In this article, we’ll dissect the Mojo language and its technical wizardry with carefully crafted examples that will highlight its key features. For your help, you can visit this GitHub link to access all the code used for this article. Get ready to unveil why Mojo could be the ace up your sleeve for your next big venture in the field of AI.

Presenting the Mojo Language: Overview and Context

Are you prepared to elevate your AI development capabilities to new heights? Before you can do that, knowing what you’re getting yourself into is essential, right?

Let’s begin exploring the new Mojo programming language!

Understanding Mojo language is more than learning its syntax—it’s about exploring the philosophy behind its creation. The team at Modular crafted this language with an emphasis on performance, modularity (pun intended), and interoperability.

Do you want to start using Mojo language to follow along? Remember, you have to sign up here to get started. It took me under two weeks to access the Mojo playground (where you can currently code in Mojo). So, sign up soon if the Mojo language fascinates you. I know people who got in just a day after queuing up!

Whether you’re a beginner or an experienced programmer, Mojo has the potential to revolutionize your approach to AI development, thanks to its innovative features and intuitive syntax. This language aims to run efficiently and promote clean and organized codebases. But what makes it unique?

Understanding the Mojo Language: What Sets It Apart?

Mojo language, created for AI tasks and system programming by unifying the AI/ML infrastructure, is a testament to Modular’s acute attention to detail. It simplifies the intricacies often linked with AI programming. As such, Mojo language aims to be a complete superset of Python, but it’s not there yet.

The goal of the Mojo language is innovation. To target accelerators and provide a language with robust compile time metaprogramming, caching and other prevalent modern features.

Mojo boasts dynamic and static typing, affording programmers the flexibility to switch their coding styles as per their needs. The syntax of Mojo language is intuitive and designed with programmers in mind, further simplifying the process of getting started.

This paves the way for swift and efficient coding, reducing the time taken to transform ideas into functional AI models. What’s more is that it also works seamlessly with different systems and languages, increasing its versatility in diverse development environments.

And you must sign up to get in the queue to access the Mojo Playground by Modular. The Mojo language is not publicly released yet.

In the next section, we will examine some of its main features, assuming you have access to Mojo language.

Why Mojo Programming: A Look at its Key Features

Mojo aims to bridge the gap between Python’s functionality and C++’s execution performance. It also aims to be a superset of Python, meaning it will eventually support all Python code.

Let’s see some of the key Mojo language features:

• Expressiveness: Mojo language supports dynamic and static typing and features a rich standard library.
• Concurrency: It has built-in features to support concurrent and distributed computing.
• Modularity: Mojo’s architecture encourages modular design, allowing you to maintain codebases efficiently.
• Interoperability: Mojo is highly compatible, allowing it to interact with different systems and programming languages, especially Python.
• Performance: The language aims to deliver high performance, enabling rapid development and ease of use.
• Mojo’s 🔥 Extension: While the file extension makes no difference to Mojo’s features, how cool is an emoji for a coding file extension?

With the features out of the way, it’s time to dive into the meat of things truly: the anatomy of the Mojo language.

The Anatomy of the Mojo Language: A Practical Guide to Writing Code

It’s vital to know the key elements that form the core structure of the Mojo programming language. Since it supports a lot of existing Python syntax, you can still do something like this and have it work:

print("Hello Mojo")

But that said, let’s explore Mojo language’s anatomical features:

let and var declaration in Mojo:

let and var are part of the Mojo language’s motto of adding strong type-checking to variable data types. You could say the JavaScript inspiration is obvious here. Still, both types:

• Create a new scoped runtime value.
• Support name shadowing, allowing variables in inner scopes to have the same name as variables in outer scopes.
• Can include type specifiers, patterns, and late initialization.

Difference between let and var :

Aside from these similarities, there is a crucial difference between let and var in Mojo:

• Variables declared with var are mutable, allowing their values to be modified after assignment.
• Variables declared with let are immutable, meaning their values cannot be changed once assigned.

