What is machine learning? Introduction to the field of the future

What machine learning is (definition)

Machine learning is a subfield of artificial intelligence that studies the development of machines able to find meaningful pattern and relationships in a large input data.

Machine learning data

Data in machine learning is the set of all the measurements and observations gathered about a phenomenon.

These observations can be any type of information, from numbers to text to images.

The data are brought together in a dataset, and divided into features: measurable characteristics of this phenomenon.

In this case, the phenomenon is a physics exam. The features are “study time” and “study place”.

NOTE: this is just an example. To teach machines properly we need huge datasets with thousands of observations.

Machine learning “teaching” approaches

Unsupervised learning

An unsupervised learning model aims to discover patterns, relationships and structures between observations in a non-labeled dataset.

Data is not labeled when all the features are independent from one another.

For example, if we train a model on this dataset, it may discover that students studying in the library were more concentrated and spent more time on books.

Supervised learning

The exam results just dropped!

Now we have a label, a feature “Result” that depends on the other features.

So we build a supervised machine learning model that finds the mathematical relationship between the independent variables X, and the dependent variable y.

In this case, X is [“Study time”, “Study place”] and y is [“Result”]. O

Reinforcement learning

A computer program interacts with a dynamic environment in which it has a goal (such as driving a vehicle, playing a game against an opponent, or making business decisions). During the learning process, the program is given feedback related to a system of rewards, which helps it progress over time. If the action taken is correct, the model gets a reward; if not, it gets punished.

Machine learning models and algorithms

What is a mathematical model?

A mathematical model is a mathematical representation of a system.

To do this, a model can describe information about the system through constants, variables, and equations.

To make things clearer, let’s look at an example.

Our system

Imagine a car moving in a straight line with a constant velocity. In physics, this is called a uniform linear motion.

Model description

Variables:

• Let S(t) represent the position of our car at a given time.
• Let S₀ be the car initial position.
• Let V be the constant velocity.
• Let Δt be the seconds that passed after the start of the motion.

Equation:

The most important equation of this system describes the car’s position depending on the time passed.

What is a machine learning model?

A machine learning model is a mathematical model that describes the input dataset.

For example, in a supervised learning problem, our model can store an equation relating the output and input features.

To learn more about building a ML model, click here.

What is a machine learning algorithm?

A machine learning algorithm is the set of instructions to build a machine learning model.

The step in which our computer executes the ML algorithm to build our model is called training.

To learn more about algorithms, click here.

Machine learning real-life applications

Machine learning systems are used in numerous sectors, here are a few examples:

Healthcare

Machine learning is revolutionizing healthcare by diagnosing diseases through image recognition models, enabling personalized medicine based on individual genetic profiles, facilitating drug discovery, and improving patient outcomes through predictive analytics.

Finance

In the financial sector, machine learning is employed for fraud detection, credit scoring, algorithmic trading to optimize investment strategies, implementation of customer service chatbots for efficient client interactions, and market prediction based on past trends.

Retail

Machine learning applications in retail encompass demand forecasting, personalized product recommendations, inventory management optimization, and the prediction of customer churn to enhance customer retention strategies.

Manufacturing

In manufacturing, machine learning is utilized for predictive maintenance, ensuring optimal equipment performance, maintaining quality control standards, optimizing supply chain processes, and improving production scheduling efficiency.

Technology

The technology sector benefits from machine learning in various ways, including natural language processing (NLP) for virtual assistants, image and speech recognition technologies, and the implementation of recommendation systems in online platforms.

Telecommunications

Telecommunications companies leverage machine learning for network optimization, predictive maintenance of infrastructure components, and customer churn analysis to enhance service quality and customer satisfaction.

Automotive

The automotive industry integrates learning into autonomous vehicles to improve safety, traffic prediction, and optimization to improve overall transportation efficiency and develop automated driving systems.

Energy

Machine learning applications in the energy sector include predictive maintenance for equipment, optimization of energy consumption patterns, and fault detection in critical infrastructure.

Agriculture

Agriculture benefits from ML through crop yield prediction models, precision farming techniques that optimize resource usage, and the detection of pests and diseases for timely intervention.

Education

In education, machine learning provides personalized learning experiences and simple assistance to students and automates grading processes to reduce assessment procedures and ensure more time for teachers to focus on youngsters’ growth.

Government

Governments utilize machine learning for fraud detection in public services, security, and surveillance applications, predictive policing strategies, and the optimization of public services through data-driven decision-making.

Marketing and advertising

In the field of marketing and advertising, machine learning is useful in targeted advertising campaigns, customer segmentation for customized marketing strategies, and in general improving the efficiency of advertisements.

