WordPress: From the Cloud to Raspberry Pi

I didn’t call it a blog, but I started my first Web site on a NeXT Cube on Thanksgiving Day, 1995. The computer was on loan from the US Air Force Academy, and when a new BYU colleague told me about this important development underway called the World Wide Web (Many thanks, Jesse!), I decided I needed to get involved.

First Web Server

It turned out that the particular computer I happened to have available was the same type of unit used to invent the World Wide Web.

Tim Berners-Lee had used a NeXT Cube for his work at the Center for Nuclear Research (CERN) in Switzerland. For this first Web site I downloaded the necessary software from the CERN FTP site and configured the Unix-based software. Voilà! I was online with my first Web presence! Almost 20 years later I created my first blog using WordPress, pretty much the gold standard for this type of functionality.

Update added in November, 2024:

That WordPress site ran on a “server in the cloud” with Bluehost for 11 years, and here we are in 2024! I recently completed the porting not only of the WordPress blog created on the Bluehost server, but also the content that resided on that NeXT computer all those years ago.

For current system, I am using a Raspberry Pi with 8GB of RAM with a terabyte hard drive, running on my home network with a URL assigned with a free registration with Cloudflare. 

I asked ChatGPT to compare the NexXT with a Raspberry Pi 4:

Raspberry Pi 4 Image and DescriptionThe Raspberry Pi 4 and NeXT Cube illustrate decades of progress. The Pi 4, with up to 8GB RAM, a quad-core 1.5 GHz ARM CPU, and a price under $100, is approximately 50-100 times more powerful than the 1988 NeXT Cube, which had 8MB RAM, a 25 MHz CPU, and cost $6,500—equivalent to about $16,000 today. The Pi’s immense power and affordability sharply contrast with the Cube’s premium cost and limited performance by today’s standards.

Of course, the NeXT had a monitor and a 256 MB magneto-optical drive, a pioneering storage technology for its time, as well as a 40-320 MB hard drive. But still, the comparison after about three decades is mind-boggling.

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LLMs for Writing Movie Scripts

As a simple test of various Large Language Models, here is a simple prompt that I submitted to three different systems. Note the intentional lack of details, which was a test to assess each model’s ability to come up with relevant and accurate information.

Here is the interaction with the most well-known model, OpenAI’s ChatGPT.

I changed the prompt slightly for a couple of other models Google’s Gemini and Anthropic’s Claude:

“I need the script for a scene for a movie. It is 27 June 1844. A rider (a well known friend of Joseph and Emma) comes to her home to alert her as to what happened. Write the dialogue for this scene.”

Click here for the result from the interaction with Google’s Gemini. Note the interaction with the chatbot to improve the accuracy of the script.

Here is the results of the same interaction with Anthropic’s Claude.ai. I was unable to get a direct link to the script, so I am pasting it here:

[A rider gallops up to the Smith home in Nauvoo and dismounts, rushing to the door and knocking urgently]

Emma Smith: [opening the door] Why, Brother William, what’s the meaning of this?

William Clayton: [catching his breath] Sister Emma…I’m afraid I bear grave news. It’s about your husband.

Emma: [fear crossing her face] Joseph? What’s happened?

William: He and his brothers were arrested this morning by a mob militia in Carthage under the governor’s order. They’ve been charged with treason and riot.

Emma: [gasping] Oh no…no, this cannot be! Treason? For what cause?

William: For his teachings and the gathering of the Saints, I fear. They claim he’s raised an army against the state.

Emma: But that’s an outrage! He’s done nothing but preach divine truth. We must go to him at once.

William: I’m afraid that won’t be possible, Sister. The jail is surrounded by the militia and an armed mob. It’s not safe.

Emma: [anguished] Then what are we to do? We cannot leave him in their hands! You know what they’ve threatened against him before.

William: [gravely] I wish I had better news…All we can do is put our faith in the Lord’s hands now and pray for a miracle.

Emma: [weeping] Oh Joseph…the Lord alone knows what shall become of this…

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Google’s Bard on Language Acquisition and Large Language Models

Prompt

Based on your understanding of the ideas and theories of Stephen Krashen write a short presentation that Krashen might give on the possibilities offered by large language models.

Bard’s Response

Good morning. My name is Stephen Krashen, and I am a professor emeritus of linguistics at the University of Southern California. I am here today to talk to you about the possibilities offered by large language models.

