Beyond the Buzz: Uncovering the Hidden Impact of LLMs on Our Future
Artificial intelligence is a (very broad) field of study that has been around since at least 1956 and it makes absolutely no sense to attribute to it anything different than the benefits of other major fields of study.
Computational systems and techniques that stem from it have been contributing to the progress of many business and scientific areas. In the past decade, the development of deep neural network architectures (one of many approaches in machine learning) have achieved extraordinary results in areas such as natural language processing (NLP), computer vision, speech recognition and synthesis, and scientific computing. Systems based on these so called “deep learning” architectures dominate the news and there is little, if any, debate that these architectures produce state-of-the-art (SOTA) results for specific tasks, in their respective areas. That last part is important to understand. The software system that is SOTA in one area is not the same system that produces SOTA results in another area. Moreover, although multimodal models are on the rise, they are not equally adept to all tasks, and it is unlikely that they will be capable of replicating the success of their special, purpose-built ‘cousins’.
