The A.I Megathread (LLM , GPT , Development)
GEPPETTO AI
GEPPETTO AI will drive the most powerful CINEMA in history, the first-ever infinite GAMES and be your partner in IMAGINATION.geppettoai.com
check out BlenderGPT
Magic3D: High-Resolution Text-to-3D Content Creation
research.nvidia.com
The Generative AI Revolution in Games | Andreessen Horowitz
To understand how radically gaming is about to be transformed by generative AI, look no further than this recent Twitter post by @emmanuel_2m. In this post he explores using Stable Diffusion + Dreambooth, popular 2D generative AI models, to generate images of potions for a hypothetical game...a16z.com
The Generative AI Revolution will Enable Anyone to Create Games | Andreessen Horowitz
Generative AI will completely reshape UGC and expand the games market beyond what many thought was possible.a16z.com
Thanks. Some would say I’m a big influence in the social media world (I hate saying it) but I’ve always wondered what’s next. Obviously this. I’m trying to combine what I’ve already done on social media with ai and kinda power up. I’m trying to find my lane now. Not really a big art guy or article guy, tho I am good with words. My thing is connecting with people and engagement thru humor.
Just trying to get in where I fit in because I really see this being big
∞-former: Infinite Memory Transformer
Pedro Henrique Martins, Zita Marinho, André F. T. MartinsTransformers are unable to model long-term memories effectively, since the amount of computation they need to perform grows with the context length. While variations of efficient transformers have been proposed, they all have a finite memory capacity and are forced to drop old information. In this paper, we propose the ∞-former, which extends the vanilla transformer with an unbounded long-term memory. By making use of a continuous-space attention mechanism to attend over the long-term memory, the ∞-former's attention complexity becomes independent of the context length, trading off memory length with precision. In order to control where precision is more important, ∞-former maintains "sticky memories" being able to model arbitrarily long contexts while keeping the computation budget fixed. Experiments on a synthetic sorting task, language modeling, and document grounded dialogue generation demonstrate the ∞-former's ability to retain information from long sequences.
edit: from google bard.
The Infinite Memory Transformer is a new type of transformer that can remember an unlimited number of past events. This allows it to model long-range dependencies in sequences, which is important for tasks like language modeling and question answering.
The Infinite Memory Transformer works by using a continuous-space attention mechanism. This allows it to attend to any point in the past, regardless of how far back it is. This is in contrast to traditional transformers, which can only attend to a fixed number of past events.
The Infinite Memory Transformer has been shown to be effective on a variety of tasks. For example, it has been shown to outperform state-of-the-art language models on tasks like text summarization and question answering.
If the Infinite Memory Transformer were combined with the Llama model, it would be able to remember an unlimited number of past events and use them to generate text. This would allow it to generate more realistic and coherent text.
I asked google bard for a primer to A.I discussions.
Here are 100-200 AI terms and phrases and their meaning in the simplest terms:
* LLM: Large Language Model. A type of AI model that is trained on a massive amount of text data. It can be used to generate text, translate languages, write different kinds of creative content, and answer your questions in an informative way.
* Model: A representation of a system or process. In the context of AI, a model is a mathematical or computational representation of a system or process that is used to understand and predict its behavior.
* Transformer: A type of AI model that is used for natural language processing tasks such as machine translation and text summarization. It is based on the attention mechanism, which allows it to learn long-range dependencies in sequences.
* Multi-modal: Describing or involving multiple modes of communication. In the context of AI, multi-modal systems are able to process and understand multiple types of data, such as text, images, and audio.
Other common AI terms and phrases include:
* Artificial intelligence (AI): The ability of a computer or machine to mimic the capabilities of a human brain, such as learning and problem solving.
* Big data: Data that is so large or complex that it is difficult to process using traditional methods.
* Deep learning: A type of machine learning that uses artificial neural networks to learn from data.
* Machine learning: A field of computer science that gives computers the ability to learn without being explicitly programmed.
* Natural language processing (NLP): The field of computer science that deals with the interaction between computers and human (natural) languages.
* Robotics: The field of engineering that deals with the design, construction, operation, and application of robots.
