices. We can set alarms using our voice, search for a bookstore on Google, and even turn/off the sound system. Some of the most popular voice assistants include Apple’s Siri, Google Assistant, Google Duplex, Amazon’s Alexa, Samsung’s Bixby, and Microsoft’s Cortana.

On the other hand, voice assistants recognize human speech using Natural Language Processing (NLP). The speech is then converted to numbers using machine language before a response is formulated and returned.

Web Services

Web services such as Email rely on machine learning algorithms to categorize messages into folders. This is an email filtering process that makes it easier for users to access the most important messages. In this case, the subject of the message is assessed and then the message is sent to the appropriate folder. An instance of this is the Gmail app that uses machine learning to arrange messages into folders such as primary, promotion, social.

What’s more, Google search and Google Translate use machine learning. The former relies on large amounts of data to determine its ranking algorithm. Google Translate, on the other hand, aids in the translation of one language to another. Furthermore, LinkedIn, Facebook, Twitter, and other social media platforms use machine learning to recommend content and ads to users of their platforms.

Finance

The financial industry has benefited greatly from the use of machine learning. Today, banks and other financial institutions can easily track credit card fraud since affected users do not have to fill heaps of papers. It means that banks can review millions of transactions faster. What’s more, the spending pattern of a user can be monitored to determine if they’d made a recent purchase or not.

As such, fraudulent transactions are easily detected and the customers can be alerted on time. Once the alert algorithm is triggered, the payment is flagged and put on hold. PayPal, a centralized cross-border payments network, also uses machine learning to combat money laundering. There’s also personalized banking being offered to individuals to enhance the customer experience.

Transportation

Ride-hailing services like Uber and Lyft employ machine learning in their products. Their apps, for instance, are able to find the most optimal route for the customer. Likewise, the app determines the amount that is to be paid using dynamic pricing, where the price on the same route is not fixed.

Instead, the price on the same route is determined using market conditions. Some of these conditions include customer demand, weather, time of the day, etc. It, therefore, differs from manual pricing where the price for the same route is always the same. Likewise, the ability to input a location, destination, and find a driver can be tied to machine learning algorithms.

Security

Machine learning has taken away the need to have security guards to note down people’s plate numbers and even stop suspicious individuals. Instead, video surveillance is used to track citizens in a country. It also helps in detecting intruders or catching criminals. Hence, the tedious task of doing each of these manually is averted.

Websites are also relying on machine language to prove that a person is not a robot. In this case, a CAPTCHA (Completely Automated Public Turing) test is carried out asking the user to pair matching objects to prove that they are human. A study has also revealed that the machine learning in data security could potentially increase big data, analytics, and artificial intelligence spending to $96 billion by 2021.

Sales and Marketing

Ecommerce sites like Amazon and Flipkart, book sites like Wattpad and Goodreads, as well as Food aggregators like Uber Eats, and Zomato are now using the user’s browser history to recommend more products to them. Their ability to do so is a result of the use of a recommendation engine that relies on machine learning. The idea is that presenting the customer with similar products can increase the potential for them to buy it.

Machine learning also eliminates the long wait time to access a company’s support system. It uses concepts of Natural Language Processing (NLP) and sentiment analysis to understand the user and even the tone at which they speak. For voice-based queries, machine learning sends the user to the appropriate support person whereas text-based queries allow the chatbot to handle the user’s query. Juniper’s research. According to a study, chatbots will help businesses to save over $8 billion yearly by 2022.

Conclusion

Machine learning has improved the way we live and transact, since things can now be done automatically, faster, and more efficiently. We now have better smartphones, banking systems, cars, security systems, etc. Despite these benefits, this modern technology comes with its own challenges that could retard its growth and adoption. Nonetheless, considering that its good side greatly outweighs these setbacks, it could become an active part of human life and our future.

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