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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!

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Book Review – Hello World: Being Human in the Age of Algorithms – Part 1

Introduction
I often think of AI as something separate from traditional computer programming, something transcendent. However, most of the advances in modern AI are not the result of revolutionary new concepts or fields of study but rather the application of previously developed algorithms to significantly more powerful hardware and massive datasets.

Hannah Fry’s take on the world of AI covers topics ranging from justice to autonomous vehicles, crime, art and even to medicine. While the author is an expert in the field, she does a great job distilling the topics down to a level understandable by a layperson, but also keeps it interesting for someone with more background in programming and AI.

My favourite quote from the first part of the book comes on page 8, where Hannah succinctly describes the essence of what an algorithm is in only one sentence:

An algorithm is simply a series of logical instructions that show, from start to finish, how to accomplish a task.

Fry, Hannah. Hello World: Being Human in the Age of Algorithms (p. 8). W. W. Norton & Company. Kindle Edition

Once you read it, it seems obvious, but trying to describe to a first-year computer science student what an algorithm is can be a challenging task. The author manages this well. Despite the complexity and depth of the subject matter, Fry is able to bring context and relevance to a broad array of topics. The remainder of my review will speak to some of the book’s many sections and how someone with a business-facing view into the topics sees them.

Data
This section covers some of the unknown giants in data-science including Peter Thiel’s Palantir. The section also touches on some very public examples where analytics has played a negative role – Cambridge Analytica’s use of private user data during the 2016 Presidential Elections.

The story here is about data brokers. Data brokers are companies who buy and collect user data and personal information and then resell it or share it for profit. A surprising fact is that some of these databases contain records of everything that you’ve ever done from religious affiliations to credit-card usage. These companies seem to know everything about just about everyone. It turns out that it is relatively simple to make inferences about a person based on their online habits.

The chapter converges to one of the major stories of 2018, the Cambridge Analytica scandal. But it begins by discussing the five personality traits that psychologists have used to quantify individuals’ personalities since the 1980s: openness to experience, conscientiousness, extraversion, agreeableness and neuroticism. By pulling data from users’ Facebook feeds, Cambridge Analytica was able to create detailed personality profiles to deliver emotionally charged and effective political messages.

Perhaps the most interesting fact though, is how small of an impact this type of manipulation actually has. The largest change reported was from 11 clicks in 1000 to 16 clicks in 1000 (less than 1 percent). But even this small effect, spread over a population of millions can cause dramatic changes to the outcome of, say, an election.

That’s the end of part 1 of this review. In Part 2, I’ll touch on some of the other sections of the book including Criminal Justice and Medicine.

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AI Everything

These days it seems like businesses are trying to use AI to do everything. At least for startups, that isn’t far off. Anywhere there is a dataset remotely large enough and an answer that is vaguely definable, companies are putting together a business model to use machine learning to solve the problem. With some incredible successes in areas like image classification and defeating humans at video games, its hard not to be impressed.

One of the best channels for following recent breakthroughs in AI is the 2 Minute Papers YouTube Channel, started by Károly Zsolnai-Fehér, a professor at the Vienna University of Technology in Austria. Károly’s videos combine interesting clips of the programs in action with well-delivered summaries of recent papers illustrating advances in artificial intelligence.

In one of his latest videos, he covers an AI that not only can copy the most successful actions that humans take in video games but can actually improve on those actions to be better than the best human players. So does that mean that AI will be displacing office workers once it learns how to do their jobs better than them? Probably, yes. But maybe not quite how you think it might.

As much of a ‘black-box‘ as AI has been in the past, modern systems are becoming better and better at explaining how they arrived at an answer. This gives human operators predictive capabilities that we didn’t have with systems of the past that could spit out an answer but gave us no indication of how that answer was formulated.

This Forbes article on Human-Centric AI provides some examples of how modern AI systems can be implemented to train employees to do their jobs better and even enjoy their jobs more while doing it! If that doesn’t sound incredible to you, you may be a machine who is only reading this page to improve your search algorithm.

So what does this all mean? A lot of research is showing that AI is actually creating many more jobs than it destroys. So, as long as you’re willing to try and understand the systems that will one day be our overlords, you should be able to upgrade your career and stay employed.

