Machine Learning: Defined and Explained
Understand Machine Learning, its definition, history and applications. Deep dive in the methods and technical challenges of Machine Learning.
What is the Definition of Machine Learning?
Here, we explain what machine learning (ML) is and what it involves. You will also find an overview of its beginnings, the characteristics of different types and an introduction to its challenges. Finally, we discuss the likely rewards that today's forward-thinking companies can reap from artificial intelligence and ML. Please read on to discover more.
How Machine Learning Works
In the expanding field of artificial intelligence (AI), ML algorithms analyze situations and predict results. The discipline employs tested computer science principles inspired by what humans learn. Let's delve deeper into the fascinating world of Machine Learning!
A Brief History of Machine Learning
ML dates back more than six decades to the early development of a computer program to play drafts or checkers. In 1959, A. L. Samuel published details* of his research into automating the board game. This first paper became a landmark in ML after the computer won a game of draughts against a human. In his paper, Samuel described early notions of machine learning. He envisaged that programming computers to learn from experience would eventually eliminate detailed coding efforts. During the 1960s, computer programmers and researchers enhanced this early model. Then, of course, the following decades saw the introduction of ever more powerful chess algorithms. Nowadays, applications and automated systems are becoming increasingly rapid and accurate in their performance.
Machine Learning Methods
ML is a subset of AI and it features a number of algorithms, ranging from statistical modeling, support vector machines, ensemble methods and artificial neural networks. In artificial neural networks, each node has threshold settings that influence whether it sends or ignores the information in response to inputs. Training and – to a lesser extent – validating the model involves adjusting the weightings between these neurons. Linear regression is another approach that is one of the most popular. It uses the principles of statistics and mathematical equations for predictive analysis, especially with fluctuating variables such as age, salary, sales and price. In essence, the technique interpolates and looks for the line of best fit among a series of dots – each a data item – on an X-Y graph. Regression techniques also help us understand how dependent variables change in response to primary (independent) variable fluctuations, assuming that other values remain the same. Another approach, logistic regression, is used for classifying true or false situations, i.e. those that result in a yes/no condition or a Boolean output of 0 or 1. Deep learning networks are artificial neural networks that have four or more layers, while a basic neural network would have just two or three.
Machine Learning vs. Deep Learning vs. Neural Networks
Although some people use the terms machine learning and deep learning interchangeably, there is a distinction between them. Deep learning uses neural networks to identify features in the data without the necessity for human intervention; it is a subset of ML. Deep learning can gain insights from labeled datasets. Unsupervised deep learning does not require labeling or tagging to function. Instead, it can process unstructured data autonomously and determine which characteristics distinguish different categories of records from each other.
Supervised Machine Learning
In supervised ML, software engineers or developers use a labeled data set to orient the machine learning model, for example, a neural network during training, validation, and testing. The labels or tags identify characteristics or properties that enable the correct classification of each record. Then, diagnostic algorithms use cross-validation techniques to ensure accuracy. The machine must receive sufficient data without causing bias due to overfitting, which results from skewed inputs or too much information of one type.
Unsupervised Machine Learning
Unsupervised learning analyses clusters of information to detect hidden patterns and find groupings. This method is beneficial when we want to discover the differences and similarities, such as in customer segmentation exercises or to develop cross-selling strategies in business.
Semi-supervised learning combines the two previous methods by using a degree of labeling or tagging to assist classification. In addition, it supports the extraction of features from a more extensive import that is unlabelled. This hybrid approach is advantageous when a lack of labeled data or training in a supervised learning algorithm would be too expensive.
Our guide to the Types of Machine Learning will enable you to get a more detailed understanding of the field.
Reinforcement Machine Learning
Reinforcement ML algorithms interact with their set environment to optimize rewards. As a result of each step in this trial and error, the ML model receives positive signals for correct actions – or negative cues for mistakes. The method enables machines to maximize performance or determine the ideal behavior in specific contexts. Data scientists use reinforcement learning to teach neural networks multi-step processes for which defined rules exist. Instead of data, the human input involves positive or negative feedback cues. However, the machine learning algorithms themselves decide what steps to take. Reinforcement learning is helpful in operations research, swarm intelligence, and simulation-based tasks to optimize resource usage. Under this model, software agents taking actions based on cumulative reward is comparable to human behavior and motivation in a broad range of situations. Analogies range from decision-making based on essential survival priorities to maximizing retail marketing returns from in-store footfall patterns. In business, reinforcement learning helps companies plan the optimal allocation of finite resources. Conversely, reinforcement learning models have learned to compete in various video games in the leisure arena.
Discover how training data can make or break your AI projects, and how to implement the Data Centric AI philosophy in your ML projects.