* Vision processing: The field of computer science that deals with the extraction of meaningful information from digital images or videos.
These are just a few of the many terms and phrases that are used in discussions about AI. As the field of AI continues to grow, new terms and phrases will continue to emerge.
>
Here are some more AI terms and phrases:
Sure, here are some more AI terms and phrases:
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Here are 100-200 AI terms and phrases and their meaning in the simplest terms:
* LLM: Large Language Model. A type of AI model that is trained on a massive amount of text data. It can be used to generate text, translate languages, write different kinds of creative content, and answer your questions in an informative way.
* Model: A representation of a system or process. In the context of AI, a model is a mathematical or computational representation of a system or process that is used to understand and predict its behavior.
* Transformer: A type of AI model that is used for natural language processing tasks such as machine translation and text summarization. It is based on the attention mechanism, which allows it to learn long-range dependencies in sequences.
* Multi-modal: Describing or involving multiple modes of communication. In the context of AI, multi-modal systems are able to process and understand multiple types of data, such as text, images, and audio.
Other common AI terms and phrases include:
* Artificial intelligence (AI): The ability of a computer or machine to mimic the capabilities of a human brain, such as learning and problem solving.
* Big data: Data that is so large or complex that it is difficult to process using traditional methods.
* Deep learning: A type of machine learning that uses artificial neural networks to learn from data.
* Machine learning: A field of computer science that gives computers the ability to learn without being explicitly programmed.
* Natural language processing (NLP): The field of computer science that deals with the interaction between computers and human (natural) languages.
* Robotics: The field of engineering that deals with the design, construction, operation, and application of robots.
* Vision processing: The field of computer science that deals with the extraction of meaningful information from digital images or videos.
These are just a few of the many terms and phrases that are used in discussions about AI. As the field of AI continues to grow, new terms and phrases will continue to emerge.
>
Here are some more AI terms and phrases:
- Artificial General Intelligence (AGI)
- Chatbot
- DeepFake
- Emotional Intelligence (EQ)
- Federated Learning
- Generative Adversarial Networks (GANs)
- Hyperparameter Optimization
- Machine Learning Operations (MLOps)
- Natural Language Processing (NLP)
- Reinforcement Learning
- Robotics
- Self-Driving Car
- TensorFlow
- Transfer Learning
- Unsupervised Learning
- Virtual Assistant
- Zero-Shot Learning
Sure, here are some more AI terms and phrases:
- AI Assistant
- AI Chatbot
- AI Data Scientist
- AI Engineer
- AI Ethics
- AI Governance
- AI Risk
- AI Safety
- AI Value
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Sparks of Artificial General Intelligence: Early experiments with GPT-4
Artificial intelligence (AI) researchers have been developing and refining large language models (LLMs) that exhibit remarkable capabilities across a variety of domains and tasks, challenging our understanding of learning and cognition. The latest model developed by OpenAI, GPT-4, was trained using an unprecedented scale of compute and data. In this paper, we report on our investigation of an early version of GPT-4, when it was still in active development by OpenAI. We contend that (this early version of) GPT-4 is part of a new cohort of LLMs (along with ChatGPT and Google's PaLM for example) that exhibit more general intelligence than previous AI models. We discuss the rising capabilities and implications of these models. We demonstrate that, beyond its mastery of language, GPT-4 can solve novel and difficult tasks that span mathematics, coding, vision, medicine, law, psychology and more, without needing any special prompting. Moreover, in all of these tasks, GPT-4's performance is strikingly close to human-level performance, and often vastly surpasses prior models such as ChatGPT. Given the breadth and depth of GPT-4's capabilities, we believe that it could reasonably be viewed as an early (yet still incomplete) version of an artificial general intelligence (AGI) system. In our exploration of GPT-4, we put special emphasis on discovering its limitations, and we discuss the challenges ahead for advancing towards deeper and more comprehensive versions of AGI, including the possible need for pursuing a new paradigm that moves beyond next-word prediction. We conclude with reflections on societal influences of the recent technological leap and future research directions.
What Is ChatGPT Doing … and Why Does It Work?
