Learning is complex. What does it take for a person to learn a language, for example? If it’s a modern language, the student buys a book, takes a class or downloads an app. In each option, the student receives specific instruction for mastering part of the language, including feedback from the teacher, checking answers in the book or interacting with the app’s interface.
What if it’s a long-dead language? Something uncovered centuries after the last speakers and readers died? Learning that type of language takes a different approach. A researcher might look for patterns in the markings or seek out similar languages from the period. This pattern-making can sometimes lead to huge breakthroughs in learning this mysterious language.
Humans learn lots of things using both types of approaches. Now that researchers are pushing the boundaries of artificial intelligence, AI approaches learning with similar, if still rudimentary, methods. Let’s explore the world of AI learning with supervised, unsupervised and reinforcement methods.
Supervised Learning vs. Unsupervised Learning vs. Reinforcement Learning
AI researchers can teach computers to mimic human behavior using all three types of learning processes. None of the learning techniques is inherently better than the other, and none take the place of the rest. Instead, each AI learning technique offers specific advantages depending on the need, time and use case.
Supervised learning is like purchasing a language book. Students look at examples and then work through problem sets, checking their answers in the back of the book. For machine learning, AI also learns to mimic a specific task, thanks to fully labeled data. Each training set is human-marked with the answer AI should be getting, allowing the machine to compare new input with the labeled sets.
Real-world applications include things like predicting housing prices. The machine receives input from recently sold houses, including specs like location and square footage, and notes the final sale price. From there, it looks at similar houses that haven’t sold yet to make a logical guess about the final price.
Supervised learning is best suited for things like:
Desired outcomes (i.e., you know what you’re looking for)
Messy data with fewer labels requires unsupervised learning. Much like trying to uncover the secrets of a lost language, unsupervised learning relies on connections, patterns and trends in whatever data is available for training.
One area common for unsupervised learning is genetics. Also, when researchers want to explore novel materials for different aspects of the supply chain, for example, unsupervised learning provides a way for AI to examine the characteristics of existing materials and predict the synthesis of new materials.
Unsupervised learning is best suited for things like:
Exploratory analysis (i.e., you don’t know exactly what you’re looking for)
Reinforcement learning comes from a different approach. Imagine a traveler lands in a country and doesn’t speak the language. With each interaction, the traveler learns more about how to communicate — some interactions produce positive results, and some don’t. When artificial intelligence engages in reinforcement learning, humans set up the parameters and reward AI with each decision. It’s up to the machine to determine how to maximize that reward.
Reinforcement learning is frequently used for parking algorithms in city traffic optimization. Recent studies have shown promise in easing traffic congestion through traffic signals optimized with reinforcement learning.
Reinforcement learning is best suited for things like:
Challenges and exploration with massive datasets
What Uniphore Has Accomplished So Far
One of the biggest fields for artificial intelligence exploration is conversational AI. Although developers thought that language modeling would be finished in a few years, the pursuit of human-like understanding has taken at least five decades. Uniphore’s work in conversational AI showcases some of the best of what the field has accomplished using these three learning devices.
Uniphore’s continued investor funding reflects the increasing demand companies have for conversational AI capabilities. Uniphore addresses the entire conversational experience for both customers and agents. Delivering both integration and automation requires AI models that can:
Analyze structured data such as relevant customer information
Automate common processes while continually learning how to increase productivity
Offer a self-serve AI-driven virtual assistant capable of responding to a variety of inputs
In addition, Uniphore acquired Emotion Research Lab earlier this year, a move that will add serious depth to existing conversational analysis through facial analysis and eye-tracking. With machine learning, agents will be able to analyze emotional responses in real-time through video analysis.
Uniphore will also work with other startups and innovation labs through the World Economic Forum’s Global Innovators Community to further research learning algorithms that will shape the next generation of conversational AI. We will continue to work on speech recognition, spoken language understanding, as well as other pragmatic applications of language models. Each exploration will build on everything accomplished in natural language processing, thanks to these three types of artificial intelligence learning.
Machine Learning Models for the Conversational AI Future
It takes a variety of learning models to even begin to approach the complexity of human language and analysis. Uniphore’s integrated products build on the foundations of supervised, unsupervised and reinforcement learning to approach the myriad of challenges presented in trying to help machines and humans work together.
For a deep dive into what machine learning can accomplish with conversational AI, download our ebook, “The Future of AI for Contact Centers,” for a practical look at what language capability and machine learning can accomplish.