Machine learning

Machine learning is intricately connected to both artificial intelligence (AI) and data science. It is a crucial part of each field, acting as a bridge between AI and data science and enabling the development of intelligent systems that can learn from data and improve over time.

As a subset of AI, machine learning focuses on creating systems that can learn from data and improve their performance over time without being explicitly programmed. It provides the algorithms and models that enable AI systems to learn from experience and make data-driven decisions.

Data science is an interdisciplinary field that involves extracting insights and knowledge from data using various techniques, including statistical analysis, data mining and machine learning. Machine learning is a key component of data science, providing the tools and methods to analyze large datasets , identify patterns and make predictions.

Data scientists use machine learning algorithms to build models that can process and interpret complex data, driving informed decision-making and uncovering hidden insights.

What is machine learning?

Machine learning definition: Machine learning is a subset of AI that uses datasets to train machines on statistical methods of analysis to identify patterns in data.

If artificial intelligence is computer systems mimicking human behavior and thinking, it is made possible by machine learning. As computers learn from datasets and past performance, artificial intelligence becomes more robust. The more machines learn, the more closely they can mimic human intelligence.

Machine learning plays a crucial role in enabling semiautonomous vehicle capabilities. For example, fully autonomous cars will need to navigate city streets without any human interaction, unlike semiautonomous vehicles today that still require some level of human input. These vehicles will rely on real-time machine learning capabilities to operate independently.

Equipped with various sensors, such as cameras, lidar and radar, semiautonomous vehicles continuously collect data about their surroundings. Machine learning algorithms process this data in real time to identify objects like pedestrians, other vehicles, traffic signs and road markings. Using deep learning techniques, the car’s AI system can recognize and classify these objects, predict their movements and make decisions accordingly. For instance, if the system detects a pedestrian stepping into the crosswalk, it can predict that person’s path and decide to slow down or stop to avoid a collision.

Additionally, machine learning models help the vehicle understand and adapt to different driving conditions, such as weather changes or road construction, by learning from vast amounts of driving data. This continuous learning and adaptation enable semiautonomous vehicles to navigate complex environments safely and efficiently, mimicking human driving behavior while reducing the risk of accidents.

How does machine learning work?

Machine learning is a crucial component of artificial intelligence, driving its rapid growth in recent years. With the vast amounts of data being generated, machine learning provides a way to harness and use this data, opening new opportunities and possibilities for what humans can achieve with computers and other machines.

Understanding how machine learning works and where it can be applied is essential. The process begins with training data being fed into a selected algorithm. This training data helps the algorithm learn to recognize patterns and make predictions. New input data is then introduced to test the algorithm’s accuracy and effectiveness.

When the training data is of high quality, the machine can process data more efficiently. This iterative process, known as the fitting process, involves refining the algorithm until its predictions and results align correctly. If the predictions are inaccurate, the algorithm is retrained with additional data until it improves. For example, the algorithms must be continually refined using incorrect responses during road tests as feedback to improve the system. This process is repeated until the desired level of accuracy and efficiency is achieved

The benefits of machine learning have been evident for years, with applications in fraud detection, cybersecurity and chatbots being fine-tuned and advanced through machine learning. In the future, machine learning will continue to play an integral role in the development of autonomous vehicles, robotics and virtual reality.

What is the history of machine learning?

Throughout the last century, machine learning has been integral to the development of artificial intelligence. Neural networking, pattern recognition and vehicle autonomy are all possible because of the research into machine learning.

• 1943, neural networking: In 1943, neural networking was a key part of the beginning of machine learning, with Walter Pitts and Warren McCulloch publishing the very first mathematical modeling of a neural network to create algorithms that mimic human thought processes.
• 1950s and 1960s, pattern recognition: Just under 10 years later, in 1952, Arthur Samuel created a program that enabled a computer to play checkers. Then, in the late 1960s, basic pattern recognition was made possible by the introduction of the nearest neighbor algorithm, first used for traveling sales associates to plan their journeys.
• 1979, the Stanford Cart: In 1979, the Stanford Cart was developed by a group of researchers at Stanford University. It was an autonomous cart able to avoid obstacles within a room.
• 1980s and 1990s, developing programs: One of the biggest developments of the 1980s was NetTalk, where Terry Sejnowski developed a program that learned to pronounce words in a comparable way to how babies learn. In the 1990s, an IBM chess program beat a world champion, and a computer scientist at IBM authored a paper on a new method of ensemble learning.
• The 21st century: The first open-source software library was available for machine learning. ImageNet was developed to help aid image recognition, and facial recognition was developed in the form of Facebook’s Deepface.

What are key types of machine learning?

There are three main categories that machine learning can be divided into: supervised, semi-supervised and unsupervised.

Supervised machine learning focuses on learning from labeled data, and from this, trains the model to make accurate predictions from unseen data. This labeled data includes inputs and correct outputs that allow the model to learn over time. When data mining, there are two types of supervised learning: classification and regression.

• Classification categorizes observations based on the dataset given to that program. For example, classification in supervised learning can classify what emails in your inbox are spam or not.
• Regression is used to predict continuous values. Instead of categorizing, regression is used for forecasting continuous outcomes.

Semi-supervised machine learning relates to some of the samples in the training data not being labeled. It is useful in situations where it is difficult or expensive to obtain enough labeled data or where supervised learning would be beneficial but large amounts of unlabeled data are available.

Unsupervised machine learning uses algorithms to cluster and analyze unlabeled datasets. Unsupervised learning has several uses that include medical imagery, customer purchasing patterns and anomaly detection to avoid security breaches and faulty equipment.

How is machine learning used?

Machine learning plays a pivotal role in autonomous driving, enabling vehicles to navigate complex environments safely and efficiently. Semiautonomous vehicles rely on a multitude of sensors, such as cameras, lidar and radar, to gather real-time data about their surroundings. Machine learning algorithms process this data to identify objects, predict their movements and make driving decisions.

Micron’s high-performance memory and storage products are crucial in supporting these AI workloads. For instance, Micron’s DRAM provides the necessary speed and bandwidth to handle the vast amounts of data generated by the vehicle’s sensors, ensuring real-time processing and decision-making. Additionally, Micron’s automotive and industrial SSD storage solutions offer the capacity and durability needed to store large datasets and training models, which are essential for the continuous learning and improvement of the vehicle’s AI systems.

By leveraging Micron’s advanced memory and storage technologies, autonomous vehicles, semiautonomous vehicles and advanced safety systems in vehicles can achieve higher levels of accuracy and reliability, paving the way for safer and more efficient transportation solutions for the future.