How AI Assistants Understand Natural Language
Nuances of Understanding Natural Language
Artificial intelligence (AI) assistants like Siri, Alexa, and Google Assistant have become ubiquitous daily. Natural language processing (NLP) allows these assistants to understand human speech and respond in natural conversational language. But how exactly do they work?
In a recent Software Spotlight podcast interview, Mike Heap, co-founder of the AI startup My AskAI, provides insights into the complex technology behind AI assistants and their ability to comprehend natural language. Mike and his partner Alex Rainey founded the bootstrapped startup in December 2022 just outside London, England.
The Challenge of Understanding Human Language
Human language is incredibly complex. Heap explains, “There are many nuances, cultural differences, and ambiguities.” Teaching a computer to understand natural language is an immense challenge.
Unlike computer languages with rigid, predefined rules and syntax, human languages are fluid and contextual. Slight variations in phrasing can completely alter meaning. Sarcasm, irony, and cultural references are difficult for an AI system to grasp.
According to Heap, the key lies in exposing the AI assistant to massive volumes of conversational data: “The models need huge amounts of data to learn all the variations and possibilities of how humans speak.”
Natural Language Processing
At the core of AI software assistants is natural language processing (NLP), which refers to the ability of computer systems to analyze, understand, and generate human language.
Heap outlines a high-level overview of how NLP works in AI assistants:
Speech recognition
The assistant captures the spoken audio and converts it into text using speech recognition algorithms.
Intent recognition
The text is analyzed to determine the user's intent. What is the purpose of their query?
Entity recognition
Key entities like people, places, and dates are extracted from the text.
Response generation
The AI generates a natural language response based on the intent, entities, and context.
This pipeline allows the assistant to intake the user's spoken query, comprehend the meaning, and respond appropriately.
The Role of Machine Learning
Modern NLP systems rely heavily on machine learning and intense learning neural networks.
Heap explains, “The AI is trained on massive datasets of human conversations to learn the patterns of natural language.” By analyzing countless examples, machine learning algorithms can decode the nuances of human speech.
Essential machine learning techniques like recurrent neural networks and transformer models underpin the NLP capabilities of AI assistants. Billions of conversation logs, web pages, documents, and more provide the data to train these AI models.
Handling Ambiguity and Context
A significant challenge in NLP is dealing with ambiguity. As Heap highlights, “A word like ‘book' has very different meanings based on context.”
AI assistants use various techniques to handle ambiguity:
Part-of-speech tagging
Labeling each word with its part of speech (noun, verb, adjective, etc.)
Intent recognition
Determining the user's goal and context
Dialog management
Asking clarifying questions to resolve uncertainty
By combining various NLP techniques, the assistant can select the correct interpretation of an ambiguous phrase by considering the user's context and intent.
Advances in Conversational AI
Recent years have seen rapid progress in conversational AI, enabling more natural interactions with AI assistants.
Heap notes two key developments:
Personalization
Assistants can learn about individual users and tailor responses accordingly.
Contextual awareness
Maintaining context and continuity throughout multi-turn conversations.
Together, these improvements allow for more human-like conversations with AI assistants.
The Future of AI Assistants
The My AskAI platform is a no-code chatbot that anyone can use, regardless of their programming knowledge. As Heap notes, AI assistants are steadily becoming “more useful, more intelligent, and more ubiquitous.”
Some critical areas of development he highlights:
Multimodal input supports inputs like images and video beyond just text or voice.
Task automation
Assistants proactively perform tasks and make recommendations. AI assistants offer numerous benefits, including increased employee productivity and the automation of repetitive and manual workflows.
Hyper-personalization
Tailoring responses to individual users based on personal context and preferences.
AI assistants are on an exciting trajectory, becoming an increasingly seamless and helpful part of our everyday lives. However, making further progress in natural language understanding remains a monumental challenge.
“The complexity of human language should not be underestimated,” Heap concludes. “We still have a long way to go.”
Try My AskAI Free on your website today.
Watch the My AskAI Software Spotlight podcast episode: Mike Heap and Alex built MyAskAI off previous AI products focused on fine-tuning GPT models. They created an easy-to-use platform for non-technical people to instantly upload content like documents and websites to create an AI chatbot. MyAskAI has seen significant traction, especially for customer support use cases, with the ability to quickly find answers in vast amounts of data and provide them to users.
