Artifical Intelligence and Machine Learning: What’s the Difference?

We developed a yield monitor system that utilises Artificial Intelligence and advanced data collection to register GPS tags every few meters. This system is designed to determine the quantity and quality grade of potatoes immediately after harvest. In a first for Australia, COREMATIC designed the first Reverse Vending Machine (RVM) manufactured in Australia.

AI-based model is black-box in nature which means all data scientists have to do is find and import the right artificial network or machine learning algorithm. However, they remain unaware of how decisions are made by the model and thus lose the trust and comfortability of data scientists. Machine learning algorithms such as Naive Bayes, Logistic Regression, SVM, etc., are termed as “flat algorithms”. By flat, we mean, these algorithms require pre-processing phase (known as Feature Extraction which is quite complicated and computationally expensive) before been applied to data such as images, text, CSV. For instance, if we want to determine whether a particular image is of a cat or dog using the ML model. We have to manually extract features from the image such as size, color, shape, etc., and then give these features to the ML model to identify whether the image is of a dog or cat.

The Relationship Between Machine Learning and Artificial Intelligence

After all, the conference collected some of the brightest minds of that time for an intensive 2-months brainstorming session. This subcategory of AI uses algorithms to automatically learn insights and recognize patterns from data, applying that learning to make increasingly better decisions. The development of AI and ML has the potential to transform various industries and improve people’s lives in many ways.

Before we jump into what AI is, we have to mark that there is no clear separation between AI and ML. However, we define Artificial intelligence as a set of algorithms that is able to cope with unforeseen circumstances. It differs from machine learning in that it can be fed unstructured data and still function. Artificial intelligence is a broad term, but it includes machine learning. If your business is looking into leveraging machine learning, it’s not a question of either or because machine learning can’t exist without AI. Also, AI can be used by Data Science as a tool for data insights, the main difference lies in the fact that Data Science covers the whole spectrum of data collection, preparation, and analysis.

How Companies Use AI and Machine Learning

The learning algorithms then use these patterns to make better decisions in the future. Basically, the main aim here is to allow the computers to understand the situation without any input from humans and then adjust its’ actions accordingly. Machine learning models are designed to handle large sets of structured data and analyze them to discover patterns and trends humans wouldn’t identify. A deep learning model is not recommended as it’s not designed to recognize trends and patterns within structured data. Data scientists focus on collecting, processing, analyzing, visualizing, and making predictions based on data.

In ML, there is a concept called the ‘accuracy paradox,’ in which ML models may achieve a high accuracy value, but can give practitioners a false premise because the dataset could be highly imbalanced. AI and ML are already influencing businesses of all sizes and types, and the broader societal expectations are high. Investing in and adopting AI and ML is expected to bolster the economy, lead to fiercer competition, create a more tech-savvy workforce and inspire innovation in future generations. Another significant quality AI and ML share is the wide range of benefits they offer to companies and individuals. AI and ML solutions help companies achieve operational excellence, improve employee productivity, overcome labor shortages and accomplish tasks never done before.

Machine Learning vs. Deep Learning

Other intelligent systems may have varying infrastructure requirements, which depend on the task you want to accomplish and the computational analysis methodology you use. High-computing use cases require several thousand machines working together to achieve complex goals. Data scientists select important data features and feed them into the model for training. They continuously refine the dataset with updated data and error checking.

The major difference between deep learning vs machine learning is the way data is presented to the machine. Machine learning algorithms usually require structured data, whereas deep learning networks work on multiple layers of artificial neural networks. There are different types of algorithms in ML, such as neural networks, that help solve problems.

Key Differences Between Machine Learning and Artificial Intelligence

When it comes to performing specific tasks, software that uses ML is more independent than ones that follow manually encoded instructions. An ML-powered system can be better at tasks than humans when fed a high-quality dataset and the right features. The other major advantage of deep learning, and a key part in understanding why it’s becoming so popular, is that it’s powered by massive amounts of data.

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