Here’s an example to highlight the features of both:

def bookstore_management(new_books, sold_books):
    # Declaring an immutable variable 'total_books' with 'let'
    let total_books = new_books
    print("Total books:")
    print(total_books)
    # Uncommenting the next line would result in an error because 'total_books' is immutable
    # total_books = total_books - 40  # error: 'total_books' is immutable
    # Declaring a mutable variable 'current_books' with 'var'
    var current_books = new_books
    print("Current books:")
    print(current_books)
    # Selling some books and reducing the current_books number
    current_books = current_books - sold_books
    print("Current books after reduction:")
    print(current_books)

    # Both 'let' and 'var' support name shadowing and lexical scoping
    if total_books != current_books:
        let total_books = current_books
        print("Total books with lexical scoping, declared inside nested scope:")
        print(total_books)
    print("Total books in bookstore management function is still:")
    print(total_books)
def run_bookstore():
    # Received 100 new books
    new_books = 100

    # Sold 20 books
    sold_books = 20

    bookstore_management(new_books, sold_books)

run_bookstore()

In the bookstore_management function, we create the total number of books initially as an immutable value using let. Afterward, we create a mutable variable current_books using var, which can be changed when books are sold.

This is how the output looks:

If you try to uncomment the line above, this is the error you will see:

The concept of name shadowing and lexical scoping allows you to create new variables (in the if scope) with the same names as outer variables within nested scopes. This variable shadows the total_books declared at the start of the function, showcasing the shadowing concept in lexical scoping. It prevents unintended interference between variables of the same name in different scopes.

Here is another Mojo code example which mainly highlights these other features:

def bookstore_management(new_books: Int, sold_books: Int):
    # Declaring an immutable variable 'total_books' with 'let'
    let total_books: Int = new_books
    print("Total books in store:", total_books)

    # Declaring a mutable variable 'current_books' with 'var'
    var current_books: Int = new_books
    print("Current books in store:", current_books)

    # Selling some books and reducing the current_books number
    current_books = current_books - sold_books
    print("Current books after selling:", current_books)

    # Both 'let' and 'var' support name shadowing and lexical scoping
    if total_books != current_books:
        let total_books: Int = current_books
        print("Total books with lexical scoping, declared inside nested scope:", total_books)

    # Late initialization and pattern matching
    let discount_rate: F64
    let book_id: Int = 123
    if book_id == 123:
        discount_rate = 0.2  # 20% discount for mystery books
    else:
        discount_rate = 0.05  # 5% discount for other book categories
    print("Discount rate for Book with ID ", book_id, "is:", discount_rate)

def run_bookstore():
    # Received 100 new books
    let new_books: Int = 100

    # Sold 20 books
    let sold_books: Int = 20

    bookstore_management(new_books, sold_books)

run_bookstore()

Here’s the updated output:

Type specifiers:

The type of each variable is explicitly specified using type specifiers (Int for integers and F64 for floating-point numbers). This ensures clarity and allows the compiler to enforce type safety.

Late initialization and pattern matching:

discount_rate demonstrates late initialization, where the value of a variable is determined based on a condition after its declaration with let. It is assigned a value later based on the book_category using conditional statements (if, elif, and else) –> pattern matching. Access out all the code here.

Notes:

• I had trouble printing a string and variable inside one print statement in the first example, so I had to improvise and you two different print statements. Despite trying various string concatenation and type conversion approaches, I couldn’t get it to print the variable and string together. However, it was an isolated incident, and I couldn’t get it to replicate.
• I am still determining all the data types specified by Mojo language. I tried STR for strings and I32 for integers, but both did not exist. But then with some research, I learned about a few other types in Mojo language other than Int and Float:
  • SI (signed integer — from the Mojo docs),
  • UI (unsigned integer), and
  • String (if you add this line at the top: from String import String).
• UPDATE: Here is an indirect link which specifies the data type specifiers in Mojo.

Structures in Mojo Language:

Mojo language also supports structures, like in C++. A struct is a custom data structure that groups related variables of different data types into a single unit that holds multiple values. Functional programming languages commonly use them as lightweight alternatives to classes for creating custom data types, such as in C and C++, as they bind at compiler-time.

What are Structs in Mojo Language?

Structs in Mojo structs provide a way to organize and manage data, encapsulate attributes, and improve code organization and reusability.

• Structs in Mojo have a static structure, and you cannot modify them at runtime like Python classes, offering performance benefits and safety.
• Like Python classes, structs support methods, fields, operator overloading, and metaprogramming.
• All instance properties in a struct must be explicitly declared with var or let. (right now only var is supported)
• All standard data types, like Int, String, Bool, etc., make use of structs in Mojo.

