A dummy’s guide to how AI is trained to understand human languages

The journey of artificial intelligence from understanding to generating human language is a complex dance of technology, linguistics, and psychology. It’s about making machines hear, listen, speak, and communicate, turning the once-distant dream of seamless human-AI interaction into our everyday reality.

Imagine a world where every whisper, every question, and every command you make is not just heard but truly understood by machines. This is not science fiction; it is the reality of today’s AI. 

From the chirpy responses of smart home devices to the nuanced dialogues with chatbots, AI has woven itself into the fabric of our daily communication. But how does this technology decode the complexities of human language, with all its characteristics, slang, and cultural usage? 

To understand the inner workings of this technology, first, we need to know that Natural Language Processing (NLP), a subfield of AI, is essentially the overarching domain that encompasses all the aspects of understanding and generating human language by AI systems. 

In this piece, TheCable will take you through the fascinating trip of how language is generated and understood by AI.

SPEECH RECOGNITION

For speech recognition, AI starts by capturing the audio input and converting it into a digital format. Here, acoustic models are used to analyse the sound waves and attempt to match them against known patterns. This process involves understanding phonemes, the smallest units of sound in speech.

The next step is language modeling, where AI predicts words or sequences that are likely to follow based on the context. It uses statistical models to guess what you might say next, almost similar to autocomplete on your phone but on a grander, more sophisticated scale.

NATURAL LANGUAGE UNDERSTANDING (NLU)

Once the speech is transcribed, the AI must parse this text. Parsing involves breaking down sentences into grammatical components to understand the structure, which includes identifying subjects, verbs, objects, and other speech structures involved.

Beyond the structure, AI interprets meaning through semantic analysis. This includes understanding synonyms, idioms, and the intent behind a query. For instance, when communicating with a voice assistant, “Can you turn on the light?” and “Could you brighten the room?” should result in the same action if the AI understands the semantics of your language. 

CONTEXTUAL AWARENESS

AI systems are increasingly equipped with contextual awareness, which involves a memory of previous interactions.

An example is chatbots, e.g., Meta AI, which keeps a record of past conversations to tailor responses more accurately over time. We can extend this to an environmental context, a situation where voice assistants like those in smart homes might adjust responses based on time, location, or the presence of other devices.

LANGUAGE GENERATION

For text generation, using large language models like BERT and GPT, AI can generate human-like text. These large language models (LLMs) are trained on vast datasets, learning patterns of human language from the internet and available literature. 

This also involves response formulation; when generating responses, AI considers relevance – how pertinent is the response to the query? It also considers coherence – does the response make sense in context? 

Then it moves to style, where it is capable of mimicking human conversational styles to make interactions feel natural.

CONTINUOUS LEARNING AND ADAPTATION

AI employs machine learning algorithms that learn from interactions, and they can do this through feedback loops where users’ corrections or preferences help refine the AI’s understanding and generation of language.

For example, suppose there is a chatbot designed to provide definitions for health terminologies. In that case, the chatbot is trained on a large dataset of texts, but it is not perfect and can make mistakes. 

Subsequently, the chatbot takes the user’s corrections and updates its internal knowledge on the subject matter.

Another way AI systems can learn is through the updating of new data. Regular updates ensure that the AI stays current with linguistic trends, slang, and even cultural shifts. 

EMOTIONAL AND SENTIMENT ANALYSIS

AI is getting better at interpreting not just what is said but how it’s said. It can detect tones of language and understand sarcasm, urgency, or politeness.

For sentiment analysis, it is capable of gauging emotional states from text or speech, which can be crucial for customer service bots or mental health applications.

ETHICAL AND PRIVACY CONCERNS

As AI moves deeper into human language, ethical issues have come up. Issues like data privacy – data collected by AI can reveal much about personal habits, necessitating robust data protection measures.

There is also the concerning issue of voice manipulation and bias, where AI is capable of inheriting biases from training data, leading to skewed interpretations or responses. 

However, efforts are ongoing to raise the bar on data (voice) protection and debiasing of these systems.

In an interview with TheCable, Temi Babalola, founder of Spitch, a text-to-speech company, said his organisation is working on a feature that would be able to classify audio and detect whether it was generated by AI or not.

“This would help clear the air regarding some audio files and some discrepancies that could come up where someone says, ‘This was generated by AI or was not.’ I mean being able to recognise and differentiate between AI-generated content and actual, real human content,” he said in the interview.

“I think that is a very good start at approaching misuse of the product with this, and that will be coming up soon. Also, nobody has come to say, ‘My voice was used’. The data we are using for generating voices is one we have the license to use.

“We have restrictions on voices. There are only four voices, and right now, nobody can bring a voice and tell us to copy it. But we can do that. We are just trying to be careful about how we are going to launch that feature.”