Tag Archives: ChatGPT

ChatGPT vs. Bard: Which AI Writing Tool is Right for You?

13 Saturday May 2023

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Greetings to all! My name is Steve and I’m the founder of Keter.net. By day, I spend my time basking in the company of my beloved dogs, Luna and Chester, while indulging in the occasional nap and camping trip. However, by night, I immerse myself in the thrilling world of artificial intelligence. As a passionate advocate of cutting-edge technology, I am particularly drawn to large language models (LLMs) and their limitless potential.

LLMs are a type of artificial intelligence (AI) that can generate text, translate languages, write different kinds of creative content, and answer your questions in an informative way. They are trained on massive datasets of text and code, and they are able to learn and improve over time.

I’ve been using two different LLMs for the past few weeks: ChatGPT and Bard. ChatGPT is a powerful tool that can be used for a variety of tasks, including writing, translation, and coding. However, I found that it could be inconsistent at times. For example, sometimes it would generate high-quality content, and other times it would produce gibberish.

Bard is a newer model, and it’s still under development. However, I’ve been impressed with its performance so far. It’s more consistent than ChatGPT, and it’s able to generate more creative and informative content.

Here is a comparison of ChatGPT and Bard:

ChatGPT

  • Pros:
    • Powerful tool that can be used for a variety of tasks
    • Easy to use
    • Free to use
  • Cons:
    • Can be inconsistent
    • Produces gibberish sometimes
  • My personal experience using ChatGPT:
    • ChatGPT is a powerful tool that can be used for a variety of tasks, but it can be a bit of a diva at times. For example, I’ve tried very hard to get it to incorporate examples in a blog post, but it’s not always cooperative. Some days it will do exactly as I asked and rewrite the entire post, including the examples I asked for. Other days, it will rewrite the blog post with examples, but with the salient points I wanted to make removed, or it will simply never respond with the examples I’ve asked for.
    • However, I do like the ability to have multiple chats ongoing simultaneously. And the prompt database that you can peruse when clicking on new chat is helpful to create your own prompts. Overall, ChatGPT has been okay to use. It’s great when it isn’t being stubborn.

Bard

  • Pros:
    • More consistent than ChatGPT
    • Able to generate more creative and informative content
    • Still under development, so it has the potential to become even more powerful in the future
    • Free to use
  • Cons:
    • Not as easy to use as ChatGPT
  • My personal experience using Bard:
    • I like Bard’s default voice – it responds in a way that feels more natural than ChatGPT. Also, today is really the first time I am using Bard to fully vet it, but I am impressed with its memory during our conversation, it was very helpful when I expressed concern about how to save my chats vs. ChatGPT and gave me instructions on how to export my conversation to a spreadsheet. The best feature I enjoy the most is its access to the internet. This feature alone makes it worthwhile to switch over to using Bard full time to help me edit keter.net. Also I have found that it is best to be extremely verbose with your Bard prompts – don’t expect it to remember all the previous steps, give it all the steps in one prompt instead.
    • Overall, I am very impressed with Bard. It is a powerful tool that has the potential to revolutionize the way we write and communicate. I encourage you to try it out and see for yourself

Overall, I think Bard is a better tool than ChatGPT. However, it’s important to keep in mind that Bard is still under development. It’s possible that it will become even more powerful in the future, however.

Here are my hopes for the future of large language models:

  • I hope that LLMs will become more powerful and consistent in the future.
  • I hope that LLMs will be used to improve the world in a variety of ways, such as by helping to solve complex problems, generating creative content, and providing educational resources.

I encourage you to try out Bard. It’s a free, powerful tool that can help you to improve your writing.

I also encourage you to leave comments and feedback. I’d love to hear your thoughts on LLMs and how you think they can be used to improve the world.

Thanks for reading!

Contextualized Lexical Embeddings: The Key to Accurate and Efficient NLP

12 Friday May 2023

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The field of Natural Language Processing (NLP) has made remarkable progress in recent years, enabling machines to interpret, comprehend, and manipulate human language. One of the most promising developments in this field is the emergence of contextualized lexical embeddings.

Traditional word embeddings represent words as fixed vectors, ignoring the surrounding context. However, this approach falls short since words can have multiple meanings depending on the context. For example, the word “crane” can refer to a bird, a machine used for lifting heavy objects, or Origami.