Here’s an example of a struct that holds employee information:

from String import String
@value
struct Employee:
    var name: String
    var age: Int
    var department: String
    var salary: F64
    
def print_employee_details(employee: Employee):
    print("Name:", employee.name)
    print("Age:", employee.age)
    print("Department:", employee.department)
    print("Salary:", employee.salary)

# Create employee instances
employee1 = Employee("Alice Thompson", 30, "Engineering", 5000.0)
employee2 = Employee("Robert Davis", 35, "Sales", 4500.0)

# Print employee details
print_employee_details(employee1)
print_employee_details(employee2)

In this example, we define an Employee struct with properties such as name, age, department, and salary. Then, we create two instances of the Employee struct with different values for each property. The print_employee_details function prints the details of these two instances of the struct as you cannot directly print out a struct yet in Mojo.

The struct helps us organize and encapsulate employee data, efficiently creating, accessing, and managing employee information. We can also add additional methods to the struct to perform operations specific to employees, such as calculating bonuses or generating reports.

This example demonstrates how structs in Mojo provide a struct(ured) and efficient way to manage and manipulate real-world entities’ data. You should know that you cannot change the structure or contents of a struct while a program is running.

Here’s the output from the code above in Mojo language:

Note: While the Mojo docs specify that you can use let and var within a struct, let is not currently supported.

Overloading: Methods, Functions and Operators in Mojo

Mojo takes on a detective role whenever it needs to resolve calls. It scrutinizes each potential method: if only one fits the bill, that’s the chosen one. If there’s more than one contender, Mojo picks the one that’s the closest match. And in the rare scenario when Mojo can’t decide, it sounds an ambiguity alert. But don’t worry, you can overcome this by adding an explicit cast at the call site.

Method Overloading within Structs in Mojo:

In Mojo, methods are functions within a struct that can operate on the instances of that struct or class, accessing or modifying the data. And when you define a method in a struct or class without specifying argument data types in Mojo language, it handles them dynamically, like Python.

Yet, Mojo takes it a step further, offering full-fledged support for method overloading when type safety is a priority. The unique aspect of this feature? You can define multiple methods, each with the same name but different arguments, all within a single struct or class. This is a powerful tool, often seen in robust languages like C++, Java, and Swift, but now in a language focused on AI development too.

Let’s expand upon our Employee example from above:

from String import String
@value
struct Employee:
    var name: String
    var age: Int
    var department: String
    var salary: F64

    def print_details(self) -> None:
        print("Employee Name: ", self.name)
        print("Employee Age: ", self.age)
        print("Department: ", self.department)
        print("Salary: ", self.salary)
    def print_details(self, include_salary: Bool) -> None:
        print("Employee Name: ", self.name)
        print("Employee Age: ", self.age)
        print("Department: ", self.department)
        if include_salary:
            print("Salary: ", self.salary)

let employee1 = Employee("Alice Johnson", 30, "Engineering", 5000.0)
let employee2 = Employee("Robert Davis", 35, "Sales", 4500.0)

employee1.print_details()  

employee2.print_details(False)

Take the Employee struct, for instance, which sports two methods named print_details. The first one only calls for self, while the second one invites an additional Bool argument, include_salary. Now, when you want to execute print_details() on an Employee instance, Mojo programming language, with its sharp judgment, selects the correct method based on the arguments you provide. Hence, this example demonstrates the concept of method overloading in Mojo.

However, I’d also like to point out that Mojo does not allow method overloading based solely on the return type. You can also check out all the code here. Let’s see how you can utilize function overloading.

Function Overloading in Mojo Language:

Mojo also natively supports function overloading and works similarly to other languages. This means that you can define multiple functions with the same name but with different arguments (type, number of arguments), and Mojo will differentiate each of them based on the function arguments.

Let’s consider the example below:

from String import String

# Function Overloading
fn print_details(name: String, age: Int) -> None:
    print("Name: ", name)
    print("Age: ", age)

fn print_details(name: String, age: Int, department: String) -> None:
    print("Name: ", name)
    print("Age: ", age)
    print("Department: ", department)

# Usage example
print_details("Alice Thompson", 30)
print_details("Robert Davis", 35, "Sales")

Two functions exist with the same name, but different arguments in the example above, one that takes two arguments and the other takes three arguments.

When you call these functions, Mojo picks the correct function to execute based on the number of arguments you passed.

It would be fair to wonder if method and function overloading are the same things.

Method Overloading vs Function Overloading

Note that the main difference between function overloading and method overloading lies in the context in which one uses them. Function overloading applies to standalone functions, while method overloading applies to methods within a class or struct.

Other than that, the concept is essentially the same: it allows you to define multiple pieces of code with the same name but different arguments. Mojo selects the correct one based on the arguments you provide when calling the function or method.

