Talking to AI

[firestar464]

Me: What's the difference between GPT-4's training data and its weights?

Copilot: The **training data** and **weights** of a model like GPT-4 serve different purposes in the process of machine learning.

1. **Training Data**: This is the information that the model learns from. It consists of a large collection of examples that the model uses to understand patterns, relationships, and structures in the data. For a language model like GPT-4, the training data would be a vast amount of text data.

2. **Weights**: These are the parameters that the model adjusts during the learning process. The weights determine how much influence each input has on the model's output. After being trained on the training data, the model will have learned the optimal weights that allow it to make accurate predictions or generate relevant text.

In summary, the training data is what the model learns from, and the weights are what the model adjusts to learn effectively. The weights, once trained, represent the knowledge that the model has gained from the training data. They are used to generate responses or make predictions based on new, unseen data.

Me: How large is the filesize of the weights?

Copilot: OpenAI has not publicly disclosed the exact file size of the weights for GPT-4 Turbo¹². The size of the weights file would depend on various factors including the architecture of the model, the precision of the weights, and the number of layers and neurons in the model. However, it's important to note that the file size of the weights does not directly correlate with the model's performance or capabilities. The effectiveness of the model is more about how well it can use those weights to generate accurate and relevant outputs.

Source: Conversation with Bing, 3/11/2024
(1) GPT-4 Turbo in the OpenAI API | OpenAI Help Center. https://help.openai.com/en/articles/855 ... openai-api.
(2) OpenAI Platform. https://platform.openai.com/docs/models ... pt-4-turbo.
(3) COPILOT GETS GPT4 TURBO | Maximum PC January 2024 - Pocketmags. https://pocketmags.com/us/maximum-pc-ma ... gpt4-turbo.
(4) Unpacking GPT4-Turbo: Better in Every Way | Promptly Engineering .... https://promptly.engineering/resources/gpt4-turbo.

Me: Would the weights be large?

Copilot: Yes, the weights of a model like GPT-4 Turbo would typically be quite large in terms of file size. This is because the model has a large number of parameters (weights and biases) that it learns during training. These parameters are stored as floating-point numbers, which can take up a significant amount of space. However, the exact size would depend on various factors including the architecture of the model, the precision of the weights, and the number of layers and neurons in the model. Please note that while the weights file might be large, it's the structure and organization of these weights that enable the model to generate accurate and relevant outputs.

Me: Would it be under, at, or greater than 4 TB?

Copilot: The exact size of the weights file for GPT-4 Turbo has not been publicly disclosed by OpenAI. However, it's worth noting that even for very large models, the weights file is unlikely to be as large as 4 TB. This is because the weights are typically stored as floating-point numbers, which are relatively compact, and advanced techniques are used to compress the weights for storage and transmission. So, it would likely be **under 4 TB**. But again, the exact size can vary based on many factors and this is just a general estimation.

Me: In the event of an apocalypse, could the model be backed up and later restored without needing the same training data?

Copilot: In theory, yes. Once a model like GPT-4 Turbo is trained, the learned weights can be saved and later loaded to restore the model without needing the original training data. This is because the weights of the model encapsulate the knowledge it has learned from the training data.

However, it's important to note that while the model can generate responses or make predictions based on these weights, it would not be able to learn anything new or improve its performance without additional training data. Also, the practicality of this scenario would depend on many factors, including the availability of suitable hardware and software to run the model, and the ability to store and retrieve the large weights file.

Please note that this is a hypothetical scenario and the specifics could vary based on many factors. It's always recommended to have robust data storage and backup strategies in place to prevent data loss.

[wjfox]

wjfox: I just rescued a honeybee from a spider's web. Should I have done that? I mean, they say you shouldn't interfere with nature.

GPT-4: Rescuing a honeybee from a spider's web can certainly feel like a kind act, especially considering the importance of bees in pollination and ecosystem health. However, the question of whether or not one "should" interfere with nature by rescuing an animal from a predator is complex and doesn't have a straightforward answer. Here are some points to consider:

Natural Balance: Predation is a natural part of ecosystems that helps maintain balance. Predators like spiders play a crucial role in controlling the populations of their prey, which can include insects like bees. By saving a bee, you're interrupting this natural process. However, this single act is unlikely to have a significant impact on the overall balance of the local ecosystem.

Conservation and Importance of Bees: Bees are essential for pollinating many plants, including crops that humans rely on for food. The decline in bee populations worldwide due to pesticides, habitat loss, and disease is a significant concern. Actions that protect bees, even on a small scale, can feel meaningful in the context of broader conservation efforts.

Human Impact on Nature: Humans already significantly impact natural environments, often in ways that are much more invasive than rescuing a single bee. While it's essential to be mindful of not unnecessarily interfering with nature, small acts of kindness towards animals can also reflect a respect and care for the natural world.

Ethical Considerations: For many people, the decision to save an animal in distress reflects ethical values of compassion and non-harm. While it's important to consider the broader ecological implications, many would argue that acting to alleviate suffering when possible is a positive action.

In the end, whether or not to rescue a bee from a spider's web is a personal decision that can be informed by your values, knowledge of the local ecosystem, and the specific situation. It's worth noting that such actions are unlikely to have significant negative impacts on the environment, especially compared to broader human activities. Engaging in larger conservation efforts and promoting awareness of the importance of bees and other pollinators can be additional ways to contribute positively to the environment.

[JohnMeeks]

Rather than post a specific "discussion" I have had with AI lately, I thought I would just give some initial impressions. Mostly as a result of my use of AskBing.