Contextualized lexical embeddings overcome this limitation by considering the surrounding words and phrases when representing a particular word as a vector. This allows machines to understand language in context, leading to more accurate and efficient NLP solutions.

Creating contextualized lexical embeddings involves training deep learning models on large amounts of text data. By doing so, the same word can have different vector representations based on context. Take sentiment analysis, for instance, which involves determining the emotional tone of a piece of text. By analyzing the surrounding context, a machine learning model can better understand the sentiment behind a particular piece of text. This is particularly vital for social media platforms and customer service interactions, where sentiment analysis can help companies measure customer satisfaction.

For example, consider a customer review for a product: “I love Apple products, but their customer service is terrible.” Without contextualized embeddings, an NLP model may struggle to classify the overall sentiment of the sentence. However, with contextualized embeddings, the model can take into account the context of each word and accurately determine the overall sentiment of the sentence to be negative towards the customer service and positive towards the product.

Another example of sentiment analysis is a review for a smartphone: “I’m really impressed with this new smartphone. It has great features and is easy to use.” Using contextualized lexical embeddings, the model can understand the context and determine that the sentiment is not just positive, but also includes a sense of enthusiasm. This is valuable information for companies looking to improve their product offerings.

Although the use of contextualized lexical embeddings is still in its early stages, it has already shown great promise in improving NLP solutions. As we continue to refine and improve these techniques, we can expect even more impressive advancements in the field of NLP. By incorporating contextual information into word embeddings, we can improve the accuracy and efficiency of NLP solutions, paving the way for new possibilities in AI and machine learning applications.

ChatGPT 101: Using ChatGPT to Interpret Unfamiliar Code

27 Thursday Apr 2023

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As we saw in our previous posts, ChatGPT can be a powerful tool for generating code and translating code between languages. But ChatGPT can also be used to help interpret unfamiliar code, even if you don’t know the language it’s written in. In this post, we’ll explore some real-world examples of how ChatGPT can help us understand code blocks from different languages.

Example 1: Interpreting Python Code

Let’s start with a simple example. Suppose you come across the following Python code block:

def is_palindrome(s):
    return s == s[::-1]

If you’re not familiar with Python, this code might be difficult to understand at first glance. But we can use ChatGPT to help us generate a natural language explanation of what the code does. Here’s the prompt we’ll give ChatGPT:

This code is written in Python. Please explain what it does:

def is_palindrome(s):
    return s == s[::-1]

And here’s the response that ChatGPT generates:

This code defines a function called 'is_palindrome' that takes a string as input. It then checks whether the string is equal to its reverse using the slice notation [::-1]. If the string is a palindrome, the function returns True; otherwise, it returns False.

Example 2: Interpreting Java Code

Now let’s consider a more complex example. Suppose you come across the following Java code block:

public static void main(String[] args) {
    int[] numbers = {1, 2, 3, 4, 5};
    int sum = 0;
    for (int i = 0; i < numbers.length; i++) {
        sum += numbers[i];
    }
    System.out.println("The sum of the numbers is " + sum);
}

Again, if you’re not familiar with Java, this code might be difficult to understand. But we can use ChatGPT to help us generate a natural language explanation of what the code does. Here’s the prompt we’ll give ChatGPT:

This code is written in Java. Please explain what it does:

public static void main(String[] args) {
    int[] numbers = {1, 2, 3, 4, 5};
    int sum = 0;
    for (int i = 0; i < numbers.length; i++) {
        sum += numbers[i];
    }
    System.out.println("The sum of the numbers is " + sum);
}

And here’s the response that ChatGPT generates:

This code defines a 'main' method that takes an array of strings as input. It initializes an integer array called 'numbers' with the values 1 through 5. It then defines an integer variable called 'sum' and initializes it to 0. The code then enters a loop that iterates over the elements of the 'numbers' array, adding each element to the 'sum' variable. Finally, the code prints the sum of the numbers to the console using the 'System.out.println' method.

Example 3: Interpreting JavaScript Code

Our final example is a block of JavaScript code:

const numbers = [1, 2, 3, 4, 5];
const evenNumbers = numbers.filter((number) => number % 2 === 0);
console.log(evenNumbers);

If you’re not familiar with JavaScript, this code might be difficult to understand. But once again we can use ChatGPT to generate a natural language explanation of what the code does. Here’s the prompt we’ll give ChatGPT:

This code is written in JavaScript. Please explain what it does:

const numbers = [1, 2, 3, 4, 5];
const evenNumbers = numbers.filter((number) => number % 2 === 0);
console.log(evenNumbers);

And here’s the response that ChatGPT generates:

This code creates an array called 'numbers' with the values 1 through 5. It then filters the 'numbers' array using a function that checks whether each element is even. The filtered array is stored in a new variable called 'evenNumbers'. Finally, the code prints the 'evenNumbers' array to the console using the 'console.log' method.

