EDITOR’ S QUESTION
“ AI” is in some ways a catch-all label often referenced as the panacea to all problems. Some refer to it as a revolution that will impact the world like the invention of the steam engine, the generation and efficient transmission of electricity, the invention of the computer or other major paradigm shifts in our global economy.
Despite the chaos of the moment, there is much to celebrate. I too celebrate the progress made and it is significant. However, it is crucial to clarify and align the three worlds so we can act based on the world that really exists as we strive for the world we would like to exist. Decisions based on a lack of clarity and understanding of AI can have large and lasting economic and human consequences.
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
Despite the chaos of the moment, there is much to celebrate. I too celebrate the progress made and it is significant.
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’.
In the last couple of years, AI hype has largely been related to a specific area of deep learning – the socalled GenAI. In the context of NLP, the main idea behind these systems is that tasks such as answers to questions, templates for writing, and summarizations of text can be created by generating content through sequences of tokens, one sequence at a time( the‘ sequence’ here can be interpreted as a sequence of tokens ranging from 1 to N tokens and a token is typically a fragment of one or more words).
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