Large language models are a type of artificial intelligence that are trained on massive datasets of text. This training allows them to learn the statistical relationships between words and phrases, and to generate text that is similar to the text they were trained on.

Large language models have a number of potential benefits for language learning. First, they can be used to provide learners with comprehensible input. Comprehensible input is input that is just a little bit beyond the learner’s current level of proficiency. It is input that the learner can understand with a little bit of effort.

Large language models can provide comprehensible input in a number of ways. They can be used to generate personalized stories, articles, and other texts that are tailored to the learner’s individual needs. They can also be used to translate texts from one language to another, which can help learners to access a wider range of materials.

Second, large language models can be used to provide feedback on learners’ output. Output is the production of language by the learner. It is the process of speaking, writing, and reading.

Feedback on output can help learners to improve their accuracy, fluency, and grammar. Large language models can provide feedback on output in a number of ways. They can be used to identify errors in grammar and usage. They can also be used to provide suggestions for how to improve the accuracy, fluency, and style of the learner’s output.

Third, large language models can be used to create new learning experiences. For example, they can be used to create virtual worlds where learners can interact with native speakers and practice their language skills. They can also be used to create games and other interactive activities that make learning fun and engaging.

In conclusion, large language models have a number of potential benefits for language learning. They can be used to provide comprehensible input, feedback on output, and new learning experiences. I believe that large language models have the potential to revolutionize language learning.


Thank you for your time.thumb_upthumb_downuploadGoogle itmore_vert

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Language Acquisition and Large Language Models

I have been wondering what language teachers and researchers are saying about the possibilities for using large language models for language acquisition. I found one reference to Krashen and ChatGPT:

A glance at Stephen Krashen’s Twitter timeline revealed nothing, so I decided to ask ChatGPT what it thought Krashen might say. Here is the prompt:

Prompt for ChatGPT

Based on your understanding of the ideas and theories of Stephen Krashen write a short presentation that Krashen might give on the possibilities offered by large language models.

I found ChatGPT’s answer to be rather inteeresting!

ChatGPT’s Response

Ladies and gentlemen,

Thank you for joining me today. Today, I want to discuss an exciting development in the field of language acquisition—the possibilities offered by large language models. As a linguist and proponent of second language acquisition, I believe that these models have the potential to revolutionize language learning.

First, let me introduce myself. I am Stephen Krashen, and my research and theories focus on the acquisition of languages. I have long emphasized the importance of comprehensible input, the idea that learners acquire language when they understand messages that are slightly beyond their current level. This concept forms the foundation of my theories.

Now, let’s delve into the possibilities presented by large language models. These models, like the one I’m speaking through today, possess an astonishing ability to generate human-like text and engage in conversations. They are trained on vast amounts of data, making them highly knowledgeable about various topics, including language.

One of the key benefits of large language models is their capacity to provide comprehensible input to language learners. They can offer learners a wide range of texts, conversations, and explanations tailored to their individual proficiency levels. This personalized approach has the potential to enhance the learning experience and make it more engaging, promoting the acquisition of the target language.

Furthermore, large language models can serve as virtual language partners, offering continuous interaction and conversation practice. Learners can engage in dialogues, ask questions, and receive immediate responses, thereby honing their speaking and listening skills. This virtual interaction can be invaluable, especially for learners who have limited access to native speakers or language immersion environments.

Another advantage lies in the diversity of language input large language models can provide. Learners can explore different registers, dialects, and genres, which enhances their overall language proficiency. By exposing learners to various linguistic patterns and styles, these models contribute to a well-rounded understanding of the language and its cultural nuances.

Additionally, large language models are not restricted by time or space. Learners can access them anytime, anywhere, allowing for flexible and convenient language learning opportunities. Whether you’re commuting, taking a break, or simply seeking a language challenge, these models are readily available to assist you.

However, it’s important to acknowledge the limitations. Large language models excel at generating text, but they may not fully replicate the dynamic nature of human interaction. Learners should strive for real-life conversations and experiences whenever possible to develop their communicative skills holistically.

In conclusion, large language models present exciting possibilities for language acquisition. They provide learners with comprehensible input, personalized interaction, and diverse language exposure. By embracing these models as valuable tools, we can enhance language learning experiences and empower learners worldwide.

Thank you for your attention, and I invite you to explore the potentials of large language models in your language learning journey.

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