Real-World Machine Learning Use Cases
Reinforcement ML is a particularly suitable technique in gaming, robotic tasks, and controlling autonomous vehicles. Supervised learning is helpful in translation engines with incomplete dictionaries. In these cases, algorithms may learn to translate written language (or speech to text) by analyzing context, checking grammar, and interpolating meaning. Similarly, semi-supervised learning can be valuable in analyzing statistics and preventing fraud. It is usually possible to teach a model to identify suspicious cases using relatively few positive examples. In this application, classification algorithms analyze similarities, looking for patterns and exceptions in banking and commercial transactions. Some of the most frequently seen examples of ML include:
Suggested products based on page visits or previous purchases.
Recommendation engines for video streaming services.
Tailored selections of news items.
Chatbots to handle initial inquiries from website visitors.
Voice recognition and control systems including Alexa and IoT (Internet of Things) devices.
Recommended courses and job vacancies for individuals, according to their qualifications, skills, knowledge, work experience, or preferences.
Implementing ML enables business development and marketing teams to understand and appreciate customers' needs in greater depth. It also means they can monitor customer behavior and create fruitful sales initiatives for products and services in different industries. Similarly, in CRM (customer relationship management), the latest software reads incoming company emails and directs them accordingly. In most cities, smartphone applications for online private hire and taxi services are ubiquitous. These popular ride services use AI algorithms to match drivers and vehicle locations to passengers' requests.
Challenges of Machine Learning
Although AI and ML have several advantages, a few factors require consideration before embarking on a project. Firstly, the cost of setting up the necessary software infrastructure can be relatively high. In addition, most projects call for the services of data scientists and skilled researchers. Also, the process may well require allocating internal resources and working time, particularly with data preparation.
Alternatively, some companies outsource this time-consuming task to an expert service supplier. The source(s), format, and quality of data imports are essential to project success. For these reasons, project planning and execution during the learning and validation phases must avoid ML bias. The latter tends to occur through overfitting, i.e. tuning the machine learning model too heavily on a subset of data that is too different from the “real-world” data. On the other hand, insufficient data is also likely to cause inaccurate output. So, we can see that expertise and experience are valuable from the design stage.
The Impact of AI on Jobs
Thanks to advances in processing power and storage capacity, technological developments are set to continue. Already, automated human resources information systems can filter through applications and identify interview candidates based primarily on keyword scanning. As well as current projects such as autonomous cars and surprisingly accurate video streaming recommendations, the technology will surely expand to other business areas and questions. In robotics, which relates to the physical world, reinforcement learning can teach machines to perform routine, repetitive tasks. Apart from manufacturing, we can expect more comprehensive automation in service transactions from food waiters to travel and customer inquiries.
As the use of ML increases in automated decision-making, so will the demand for training and validation data. However, these imports often contain potentially sensitive information, often in the medical and finance domains. Data protection legislation, including GDPR, requires the safeguarding of personal data. Article 35 of that directive compels organizations to analyze, identify and minimize data protection risks for every algorithm and project. To address this critical need, open-source tools such as ML privacy meters enable developers to quantify privacy risks. The tool highlights training data records that could leak through model parameters or algorithm predictions.
Bias and discrimination
Algorithms trained on similar data are liable to result in unreliable output that does not reflect real-world situations. At best, these flawed systems are irrelevant or inaccurate. At worst, they could fail or be discriminatory by excluding specific customer profiles or populations.
Currently, AI models need extensive training to optimize them to perform a single task. In the future, researchers look set to produce more flexible models. It may become feasible to develop techniques that enable a machine to retain learning from the context of one or more previous tasks and apply it to new jobs. However, concerns have arisen about the idea of AI surpassing human intelligence. While this superintelligence is unlikely to occur soon, some commentators have indicated it might happen within a few decades. Consequently, they have suggested keeping any superior intellect on a short leash if it outperforms humans in creativity, wisdom, and social skills.
Accountability and ethics
Other issues include questions such as: to whom would the occupant(s) of a driverless car be responsible for an accident? Recent advances in motion systems, sensor accuracy, and processor speed mean that robots are becoming more nimble and life-like. Four-legged dog-like machines now form part of explosive ordnance disposal equipment. However, the possible military repurposing of these defensive robots into highly agile machines that can run, pounce, and attack is a concern for those advocating pacific uses. By last year (2021), news stories** featured the US Army testing armed robots with sniper rifles.
Today, AI is a rapidly growing field. In ML, we use statistical methods to classify data, make predictions and provide valuable insights. Deep learning powers the most advanced applications in AI. Enterprise technology vendors such as Microsoft, Google, IBM and Amazon offer various platform services and competition look set to intensify. However, Kili Technology is the complete solution to iterate smoothly on your training data, train your AI faster, and ship turnkey projects ready for production. With Kili, you can better manage training data, whether images, video, text, voice, or PDF files. The benefits of this leading solution include rapid annotation, quick collaboration, intuitive quality control, and straightforward data management. The software is available either as a cloud service or local installation to match all requirements. Collaboratively, Kili eases the alignment between SMEs and ML experts. It enables developers to build machine learning algorithms using the latest techniques and accessible languages, including Python. If you are a CTO, technical CXO, or business decision-maker and would like to discuss your requirements, please go here. Our team of approachable experts will be delighted to answer any questions you may have.