Machine learning problems and limitations

Training resources

The biggest obstacle to training a model is creating a suitable dataset for its training, which in cases of very sophisticated machines like ChatGPT can weigh as high as 570 GB, and the time and power that training a large model requires. In 2023 big tech companies like Amazon, Google, and Microsoft invested over $6 billion in training AIs.

The black box theory

The “black box theory“ poses another significant challenge to artificial intelligence development. It refers to a situation where the machine’s process of making decision output is entirely opaque, meaning that even the coders of the algorithm can’t fully understand the pattern that the program extracted out of the data.

This can cause a lot of problems and in the past has led to tragic incidents, when in 2018 a prototype of a self-driving car ran over a pedestrian, or when in some cases the Bing AI chatbot generated hostile and offensive prompts.

Artificial intelligence vs machine learning vs deep learning

Artificial Intelligence

An artificial Intelligence is a machine that can mimic human behaviors and cognitive skills for problem-solving or learning, such as speech recognition, decision-making, and translation.

There are three types of artificial intelligence, one more developed and powerful than the other in the direction in which they are displayed:

ANI (Artificial Narrow Intelligence)

An intelligent machine that can do only a specific and limited number of tasks that it is programmed to do, and it has no “consciousness” of what is outside the field in which it operates.

AGI (Artificial General Intelligence)

An intelligent machine that can understand and perform a wide range of instructions, but it can also adapt to new situations and decide “how to move” based on past experiences, just like humans.

ASI (Artificial Superintelligence)

A superhuman intelligence machine with cognitive abilities much superior to ours that overcomes every aspect of human intelligence.

Machine learning

Machine learning is a subfield of artificial intelligence that studies the development of algorithms that can learn patterns from given input data.

Deep learning

Deep Learning is a subset of ML based on the neural network model, made of different layers of artificial neurons that interact like the ones in our brains.

Machine learning history

Even though the first machine-learning model, capable of evaluating each checker side’s winning chances was programmed in 1950 by Arthur Samuel (one of the fathers of artificial intelligence who also coined the term in 1959), in 1947 Alan Turing, famous computer scientist, mentioned computer intelligence in a public speech in London, saying, “What we want is a machine that can learn from experience,” and that the “possibility of letting the machine alter its own instructions provides the mechanism for this.”

Only a year later, in 1948, he introduced some basic concepts of artificial intelligence, like a model composed of artificial neurons that emulates the brain’s thinking process (actual neural network).

In 1950 Turing invented the Turing Test, a way to identify if a system is “intelligent” or not based on whether a human, during a conversation, can identify if it’s a machine or a human being.

By the early 1960s an experimental “learning machine” with punched tape memory and trained by humans through reinforced learning, called Cybertron, had been developed. It could analyze sonar signals, electrocardiograms, and speech patterns.

The future of machine learning

Although artificial intelligence technologies will certainly be part of our tomorrow and the ML market value is expected to grow by 1270% from 2022 to 2029 (from $30 billion to $410 billion), the future of this field is still unknown.

Machine intelligence is the last invention that humanity will ever need to make

Nick Bostrom, philosopher at the University of Oxford

Future trends

ML experts have recognized some trends that can shape the future of this interesting and life-changing field.

1. Increased speed with quantum computing

The development of quantum computers is believed to contribute to the industry by making model training processes much faster.

2. The creation of big models

The creation of huge models with knowledge and skills in numerous areas of work and daily life.

3. More adaptable algorithms

Scientists will no longer need to rewrite new algorithms from scratch for each of their projects. Instead, they will be able to integrate their work into the new systems, along with the user data.

4. Loss of importance of codes

With the evolution of numerous libraries, in the future, it will be less and less common to write long and tedious code. The focus will shift more toward understanding mathematical algorithms and how they make decisions than to writing the program itself.

5. The rise of reinforcement learning

As reinforcement learning is the best method of instructing a model on how to make correct decisions, it will be of utmost importance in the future, precisely because it will help us to make small, everyday decisions and also businesses to choose the right marketing or investment strategies.

Coexistence between man and machines: will AI replace humans?

Following the latest advancement in ML people are rightly asking if artificial intelligence will replace us humans in every of our functions.

Although a future in which machines will have dominion over the world is dystopian and highly unlikely, people are worried about the stability of their jobs.

In a pessimistic view of the future, the constant development of ML has created algorithms that overcome every aspect of human intelligence, leading to the disappearance of most jobs. Unemployment is skyrocketing and the social divide is increasingly evident.

In a positive view of the future AI helps human workers be more productive and contributes to technological, scientific, and medicinal development.

In both forecasts, the jobs affected will be reasoning jobs. Compared to the latter, the time when AI will have the skills needed to perform manual jobs will be longer, while professions involving emotions and human interactions will never disappear.