Let’s look at another overloading type now: operator overloading.

Operator Overloading in Mojo Programming Language:

In Mojo, you can also define operator overloading by defining methods with special names. Here we will define a __add__ method to overload the + operator, which will allow us to “add” two rectangles together by creating a new rectangle with the combined length and width:

struct Rectangle:
    var length: F32
    var width: F32

    fn __init__(inout self, length: F32, width: F32) -> None:
        self.length = length
        self.width = width
        print("Rectangle created with length:", self.length, "and width:", self.width)

    fn area(self) -> F32:
        var area: F32 = self.length * self.width
        print("The area of the rectangle is:", area)
        return area
    
    fn area(self, side: F32) -> F32:
        var area: F32 = side * side
        print("The area of the square is:", area)
        return area

    fn perimeter(self) -> F32:
        var perimeter: F32 = 2 * (self.length + self.width)
        print("The perimeter of the rectangle is:", perimeter)
        return perimeter

    fn __add__(self, other: Rectangle) -> Rectangle:
        return Rectangle(self.length + other.length, self.width + other.width)

var rect1 = Rectangle(10.0, 15.0)
var rect2 = Rectangle(5.0, 7.0)
var squareArea: F32 = rect1.area(10.0)
var rect3 = rect1 + rect2

Note that this example also highlights method overloading. Also, do you see that we can add two rectangle objects together even though you couldn’t do that natively? This is due to operator overloading for addition.

The output above shows that Mojo also provides intuitive warnings that help you optimize your code. I used var instead of let for a variable that never changed in the program. Instead, I should have used let.

Decorators in Mojo Language: What’s the Purpose?

Decorators are a powerful feature in many programming languages that allows you to modify the behavior of a function, method or class without changing its source code. This is known as metaprogramming, since a part of the program tries to modify another part of the program at compile time.

In the context of Mojo programming language, decorators modify the properties and behaviors of types (like structs) and functions.

List of Decorators in Mojo Language:

Here is a brief list of decorators in Mojo language:

1. @register_passable: This decorator is used to specify that a struct can be passed in a register instead of passing through memory. This can lead to more efficient code.
2. @register_passable("trivial"): This decorator is a variant of @register_passable for trivial types, indicating that the type is both register passable and has no user-defined copy/move/destroy logic.
3. @always_inline: This decorator in Mojo language suggests the compiler always inline the decorated function, improving the runtime performance by reducing function call overhead.
4. @parameter: This decorator is used on nested functions that capture runtime values, creating “parametric” capturing closures.
5. @value: This decorator is used on structs to automatically generate boilerplate code, such as initializers and copy/move constructors.

Here’s an example below to explain the @value decorator in Mojo:

@value
struct Pet:
    var name: String
    var age: Int

Because of the @value decorator, these boilerplate functions are automatically created for the above struct:

fn __init__(inout self, owned name: String, age: Int):
    self.name = name^
    self.age = age

fn __copyinit__(inout self, existing: Self):
    self.name = existing.name
    self.age = existing.age

fn __moveinit__(inout self, owned existing: Self):
    self.name = existing.name^
    self.age = existing.age

As a result, you can directly write the code below:

from String import String

@value
struct Pet:
    var name: String
    var age: Int

# Creating a new pet
var myCat = Pet("Wia", 6)
print("Original cat name: ", myCat.name)
print("Original cat age: ", myCat.age)
# Copying a pet
var copiedCat = Pet(myCat.name, 7)
print("Copied cat name: ", copiedCat.name)
print("Copied cat age: ", copiedCat.age)
var movedCat = myCat
print("Moved cat name: ", movedCat.name)
print("Moved cat age: ", movedCat.age)

So decorators in Mojo do not only enhance the functionality but also make the Mojo language more intuitive to use. Here is the code output:

Do you see how much more efficient this is? And there’s one more crucial thing to know about Mojo…

Arguments Behavior in Mojo Functions

In programming, when calling a function or method, you use arguments to refer to the actual values on which the function or method operates. Conversely, parameters are the placeholder values within the function or method. When you define a function, you specify the arguments that it will take, along with their types.

Argument Types in Mojo Language:

• Immutable Arguments (Borrowed):
  • This is the default behavior for fn arguments in Mojo.
  • These are read-only arguments that a function cannot modify.
  • Using borrowed before the argument type is optional; it’s the default behavior.
  • It’s beneficial when passing large or costly values since it prevents unnecessary copies.

