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AI race: The future is coming too fast
Knowing how to "use" AI in order to provide desired output will put people at an advantage over those who does not know how to command AI.
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High school English and philosophy teacher Eric Vanoncini is seen heading a workshop on ChatGPT organized for teachers by the School Media Service (SEM) of the public education of the Swiss canton of Geneva, on Feb. 1.
(AFP /Fabrice Coffrini)
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here have been many debates and much criticism about the recently viral ChatGPT. In addition to those who are "wowed" by its capability, some people note ChatGPT sometimes "hallucinates" with its output. This is correct and I have experienced it myself when using ChatGPT to assist me on my research on water governance or environmental regulation, the domains of my expertise.
However, people often mistake ChatGPT with GPT (Generative Pre-trained Transformer) or AI "language models" in general. I am no expert in AI, but I have been using these AI tools for some time now to help me with my research.
First, ChatGPT is only a "fine-tuned" version of OpenAI's GPT (the company that created it). A better sense of what the GPT can do can be achieved by using OpenAI’s playground, where we select many versions of the GPT models.
Second, the GPT models, including ChatGPT, can have better results if the user better manipulates their prompts, something known as "prompt engineering".
Third, the GPT models can have better output if they are "fine-tuned" (further trained with specific context) or fed with contextual documents (a process called "embedding"). GPT-4, the latest version, which was released on March 14, is said to be multimodal, capable of receiving image and text input and claimed by OpenAI to have better performance in various academic benchmarks.
That was OpenAI. The race in Large Language Models (LLMs) has just been started. Facebook, Google, Microsoft, Baidu as well as various research institutes and universities have pressed the pedal on the AI race.
So, what are the socioeconomic impacts?
