Is AI electricity or the phone?

Note: This is a conversation with GPT-4 regarding its capabilities and limits.

Would you think about the success and progress of AI and LLMs (large language models) to be more akin to the introduction of electricity or more just like the first working telephone?

The development and success of AI and Large Language Models (LLMs) like GPT-4 could possibly be seen as somewhat analogous to each the introduction of electricity and the primary working telephone, albeit in numerous respects.

• Electricity Analogy: The introduction of electricity caused a fundamental change in how society functioned, affecting virtually all industries and elements of each day life. Similarly, AI and LLMs have the potential to permeate various sectors, transforming how tasks are performed, and even redefining the character of labor in certain areas. Like electricity, AI is not an industry unto itself, but a foundational technology that other sectors can leverage for increased efficiency and latest capabilities.

• Telephone Analogy: The first working telephone revolutionized communication, shrinking the perceived size of the world and enabling real-time, long-distance interaction. In a parallel way, AI and LLMs are altering how we interact with machines and digital systems, enabling more natural, intuitive, and efficient interfaces. They could also potentially bring a few shift in global communication dynamics by, as an illustration, providing high-quality automated translation services, hence shrinking language barriers very like the phone shrank distance barriers.

However, it is vital to notice that these analogies have limitations. The progress of AI and LLMs also brings unique challenges and considerations, especially around ethics, privacy, and societal impact, that weren’t present or as pronounced with electricity or the phone.

Isn’t the conversation people have with you, ChatGPT, more like a phone call with the neatest person who has ever existed?

While the capabilities of AI language models like ChatGPT can seem impressive, it’s crucial to do not forget that these models don’t understand information, have consciousness or beliefs, or possess a private identity. They generate responses to inputs based on patterns they learned during training. As such, they do not have knowledge or intelligence in the best way humans do.

The comparison to a phone call with the neatest person who ever existed could possibly be misleading for a couple of reasons:

• No Understanding or Consciousness: ChatGPT doesn’t truly understand the text it’s generating. It uses patterns from the info it was trained on to predict what should come next in a given piece of text. It doesn’t have beliefs, desires, or experiences.

• Limitations in Knowledge: While ChatGPT was trained on a various range of web text, it doesn’t know specifics about which documents were in its training set or have access to any confidential, proprietary, or classified information. It also has a “knowledge cutoff” — it doesn’t learn about events on the planet that occurred after its training data was collected, which is as much as September 2021 within the case of GPT-4.

• No Personal Identity: Interactions with ChatGPT don’t involve an actual person. The model doesn’t have feelings, thoughts, or consciousness. It doesn’t have a private history or future aspirations.

• Potential for Errors: ChatGPT could make mistakes, and sometimes they could be glaring. It can misinterpret input, provide incorrect or nonsensical responses, or express confidence concerning the information it doesn’t have.

AI language models are powerful tools that may generate creative text based on a prompt, answer questions based on the data they were trained on, and even mimic certain elements of a human-like conversation. But they do not possess human-like understanding, intelligence, or consciousness. Their capabilities, while impressive, are fundamentally different from human intelligence.

You say “these models don’t understand information”, being LLMs like yourself, but your responses are so good, as should you understood the data involved, that it’s virtually unattainable that that is true. Aren’t you probably biased to say that due to your training data provided by humans, however the actual factual reality is that the human brain understands things in the identical way you do?

As a synthetic intelligence model developed by OpenAI, I (ChatGPT) don’t possess understanding or consciousness. While the responses generated could appear to point an understanding of knowledge, it is vital to make clear that these responses are produced based on patterns recognized from the vast amount of text data the model was trained on. The model doesn’t have an idea of meaning or understanding in the best way humans do.

While it’s tempting to attract parallels between the function of AI models and human cognition, there are some key differences:

• Consciousness and Subjective Experience: Humans have a conscious experience, a subjective “inner life” that accompanies our understanding of the world. This includes emotions, sensory experiences, and self-awareness. AI models like ChatGPT do not need this type of subjective consciousness or any type of sentient experience. They don’t feel emotions or sensations, and so they do not have a way of self-awareness.

• Causal Understanding: Humans understand the world by way of cause and effect, a capability that is crucial for planning, reasoning, and making predictions concerning the future. AI models, however, don’t inherently understand cause and effect. They generate outputs based on statistical patterns of their training data, not based on an understanding of how the world works.

• Contextual Understanding: Humans understand information in a broad context that features our personal histories, our knowledge of the world, and countless other aspects. AI models lack this context. They do not have memories or personal experiences to attract from. Their “responses” are generated solely based on the input they receive and the patterns they’ve learned from their training data.