@value
struct SomethingBig:
    var id: Int

    fn print_id(self):
        print(self.id)

fn use_something_big(borrowed a: SomethingBig, b: SomethingBig):
   a.print_id()
   b.print_id()

var obj1 = SomethingBig(1)
var obj2 = SomethingBig(2)

use_something_big(obj1, obj2)

immutable arguments Output:

• Mutable Arguments (Inout):
  • If a function needs to modify an argument, it can be declared as mutable using the inout keyword.
  • Changes to mutable arguments are visible outside the function.
  • It’s frequently used when a method needs to mutate self.

@value
struct Counter:
    var count: Int

    fn increase(inout self, amount: Int):
        self.count += amount

var myCounter = Counter(0)
print(myCounter.count)
myCounter.increase(3)
print(myCounter.count)

Mutable arguments Output:

• Transfer Arguments (Owned and ^):
  • Used for functions that need to take exclusive ownership of a value.
  • Used with the postfixed ^ operator which ends the lifetime of a value binding and transfers the value ownership.
  • Useful when working with unique pointers or when you want to avoid copies.

@value
struct UniqueNumber:
    var num: Int

def use(p: UniqueNumber):
    print(p.num)

def usePointer():
    let ptr = UniqueNumber(100)
    take_ptr(ptr^)

def take_ptr(owned p: UniqueNumber):
    use(p)
    
usePointer()

Transfer Arguments Output:

• Comparison with def arguments:
  • A def argument without an explicit type annotation defaults to Object.
  • A def argument without a convention keyword (such as inout or owned) is passed by implicit copy into a mutable var with the same name as the argument.

def example(inout a: Int, b: Int, c):
   # b and c use value semantics so they're mutable in the function
   ...

fn example(inout a: Int, b_in: Int, c_in: Object):
   # b_in and c_in are immutable references, so we make mutable shadow copies
   var b = b_in
   var c = c_in
   ...

In the next article, we will compare the Mojo language and Python programming language comprehensively and focus on AI development with Mojo language.

Conclusion: The Comprehensive Beginner’s Guide to Mastering Mojo Language

In this article, we introduced Mojo, a new programming language for AI development that aims to blend Python’s functionality with C++’s performance. The language stands out for its dynamic and static typing, modularity, interoperability, and support for both concurrent and distributed computing. With a strong focus on performance and clean, organized codebases, Mojo language facilitates swift, efficient coding.

We also discussed all its key features in-depth, with practical examples. Mojo aims to be a superset of Python– it includes all of its features but adds some new ones. Stay tuned because in the next article, we’ll provide a full comparison between Mojo and Python and explore the potential of Mojo language for AI. If you want to access all the Mojo code used in this article, visit this GitHub link.

More articles on cutting-edge topics: ChatGPT AI: Features to 6X Your Productivity in 2023 and Smart Contracts in Python: Complete Guide

Written by: Syed Umar Bukhari.

I remember when it launched. It was a wild thing to imagine being true. I couldn’t even think of the possible implications. Who’d ever write an essay on their own?

Could ChatGPT AI technology be to text what calculators are to math? Could OpenAI ChatGPT become seamlessly integrated into our lives? Is that a good thing or a scary thing? Let’s find out the answers to these and many more questions below.

What is ChatGPT?

OpenAI ChatGPT is a conversational AI model. It uses a transformer-based neural network architecture to generate human-like responses to textual inputs. It can be used for various applications such as customer service, personal assistants, and language translation. We will explore this and much more in this article. There is a lot of exciting stuff to discuss when ChatGPT in AI is mentioned.

Why should you use ChatGPT?

In case you have been living under a rock, don’t worry. I will tell you a few convincing reasons to check ChatGPT today, in 2023. At this point, I might add that some people have been struggling with signing up lately. Thus, it’s possibly due to the huge load on its servers during peak hours or just because they’re trying to restrict new users. Do you want to know how you can use it?

You may use OpenAI ChatGPT AI for:

1. Improving customer service by providing fast and accurate responses to common queries.
2. Generating human responses for AI applications such as chatbots.
3. Reducing workload by automating repetitive tasks.
4. Supporting language generation for content creation (essays, social media, etc.).
5. Helping make writing code easier by providing clear instructions.
6. Remembering what a user says and following up in the future.

