Erik Delorey, Director of Innovation Engineering at Miratech summarizes the history of AI, from simple tools to CX transformation
Artificial Intelligence (AI) has been around for over six decades, debuting in 1956. While early AI applications such as speech recognition became available to the public in the 1990s, recent advancements in generative AI and natural language processing have driven a surge in mainstream adoption and interest.
Today, businesses are eager to adopt powerful AI tools in the hopes of gaining a competitive advantage or, at the very least, staying afloat in fast-evolving industries. However, not all AI is created equal.
Take generative AI, for example. It’s like a universal remote interfacing with other AI systems and providing a powerful new way to interact with the advanced technologies already operating behind the scenes. Yet Gen AI, as it’s known for short, is just one area of AI as a whole. In this article, we’ll explore the different categories of AI and how each can help accelerate business growth and efficiency.
The Evolution of AI Systems
1. The Birth of AI: Classification Systems
Simple classification tools were the first artificial intelligence systems created in the 1950s. They solved single-dimensional problems by responding to simple yes-or-no questions. “Cat or not cat?” is an example of an AI-based image classification system.
Classification schemes are still essential nowadays. “Fraud” or “Not Fraud” in banking, or “Resolved” versus “Not Resolved” in contact centers, are examples of classification outcomes that AI can help to determine. Over time, these classification systems have been aggregated, grouped, and combined into core libraries and AI can help perform billions of classifications per second.
2. The Rise of Machine Learning: Feedback Loops
From here, feedback systems followed. Here’s how it worked: people evaluated a machine’s outputs, assigned a “Pass” or “Fail” rating, commented on how correct it was, and provided feedback on its accuracy. The algorithm then refined its logic to increase the classification skills as it examined bigger databases.
These systems helped the classification systems to improve, sometimes with human interpreters, and sometimes with other computer-based evaluations. This ushered in the age of machine learning, marking a critical shift from static to dynamic systems capable of adapting and evolving.
3. Predictive Systems: Forecasting the Future
AI quickly developed predictive capabilities, building on classifications and feedback. By analyzing historical data, machines started making prediction patterns.
These systems have unquestionably changed industries, and they underpin a wide range of applications, including supply chain forecasting and stock price predictions. One of the earliest examples in healthcare is the MYCIN, a 1970s medical diagnostic system that used AI to identify bacterial infections and recommend tailored antibiotic treatments, demonstrating the potential of predictive AI in medicine.
4. Decision-Making Systems: The Game Changers
After laying the groundwork with prediction capabilities, AI evolved into decision-making. Game theory drove this advancement, making machines proficient in strategic games like chess and pong. Logistics planning, operations management, and even real-time strategy optimization are now driven by AI decision-making systems.
5. Recommendation Systems: Guiding Choices
As AI evolved, recommendation systems emerged, which suggest products, content, or actions based on patterns from similar users or scenarios. Think about how social media algorithms show you personalized news and updates, Netflix selects your next binge-worthy series, or Amazon suggests your next purchase. These systems don’t just analyze; they engage. They keep you clicking, browsing, and exploring by appealing to your interests or evoking strong feelings.
6. Reinforcement Learning: Reward and Repeat
Unlike recommendation systems, reinforcement learning introduces a feedback loop that rewards desired behaviors.
This might take the form of receiving a badge or hearing a chime when you reach a target in workforce management and learning systems. Similar reinforcement loops are used internally by machines that modify their algorithms in response to feedback such as post-interaction surveys or thumbs-up buttons.
7. The Revolution of Natural Language Processing (NLP)
Finally, we arrive at the transformative power of Natural Language Processing (NLP)—the technology that bridges human communication and machine understanding.
For many years, people had to write code to communicate with machines. By making it possible for machines to comprehend human language, NLP revolutionized the game. Machines now interpret meaning, intent, and emotion by deconstructing letters, phrases, and sentences into mathematical tokens.
Since language can now be processed mathematically, it can be understood by machines. Natural Language Understanding (NLU) has enabled scientists to ‘generate’ responses that humans can understand. This breakthrough is why Generative AI is now considered the “new keyboard and mouse,” allowing people to interact with technology in previously unthinkable ways. Speaking the computer’s language is no longer necessary; instead, the focus is on machines comprehending ours.
How Generative AI Powers Contact Centers Technology
How we work, live, and interact with technology has evolved as AI systems have progressed over time, from basic classification capabilities to natural language processing. In a modern contact center, generative AI seamlessly integrates into the customer experience to resolve common issues, such as handling a billing complaint:
- A customer reaches out via chat to dispute an unexpected charge. The AI bot quickly classifies the issue as a billing concern and detects frustration in the customer’s tone of voice.
- Predictive systems analyze past billing inquiries and identify similar cases, anticipating possible resolutions.
- Decision-making systems assess the customer’s eligibility for a refund or account adjustment based on policy guidelines.
- Recommendation systems suggest tailored solutions such as a refund or a personalized payment plan to retain customer satisfaction.
- Reinforcement learning ensures future interactions improve by incorporating feedback from this experience.
- Each AI-facilitated layer works in harmony, ensuring the customer’s experience is both efficient and personalized.
Bringing It All Together
The history of AI spans many decades, and recent advancements mean its transformative potential for businesses is now huge. Generative AI is the gateway to a suite of tools and capabilities that are no longer the preserve of experts. As you embark on your AI journey, focus on the practical outcomes you want to achieve. AI is a tool, and, like any tool, its value lies in how you use it.
At Miratech, we’re here to help visionaries execute their vision and transform their business—one intelligent system at a time. Ready to get started? Contact Miratech today.
