An Introduction to Levels of Artificial Intelligence (AI) and Spend Analytics

It is increasingly difficult to read any sort of business publication these days without seeing how AI is going to change the way business is done. Some of this is reality but some of it does look a bit like hype and the latest silver bullet to solve all problems. CEO after CEO is informing the world about their plans for AI, but there does appear to be a lack of deeper awareness of how AI works and how it can be applied.

For the purposes of this document we have looked at three major areas of AI; Artificial Intelligence at a general level and two prominent subfields; Machine Learning and Deep Learning, each with its own distinct characteristics and applications. Machine Learning and Deep Learning are both critical components of modern AI systems but the way in which the different elements of AI work to process data differs which in turn can have an impact upon the speed and the accuracy of data processing.

Artificial Intelligence

In the broadest sense, AI is a transformative field of computer science that aims to create intelligent machines capable of performing tasks that typically require human intelligence. The way that AI generally works here is using rule-based systems which is a set of predefined rules to make decisions or perform tasks. These rules are typically created by human experts in a specific domain and are encoded into the system. Rules based AI is used for applications such as chat-bots that can handle a basic set of questions and then hand the client over to a human if the request cannot by handled by AI alone.

Machine Learning:

Machine Learning is a subset of AI focused on developing algorithms that enable computers to learn patterns and make predictions or decisions from data without being explicitly programmed. In traditional programming, developers write explicit instructions for computers to follow. In contrast, machine learning algorithms learn from large datasets to recognize patterns and make decisions or predictions. Some companies are starting to harness machine learning as the basis of their spend analytics offering.

Deep Learning:

Deep Learning is a specialized subset of machine learning that involves artificial neural networks, inspired by the structure and function of the human brain. Deep Learning algorithms, also known as artificial neural networks, consist of many layers of interconnected nodes that process data in a hierarchical manner. These networks can automatically learn representations of data through the process of training on large datasets. Deep Learning has gained significant attention and popularity in recent years due to its remarkable performance in various tasks, such as image recognition, natural language processing, and speech recognition. ProcureVue uses deep learning to power the swift processing of cleaning, harmonising and categorising spend data

What Are The Differences Between General AI, Machine Learning, and Deep Learning?
General AI, often referred to as Artificial General Intelligence (AGI), is the ultimate goal of AI research. AGI aims to create machines that possess human-like intelligence and can perform any intellectual task that a human can. However, AGI remains largely theoretical and is yet to be achieved. Rodney Brooks, a roboticist at the Massachusetts Institute of Technology and cofounder of iRobot, stated in multiple articles that he believes true AGI won’t arrive until the year 2300.

Machine Learning is a broader concept that encompasses various techniques and algorithms for enabling computers to learn from data and improve their performance over time. It includes traditional statistical methods as well as modern approaches like Deep Learning.

Deep Learning, as mentioned earlier, is a specific subset of machine learning that utilizes neural networks with many layers (hence the term "deep"). Deep Learning has shown remarkable success in tasks involving large amounts of data, complex patterns, and unstructured data types like images, text, and audio.

What Are The Benefits of Using Deep Learning for Spend Data Analytics?

Deep Learning offers several advantages for spend data analytics, particularly in handling large volumes of heterogeneous data and extracting meaningful insights. Here's why it's the optimal tool for analysing spend data:

• Feature Learning: Deep Learning models can automatically learn relevant features from raw spend data, eliminating the need for manual feature engineering. This capability is crucial for dealing with complex spending patterns and diverse data sources.
• Complex Patterns: Spend data often contains intricate patterns and relationships that may not be easily discernible through traditional analytics approaches. Deep Learning algorithms excel at uncovering these complex patterns, leading to more accurate predictions and insights.
• Scalability: Deep Learning models can scale effectively to handle large datasets with millions of transactions, making them well-suited for analyzing extensive spending data from diverse sources such as procurement systems, invoices, and financial records.
• Adaptability: Deep Learning models can adapt to changing spending patterns and business dynamics without extensive manual intervention. This adaptability ensures that spend analytics solutions remain effective and relevant over time, even as the business environment evolves.

In summary, Deep Learning offers a powerful toolkit for analyzing spend data, enabling organizations to derive actionable insights, optimize procurement processes, and make informed strategic decisions. Its ability to automatically learn from data and extract complex patterns makes it a valuable asset in the realm of spend analytics.

How can ProcureVue™ help you? 
ProcureVue™ will work with you to clean and analyze your spend data. Once cleansed, the data is harmonized, categorized, enriched, and analyzed to generate an accurate snapshot of your company’s performance. The result… your populated spend cube and potentially powerful insights. 

From there, ProcureVue™ analysts transform your spend cube into easily digestible visualized insights with identified quick-hitting areas that you can engage immediately.