ChatGPT and generative AI can't tell the truth
Generative AI, such as ChatGPT, has the ability to create content but struggles to differentiate between fact and fiction. It presents its creations with unwavering confidence.
State of play:
All the way back in 2022, ChatGPT debuted, and the scenario remains the same today. Despite this, the tech industry persists in reshaping the digital landscape around AI as if these issues do not exist.
Why it matters:
The unreliability of generative AI may seem like a minor inconvenience when used for simple tasks like providing recipes or video recommendations.
Driving the news:
Popular AI "answer engine," Perplexity, is known to produce inaccuracies and muddled versions of published content, as reported by publications like Forbes and Wired.
Catch up quick:
Initially impressing with their ability to mimic famous authors and artists, tools like ChatGPT and Dall-E are now marketed as user-friendly interfaces for various tasks.
Fun fact:
Shortly after the launch of ChatGPT, computer scientists declared it as a "bulls--t generator," sparking discussions and even leading to research papers on the topic.
Between the lines:
The errors and fabrications produced by generative AI are not mere bugs; they are inherent to how this technology functions.
Model builders
These developers can fine-tune the level of randomness in AI models to balance between inventiveness and reliability.
Zoom out:
The ongoing debate about AI's tendency to produce unreliable information continues as the concept is still relatively new to the public.
Potential solutions:
- More accuracy: AI developers may enhance the trustworthiness of their tools through better data and training methods.
- Less confidence: Modifying chatbots to express uncertainty and admit when they lack definite answers could be an approach.
- Embrace BS: Some suggest reframing the fabricated nature of AI as a creative feature rather than a flaw.
The bottom line:
It's challenging to market a technology as revolutionary or civilization-saving when its output cannot be reliably explained, potentially jeopardizing its impact on society.
