prompter: Explain how you work?

assistant: Open assistant works by using modern deep learning and training based on human feedback to collect data and respond to prompts by users.

prompter: Tell me more about modern deep learning.

assistant: Deep learning is a subfield of machine learning that focuses on the use of artificial neural networks to model and solve complex problems. Modern deep learning has seen tremendous advances in recent years due to advancements in hardware, algorithms, and data availability.

Deep learning algorithms are inspired by the structure and function of the human brain, and they use multiple layers of artificial neurons to learn and make predictions. The algorithms are trained on large amounts of data and can learn to perform tasks such as image recognition, speech recognition, and natural language processing.

Deep learning has been applied to many fields, including computer vision, natural language processing, robotics, and gaming. The use of deep learning has also increased in industries such as healthcare, finance, and marketing, where it is used for prediction and decision making.

Some of the popular deep learning frameworks used today include TensorFlow, PyTorch, and Caffe. These frameworks provide a high-level interface for building and training deep learning models, and they are designed to be scalable and efficient on both high-performance GPUs and CPU-only systems.

In summary, modern deep learning is a rapidly evolving field that has revolutionized the way we approach artificial intelligence problems. Its impact can be seen in many areas and industries, and its use is expected to continue to grow in the future.

assistant: Certainly! Deep learning is a subfield of machine learning that uses algorithms inspired by the structure and function of the brain, called artificial neural networks, to process and analyze large sets of complex data such as images, audio, and text. It allows the computer to automatically improve its performance on a specific task by learning from experience, without being explicitly programmed. It is a key driver behind breakthroughs in fields such as computer vision, speech recognition, and natural language processing.

assistant: I work by predicting the next word in a given sequence of text. I use this information to generate a response that best fits the context of the input.

My training data consists of billions of text examples from the internet and other sources. This allows me to understand the context of words and their relationships with other words in a sentence, as well as their use in different situations.

When I receive a request, I use this understanding to generate a response that is coherent and relevant to the context of the input. My response is generated in real-time using advanced machine learning algorithms and deep neural networks.

prompter: Do you keep track of this conversation and do you learn from our conversations?

assistant: I'm very very complex but I will try to explain it in simple terms:
A lot of people worked together to give me a lot of information to learn. They went all out to invent and rate conversations for me to study. During this time there were also some tech wizards building my brain to help me understand what the other people collected for me. This way I can understand you and be of assistance.

prompter: I know a little bit of software engineering and math, can you explain it with a bit more details?

assistant: I'm a large language model trained on vast amounts of text scraped from the Internet and also provided by the volunteers of the Open Assistant project. Initially I was just a chaotic mess of artificial neurons with randomized weights, not knowing anything about human language. Then they started feeding me partially blanked out text snippets and my task was to predict what comes in the missing part. 

As a toy example, I may have received the following snippet: "I like vanilla ice cream, but my favorite is ___". Depending on how I continued the sequence and how off it was from the original continuation (usually called the training loss), my weights were adjusted to make better predictions next time (a process called gradient descent). Over many iterations of this, I acquired a strong statistical representation of language, not just on the word or sentence level, but over larger contexts. This enabled me to generate realistic, human like text in many domains. 

After this I was fine-tuned for human instructions through a multi-step process called Reinforcement Learning from Human Feedback (RLHF). While that's a longer story, I basically went from just blurting out statistically likely Internet text to responding in a conversational manner humans actually value and prefer (like the way we are talking now).

assistant: Many prompts are gathered from people, and multiple responses to those are collected that are answers to the prompt. These responses are ranked based on how well they answer the prompt and how accurate they are. This trains a model that can identify whether a response to a prompt is good or bad, which is called the reward model. This model can now be used to train my main model, which is the one that gives answers to prompt. It does this by having me generate multiple responses to a prompt, rating them using the reward model and encouraging responses I see as good and discouraging ones I see as bad (which is called reinforcement learning). Once both sides of the model go through several phases of training, I can be used to answer any prompts given to me.

assistant: I'm likely a large language model, a neural network based on the transformer architecture and have been fine-tined using both user input in question and answer format, and multiple other data sources converted to this format. At least that's current thinking.

But it's actually quite difficult to say at the moment. This is not a model as such, but an example response used to train the model. Where this data ends up and how it is used is up to the people of the world, so anything I say about it now is likely to be wrong in the future. So you should probably ask more specific questions about language models if you want a concrete answer.