What is Machine Learning? Why is it Important?

What is Machine Learning?

Machine learning (ML) is a field of research devoted to understanding and creating methods that “learn”, that is, methods that take advantage of data to improve performance on some set of tasks. It is considered part of artificial intelligence. Machine learning algorithms build a model based on data samples, known as training data, to make predictions or make decisions without being explicitly programmed to do so. Machine learning algorithms are used in a wide variety of applications, such as medicine, email filtering, speech recognition, agriculture, and computer vision, where it is difficult or impossible to develop traditional algorithms to perform the necessary tasks.

A subset of machine learning is closely related to computational statistics, which focuses on making predictions using computers, but not all machine learning is statistical learning. The study of mathematical optimization introduces methods, theory, and application areas in the field of machine learning. Data mining is a related field of study, with an emphasis on exploratory data analysis through unsupervised learning. Some machine learning applications use data and neural networks in a way that mimics the functioning of a biological brain. Machine learning in its application through business problems is also known as predictive analytics.

How does Machine Learning Work?

UC Berkeley (link is outside IBM) divides the learning system of its machine learning algorithm into three main parts.

Decision Making: Generally, machine learning algorithms are used to make prediction or classification. Based on some input data, which may or may not be labeled, your algorithm will produce an estimate of a pattern in the data.
Error function: The error function evaluates the prediction of the model. If there are known examples, the error function can perform a comparison to evaluate the accuracy of the model.
Model optimization process: If the model can better fit the data points in the training set, the weights are adjusted to reduce the discrepancy between the known example and the model estimate. The algorithm will repeat this “evaluation and optimization” process, updating the weights independently until the accuracy limit is reached.

Machine Learning Methods

Machine learning models fall into three main categories.

Supervised machine learning: Supervised learning, also known as supervised machine learning, is defined by the use of labeled datasets to train algorithms to accurately classify data or predict outcomes. When input data is entered into the form, the form adjusts its weights until it fits properly. This happens as part of the cross-validation process to ensure that the model avoids overfitting or improper fitting. Supervised learning helps organizations solve a variety of real-world problems at scale, such as sorting spam into a folder separate from your inbox. Some of the approaches used in supervised learning include neural networks, naïve bays, linear regression, logistic regression, random forests, and support vector machine (SVM).

Unsupervised machine learning: Unsupervised learning, also known as unsupervised machine learning, uses machine learning algorithms to analyze and aggregate unlabeled data sets. These algorithms discover hidden patterns or data sets without the need for human intervention. This method’s ability to detect similarities and differences in information makes it ideal for exploratory data analysis, cross-selling strategies, customer segmentation, and image and pattern recognition. It is also used to reduce the number of features in a model through a dimension reduction process. Principal component analysis (PCA) and singular value decomposition (SVD) are two common approaches to this. Other algorithms used in unsupervised learning include neural networks, k-means clustering, and probabilistic clustering methods.

Semi-supervised learning: Semi-supervised learning provides a happy medium between supervised and unsupervised learning. During training, it uses a smaller, labeled dataset to guide classification and extract features from a larger, unlabeled dataset. Semi-supervised learning can solve the problem that there is not enough labeled data for a supervised learning algorithm. It also helps if it is too expensive to categorize enough data.

Also Read, Cyber Security 

Machine Learning Challenges

With the development of machine learning technology, it has definitely made our lives easier. However, the application of machine learning in companies has also raised a number of ethical concerns about AI technologies. Some of these include:

Technological exclusivity: While this topic attracts a lot of public interest, many researchers are not worried about the idea of ​​artificial intelligence surpassing human intelligence in the near future. The technological singularity is also known as strong artificial intelligence or superintelligence. Philosopher Nick Bostrom defines superintelligence as “any intelligence that far exceeds the best human minds in almost every field, including scientific creativity, general wisdom, and social skills.” Despite the fact that superintelligence is not forthcoming in society, the idea of ​​it raises some interesting questions when we think about the use of autonomous systems, such as self-driving cars. It is unrealistic to think that a self-driving car will never have an accident, but who is responsible under the circumstances? Should we continue to develop self-driving vehicles or limit this technology to semi-autonomous vehicles that help people drive safely? The jury is still out on this one, but these are the kinds of ethical debates that happen with the development of innovative new AI technology.

The impact of artificial intelligence on jobs: While much of the public perception of AI focuses on job losses, this concern likely needs to be reframed. With each new disruptive technology, we see the market demand for specific job roles change. For example, when we look at the auto industry, many manufacturers, like General Motors, are shifting to focus on producing electric vehicles to go along with green initiatives. The energy industry will not disappear, but the source of energy is changing from fuel economy to electricity.

Likewise, artificial intelligence will shift job demand to other fields. There must be people to help manage AI systems. People will still need to be there to tackle more complex issues within industries that are most likely to be affected by changes in labor demand, such as customer service. The biggest challenge of AI and its impact on the labor market will be to help people transition into the new roles that are in demand.

Privacy: Privacy tends to be discussed in the context of data privacy, data protection, and data security. These concerns have allowed policymakers to move forward in recent years. For example, in 2016 the GDPR was created to protect the personal data of individuals in the European Union and European Economic Area, giving individuals greater control over their data. In the US, individual states are developing policies, such as the California Consumer Privacy Act (CCPA), which was introduced in 2018 and requires companies to inform consumers about the collection of their data. Legislation like this has forced companies to rethink how they store and use personally identifiable information (PII). As a result, investments in security are becoming an increasing priority for companies as they seek to eliminate any vulnerabilities and opportunities for surveillance, hacking, and cyberattacks.

Prejudice and discrimination: Cases of bias and discrimination in different machine learning systems have raised many ethical questions regarding the use of artificial intelligence. How can we protect against bias and discrimination when the training data itself is generated by biased human processes? While companies often mean well for their automation efforts, Reuters (link is outside IBM) highlights some of the unintended consequences of integrating AI into hiring practices. In its efforts to automate and streamline the process, Amazon inadvertently discriminated against gender candidates for technical positions, and the company was ultimately forced to cancel the project. The Harvard Business Review (link resides outside of IBM) has raised other specific questions about the use of AI in hiring practices, such as what data you should be able to use when evaluating a candidate for a position.

The Future of ML

For all its shortcomings, machine learning remains critical to the success of AI. However, that success will depend on another approach to AI that addresses its weaknesses, such as the “black box” problem that occurs when machines learn without supervision. This approach is symbolic artificial intelligence, or a rule-based methodology for processing data. The symbolic approach uses the knowledge graph, which is an open box, to define semantic concepts and relationships.

Together, machine learning and symbolic AI constitute hybrid AI, an approach that helps AI understand language, not just data. With more knowledge about what was learned and why, this powerful approach transforms the way data is used across the organization.

Leave a Reply

Your email address will not be published. Required fields are marked *