Ryan Kirby, Simatree’s Senior Technology Manager, Application Architecture, has developed a series of Insights to demystify various aspects of artificial intelligence. Read the first two post in the series here – “Discover 10 Unexpected Uses for AI Chatbots You Might Have Missed” and “Want to be the Person Who Steals Your Job?”. Plus, read more of Simatree’s Insights on AI here.
Semantic inference is the understanding of the meaning behind language. It goes beyond recognizing the words themselves. It’s actually more about comprehending intent, context, or even relationships between different concepts. It’s being able to read between the lines instead of just taking things literally. When you ask the question “Where can I grab a bite around here?”, a system with semantic inference capabilities would infer that you’re looking for nearby restaurant or food recommendations, rather than taking it literally. This brings a much more natural and intuitive communication to an experience.
In our increasingly “AI” world, we engage with language models more and more, whether through virtual assistants, chatbots, search engines or other applications. Semantic inference is crucial for these systems to understand us and provide relevant and helpful responses. Semantic inference bridges the gap, allowing for smoother exchanges and better answers. (It’s likely that you have experienced frustration with older assistants like Alexa or Siri and the lacking semantic inference.)
At the technical core, a language model’s ability to understand semantics are word embeddings. These are vector representations of words, where words with similar meanings are positioned closer together in the high-dimensional vector space. Here’s a oversimplified example, take the two sentences “Kittens are soft” and “Beds are soft”. Mathematically speaking, we can know that kittens and beds are semantically related generally because they appear in more internet content, near each other, more often than say the word cacti. These word embeddings are learned during the pre-training phase of the language model, where the model is fed almost incomprehensible amounts of prepared text data and tasked with predicting the next word in a sequence based on the context.
While semantic inference is not a new technology, it’s still an active area of research and development. As these capabilities are improving at speed, our interactions with AI will become increasingly seamless and natural. So, while you may not think about semantic inference explicitly, it’s playing a big role in shaping the future of human-computer interaction. Understanding what it is and why it matters puts the amazing potential of AI communication into perspective.
