Your Cart

Pooring 386 KG of GPTChat cyber security and privacy concerns in a bottle. What happens?


Top 30+ security vulnerabilities of GPTChat: AI-Powered ML Chatbot, Privacy Concerns, and Cybersecurity Concerns

GPTChat (Generative Pre-trained Transformer [Chatbot ver. 3] aka. GPT3) is a chatbot powered by artificial intelligence and machine learning that improves and learns from user input. It is programmed on around 175 billion neural network samples and will reach approximately 120 trillion in the upgraded version 4. It means that the Chatbot can comprehend human speech and respond to us as though we were conversing with another person. GPTChat is receptive to feedback and reinforces it programmed model to enhance its capabilities. It means, feedback is welcomed by GPTChat, which then strengthens its programming model to increase its functionality. It is based on OpenAI and uses Large Language Models, which are machine learning models based on supervised and unsupervised neural networks. Programmers can get information about their code on GPTChat. If you’re a marketer, it can create your hero-hook. It can offer security solutions if you are a security expert.

If you’re a student, it can help you with your assignments. It can provide fantastic content for you if you write columns. It can craft or direct you to a certain webpage if you are a hacker looking to learn more about it. It will also warn you not to engage in hacking operations. Example: let say you want to write a malware program; you can ask the indirect questions and probe to craft the malware code. It can write lyrics for songs for you if you sing. It can fine tune reports and assignments for 5 years old and 55 years old person to apprehend.

GPTChat only uses data streams till 2021. You might wonder how this Open AI chatbot functions.

GPTChat has surpassed the roughly 86 billion neurons in the human comparable neural network. This indicates that the AI chatbot is more powerful than we could have envisioned. GPTChat uses data samples that have been trained before correlating them to rewards to reinforce learning. A new, more sophisticated reward-based model is created as this help to advance. By ranking the output from best to worst, it aids the chatbot’s ability to generate output. In essence, it has been trained to produce text-based output based on text summaries, language level understanding, pattern reorganization, neural language-based processing, and responding in text or languages that are understandable to humans.

Full report here.