As we can see, ChatGPT can be a powerful tool for interpreting unfamiliar code. By giving ChatGPT a code block and asking for a natural language explanation, we can quickly understand what the code does, even if we’re not familiar with the language it’s written in. This is just one of the many ways that ChatGPT can be useful for developers, and we look forward to exploring more in future posts.

Chat with KeterBot

26 Wednesday Apr 2023

Posted by in AI, ChatGPT

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Now that I am getting established in the ChatGPT community, I wanted the AI to respond more like myself. To achieve this, I created an AI agent at character.ai and trained it using dozens of my own conversations. Although it’s not perfect, KeterBot now has a distinctive voice that sets it apart from other ChatGPT bots. I’ve grown fond of it and it’s quickly become my favorite pen pal! Please note that all conversations are private and not stored, but as with any other site, refrain from sharing any personal information.

Click here to chat with KeterBot

ChatGPT 101: How to Use ChatGPT for Text Generation

26 Wednesday Apr 2023

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ChatGPT is a powerful AI language model developed by OpenAI that can be used for a variety of tasks, including text generation. In this tutorial, we’ll go over the steps to generate text using ChatGPT and how to customize the parameters to control the quality of the generated text.

What are the steps to generate text using ChatGPT?

Here are the basic steps to generate text using ChatGPT:

  1. Choose a platform: There are several platforms that allow you to interact with ChatGPT, such as the OpenAI website, FlowGPT.com, the GPT-3 API, and various third-party applications.
  2. Create a prompt: A prompt is the input text that you give to ChatGPT to generate a response. It can be a sentence, a paragraph, or even a whole article.
  3. Customize parameters: ChatGPT has several parameters that can be customized to control the quality and length of the generated text, such as temperature, top_p, and length.
  4. Generate text: Once you’ve created a prompt and customized the parameters, you can generate text by feeding the prompt to ChatGPT and waiting for the response.

How to input a prompt and generate text based on it

Let’s look at an example of how to input a prompt and generate text based on it. Suppose you want to generate text about the topic of “How to change a light bulb.” Here’s a sample prompt you could use:

Prompt: Can you provide instructions on how to change a light bulb in a ceiling fixture?

Response: Of course! Here are some general instructions for changing a light bulb in a ceiling fixture:

1. Turn off the power to the light fixture by flipping the corresponding circuit breaker in your electrical panel or by unscrewing the light bulb from the fixture.

2. Wait for the light bulb to cool down if it has been on recently.

3. Locate the light fixture's cover or dome, which is typically held in place by screws, clips, or a twisting motion. Remove the cover by unscrewing the screws, pressing the clips, or twisting it counterclockwise.

4. Gently remove the old light bulb from the fixture by twisting it counterclockwise. If the bulb is stuck, try gently wiggling it back and forth while turning it.

5. Insert the new light bulb into the socket and twist it clockwise until it is securely in place.

6. Replace the light fixture's cover by screwing in the screws, snapping the clips into place, or twisting it clockwise.

7. Turn the power back on to the light fixture by flipping the circuit breaker or screwing in the light bulb.

And that's it! You should now have a new light bulb in your ceiling fixture.

Next, we’ll customize the parameters to control the quality of the generated text.

How to control the quality of the generated text

ChatGPT has several parameters that can be customized to control the length and quality of the generated text. Here’s a brief overview of the most important parameters:

  • temperature: Controls the level of randomness and creativity in the generated text. A higher temperature value leads to more unexpected and varied responses, while a lower value leads to more predictable and conservative responses. The default value is 0.7.
  • top_p: Controls the diversity of the generated text by only considering the most probable tokens according to a cumulative distribution function. A higher value leads to more diversity, while a lower value leads to more common responses. The default value is 1.0.
  • length: Controls the length of the generated text in tokens. The default value is 50.