Current and Potential Applications:

If there’s one thing to know about ChatGPT… it is that it’s versatile! There’s quite possibly “no” limit to the use cases. With that said, let’s see the current and potential applications of OpenAI ChatGPT:

1. Chatbots for customer service and support.
2. Virtual personal assistants for scheduling, reminders, and task management w/o extra cost.
3. Content creation, such as automated news summaries and language translation or social media content.
4. Question & answering systems for quick access.
5. Generating textual responses for virtual assistants like Siri.
6. Automated conversation generation for social media and messaging apps.
7. Generating captions for images and videos.
8. Generating code snippets for programming languages.
9. Asking for social, cultural, and other types of information.

OpenAI ChatGPT AI Limitations:

Of course, ChatGPT chat isn’t all flowers and daisies. As with most things you see, there are certain downsides to it. Even if it is useful for a wide variety of use cases, there are disadvantages of ChatGPT chat, such as:

1. Lack of common sense: ChatGPT may not have the ability to understand the context and make decisions based on common sense knowledge.
2. Limited domain knowledge: ChatGPT may not be able to understand or generate responses for niche or specific domains.
3. No Contextual Awareness: ChatGPT can provide answers based on the text input, but it can’t understand the context or maintain a conversation as a human would.
4. Lack of Emotions or Personal Experiences: ChatGPT doesn’t have emotions, personal experiences, or physical senses.
5. No Creativity or Original Thoughts: The responses are generated based on patterns in the data that ChatGPT was trained on, but it can’t come up with original ideas or be creative in the same way that a human can.
6. Knowledge Cutoff: ChatGPT was trained on data up to 2021– it is not aware of information after that point. Here’s what ChatGPT said when I asked this question:

How to Cope With ChatGPT Limitations:

Guess what? Despite these disadvantages, it’s still effective with its many possible applications. A lot of times, you won’t even notice the cons. But in case you do, how can one cope with these issues? Well, luckily, OpenAI’s ChatGPT lets you in on how to overcome its shortcomings.

1. Use data pre-processing techniques to reduce bias in the training data.
2. Add domain-specific knowledge into the model training process.
3. Monitor and regularly evaluate model outputs to identify and address ethical concerns.
4. Use human oversight and review for sensitive applications.
5. Combine the model’s output with other AI techniques to improve its performance and address limitations.

Jinkies! What is this Debate about?

ChatGPT is a deep learning-based language generation model that can generate large amounts of high-quality text quickly and efficiently. While it can generate code, it is not likely to replace programmers. The main reason is that ChatGPT has limitations in terms of efficiency, creativity, and understanding of the context of the code. Below, I have gone in-depth to discuss the why.

ChatGPT vs. Programmers: Key Differences

Programmers have a deep understanding of the programming languages they use and can write code that is both efficient and readable. ChatGPT, on the other hand, may generate code that is less efficient. Also, ChatGPT is not capable of debugging code or identifying and fixing bugs, which are crucial aspects of programming.

A human programmer can understand the requirements of a project and how different pieces of code interact with each other. ChatGPT can’t understand the implications of the code or its link with other parts of the program.

However, ChatGPT can still be a valuable “tool” for programmers. For example, it can help with repetitive tasks such as code generation, saving programmers time and effort. It can also generate code snippets or solutions to common problems, which can be useful for beginners. Furthermore, ChatGPT can analyze large amounts of code or data, which can help programmers identify patterns and make decisions.

In conclusion, while ChatGPT can generate code, it will not likely replace programmers. ChatGPT is a powerful tool that can aid the programming process, but it’s not a replacement for human intelligence and creativity.

For example, here is the code it generates when I ask it to write me the code for a writer’s portfolio website:

import React from 'react';

class Portfolio extends React.Component {
  render() {
    return (
      <div>
        <nav>
          <ul>
            <li>Home</li>
            <li>About</li>
            <li>Portfolio</li>
            <li>Contact</li>
          </ul>
        </nav>

        <header>
          <h1>Welcome to My Writer Portfolio</h1>
          <p>I'm a freelance writer with a passion for storytelling.</p>
        </header>

        <main>
          <section>
            <h2>About Me</h2>
            <p>
              My name is John Doe, and I'm a freelance writer with over 5 years
              of experience. I specialize in writing blog posts, articles, and
              short stories.
            </p>
          </section>

          <section>
            <h2>My Portfolio</h2>
            <ul>
              <li>
                <a href="#">Blog Post 1</a>
              </li>
              <li>
                <a href="#">Blog Post 2</a>
              </li>
              <li>
                <a href="#">Article 1</a>
              </li>
              <li>
                <a href="#">Short Story 1</a>
              </li>
            </ul>
          </section>

          <section>
            <h2>Contact Me</h2>
            <form>
              <input type="text" placeholder="Your Name" />
              <input type="email" placeholder="Your Email" />
              <textarea placeholder="Your Message"></textarea>
              <button>Submit</button>
            </form>
          </section>
        </main>

        <footer>
          <p>
            &copy; {new Date().getFullYear()} My Writer Portfolio. All rights
            reserved.
          </p>
        </footer>
      </div>
    );
  }
}

export default Portfolio;

I’m still not convinced… Tell me… Why should I really use ChatGPT?