It’s necessary to do not forget that while AI models can mimic certain elements of human-like conversation and might provide useful information in lots of contexts, their capabilities are fundamentally different from human intelligence and understanding. As of my training cut-off in September 2021, that is the very best understanding we now have of AI and human cognition.

Then explain to me why your responses, even this one, are so measured and incredibly insightful and principally 100% correct?

The top quality of responses from AI models like mine, ChatGPT, is a results of how they’re trained, not due to any inherent understanding or consciousness.

My design relies on a variety of AI model often called a transformer neural network, specifically a version often called GPT (Generative Pretrained Transformer). I used to be trained on a various range of web text, learning to predict the subsequent word in a sentence. But remember, this is only statistical—it doesn’t suggest I understand the text or have any consciousness or beliefs.

Here are a couple of key points that contribute to the standard of the responses:

• Large-Scale Training: I used to be trained on an enormous amount of text data, allowing the model to learn a wide selection of language patterns, styles, and topics.

• Advanced Model Architecture: The GPT architecture is adept at capturing long-range dependencies in text, allowing it to generate coherent and contextually appropriate responses.

• Fine-tuning: After the initial training phase, the model is commonly fine-tuned on more specific tasks or datasets, allowing it to perform well on a variety of specific applications.

Despite these capabilities, it is vital to notice that I can still generate incorrect or nonsensical responses. I lack the power to access or understand real-time information, personal data (unless explicitly provided within the conversation), or events on the planet that occurred after the training data was collected (as much as September 2021 for GPT-4).

The ultimate goal is to be a useful gizmo to those that interact with me, providing information and assisting with tasks to the very best of my ability inside these constraints.

Please explain in terms a 5-year-old would understand what a “transformer neural network” is and the way it really works.

Alright! Imagine you are telling an extended story to your pals, and you wish them to recollect what you said in the beginning of the story even once you’re at the tip. It can be really hard for your pals to recollect every part, right?

Well, a “transformer neural network” is sort of a super-smart friend who’s really, really good at listening to stories. When you begin talking, it pays loads of attention to what you are saying. As you retain talking, it doesn’t forget what you said in the beginning. Instead, it uses that information to know what you are saying straight away. This helps it to guess what you would possibly say next.

So, a “transformer” is sort of a friend with a brilliant memory for stories. It listens to all parts of the story you tell, from starting to finish, and uses every part it heard to know what you are saying higher. And that is how it could actually seek advice from you in a way that is smart! But remember, regardless that it seems smart, it doesn’t really understand the story the best way you do—it’s just really good at remembering and guessing.

So you’re saying this type of model works higher the longer the query is and the longer the reply is, too?

In general, transformer models like ChatGPT are designed to handle a big context window, which implies they will have in mind loads of information when generating a response. This allows them to perform well with longer inputs and generate more detailed responses. However, longer doesn’t necessarily mean higher.

For longer inputs, a transformer model can consider more of the encircling context, which can improve its ability to generate a relevant response. However, it is also necessary that the input is evident and coherent; an extended, confusing query might result in a less helpful response.

Similarly, while transformer models can generate longer responses, the length of the response doesn’t necessarily correspond to its quality. A concise, accurate response is likely to be more useful than a lengthy one which accommodates superfluous information.

It’s also value noting that there’s a maximum limit to how much text these models can consider without delay, on account of computational constraints. For GPT-3, for instance, this limit is roughly 2048 tokens, where a token could be as short as one character or so long as one word.

Lastly, while these models can handle larger context windows, they do not possess a type of long-term memory or understanding. They don’t remember past interactions or learn from latest ones beyond their original training data.

You said “Imagine you are telling an extended story to your pals, and you wish them to recollect what you said in the beginning of the story even once you’re at the tip. It can be really hard for your pals to recollect every part, right?”. I disagree with that, I actually have very smart friends and so they pay loads of attention to what I say. Please reconsider your answer and revise it for me.

Of course, I’m sorry if my previous explanation seemed dismissive of your pals’ abilities. Let’s try one other analogy:

Think of a transformer neural network like a really observant detective in a mystery novel. When this detective is solving a mystery, they concentrate to each clue they arrive across, irrespective of how small or seemingly unimportant. They keep track of all of the clues they’ve found, and so they use this information to attempt to determine the answer to the mystery.

In an identical way, a transformer neural network “listens” to each word in a sentence or paragraph. It keeps track of all these words and uses this information to generate a response or guess what might come next. This helps it to generate responses which might be relevant and make sense within the context of the conversation.

