An essential characteristic of wisdom is the alignment between three worlds: the world that we think that exists, the world that really exists and the world that we would like to exist. The greater the alignment the more likely it is that we will make good decisions.

It’ s been more than two years since the introduction of ChatGPT and the unprecedented mix of confusion, inflated expectations, and exaggerated valuations associated with the“ AI” hype cycle. As with most innovations, that hype cycle now begins to subside. However, large consulting companies and a variety of software vendors, who aim at maximizing the value that they can receive from all the commotion, have no interest in letting it subside. They work to keep the hype going rather than helping the industry align on the world that really exists and ground itself as to what is possible. We have perhaps lived through a historical case study of the result on markets of extreme hype on a subject most people know so little about.

“ 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 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 so-called GenAI. In the context of NLP, the main idea behind these systems is that tasks such as answers to

The lasting value of LLMs will be the ability to create computer interfaces based on natural language.

Intelligent CIO North America Issue 59 | Page 33

An essential characteristic of wisdom is the alignment between three worlds: the world that we think that exists, the world that really exists and the world that we would like to exist. The greater the alignment the more likely it is that we will make good decisions.