Machine learning can get smarter by overcoming data quality, bias, complexity and maintenance issues.
Organizations are dealing with huge amounts of data in the pandemic era.
Machine learning is rapidly changing with the rise of the metaverse, quantum computing, NFTs and the internet of things (IoT). There are some key issues that are preventing machine learning from reaching its potential.
Machine learning is a subset of artificial intelligence (AI), which focuses on the use of data and algorithms to imitate the way that humans learn, gradually improving its accuracy.
The iterative aspect of machine learning is important because as models are exposed to new data, they are able to independently adapt.
Machine learning algorithms are used in a wide variety of applications, such as in medicine, email filtering, speech recognition, and computer vision, where it is difficult or unfeasible to develop conventional algorithms to perform the needed tasks.
Machine learning (ML) is powering the 4th industrial revolution amid the Covid-19 pandemic. Whether a business is trying to make recommendations to customers, hone its manufacturing processes or anticipate changes to a market, machine learning can assist by processing large volumes of data to better support companies as they seek a competitive advantage.
Here are 5 trends that are shaping machine learning:
Self driving cars
Machine Learning as a Service
No Code Machine Learning
Machine learning classifiers fall into four primary categories:
1. Supervised learning is defined by its use of labeled datasets to train algorithms that to classify data or predict outcomes accurately.
2. Unsupervised learning uses machine learning algorithms to analyze and cluster unlabeled datasets.
3. Semi-supervised learning offers a happy medium between supervised and unsupervised learning.
4. Reinforcement learning is the training of machine learning models to make a sequence of decisions.
Every year, machine learning researchers fascinate us with new discoveries and innovations, but there are some challenges and limitations.
Here are some key practical issues that are harming machine learning to truly reach its potential.
Machine learning systems rely on data. That data can be broadly classified into two groups: features and labels.
Machine learning relies on the relationships between input and output data to create generalisations that can be used to make predictions and provide recommendations for future actions. When the input data is noisy, incomplete or erroneous, it can be extremely difficult to understand why a particular output, or label, occurred.
Many companies use machine learning algorithms to assist them in recruitment. For example, Amazon discovered that the algorithm they used to assist with selecting candidates to work in the business was biased. Also, researchers from Princeton found that European names were favoured by other systems, mimicking some human biases.
The problem here isn’t the model specifically. The problem is that the data used to train the model comes with its own biases. However, when we know the data is biased, there are ways to debias or to reduce the weighting given to that data.
Building robust machine learning models requires substantial computational resources to process the features and labels. Coding a complex model requires significant effort from data scientists and software engineers. Complex models can require substantial computing power to execute and can take longer to derive a usable result.
Machine learning models are part of a longer pipeline that starts with the features that are used to train the model. Then there is the model itself, which is a piece of software that can require modification and updates. Each model requires labels so that the results of an input can be recognised, then used by the model. And there may be a disconnect between the model and the final signal in a system. Maintenance itself can be costly.
Machine learning offers significant advantages to organizations. The ability to predict future outcomes to anticipate and influence customer behaviour and to support business operations are substantial. However, machine learning also brings challenges to businesses. By recognising these challenges and developing strategies to address them, organizations can ensure they are prepared and equipped to handle them and get the most out of machine learning technology.