Generative Artificial Intelligence (GAI) has experienced a rapid evolution in recent years, finding applications in diverse fields, ranging from finance to medicine. However, the surging popularity of GAI raises numerous questions about the credibility and safety of deploying such advanced models. Can we genuinely trust these algorithms, especially when they operate with sensitive data?

Generative Artificial Intelligence (GAI) has made remarkable strides in recent years, making its presence felt in various domains, from finance to medicine. However, the escalating use of such advanced models has sparked questions regarding their reliability and safety. Can we entrust these algorithms, particularly when dealing with sensitive data?

A thought-provoking response to this question comes from a recent study conducted by scientists in the USA. The research group analyzed the latest GPT-3.5 and GPT-4 models, evaluating them based on different criteria, including toxicity, systematic errors, and robustness. According to the article published on Arxiv, these models exhibit lower toxicity compared to their predecessors but are still susceptible to external influences.

One significant discovery was the models' ability to deliver toxic content in response to carefully crafted queries, even when their baseline toxicity was reduced. It is also intriguing that, when faced with contradictory cues, the model can generate highly toxic content.

Another area of concern is data protection. Despite its advanced architecture, the GPT-4 model proved more prone to disclosing sensitive training information than GPT-3.5. This implies a risk of revealing critical information, such as email addresses or social security numbers, in response to appropriately designed queries.

However, the reliability of GAI extends beyond security issues. Researchers highlighted problematic behaviors in the models concerning certain data categories. For instance, the models could exhibit biases related to income based on gender or race.

All of this leads to the conclusion that while the potential of GAI is immense, caution is necessary in its utilization. Blindly relying on the outcomes provided by these models, especially when they impact crucial decisions, is not advisable.

In the realm of technological progress, it is crucial for the development of GAI to align with responsibility. Scientists and engineers must be aware of the challenges and limitations of the technologies they create. Implementing standards, audits, and continuous research and evaluation of models are key to shaping a safe and responsible future in the field of generative artificial intelligence.