Artificial intelligence has become the most prevalent technology of modern times and continues to revolutionize various industries. AI has been around for a while, with use cases in voice assistants like Sir and Alexa, search engine algorithms used by Google and Bing, and even computer games like chess. These forms of AI are today known as traditional, narrow, or weak AI and rely on existing data and programmed strategies to respond to specific inputs.
However, the introduction of generative AI in 2022 changed the AI landscape forever, expanding the technology’s capabilities beyond previous realms. Unlike traditional AI, which doesn’t create anything new but relies on existing data and strategies to make human-like decisions, generative AI is capable of creating new data and results based on its training. Here’s an overview of traditional and new AI and their different use cases:
Traditional AI vs Generative AI
Both traditional AI and generative AI have a lot in common and use machine learning to imitate human decision-making. Generative AI is a more specific component of AI that understands context and is capable of generating new human-like content. Gen AI can generate text, codes, descriptions, audio, videos, music, data, and more. Traditional AI tools use historical data patterns to predict outcomes used in specific applications. As such, traditional AI has defined-use cases like playing chess, detecting fraud, and identifying anomalies in images. Generative AI has more general use cases, from answering complex questions to creating new files and data.
AI text to video technology is a transformative innovation that automates the process of creating video content from textual input. This cutting-edge capability harnesses artificial intelligence to interpret and convert written text into visually dynamic and engaging video presentations. By utilizing sophisticated algorithms and deep learning models, AI text-to-video systems can generate scenes, animate characters, and synchronize audio seamlessly.
Generative AI uses human interfaces like chat boxes on web browsers and apps, while traditional AI is utilized in case-specific applications like software dashboards, BI reports, and call center portals. This restricts traditional AI to tech-savvy individuals with specialized skills and knowledge. Anyone can use generative AI.
Modern AI Use Cases
The introduction of generative AI and large-language models has resulted in a world dominated by general-purpose AI systems that can perform pretty much everything. These AI systems are used to augment the work of artists, designers, writers, creators, coders, and manufacturers, thanks to their ability to use training data to develop new data. Modern AI use cases include generating text, images, videos, and music, facilitating product design, analyzing large volumes of data sets, and enhancing cybersecurity and personalization. It seems like all new applications, electronics, and digital developments are powered by some form of AI system. Modern AI systems have dramatically changed creative industries by providing tools that augment the work of artists and musicians. An exciting development in this sphere is songwriting with AI, which allows users to generate original music effortlessly. This capability enhances the user experience significantly, streamlining tasks previously requiring extensive musical expertise.
In regions like Canada and the USA, modern AI systems have been adopted across the board, with applications in education, gaming, finance, healthcare, energy, retail, and more. For instance, leading Canadian casinos use AI to deliver more personalized experiences for players looking to enjoy slots, roulettes, poker, and other real money games. These AI models are deployed in live chat customer service, bonus recommendation, and activity tracking and analysis. AI models can capture player patterns and use them to detect unusual activity, which helps to flag and block unauthorized access, thereby enhancing cybersecurity.
Traditional AI Use Cases
AI has traditionally been used in various ways, including to boost automation, security, analytics, chat, and speed of command execution. The technology has limitless use cases in nearly all industries, from education to healthcare, finance, and manufacturing. Automation is perhaps the most obvious application of traditional AI models. AI’s ability to automate complex tasks allows users to do more in less time. IT teams use AI to automate signups, configure networks, provision devices, and monitor access and usage more efficiently. AI is also the foundational technology for robotic process automation in manufacturing plants.
With technologies like natural language processing, deep learning, and neural networks, AI can quickly analyze and manipulate large volumes of data from different sources. Companies can use AI models to automate repetitive tasks, reducing the demand for human workers. AI tools also help in identifying and responding to trends and inspecting data faster than any human.
Other applications of traditional AI include customer service chatbots, data analysis and reporting, and cybersecurity. For instance, AI systems can analyze data, networks, and activities for anomalies and inform designated teams or execute specific programs.
Key Takeaways About AI Usage
Artificial intelligence is a fast-growing technology with limitless applications. Both traditional and generative AI are used in tandem, with traditional AI models helping in application creation and generative AI helping in simplifying and streamlining interactions between humans and machines. More tech companies are integrating AI in their developments, with the technology even being used to develop AI-powered weather balloons to increase the accuracy of weather prediction. AI is already being used in the supply chain to provide more accurate demand forecasts. The technology aids any process that involves analyzing data, creating summaries, identifying patterns, forming suggestions, and automating tasks based on preset rules.