Stephen Wolfram explores the broader picture of what's going on inside ChatGPT and why it produces meaningful text. Discusses models, training neural nets, embeddings, tokens, transformers, language syntax.
writings.stephenwolfram.com
What Is ChatGPT Doing … and Why Does It Work?
February 14, 2023FYI: it's a long read.
OK, as an assistant director of a facilities department at a school campus that has maintenance, landscaping, and janitorial departments that we oversee, how can I harness this new power to help streamline some of of communications, duties, or delegation processes?
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I asked google bard your question....
With access to ChatGPT and Google Bard, you can harness their power to help streamline some of your communications, duties, or delegation processes in several ways:
- You can use ChatGPT to create chatbots that can answer common questions from students and staff, or to provide information about campus facilities.
- You can use Google Bard to generate reports and presentations, or to create training materials for your staff.
- You can use ChatGPT and Google Bard to automate tasks, such as scheduling appointments or sending out emails.
- You can use ChatGPT and Google Bard to collaborate with other staff members, or to communicate with students and parents.
edit; needs more fine tuning...
what are some creative ways an assistant director of a facilities department at a school campus that is in charge of maintenance, landscaping, and janitorial departments can be more productive with chatgpt or google bard? be as detailed as possible.
Sure, here are some creative ways an assistant director of a facilities department at a school campus that is in charge of maintenance, landscaping, and janitorial departments can be more productive with ChatGPT or Google Bard:
- Use ChatGPT to create a chatbot that can answer common questions from students and staff. This chatbot could be powered by ChatGPT and could be trained on a dataset of frequently asked questions. The chatbot could then be deployed to your campus website or to a messaging platform like Slack or Facebook Messenger. This would free up your time so that you can focus on more important tasks, such as responding to more complex questions or providing in-person assistance.
- Use Google Bard to generate reports and presentations that can be used to track the progress of your department's work. This could be helpful for tasks such as preparing for budget hearings or providing reports to the campus administration. Google Bard could also be used to generate presentations that can be used to train new employees or to give presentations to students or parents. This would save you time and improve the quality of your work.
- Use ChatGPT and Google Bard to automate tasks that are currently done manually. For example, you could use ChatGPT to generate work orders for maintenance and landscaping projects, or to generate invoices for janitorial services. This would free up your time so that you can focus on more important tasks, such as overseeing the work of your department's staff or developing new programs and services.
- Use ChatGPT and Google Bard to collaborate with other departments on campus. For example, you could use ChatGPT to communicate with the campus facilities department to coordinate maintenance and landscaping projects, or to communicate with the campus food services department to coordinate janitorial services. This would help to improve communication and coordination between departments, which would lead to a more efficient and effective campus.
- Use ChatGPT and Google Bard to communicate with students and parents. For example, you could use ChatGPT to answer questions from students and parents about campus facilities, or to send out updates about campus maintenance and landscaping projects. This would help to improve communication between the department and the campus community, which would lead to a more positive and productive campus environment.
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This prompt structure will dramatically improve your generations inside ChatGPT, whether you are using GPT 3.5 or 4. Derived from reading this [2303.11366] Reflexion: an autonomous agent with dynamic memory and self-reflection
I've used this to insanely improve Llama 7B as well....its wildly good.
User: [Topic or question]
Assistant Hypothetical Response: [Brief or simplified answer to the topic or question]
Agent Reflection: [Critique of the hypothetical response, highlighting the limitations, inaccuracies, or areas that need improvement or expansion, while providing guidance on how to address these issues in the revised response]
GPT-4 Actual Response: [The natural and contextually appropriate answer to the topic or question, as generated by the advanced language model, which incorporates the suggestions and improvements from the agent reflection for a more comprehensive and accurate response]
I've used this to insanely improve Llama 7B as well....its wildly good.
User: [Topic or question]
Assistant Hypothetical Response: [Brief or simplified answer to the topic or question]
Agent Reflection: [Critique of the hypothetical response, highlighting the limitations, inaccuracies, or areas that need improvement or expansion, while providing guidance on how to address these issues in the revised response]
GPT-4 Actual Response: [The natural and contextually appropriate answer to the topic or question, as generated by the advanced language model, which incorporates the suggestions and improvements from the agent reflection for a more comprehensive and accurate response]