To customize these parameters, you can add them to the prompt as follows:

Prompt: Can you provide instructions on how to change a light bulb in a ceiling fixture?
Temperature: 0.3
Top_P: 0.9

Response: Certainly! Here are some simple steps you can follow to change a light bulb in a ceiling fixture:

1. Turn off the power to the fixture. You can do this by either turning off the switch that controls the light or by turning off the circuit breaker that supplies power to the fixture.

2. Wait for the light bulb to cool down. If it has been on recently, the bulb may be hot to the touch, so wait a few minutes before attempting to replace it.

3. Locate the cover or dome of the fixture. The cover may be held in place by screws or clips, or it may twist off.

4. Remove the cover or dome to access the bulb. If the cover is held in place by screws, use a screwdriver to remove them. If it is held in place by clips, gently pry them open with your fingers or a flat tool. If it twists off, turn it counterclockwise until it comes loose.

5. Carefully remove the old bulb by twisting it counterclockwise. Be gentle and avoid pulling on the bulb too hard.

6. Insert the new bulb by twisting it clockwise. Make sure it is securely in place.

7. Replace the cover or dome by reversing the steps you took to remove it. If it was held in place by screws, replace them and tighten them until the cover is snug. If it was held in place by clips, snap them back into place. If it twists on, turn it clockwise until it is secure.

8. Turn the power back on to the fixture by either flipping the switch or turning the circuit breaker back on.

And there you have it! Your light bulb has been successfully replaced.

In this example, we’ve set the temperature to a low value of 0.3, which should result in more conservative and predictable responses. We’ve also set the top_p value to 0.9, which should result in more diverse responses.

Now let’s see how the generated text changes with different temperature and top_p settings:

Prompt: Can you provide instructions on how to change a light bulb in a ceiling fixture?
Temperature: 1.0
Top_P: 0.3

Response: Sure, here are some steps for changing a light bulb in a ceiling fixture:

1. Turn off the power to the light fixture.

2. Wait for the bulb to cool down.

3. Remove the fixture's cover or dome.

4. Unscrew the old bulb.

5. Screw in the new bulb.

6. Replace the cover.

7. Turn the power back on.

That's it!

ChatGPT 101: Code Translation

26 Wednesday Apr 2023

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As a retired systems administrator, I have seen how frustrating it can be to migrate from one programming language to another. It can feel like you’re starting from scratch. But with the help of ChatGPT, code translation can be a breeze!

In this post, we will explore the power of ChatGPT’s code translation capabilities. We’ll take a PowerBuilder program and translate it to .NET code. But first, let’s go over the basics of code translation.

Code translation is the process of converting one programming language to another while preserving the functionality of the original code. It can be a time-consuming and error-prone task, but ChatGPT can help streamline the process.

Now let’s get to the fun part! Here is an example of translating a PowerBuilder program to .NET code using ChatGPT:

Input (PowerBuilder code):

// PowerBuilder code to retrieve employee data
String sEmpName, sEmpID
Integer iEmpSalary

// Retrieve employee name and ID
sEmpName = dw_employee.GetItemString(1, "emp_name")
sEmpID = dw_employee.GetItemString(1, "emp_id")

// Retrieve employee salary
iEmpSalary = dw_salary.GetItemNumber(1, "emp_salary")

// Display employee information
MessageBox("Employee Information", &
  "Name: " + sEmpName + "~r~n" &
  "ID: " + sEmpID + "~r~n" &
  "Salary: " + String(iEmpSalary, "#,###.##"))

Output (.NET code):

// .NET code to retrieve employee data
string sEmpName, sEmpID;
int iEmpSalary;

// Retrieve employee name and ID
sEmpName = Convert.ToString(dw_employee.GetItemString(1, "emp_name"));
sEmpID = Convert.ToString(dw_employee.GetItemString(1, "emp_id"));

// Retrieve employee salary
iEmpSalary = Convert.ToInt32(dw_salary.GetItemNumber(1, "emp_salary"));

// Display employee information
MessageBox.Show("Employee Information" + Environment.NewLine +
  "Name: " + sEmpName + Environment.NewLine +
  "ID: " + sEmpID + Environment.NewLine +
  "Salary: " + iEmpSalary.ToString("#,###.##"));

As you can see, ChatGPT was able to accurately translate the PowerBuilder code into .NET code while preserving the functionality of the original code. This can save developers hours of time and reduce the risk of errors.

