What is machine learning? Effects on everyday life and privacy

Machine learning (ML) is a forward-thinking branch of artificial intelligence. It aims to build machines imitating human behavior, primarily the way we learn. In simpler terms, machine learning is all about giving devices the power to analyze and interpret data.

We can find many examples of machine learning in our everyday life. For instance, machines likely predicted the products recommended to you online. Furthermore, we rely on such systems to flag and delete spam on email services. Lastly, automatic caption generation on videos also depends on ML.

Wait, is machine learning the same as AI?

The hype around artificial intelligence is undeniable. Sometimes, you might notice AI and machine learning mentioned interchangeably. However, remember several factors differentiating these related but distinct concepts:

• Artificial intelligence is a broader term, and it also includes ML.
• Machine learning does not fully define AI, as it is only a component of the intricate AI concept.
• So, AI is a technology fueling machines to simulate different human behaviors. On the other hand, machine learning is a subset that lets systems automatically learn from past data.

Understanding machine learning

Machine learning is an approach to artificial intelligence, powering many astonishing and futuristic technologies. For instance, apps like Voila and similar editing apps all follow modern AI and machine learning concepts.

It focuses on generating machines capable of solving complex problems and responding without explicit programming.

Typical machines rely heavily on their creators’ code, meaning developers must evaluate each possible scenario. Then, they need to establish definite steps for each situation, giving clear-cut instructions for the machine.

ML aims to lift some of the burdens off developers’ shoulders. The ideal is to build machines capable of human-like intelligence and adaptation.

Therefore, machine learning allows systems to learn how to perform tasks independently. Usually, such gradual understanding gets achieved by feeding large sets of data to computers. The more data machines get, the “smarter” they become.

Then, systems analyze these information bulks and teach themselves appropriate solutions and strategies. Over time, machine learning also optimizes the processes, finding more efficient ways to complete tasks. Ideally, machines accomplish their goals without direct human input.

This technology has transformed and reinvented many industries. However, its contribution to digital privacy has also made it easier to uncover valuable insights. Furthermore, bias and unintended outcomes like reflecting inequities also remain matters of concern.

How does machine learning work?

Machines and humans learn and operate differently. Traditionally, computer systems can perform complex tasks at incredible speeds. However, humans still need to do the actual thinking, instructing systems what needs to be done.

Machine learning enables computers to become thinkers, executing typically human-supported commands quickly and efficiently. Research indicates that machine learning makes material science calculations 40,000 times faster than usual.

But how do specialists teach computers how to learn from supplied data sets? Let’s look at a simple example illustrating ML operations.

Bananas or apples? A simple machine learning example

1. Say we want to train our machine to differentiate between apples and bananas on a conveyor belt successfully.
2. For this, we need to create a machine learning algorithm.
3. The first step of our process will focus on gathering training data. The goal of training is to build a model that can answer questions or perform tasks we need. In this case, we wish that the machine would place the correct labels on fruits.
4. For the training data, we classify fruits based on their differences. For instance, we could pick weight (in grams) and color (wavelength) as the main features.
5. Our data should have many entries as it will directly influence the accuracy of our algorithm. The more entries, the better results we can expect. Also, it is better to avoid data imbalance, like adding more than half examples of apples.
6. We also add our data in an appropriate setting like Anaconda, the Python distribution. Then, we set the algorithm to make observations of the supplied data.
7. It is important to note that we use the supervised learning technique. It means that we supply our machine with a dataset of inputs and correct outputs. The model will gradually improve its accuracy and reduce errors according to this information.
8. Finally, we supply another list of data points, seeing how well our ML algorithm manages to predict fruits.
9. At first, the machine learning algorithm will struggle to identify apples and bananas correctly. However, it will learn to make appropriate predictions by recognizing patterns in the training data. For instance, it can notice that most bananas are yellow, making it a rule to identify yellow data points as bananas.

Please note that this simplified example gives you a broad idea of how machine learning works. It can require more features, data points, and different types of ML. However, sophisticated algorithms significantly contribute to applications and businesses, influencing key growth metrics.

Types of machine learning

Experts can turn to different ML types depending on the goal, data sets, and data complexity.

• Supervised learning. It is a model for which specialists feed the algorithm structured data. Organized spreadsheets are one of the examples that experts can supply.
• Unsupervised learning. It is a model in which computers work on unstructured data. This type might be common for information that humans struggle to organize. For instance, spam detection algorithms can use it for analyzing emails and identifying common red flags.
• Semi-supervised learning. In such machine learning, humans monitor the computer system and guide it. For instance, a machine receives a smaller organized dataset which it needs to use to classify unlabeled data.
• Reinforcement learning. In such cases, sample data does not train the algorithm. Instead, it operates entirely on a trial-and-error basis. It gets rewarded for desired behaviors and punished for unwanted ones. Thus, such learning adds feedback into the mix.

Real-life integrations machine learning

You encounter machine learning algorithms daily. Here are some examples that empower the digital space as you know it:

• Recommendation engines. Online services are eager to suggest products or movies you might like. For instance, many streaming services use machine learning algorithms to predict titles suitable for your taste.
• Enhancing search experiences. The infamous Google algorithms use ML to make predictions on users’ intentions. Thus, it supplies a more personalized experience depending on users’ previous interactions.
• Customer service. Many chatbots replace humans when it comes to supporting clients. Now, algorithms can answer common questions customers have, like on prices, shipping, or give personalized advice.
• Working with machines. People fear that AI and machine learning capabilities threaten their livelihood. The biggest concern is jobs, with some experts arguing that AI could replace doctors. However, such problems are not exactly accurate. A more valid prediction is that doctors work hand-in-hand with AI, which has already become a reality.
• Speech recognition. Many services allow users to dictate texts instead of typing them. Such text transcripts also rely on ML.
• Detecting devices in need of repair. Studies suggest how convenient machine learning can be for maintenance. Data collected over time can and does help monitor the state of different equipment.

Do machine learning and AI threaten privacy?

Virtual assistants heavily rely on machine learning to improve their voice recognition technologies. Thus, it is fair to suggest that businesses use ML as one of the means for achieving their strategic objectives. For virtual assistants like Siri and Alexa, the goal is to understand natural language and generate appropriate answers. However, achieving corporate goals can be a priority above ethical and just data analysis.

We see risks to individuals’ rights and freedoms in the AI processing of personal data. For one, racial bias is one of the biggest challenges for ML algorithms.

Data sets tend to have blind spots, and the assumptions algorithms make can be dangerous. For example, inaccuracies in algorithms dealing with health care could be even more high-risk.

Another concern is that ML and AI enable companies to study large data sets effectively. While it is incredibly business-wise, meaning corporations can draw assumptions about users from seemingly disorganized data.

Other concerns include the following:

• The ability to re-identify personal information using large datasets.
• Experts require more regulations against pairing extensive scale surveillance with machine learning methods.
• Who is responsible for systems making decisions that negatively impact people’s lives?
• Racial bias or other types of dangerous profiling, like gender discrimination during hiring processes.
• Lack of transparency over how companies will use the generated insights and how long they keep the data.
• Facilitating different privacy issues through AI-powered systems, like deepfakes.

Last notes

AI and machine learning are potentially the most exciting technologies of our age. They can benefit users and companies, polishing product quality and enabling customization.

However, lack of regulations and constraints ruin the fun, especially with algorithms integrated into online platforms and industries from health to retail. Firms capturing more details about their customers might use this knowledge unethically.

With proper foresight, it could be possible to subdue the overwhelming effect of AI and machine learning. It has a bright future, but it needs adequate control and repercussions for unintended outcomes.