Whenever a novel AI model is introduced, its performance is often trumpeted as surpassing expectations on a series of benchmark tests. OpenAI’s GPT-4, launched in May, demonstrated exceptional performance by outpacing every other AI company’s latest model across multiple benchmark tests.
Poorly conceived benchmarks hinder progress, as their flawed design, coupled with arduous reproduction efforts, and reliance on arbitrary metrics, undermines the value of research findings. The degree of scrutiny that artificial intelligence models receive is determined by their performance against established benchmarks and criteria.
As AI companies repeatedly tout benchmark performances as proof of their innovative models’ prowess, those very same metrics are also being integrated into government strategies for AI regulation. However, they may not yet be equipped to effectively utilize this approach.
While generative AI has become surprisingly adept at conversing with us, creating visual content, and even composing music, its ability to assist us in tangible, practical ways remains somewhat limited.
AI brokers vow to transform this. Last week, researchers published a groundbreaking paper detailing their findings on how they successfully…
Artificial intelligence systems designed to replicate human appearance and behavior will soon take autonomous action on our behalf in the near future? As these instruments become more affordable and easier to manufacture, they will raise numerous ethical concerns, with two fundamental issues emerging to the forefront. .