ChatGPT’s code translation capabilities can make the migration to a new programming language much smoother. With just a few prompts, ChatGPT can accurately translate code while preserving its functionality. So don’t be afraid to take on a new language – let ChatGPT do the heavy lifting for you!

ChatGPT 101: Code Generation

25 Tuesday Apr 2023

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Welcome back to the ChatGPT 101 series! In the last post, I introduced readers to OpenAI’s language model called ChatGPT, which uses machine learning algorithms to generate human-like text. The post highlights the potential applications of ChatGPT in code generation and translation, including generating code based on a prompt and translating code between programming languages. In this post, we’ll focus on using ChatGPT for code generation.

Code Generation with ChatGPT:

ChatGPT can generate code in a variety of programming languages, including Python, JavaScript, and PowerShell. Let’s take a look at an example of using ChatGPT to generate a PowerShell script to connect to a remote server and troubleshoot a TCP/IP routing issue, all using natural language.

Prompt:
Create a PowerShell script that will connect to a remote server named [TARGET-1] and then diagnose and troubleshoot any TCP/IP routing issues that may exist between [TARGET-1] and another server named [TARGET-2].

Response:

$targetServer = "TARGET-1"
$username = "domain\username"
$password = "password"

$securePassword = ConvertTo-SecureString $password -AsPlainText -Force
$cred = New-Object System.Management.Automation.PSCredential ($username, $securePassword)

Enter-PSSession -ComputerName $targetServer -Credential $cred

$targetServerIP = Resolve-DnsName -Name $targetServer | Select-Object -ExpandProperty IPAddress
$target2ServerIP = Resolve-DnsName -Name "TARGET-2" | Select-Object -ExpandProperty IPAddress

Test-NetConnection -ComputerName $target2ServerIP -TraceRoute -InformationLevel Detailed

ChatGPT is smart enough to include in the script a prompt for the credentials needed to connect to [TARGET-1], then establishes a PowerShell session with that remote server. It then uses the Resolve-DnsName cmdlet to resolve the IP addresses of both [TARGET-1] and [TARGET-2]. Finally, it runs the Test-NetConnection cmdlet with the -TraceRoute parameter to diagnose any TCP/IP routing issues between [TARGET-1] and [TARGET-2].

Conclusion:

We’ve seen how ChatGPT’s language model can be leveraged to generate complex and useful PowerShell scripts, such as the one we created to connect to a remote server and troubleshoot a TCP/IP routing issue. What’s impressive is not just the syntax and logic of the code itself, but also the ability of the model to understand the context of the problem and prompt for missing information, like the credentials required for remote access. This shows the power of natural language processing and machine learning, and how it can augment the work of system administrators, enabling them to focus on higher-level tasks and delegate some of the more mundane and time-consuming ones to AI-powered tools like ChatGPT.

So if you’re a sysadmin looking for ways to streamline your workflow and automate repetitive tasks, don’t hesitate to give ChatGPT a try. You may be surprised by the quality and efficiency of the code it generates, and the intelligence it demonstrates in understanding your needs and requirements.

In the next post, we will cover how to use ChatGPT for code translation, such as rewriting PowerBuilder code into a .NET app. Stay tuned!

ChatGPT 101: An Introduction to OpenAI’s AI-Powered Language Model for Code Generation and Translation

25 Tuesday Apr 2023

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Hello and welcome to ChatGPT 101! As a retired systems administrator with a passion for technology, I’m excited to introduce you to OpenAI’s AI-powered language model – ChatGPT – and its potential applications in code generation and translation.

At its core, ChatGPT is a language model that uses machine learning algorithms to generate human-like text. This can be incredibly useful for generating or translating code, especially when dealing with complex or unfamiliar syntax.

For example, ChatGPT can be used to generate code based on a given prompt, such as a description of a desired functionality or a high-level design document. It can also be used to translate code between programming languages, which can be incredibly useful when working on projects with multiple language requirements.

But as with any technology, it’s important to understand both its capabilities and limitations. ChatGPT may struggle with understanding context or generating code that is optimized for performance, so it’s important to review and test its output thoroughly.

In this beginner’s guide to ChatGPT, we’ll explore the ins and outs of this powerful language model with a focus on its potential applications in code generation and translation. We’ll cover everything from how it works to its potential applications and ethical considerations.

So, whether you’re a programmer, a systems administrator, or simply a technology enthusiast, I hope you’ll join me on this exciting journey into the world of ChatGPT!”