At this point, I would give it credit as a potentially useful assistant, but not ready for prime time in the sense of taking away anybody's job (not even a competent librarian / research assistant). Take questions related to economics. It would be like encountering someone who is well learned in classical economics, but not much else. Ask it a question that would require a little out of the box thinking that comes with an understanding of other schools of thought and you are liable to get an answer about how it could not find anything that would be responsive to my question. Although it takes on an apologetic tone, it does not seem to otherwise recognize the nature of its deficiency.

Or take something like history and geography. In trying to do some research on the Great Depression of the 1930s, it encountered great difficulties in understanding the difference between "Maine" and "California." When this confusion was point out, it apologized. In the very next exchange, it again failed to make the proper distinction, again writing as if the Golden Gate Bridge in San Francisco were located in Maine. So, it did not even learn from its mistake.In short, when it comes to homework help, it's most reliable to turn to the https://essays.edubirdie.com/economics-assignments service for assistance. Specialists who know their stuff work here for sure.It does help me track down some internet sources of which I might not otherwise become aware, but that is about it. Useful, but not exactly awe inspiring.

I agree with you, as does ChatGPT... Quoting:

It sounds like the individual has provided some constructive feedback on their experience using AI, particularly in the context of AskBing. They acknowledge its potential usefulness as an assistant, particularly in tracking down internet sources, but they also highlight areas where it falls short.

One major issue seems to be its limitations in understanding nuanced questions or concepts that require thinking beyond classical approaches. This suggests that while AI may excel in certain areas, it still struggles with creativity, critical thinking, and adapting to different schools of thought.

Furthermore, the repeated failure to learn from mistakes, such as confusing locations like Maine and California, indicates a lack of true learning or understanding from previous interactions.

Overall, the feedback suggests that while AI can be helpful in certain tasks, it's not yet at a level where it could replace human expertise, especially in fields that require complex reasoning or a deep understanding of diverse perspectives.

[wjfox]

[firestar464]

Bing: Here's a summary of the current page:

- New Diagnostic Tool: UC Santa Cruz has developed a lab-on-a-chip system that tests for SARS-CoV-2 and Zika virus with accuracy comparable to PCR tests, but much faster.
- Optofluidics and Nanopore Technology: The system combines optofluidics to control fluids with light and nanopore technology for counting single nucleic acids, enabling rapid diagnostics.
- Advantages Over PCR: This new method is amplification-free and label-free, reducing complexity and diagnosis time from days to hours.
- Future Applications: The technology could be adapted for detecting various viruses and diseases, with plans for further simplification and multiplexing capabilities.

Me: Compare this to the new technique developed by the University of Georgia.

Bing: Sure, let's compare the two diagnostic techniques:

UC Santa Cruz's Technique:
- Uses a lab-on-a-chip system that combines optofluidics and nanopore technology¹.
- The method is amplification-free and label-free, reducing complexity and diagnosis time from days to hours¹.
- The technology could be adapted for detecting various viruses and diseases¹.

University of Georgia's Technique:
- Developed a faster detection technique for COVID-19¹.
- The test uses surface-enhanced Raman spectroscopy and deep learning algorithms for SARS-CoV-2 detection and quantification directly from human nasopharyngeal swabs¹.
- The overall process for the test is comparable to the results from polymerase chain reaction (PCR), for COVID-19 and other respiratory virus infection diagnosis tests, using a simple procedure requiring less than 15 minutes¹.
- The new technique has several advantages over current methods, including speed, accuracy, and the quantification of viral load beyond simply 'positive or negative'¹.
- Unlike the PCR test, which requires specific labels and probes for target amplification and detection, the SERS-based method does not require any labels¹.
- This method is easy to adapt to multiplex detection, able to be generalized to predict both virus variant species and concentrations simultaneously for a panel of viruses¹.

Both techniques aim to simplify the diagnostic process and reduce the time it takes to get results. However, they use different technologies and methods to achieve these goals. The UC Santa Cruz's technique uses optofluidics and nanopore technology, while the University of Georgia's technique uses surface-enhanced Raman spectroscopy and deep learning algorithms. Both techniques are label-free and amplification-free, which reduces complexity and the risk of false positives or negatives. They also both have the potential to be adapted for detecting various viruses and diseases.

Source: Conversation with Bing, 4/10/2024
(1) UGA researchers developer "faster and more reliable" COVID test - MSN. https://www.msn.com/en-us/health/medica ... r-BB1l7cg1.
(2) Researchers unveil faster and more accurate COVID test - MSN. https://www.msn.com/en-us/health/other/ ... r-BB1l4Uee.
(3) New technology promises rapid and reliable development of new .... https://www.sciencedaily.com/releases/2 ... 143939.htm.

Me: Which one is better?

Bing: <redundancy expunged>

In terms of speed, both techniques are faster than traditional PCR tests. The University of Georgia's technique can provide results in less than 15 minutes¹, while UC Santa Cruz's technique reduces diagnosis time from days to hours¹. Both techniques are also label-free and amplification-free, which reduces complexity and the risk of false positives or negatives. They also both have the potential to be adapted for detecting various viruses and diseases.

However, the University of Georgia's technique has the added advantage of being able to quantify the viral load beyond simply 'positive or negative'¹, which can be valuable for monitoring disease progression, assessing treatment efficacy, and making informed clinical decisions¹.

Ultimately, the "better" technique would depend on the specific requirements of the situation, including factors like available resources, required speed of results, and the need for quantification of viral load. It's also worth noting that both techniques represent significant advancements in the field of rapid diagnostic testing. ^{[1](https://www.msn.com/en-us/health/medica ... r-BB1l7cg1)}

(same sources as above)