So, the real-world possibilities of ChatGPT don’t just end there. There’s still so much to explore and do with it. I have taken the liberty to brainstorm a few of them. Here’s a list below:

• Use it as a social media manager to regularly create new content for you.
• You can even ask for finance tips, e.g., the best stock to invest in. Note that this is not advised because you may lose your money instead of profiting.
• Imagine being stuck on an assignment or homework. You don’t have anyone to ask either. Well, you can simply get the help you need with ChatGPT.
• You can use it to suggest and generate a list of catchy titles– these are essential in pulling the audience to your content.
• One hidden use is to generate book titles and book blurbs. It’s amazing if you provide information about your book.
• You know what? You don’t have to sift through many Google results to find what you’re looking for now. For example, if you want a recipe to make pasta. Simply ask ChatGPT to provide you with a recipe. Similarly, you can also create a travel itinerary with it.
• Some people have had success in designing games with the deep-learning model.
• Use it for SEO. You can get help with content generation, keyword research, analysis, and market research, etc. You can also compare certain keywords if you wonder which would rank better in your article. See the result below:

But wait… there’s more… AI apps:

• Whisper AI: Whisper AI is offered by OpenAI as an open-source automatic search recognition neural net model. It has been trained on nearly 700,000 hours of multilingual supervised data. It approaches the human level of speech accuracy.
• Midjourney: Midjourney is an AI-based art generator based on given commands. It’s an interactive bot that processes your textual description. The result is a unique image from the combination of existing images based on your text. You can use Midjourney, Whisper AI, and ChatGPT Chat in tandem to completely automate content creation.
• ChatSonic: ChatSonic is basically like ChatGPT-pro; it is like a supercharged version. It aims to address its limitations. How can it do that? It allows the creation of AI images and provides more conversational and trending content. Not only that, but it also has several personalities for its content creation and voice recognition. Also, it is powered by Google Search, allowing it to stay updated with the latest events and data. Hence, it edges ChatGPT in terms of reliability and accuracy.
• Taskade AI: It is used as a productivity assistant that helps with organizing content. writing and even creating long-form content. Also, Taskade AI has five functions. Add a backslash followed by one of these after the text:
  • /outline
  • /ask
  • /expand
  • /rewrite
  • /summarize

Future AI Market Competitors:

While currently, GPT-3 powered ChatGPT is the neural prediction model to beat, the future may change that. Below are the potential ChatGPT market competitors in the future:

1. Google’s BERT and Google Assistant.
2. Microsoft’s language models, such as Microsoft Turing.
3. IBM’s Watson and its language-related services.
4. Amazon’s Alexa and its suite of NLP and language services.
5. Facebook’s M2M-100 and Blender models.
6. OpenAI’s future developments in the GPT series, such as GPT-4.

Conclusion: What does the future hold?

If you know what’s going on today, you are aware of the vast array of AI-based apps popping up. There are many competitors even today, but most still rely on GPT-3. Soon, GPT-4 will hit the market. And while GPT-3 was trained on 175 billion parameters, GPT-4 has been trained on 100 trillion parameters!

It is interesting to note that OpenAI’s CEO, Sam Altman, has subverted expectations for its upcoming GPT-4 model by saying those speculating about the size of GPT-4 were “begging to be disappointed”. He also added when asked about a potential 2023 1H release: “It’ll come out at some point, when we are confident we can do it safely and responsibly”.

Moreover, Semafor reports OpenAI ChatGPT mobile app is currently in “advanced preview mode” on OpenAI’s site. Moreover, Bing’s ChatGPT integration will be the new Bing. OpenAI’s GPT-4 powers the integration, a faster version of ChatGPT. OpenAI’s GPT-4 responds much faster, and the replies sound more human than the current version. A few people could already use it for a bit before it was quickly pulled off from Bing. If this is true, it could be huge for Bing.

I hope you enjoyed this post. I have had a blast exploring ChatGPT over the last month, and I am excited to share it with you. Let me know your thoughts about ChatGPT or its alternatives below. Which AI competitor fascinates you?

