Aight, you’ve probably already seen this logo. Chatbots have become increasingly popular in recent years, with a growing number of businesses using them to interact with customers and provide assistance. One type of chatbot, known as a Generative Pre-trained Transformer (GPT) chatbot, has gained particular attention due to its ability to generate human-like responses and engage in conversation. In this article, we will explore the basics of GPT chatbots and discuss their potential benefits and drawbacks. We will also examine some of the challenges and considerations involved in developing and implementing these chatbots, as well as potential future directions for this technology.

Overall people seem to be confused about how it actually works and what can it do, the goal of this article is to provide a comprehensive overview of GPT chatbots and their role in the world of chatbot technology. The early chatbots were slammed and dismissed by how “dumb“ they were. Machine learning models are only as good as the data they are trained on, and if the data contains biases or errors, the model may also exhibit those biases or make mistakes. While ChatGPT is able to remember what the user has said earlier in the conversation, there is a limit to how much information it can retain. The model is able to reference up to approximately 3000 words (or 4000 tokens) from the current conversation - any information beyond that is not stored. This is why OpenAI’s model seems different and more engaging.

So after playing specifically with Chat GPT by OpenAI, I can say that it can be helpful but only if you know what you're doing. These models work by prediction, and by that, I mean that they take a prompt and predict what would the next words will be based on the data it was trained on. But they can sometimes produce responses that are unrelated to the input or that do not make sense in the context of the conversation. This is because Chat GPT, like other GPT models, is trained on a large dataset of text and is not able to understand the meaning or context of the words it generates. It does not have an understanding about the truthfulness of what’s generating and how to identify facts over incorrect data or speculation. Basically it generates text that sounds good but can be factually incorrect. If you can spot the mistakes and correct them or retrain the model by reinforcement learning, then it can be performing really good at tasks. Nor are they sentient or actively learning in real time and it is not being aware of the present. Here is a technical video explanation of how Google’s lambda works.

One way to improve the performance of Chat GPT is to fine-tune it on a specific domain or topic. For example, if you are building a chatbot for customer service, you can fine-tune the model on a dataset of customer service conversations to improve its ability to understand and respond to common questions and concerns.

Another option is to use Chat GPT in combination with other NLP techniques, such as named entity recognition or sentiment analysis, to provide additional context and help the chatbot generate more relevant responses.

Overall, Chat GPT can be a useful tool for building chatbots, but it is important to carefully consider its limitations and how to best utilize it in your specific application.

Now, let’s talk about other interesting things and AI Jailbreaks.

First one’s amazing and weird at the same time:

https://www.engraved.blog/building-a-virtual-machine-inside/

Here look at the context.

Apparently ChatGPT has the training dataset from Github Copilot, which makes it good at writing actual running code and also can fix bugs on the fly just by asking it to. However you still need to oversight it because it doesn’t always make sense.

Look here. It is plain wrong and factually incorrect affirming that GitHub Copilot is something else and it’s not even aware that itself is using it.One interesting aspect is thatit feels like copilot is coding for you but ChatGPT is coding with you, making it more flexible and dynamic.

I imagine this is also the end of bad formatted scams with obvious typos.

Visit their examples page for more hints on how to use it.

And then we ask ourselves: Why there are so many scams in crypto, who could’ve predicted this?

https://twitter.com/cz_binance/status/1603295726749904896

As well as this lovely experiment by Yannic Kilcher where it used the 4chan to train the GPT-3 model. Head here for a detailed video about ChatGPT jailbreaks if u feel like it.