Tag: Python

Posted on

How ChatGPT Helped Me Visualize Global Media Funding: A Unique Application of AI on ApplyingAI.com, Featuring a Dash of Twitter Drama

Introduction

At ApplyingAI.com, we’re passionate about exploring the immense potential of artificial intelligence (AI) in global macro investing, electric vehicles (EVs), autonomy, space travel, and free markets. Our goal is to empower the future of finance and innovation by showcasing real-world AI applications that are revolutionizing industries. In this article, we’ll share a fascinating story of how OpenAI’s ChatGPT helped create an engaging visualization of global media funding, peppered with a few laughs inspired by a recent Twitter thread.

The Idea

In a world where media plays a crucial role in shaping public opinion and influencing decision-making, understanding the relationship between media companies and their funding sources is vital. We wanted to create an impactful visual representation of government funding for major media companies worldwide, including prominent names such as BBC, NHK, CCTV, and CBC, as well as US-based networks like PBS, NPR, and CNN. Little did we know that our creation would coincide with a humorous Twitter exchange!

The Twitter Drama

As fate would have it, a recent Twitter thread surfaced discussing the Canadian Broadcasting Corporation (CBC) and its government funding. The public broadcaster took issue with being labelled as “government-funded media,” arguing that it undermined their credibility. Elon Musk chimed in, suggesting a 70% government-funded label, followed by a tongue-in-cheek compromise of 69% to “give them the benefit of the doubt.” The exchange lightened the mood and highlighted the importance of accuracy (and humor) in media funding discussions.

The Solution

With the assistance of ChatGPT, we generated a Python script that utilized popular visualization libraries such as matplotlib and seaborn. The script produced a striking horizontal bar chart that showcased the percentage of government funding received by each media company, along with the country they are based in. The visualization, complete with vibrant colors and annotations, allowed for an easy comparison of the government funding landscape across the globe – and a subtle nod to the ongoing Twitter debate.



import matplotlib.pyplot as plt

import seaborn as sns



# Data

media_companies = [

    "BBC", "NHK", "CCTV", "France Télévisions", "ARD",

    "RAI", "RTVE", "ABC", "SABC", "CBC/Radio-Canada",

    "PBS", "NPR", "FOX", "CNBC", "CNN"

]

government_funding = [

    75, 95, 100, 80, 85, 70, 90, 95, 20, 65,

    15, 10, 0, 0, 0

]

countries = [

    "United Kingdom", "Japan", "China", "France", "Germany",

    "Italy", "Spain", "Australia", "South Africa", "Canada",

    "United States", "United States", "United States", "United States", "United States"

]



# Set seaborn style

sns.set(style="whitegrid")



# Create a horizontal bar plot

plt.figure(figsize=(12, 8))

ax = sns.barplot(x=government_funding, y=media_companies, palette="viridis")



# Add title and labels

plt.title("Major Media Companies and Their Government Funding Percentages")

plt.xlabel("Government Funding (%)")

plt.ylabel("Media Companies")



# Annotate the bars with the percentage values and country names

for i, (value, country) in enumerate(zip(government_funding, countries)):

    ax.text(value + 1, i, f"{value}% ({country})", va="center")



# Show the plot

plt.show()

The Impact

The resulting bar chart, generated with the help of ChatGPT, has garnered attention and sparked discussions among our audience, as well as some chuckles inspired by the Twitter thread. By understanding the connection between media companies and their funding sources, investors, policymakers, and the general public can make more informed decisions about the future of media, finance, and innovation – and perhaps share a laugh or two along the way.

Conclusion

This unique application of AI demonstrates the power of tools like ChatGPT in simplifying complex tasks and generating insightful outputs, with a sprinkle of humour to keep things light-hearted. We at ApplyingAI.com aim to continue empowering the future of finance and innovation in various domains, such as global macro investing, EVs, autonomy, space travel, and free markets. Stay tuned for more exciting stories, insights, and a few laughs as we continue to explore the captivating world of AI applications!

Posted on

Python Is Awesome

If you’ve ever looked into what it takes to become a developer of AI software, you probably know that Python is the language of choice for 95% of Machine Learning applications out there.

So, why Python? It’s not super easy to learn. Students can learn graphical programming languages like Apple’s Swift much faster. It’s not the speediest. There are other programming languages that are better optimized to develop the GPU and CPU intensive tasks that Machine Learning software requires. Unfortunately, it’s not even the most ubiquitous (for applications outside of machine learning and data science). Many programmers are much more familiar with Javascript for web development. It is free, so it does have that going for it.

Here’s what Quora has to say about it:

“…it is a general language that does a little of everything at a good enough complexity-performance tradeoff with a full suite of tools for productionizing machine learning.”

Thia Kai Xin, Head of Data (Tech In Asia), Co-Founder of DataScience SG.

Essentially, Python is effective enough to get the job done. Major companies like Google, Facebook, and Uber all use Python for the majority of their ML software development, so that helps. If you want a job at a major tech company, and you want to develop artificial intelligence applications, you’ll probably need to understand Python pretty well.

So, what can Python do? It’s built on an open-source licence, so there’s no need to worry about licensing fees. Python comes pre-installed on all Apple desktops and can be easily installed on Windows or Linux builds. The latest version of Python (Python 3.7) can be easily integrated with mathematical packages like NumPy and with clever development visualization tools like Jupyter Notebooks.

“The Jupyter Notebook is an open-source web application that allows you to create and share documents that contain live code, equations, visualizations and narrative text. Uses include: data cleaning and transformation, numerical simulation, statistical modeling, data visualization, machine learning, and much more.”

Project Jupyter – jupyter.org

There are many, many online and in-person courses available that teach Python, including many free and low cost options. Udacity offers a free intro to Python course that you can sign up for right now. 

Python is easy to learn and powerful. It’s accessible to pretty much anyone with a computer and there are lots of ways to get started. Here’s an example of what you can do with Python in only a few days of practice:

Using only freely available libraries and packages, along with some tutelage from Udacity’s AI Programming Fundamentals program, a student can learn to program a deep-neural-net, a type of machine learning tool, that is able to distinguish images of various types of animals, including dog breeds, with a high degree of accuracy. That’s pretty amazing. Someone with limited programming experience can learn how to build their own AI program in less time than it takes to fail your first University midterm.

If you’re interested in programming these types of tools or if you’re curious about how they work, I highly suggest you head over to the 3 Blue 1 Brown YouTube channel and watch his videos on Neural Networks. The animations are world-class and the topics are simplified enough to be understood but still cover the topic in great depth. I’ve linked the first video in the series below and I can’t recommend the channel enough.

So where is this all going and what applications does this have for people in finance?

Using the tools described above, I have already created software that tracks the share price of several tech giants and tries to predict their short-term market performance. Mind you, I have about a decade of software development experience, but nearly all of my experience is outside of the machine learning space.

Python is a great tool for experienced programmers and beginners alike to build some amazing software. Try it out for yourself, and when you get hooked, don’t blame me when you inevitably find yourself up past midnight solving the world’s problems one line of code at a time.