Large Language Model Inflection Point?

For most people, it seems as though artificial intelligence has suddenly emerged as an idea and set of possibilities. In truth, AI has been gestating for many many decades. But forms of AI already are used in consumer appliances such as smart speakers, recommendation engines and search functions.

What seems to be happening now is some inflection point in adoption. But consider the explosion of interest in large language models or generative AI. Development has been under way for more than 70 years. 

Search engines, smart phones and smart speakers have been using AI to support speech interfaces. In that sense, consumers have routinely been using AI-assisted devices and apps for some time. 

Large Language Models, or Generative AI, have lots of potential applications in virtually any setting where language, questions and answers or content creation--including development of computer code--is involved. 

source: Java T Point

Compared to earlier supervised learning models, large language models are self-supervised, able to scour huge amounts of internet data to predict the next word in a sentence. 

A large language model “is a type of artificial intelligence (AI) algorithm that uses deep learning techniques and massively large data sets to understand, summarize, generate and predict new content,” consultant Sean Kerner says. 

Right now, the obvious use cases are text summarization, chatbots, search, and code generation. Other use cases undoubtedly will develop. That suggests early use for customer service, text generation, writing of code and information retrieval tasks. 

It seems clear that large language models, as a subset of AI, have reached an inflection point. What remains unclear is the degree of progress and adoption. At most inflection points there is a quantitative shift in usage that often leads to qualitative impact. 

We will see quantitative change a lot faster than potential qualitative effects, near term. The qualitative changes will take longer, but should be far deeper than we now envision. That is just the way technology change tends to happen.