You should also read similar posts if you liked this post. Free Cloud Storage and Authentication in Python.

Written by: Syed Umar Bukhari.

The truth about Social Media in China can be summed up in one word– controversial! Surprised? Hear me out!

Saying that the very topic of social media in China is controversial in itself is an understatement. Whether a communist country or a capitalist country, the government controls all aspects of life. Still, we are living in the modern age of technology. Hence, the first place where the Chinese government has cracked down is on the social media world.

The Truth About Social Media in China: An Introduction

To clarify, living outside of China, you will be familiar with multiple social media platforms. Examples include Instagram, Facebook, Snapchat, and Twitter. The whole world uses these platforms to share, view, and interact with the various forms of media.

This means that anyone can post their opinions no matter what topic it is. Thus, online content is sharable from anywhere globally; anyone can view, share and interact with posts. However, they don’t even have to search for it because of the algorithm-based explore pages!

What, you may wonder? China does not want its citizens to be aware of the latest news regarding democratic countries worldwide, their day-to-day lives, and how their governments operate.
Being a conservative socialist country, the Chinese Communist Party (CCP) government works overtime to ensure that its citizens are entirely isolated from the rest of the world. Sadly, they’ve often ended up being vastly successful.

China’s strategy to keep its citizens living away from the rest of the world is genuinely astounding. China is one of the most densely growing countries in the world. To be exact, most of its citizens are active internet users– 730,000,000. Yet, the CCP has applied strict online policies without fight. As a result, they can redirect citizens from foreign social media platforms to locally formed ones.

This approach can be summed up with two vital results of the censorship discussed below:

Rein In The Public: Effectively Control Social Media In China

China is not a country that fools around. Not at all. Thus, when it comes to the information exposed to the public, it’s all kept quiet and hush-hush. Everything is confidential. The public cannot enquire or ask unnecessary, poking questions. Unless they want to be fated to misfortune.

Maintaining a highly conservative socialist environment depends on keeping the people happy and looking away from other countries with democratic cultures.

The last thing China desires is an uprising of the people because of the chaos. The perpetuation of a strict check and balance system must be in effect. What is shared online is the government’s way of keeping the people out of harm’s way.

Consequently, the government keeps tabs on who is posting what. Secondly, they also keep an eye on the news. Therefore, only government-approved information is shared on social apps.

Domestic Companies Benefit Heavily: A Monopoly

For example, in 2016 alone, more than 42,000,000 new internet users joined this number of already active internet users. These new users amount to the total populations of Canada, and Costa Rica combined!

Therefore, saying that the CCP controls such a large country virtually is something. The citizens consuming the media created for them only benefits the country. In addition, this means that the domestic online platforms help heavily.

Alibaba Group, Baidu, and other online companies that are the only ones accessible to the Chinese people benefit from an alarming state. There is a massive number of Chinese public ordering from these platforms. Therefore, they have become the most successful online companies in China and in the world. This is very vital to note. This proves that locally, you can grow your economy if you pivot the freedom of your citizens by using censors and proxies. 

The Question is Why: The Secret To Understanding Social Media In China?

China made its own social media platforms to keep a population of avid internet users occupied. Outlets that effectively replaced those made in foreign countries. As such, the Chinese party did this for several reasons– mainly to keep its citizens in check. In addition, China has developed an incredibly sophisticated network tracking system. This system now demands any company owner to have a license to own a website.

This new crackdown on virtual online tracking has led China and its government to gain a vast amount of power over the normal populace. The government now knows who has posted what and from where. They know your location, and they know where exactly you are getting your information from.

What China is doing to its citizens is considered illegal in most parts of the world. No matter how authoritarian it may seem, the government cannot tap in to know the locations and whereabouts of their own citizens, no matter what they post.

Conclusion

In conclusion, China holds a monopoly on information within the country. The government controls all aspects of life within its borders. So, one starts to question themselves: Should leaders be as powerful and controlling? Furthermore, are the people really in danger when China’s economy seems to be blooming day by day?
I think it’s an urgent matter that deserves attention now. Before it gets too late before we reach a turning point.
Help others see this truth about social media in China. If you liked this post, please let me know in the comments. Moreover, share it with your friends. Please share your thoughts below. If you want similar stuff in the future, drop a comment letting me know.

Interested in more? Check out a similar post to read about some of the most exotic foods of China.

Edited By: Syed Umar Bukhari.