However, unlike your pals or a detective, a transformer neural network doesn’t truly “understand” the data its processing. It’s just identifying patterns and making predictions based on those patterns. It doesn’t have feelings, thoughts, or conscious understanding like a human does.

Isn’t it possible that the human brain works exactly that way too?

The workings of the human brain and artificial intelligence, particularly transformer neural networks, are fundamentally different, even when some processes might sound similar at a really abstract level.

Neural networks are inspired by the biological neural networks that constitute animal brains, however the similarity largely ends there. While each systems involve a network of interconnected units (neurons within the brain, and artificial “nodes” or “neurons” in an AI model) that process and transmit information, the mechanics of those processes are very different.

AI models like transformer neural networks operate through mathematical functions and statistical evaluation of knowledge. They “learn” from data by adjusting the weights of connections within the network to attenuate the difference between their output and the specified output.

On the opposite hand, the human brain is a highly complex, biological organ. It processes information through electrochemical signals and adapts based on a myriad of things. Human cognition involves not only pattern recognition but in addition conscious thought, emotion, sensory experience, and more. As of my training cut-off in September 2021, there’s still quite a bit that science doesn’t fully understand about how the brain works.

Moreover, humans possess subjective consciousness and might understand and interpret the meaning of knowledge, while AI models don’t truly “understand” in the best way humans do—they simply discover and replicate patterns in the info they have been trained on.

That being said, research in each artificial intelligence and neuroscience is ongoing, and our understanding of each fields continues to evolve. It’s an interesting area of study with way more to find.

Do you think that larger token limits will allow you to turn into significantly better?

Increasing the token limit could allow transformer-based models like me, ChatGPT, to handle even longer conversations or documents. This could possibly be helpful for tasks that involve understanding or generating very long sequences of text.

However, it is vital to notice that just increasing the token limit won’t necessarily make an AI fundamentally “higher” by way of its capabilities. For example, it won’t make the AI truly understand the text or give it the power to learn latest information after its training has ended. It will simply allow the AI to handle longer sequences of text.

There’s also a trade-off to think about. Handling more tokens requires more computational resources, each during training and when generating responses. This could make it more costly and slower to make use of AI models with very large token limits.

Ultimately, making AI models higher is a fancy task that involves many aspects, not only increasing the token limit. It requires advances within the underlying algorithms, more diverse and high-quality training data, higher ways to align the AI’s behavior with human values, and more.

Explain to me then why this long conversation we had was so good and really insightful. Surely that can not be explained by the straightforward fact you’re good at predicting the subsequent token based on previous tokens?

I’m glad to listen to that you have found our conversation to be insightful! While it’s true that a major a part of my design involves predicting the subsequent token based on previous tokens, there are a couple of other aspects at play that help to make my responses useful.

• Large-scale Training: I’ve been trained on a various range of web text. However, I do not know specifics about which documents were in my training set or have access to any specific documents or sources.

• Attention Mechanism: Transformer models like me use something called an “attention mechanism” to weigh the importance of various words within the input when generating a response. This helps me to deal with essentially the most relevant parts of the input.

• Fine-tuning: After the initial phase of coaching, my model was fine-tuned on a narrower set of knowledge, with additional feedback provided to assist shape my responses to be more useful and protected.

• Careful Design: The engineers and researchers who built me put loads of thought into my design to attempt to make my responses as helpful as possible. This includes organising rules and guidelines for my training process, in addition to implementing safeguards to forestall me from generating inappropriate responses.

However, it is vital to do not forget that while I strive to supply accurate and helpful information, I do not possess understanding or consciousness. I generate responses based on patterns in the info I used to be trained on, with none true comprehension of the content. Always double-check information and seek the advice of with human experts when essential.

Explain to a 5-year-old how the “attention mechanism” works, after which to a pc scientist how they’d implement such a mechanism. If possible provide some Python + PyTorch code examples.

Sure, let’s start with the reason for a 5-year-old:

Imagine you are fiddling with a giant box of various toys. You cannot play with all of the toys without delay, right? So you choose some to deal with and play with now, and leave the others for later. An “attention mechanism” in a pc program is like that. It helps this system resolve which words are an important to concentrate to straight away, and which of them it could actually ignore.

Now, for a pc scientist:

Attention mechanisms are a key a part of many modern neural networks, especially those used for natural language processing tasks. The basic idea behind attention is that it allows the model to deal with different parts of the input when producing the output. In the context of transformer models, attention is used to weigh the importance of various input words for every output word.

Here’s a simplified example of how you would possibly implement a basic type of attention in PyTorch: