How does AI work – From ML to all applications

Artificial intelligence (AI) is a subset of data science that goes back to the 1950s. It has evolved by combining extensive data sources with powerful computer processors and advanced algorithms.

The hallmark trait of AI solutions is that they can learn to accurately predict the answer or outcome of a query. Today, AI encompasses a whole ecosystem of connected technologies that make it possible for machines to engage in problem solving and perform human-like tasks.

Here are a few of the underlying components that give AI its superpowers:

• Natural language processing (NLP) allows humans to query models using conversational terms instead of having to translate the request into computer science terminology. When you ask AI-powered apps and chatbots, such as ChatGPT, to answer a question or write some text, you are prompting it using NLP.
   
  The same natural language processing is also integral to other types of bots, like voice assistants. Speech recognition allows voice assistants to understand and respond to spoken queries and commands.
• Computer vision enables software to learn and recognize all kinds of images, from animals to road signs. Applications for computer vision include robotics, autonomous vehicles, and self-driving cars.
   
  Within ag-tech, image recognition powers smart machines that can tell a weed from a row crop and spray pesticide only on the weeds to reduce the amount of chemicals used in conventional agriculture.
• The Internet of Things (IoT) consists of all our wearable trackers, sensors, voice assistants, and smart home devices. IoT devices provide a constant stream of data that use machine learning models to learn and adjust to new information as it comes in.
• Application Programming Interfaces (APIs) turn AI functionality into neat packages of code that you can integrate into existing products to provide next-level customer experiences.
• Machine learning (ML) is a major subfield of AI. Machine learning models teach AI systems how to learn. They use principles of statistics and psychology to train algorithms in the art and science of classifying data and predicting outcomes. In this way, ML models can reveal new insights that enable businesses as well as individuals to make better decisions.
   
  When training AI models based on machine learning principles, a training data set is required to support the machine learning process with reading or identifying a specific type of data. Data formats include text, number, image, and video formats. The more data available, the better the system functions.
   
  Big data analytics can make sense of large amounts of data by recognizing trends and patterns and machine learning can speed up this process by employing decision-making algorithms. This type of data analysis is particularly valuable to businesses and can help shape operations decisions.

The earliest ML models required structured data and guidance from a human being to learn right from wrong.

Data scientists call this type of ML supervised learning because it involves using pre-labeled datasets to train the algorithm. Today, we also have unsupervised learning models that can learn on their own. Essentially, they use other algorithms to study and classify data that is not labeled by a human.

A sub-category of machine learning algorithms, called neural networks, has interconnected layers just like the human brain, which allows these models to find patterns in extremely complex information. And within the field of neural networks, deep learning algorithms can take large bodies of raw, unstructured information and identify patterns and categories within the dataset.

Reinforcement learning is a machine learning training method that rewards correct behaviors and punishes incorrect ones. Generally, a reinforcement learning agent (the entity being trained) is able to interpret its environment, take actions, and learn by a process of trial-and-error.

The iterative nature of machine learning means models are independently able to adapt as they are exposed to new data and they produce more reliable decisions and results.

We tend to associate AI with the idea of replacing humans, but its true potential lies in the opportunity to augment human capabilities. As Stanford Digital Economy Lab Director Eric Brynjolfsson explains in a 2022 paper, focusing efforts on augmentative AI versus automation that replaces human labor creates new capabilities, products, and services, ultimately generating more value for society.

Modern AI can be used in many real-world scenarios, such as evaluating job applications in hiring, analyzing sentiment to resolve customer support requests, or understanding individual biomarkers to recommend lifestyle health interventions. The possibilities truly are limitless.

At this point in its evolution, AI has been deployed in just about every major industry – as voice-powered virtual assistants such as Amazon’s Alexa and Apple’s Siri, chatbots, recommendation engines, translators, photo tagging agents, and data analysts.

Here are a few common applications of AI:

• Healthcare companies can use AI to analyze medical images and help practitioners make accurate determinations. In an emerging use case, an image generating AI called Stable Diffusion is being studied to create training images for medical doctors to learn about rare diseases.
• Retailers have embraced the potential of AI to enable conversational commerce, live virtual shopping, and personalized experiences that complement e-commerce experiences, increasing engagement and loyalty.
• Manufacturers rely on AI to forecast demand as well as load vs. capacity on their equipment. They also can get insight from analyzing companywide data to improve decision-making and detection of quality issues.
• Global supply chains that use AI-driven systems benefit from more accurate capacity planning, enhanced demand forecasting, improved productivity, and lower supply chain costs.
• Financial services providers have implemented AI-powered fraud detection and smarter credit scoring for their banking customers to improve safety and efficiency.
• Cybersecurity solutions use AI to detect and respond to cyber threats in real-time. AI can recognize patterns and act on the user’s behalf, strengthening defences against cyber-attacks.
• Content creators and digital marketers use AI to speed up productivity and optimize workflows. Using artificial neural networks to identify patterns and structures within existing data, generative AI can create new and original copy and images at lightning speed. This AI-generated content can be used for blogs, marketing copy, or social media messaging.

At Sitecore, we’re leading the way with AI-powered search, enabling visitors to get hyper-relevant results from complex queries across all digital channels and assets. It may sound counterintuitive, but we believe deploying AI to assist with routine and repetitive tasks will help brands become more human in their interactions with customers.

And as AI technologies gain further decision-making capabilities along with widespread adoption, we anticipate a new wave of innovation that will benefit marketers, developers, customers, and society.