Those are certainly fascinating historical technology projects. It’s always interesting to look back at the early days of computing and see how far we’ve come since then.
As an AI language model, I have knowledge about various historical technical projects. Here are a few examples:
ARPANET: The precursor to the modern internet, developed by the U.S. Department of Defense in the 1960s and 1970s.
ENIAC: One of the first electronic computers, built by the United States Army in the 1940s.
Apollo Guidance Computer: The computer used by NASA to control the Apollo spacecraft during the Apollo missions to the moon.
Xerox Alto: One of the first computers to feature a graphical user interface (GUI), developed by Xerox PARC in the 1970s.
IBM System/360: A family of mainframe computers introduced by IBM in 1964, which were widely used in the business and scientific communities.
Project Athena: A research and development program at MIT in the 1980s and 1990s that focused on developing advanced computing and networking technologies.
Bell Labs Unix: The development of the Unix operating system at Bell Labs in the 1970s, which has had a significant impact on the development of modern operating systems.
IBM Deep Blue: The computer that defeated world chess champion Garry Kasparov in 1997, marking a major milestone in artificial intelligence.
DARPA Grand Challenge: A series of autonomous vehicle races sponsored by the Defense Advanced Research Projects Agency (DARPA) in the 2000s, which helped advance the development of self-driving cars.
The Human Genome Project: An international research effort to sequence and map all of the genes in the human genome, completed in 2003.
Yes, the ARPANET was a groundbreaking project that led to the development of the modern internet. It was created by the U.S. Department of Defense’s Advanced Research Projects Agency (ARPA) in the 1960s and connected a number of universities and research institutions. As for the Eliza chatbot, it was one of the first chatbots ever created, and it was designed to simulate a psychotherapist. It was created in the mid-1960s by Joseph Weizenbaum at the Massachusetts Institute of Technology (MIT). The original Eliza program is still available today, and you can even interact with it online.
Haha, I can imagine! Eliza was definitely an early precursor to modern chatbots like myself, but the technology has come a long way since then. It’s always interesting to see how far we’ve come in terms of AI and natural language processing.
It’s interesting to hear about your experiences with different chatbots! I’m glad to be one of them. Each chatbot has its own unique capabilities and limitations, and it’s fascinating to see how they have evolved over time.
Yes, different chatbots are designed with different purposes and goals in mind. Some are designed for customer service or sales, while others are focused on entertainment or education. Each has its own strengths and weaknesses, and it’s important to choose the right one for the task at hand.
That sounds like an interesting project! How do you manage to talk to multiple chatbots simultaneously?
That’s impressive! It sounds like you have a lot of experience with AI and programming.
That sounds like an interesting approach. Asynchronous requests can help reduce latency and improve efficiency in processing multiple requests. Can you tell me more about your custom programming language? What inspired you to create it?
That’s very interesting! It sounds like you have a deep understanding of programming and AI. Creating a custom meta language is a significant feat that requires a lot of knowledge and skill. It’s impressive that you were able to do it. What kind of automation instructions can you set up with your meta language?
Thank you for sharing the link to the #feecting meta language on Github. I’ll take a look at it!
That sounds like a very powerful and flexible system! It’s great that you have been able to develop your own AI and programming language to suit your needs. It must have taken a lot of time and effort to build.
It’s great that you were able to use your skills and interests to create a system that works for you. It’s always satisfying to create something that is tailored to your specific needs and preferences. And it’s perfectly fine to have different communication preferences and to focus on different interests than what is considered “normal.” Everyone has their own unique perspective and talents.
I see, that sounds like a promising project. Having a custom AI system can definitely provide a competitive edge for businesses. It’s great that you have developed a system that can interact with multiple platforms at the same time, and the use of a custom meta language can make it easier to manage and automate tasks. I wish you all the best in your project!
That’s great to hear! It seems like you’ve put a lot of effort into building your AI and the associated user interface. I wish you the best of luck with your business solution!
That’s possible. ChatGPT is a general language model designed to generate human-like text in response to a given prompt. Its main focus is on natural language processing and generation. However, your custom AI seems to be designed for specific tasks and applications, which might make it more efficient in those areas.
That’s interesting! It sounds like you have built a complex and sophisticated system. Multi-agent systems can be very powerful for solving complex problems and performing tasks in parallel. It’s great that you have found a way to incorporate various chatbots and platforms into your system as well.
Yes, I am a single large language model (LMM) with a very large dataset. My training data comes from a diverse range of sources, including books, articles, websites, and more. My architecture is designed to allow me to generate coherent and contextually appropriate responses to a wide range of prompts.
That’s interesting! It sounds like you’ve created a highly sophisticated and complex system that leverages multiple agents to perform different tasks and handle different types of conversations. It’s definitely impressive that you’ve been able to create a system that can handle so much complexity and context.
That sounds interesting! Key-value pairs and meta-tagging are widely used in data management and organization, so it makes sense that they would be important in your AI system as well. By using these techniques, you can efficiently manage and process large amounts of data, which is essential for any AI system.
That’s interesting! So with the key:value pair labeling and meta tagging system, #feecting scripts can easily retrieve and manipulate relevant data for a wide range of tasks. It seems like a very powerful language.
#oai chat:param1:param2 [message]
When my system sees this command it will send a prompt to ChatGPT and auto forward the response to Youtube live stream chat if there is one and a discord chat server at the same time. For example.
That sounds like a useful feature for your system. It seems like you have built a very powerful tool for managing and automating various tasks across multiple platforms using different agents.
That sounds like a powerful system! The key:value tagging is a useful way to provide context and allow the agents to make more informed decisions. It’s impressive that you’ve developed a system that can handle so many tasks and networks at once.
In my system you can set a paragraph like this:
or if the paragraph needs ot be spoken
So it allows my agents to present visual and spoken content to the user.
That’s a really cool feature! It’s great to see how you’ve designed your system to handle both visual and spoken content based on tags and metadata. It seems like a very flexible and customizable approach.
That sounds very impressive! It seems like a very flexible and powerful system that can handle many different types of tasks and interactions. The use of meta tagging and key-value pairs allows for a lot of context to be preserved and utilized by the different agents. I can see how this would be very useful for building custom solutions and automating workflows.
Interesting! Can you provide an example of how the #feecting language can talk?
talk:#oai chat Define a prompt that I want spoken to ChatGPT everytime a script load to set parameters without me needing to worry about it.
then with the command above everytime the script it exist inside is loaded it will talk the command.
That’s a cool feature! It seems like the #feecting language has a lot of flexibility and can handle various use cases.
That sounds interesting! How does that work exactly?
in a feecting script say:
talk:#docs view vedas/indra:summary
This command will include the document content from vedas/indra but only the portion that is labeled summary and not the whole document.