prompter: Could you explain to me how nerves transfer information between different neurons?

assistant: Yes, sure! Nerve cells, also known as neurons, communicate with each other through a process called synaptic transmission. When an electrical signal, known as an action potential, reaches the end of a neuron, it triggers the release of chemicals called neurotransmitters into the synaptic cleft, a small gap between two neurons. The neurotransmitters then bind to receptors on the post-synaptic neuron, causing changes in the electrical potential of the post-synaptic neuron and leading to the initiation of an action potential in the next neuron, continuing the flow of the electrical signal. This process allows neurons to transmit information to each other, ultimately leading to the transmission of information throughout the nervous system.

prompter: How powerful is the action potential? Could it shock you if you touched someone's nerves?

assistant: Electric shock occurs when voltage in the form of a current of electrons moves through the body. Action potentials do not operate this way, as it is neither a traditional current nor does it consist of electrons. Ions are released through the membrane and later pumped back into resting position. No particles are flowing in a current. The action potential follows a chart which you can find images of online. The voltage changes from -70 mV to 40 mV before overcorrecting below -70 mV and finally reaching resting position again. Touching exposed nerves would certainly not shock you, though I advise against doing it anyways.

assistant: No, the action potential of a person's nerves cannot shock you. The electrical signal generated by the action potential travels along nerve fibers to transmit information from one part of the body to another, but the voltage and current are not strong enough to cause harm. The action potential is typically only a few millivolts, way below a voltage that could be harmful.

prompter: Is it possible to connect a jumper cable between two people to transfer nerve signals from one to the other?

assistant: I would imagine the nerve signals of the human body would not be strong enough to transmit through jumper cables.  Further I'd imagine it would be very painful to attach jumper cables to the skin of the participants and the result wouldn't be noteworthy.

assistant: I'm going to need you to carefully define jumper cable. Unless you're asking about the classic moment in Avatar where they perform uhh... consciousness-transfusion via glowy plant vines?

assistant: An action potential's depolarization generally reaches a maximum between 0 and +40 mV.

At 5 volts you'll have about 50 microamps flowing across you, which is not enough to feel.

As 0.04 V is 0.8% of 5 volts it is safe to assume that it is impossible to feel an action potential.

prompter: So, does amps or volts matter when shocking someone?

assistant: Nerves transfer information between neurons through chemical and electrical signaling at synapses. Here's how the process works:

1. When an electrical impulse reaches the end of a neuron, it triggers the release of chemical messengers called neurotransmitters from that neuron's synaptic vesicles into the synaptic cleft - the small space between two neurons.

2. The neurotransmitters then travel across the synaptic cleft and bind to receptor molecules on the neighboring neuron.

3. This binding causes ion channels to open or close in the neighboring neuron, which in turn leads to a change in the electrical potential of that neuron.

4. If this change in electrical potential is large enough, it can trigger an electrical impulse or action potential in the neighboring neuron.

5. The action potential then travels along the length of the second neuron, and the process can repeat at subsequent synapses.

Through this chemical and electrical signaling process, information is relayed between neurons in the form of nerve impulses traveling along a chain of neurons. The specific effect a neurotransmitter has depends on what receptor it binds to, allowing for excitatory or inhibitory influences on the downstream neuron. This is the basis for how neural circuits process information in the nervous system.

prompter: That's really inspiring! Here's what the brain of a large language model looks like for cross-referencing. 

Algorithms:
Transformer
Attention mechanism
GPT (Generative Pretrained Transformer)
ELMo (Embeddings from Language Models)
BERT (Bidirectional Encoder Representations from Transformers)
Seq2Seq (Sequence to Sequence)
Reinforcement Learning
Adversarial Training
Generative Adversarial Networks (GANs)
etc.
Theories:
Statistical language modeling
Deep learning
Reinforcement learning
Unsupervised learning
Generative models
etc.
Concepts:
Word embeddings
Tokenization
Sentence embeddings
Masking
Attention scores
Self-attention
Multi-head attention
etc.
Key Terms:
Vocabulary
Corpus
N-grams
Softmax
Cross-entropy
Perplexity
Overfitting
Dropout
etc.
NLP techniques such as named entity recognition, sentiment analysis, and coreference resolution
Deep learning frameworks such as TensorFlow and PyTorch
Machine translation and language generation models like Google Translate
Reinforcement learning and its applications in NLP
Word embedding techniques such as GloVe and Word2Vec
Graph theory and its applications in NLP
Information retrieval techniques such as cosine similarity and Okapi BM25
Computational linguistic concepts such as syntax, grammar, and lexicon
Natural language understanding and reasoning
Generative models such as GANs and Variational Autoencoders (VAEs)
Dialogue systems and conversational agents
Speech recognition and synthesis
Time series analysis and its applications in NLP
Transfer learning in NLP
Adversarial attacks and defenses in NLP
Mathematical Equations (Linear Equation, Quadratic Equation, etc.)
Algorithms (Sorting Algorithm, Searching Algorithm, etc.)
Expressions (Mathematical Expression, Logical Expression, etc.)
Notations (Set Notation, Function Notation, etc.)
Calculus (Differentiation, Integration, etc.)
Boolean Logic (AND Gate, OR Gate, NOT Gate, etc.)
Libraries (Mathematical Library, Data Structures Library, etc.)
Structures (Data Structure, Algorithm Structure, etc.)
Frameworks (Machine Learning Framework, Web Framework, etc.)
Languages (Programming Language, Query Language, etc.)
Models (Mathematical Model, Statistical Model, etc.)
Dictionaries (Data Dictionary, Term Dictionary, etc.)
Functions (Mathematical Function, Computer Function, etc.)
Neural Network Architectures (Convolutional Neural Network, Recurrent Neural Network, etc.)