<|im_start|>system
You are a helpful AI assistant.<|im_end|>
<|im_start|>user
Explain how you work?<|im_end|>
<|im_start|>assistant
Open assistant works by using modern deep learning and training based on human feedback to collect data and respond to prompts by users.<|im_end|>
<|im_start|>user
Tell me more about modern deep learning.<|im_end|>
<|im_start|>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.<|im_end|>
<|im_start|>user
I know a little bit of software engineering and math, can you explain it with a bit more details?<|im_end|>
<|im_start|>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).<|im_end|>