The Simplified AI series summarizes our community members’ thoughts to help people better understand complex AI topics.
In last month’s article, we learned more about the future of AI, AI Explainability, and Human Compatible AI. In this article, I shift the focus to the more technical side of AI.
A glance at some of the definitions
Before we dive into the topic, we’ll go through a few key phrases related to AI. Many people use artificial intelligence to describe computer systems that can carry out human-level tasks. On a deeper level, goal-directed adaptive behaviour refers to using advanced AI systems in various industries.
One subtype of artificial intelligence (AI) that automates learning and improves with experience without explicit programming refers mainly to supervised machine learning, unsupervised machine learning, and reinforcement machine learning.
Besides ML, deep learning, which is also a subtype of machine learning (ML), we are able to perform complex tasks such as image and speech recognition. For this to happen, the software must first be programmed with neural networks that consist of multiple layers. Then it can learn by itself by exposing the neural networks to a significant amount of data.
Discovering the purpose of machine learning and auto-ML
To be honest, 2020 was both a year of surprises for businesses and a wake-up call for leaders. Because of the worldwide pandemic, the global recession had unanticipated financial consequences for businesses, setting the stage for unprecedented economic hardship. Because of the current ‘New Normal’, some business leaders are beginning to use technology to better deal with future changes and operations. Until now, it has been challenging to comprehend and apply.
Still, this is a massive opportunity for artificial intelligence. AI and ML can assist businesses in identifying issues and responding more quickly by presenting a current state, an earlier state, and a predicted state. In this way, leaders can implement critical decisions that affect operations, reduce costs, save time, or increase prices, profits, and productivity.
Automatic Machine Learning (AutoML) is bridging these gaps. AutoML is utilizing current AI-based systems to find optimal solutions in the real world. It combines mathematics and statistics with computer science, but it is also essential to comprehend the underlying data sets.
It is important to emphasize that AutoML is rapidly increasing in prevalence, making the development process go faster. While embracing AutoML offers significant time-saving elements, it also empowers data scientists by upskilling them with domain expertise; as a result, AutoML does not make data scientists redundant, on the contrary, it makes them more valuable by enabling them to address more complex questions surrounding AI.
Example ML tools include; Power BI, Microsoft Azure, Google’s Auto ML, IBM’s Watson Studio, and Data Robot. As per your application, the appropriate platform should be selected, and accessibility should be enhanced to empower analysts and business partners. This helps organizations by increasing the libraries of built-in Auto ML models.
The technique of AutoML (i.e., machine learning) can also assist in feature engineering, early focus on suitable models, and learning proper hyperparameters. AutoML also can assist in switching the neural network architecture to the problem at hand.
More advanced neural networks and beyond
The most basic explanation of how neural networks work is to think of them as computer simulations of the human brain, known as ANN (Artificial Neural Networks). These networks use artificial neurons that loosely mimic neurons in a biological brain.
The edges are referred to as connections. In general, neurons and edges will have adjustable weight as they learn. Neural networks frequently misinterpret the temporal, hierarchical, and memory attributes. What defines us as human beings is the ability to communicate with other people to be brief and contextual. Another way to think of it is that thoughts are a continuum, and lexicons (languages) are discrete.
To better mimic the human brain, neuroscience is the new and evolving era of neural networks, and it requires the implementation of a hierarchical temporal model.
Thus, this blog covered a diverse spectrum of classical AI, machine learning, and deep learning. Based on the application that we are implementing and the industry, customer experience being at the heart of the implementation, creating purposeful AI applications with ethical frameworks, and proactive augmented intelligence should be a vision for business leaders for the success of AI.