Whether you still want the job that remains is another question entirely.

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Turning Your Selfie Into a DaVinci

Transfer learning. It’s a branch of AI that allows for the style transfer from one image to another. It seems like a straightforward concept: take my selfie and make it look like a Michelangelo painting. However, it is a fairly recent innovation in Deep Neural Networks that has allowed us to separate the content of an image from its style. And in doing so, to combine multiple images in ways that were previously impossible. For example, taking a long-dead artist’s style and applying it to your weekend selfie.

Just to prove that this is pretty cool, I’m going to take my newly built style transfer algorithm and apply it to a ‘selfie’ of my good dog, Lawrence. Here’s the original:

And here’s the image that I’m going to apply the style of:

That’s right, it’s Davinci’s Mona Lisa, one of the most iconic paintings of all time. I’m going to use machine learning to apply Davinci’s characteristic style to my iPhone X photo of my, admittedly very handsome, pupper.

If you’re interested, here’s a link to the original paper describing how to use Convolutional Neural Networks or CNNs to accomplish image style transfer. It’s written in relatively understandable language for such a technical paper so I do recommend you check it out, given you’re already reading a fairly technical blog.

So what is image content and style and how can we separate out the two? Well, neural networks are built in many layers, and the way it works out, some of the layers end up being responsible for detecting shapes and lines, as well as the arrangement of objects. These layers are responsible for understanding the ‘content’ of an image. Other layers, further down in the network are responsible for the style, colors and textures

Here’s the final result next to the original.

Pretty striking, if I do say so myself.

Using a pre-trained Neural Network called VGG19 and a few lines of my own code to pull the figures and what’s called a Gram Matrix I choose my style weights (how much I want each layer to apply). Then using a simple loss function to push us in the right direction we apply the usual gradient descent algorithm and poof. Lawrence is forever immortalized as a Davinci masterpiece.

Impressed? Not Impressed? Let me know in the comments below. If you have anything to add, or you think I could do better please chime in! This is a learning process for me and I’m just excited to share my newfound knowledge.

Here’s a link to my code in a Google Colab Notebook if you want to try it out for yourself!

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We’re Hiring!… And Raising Money!

My company, InOrbis Intercity, is doing a capital raise. We’ve been operating profitably for nearly two years now and we’re growing at a steady 10-20% per month. It’s exciting, to say the least, but this is the first time I’ve raised outside money. I have read book after book on Venture Capital, Angel Investment, Private Equity, and even raising money via an ICO (Initial Coin Offering). I have pitched our company several times, but the interest has been limited in this neck of the woods (Alberta, Canada). I think it’s time to start investigating alternative funding sources.

I know this business can be a billion dollar company, but we need to build scalable software to allow us to operate in more markets. We have developers, but we need more, and we need to hire some of the best in the business to work on our team. We’re building something great, applying AI to make intercity travel a better experience for millions of people. 

Raising money is a battle, only about 0.01% of companies actually manage to raise the funds for a billion-dollar valuation. There are hundreds of unreported examples where companies raise multiple-millions, the company grows and is sold for tens of millions but the founders are left with nothing.

It is a monumental risk, to be sure, but at this point, it seems like the best option we have available to grow the business.

If you’re interested in being part of something special, the future of autonomous, sustainable, intercity transportation, and you are a passionate, intelligent developer, or software engineer, willing to risk it all on a moonshot (I know, there must be at least one or two out there), send me a message and we’ll meet for coffee.

On the flip side, if you’re an Angel or VC and you’d like to learn more, drop us a line. I’d be happy to pull out our pitch deck and wow you with this vision. We will make it happen, but your help could make it a reality that much sooner.

Cheers,

Rosario

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What is this about?

My name is Rosario Fortugno. I’m an electrical engineer, MBA, and clean-tech entrepreneur making my way into the worlds of finance and AI. 

This website is meant to provide some insight into my journey. My hope is that it communicates some of what I learn as I pursue my CFA (Chartered Financial Analyst) designation, highlighting examples from my business, as well as what I’m learning through the courses I’m taking in AI and machine learning from MIT and Udacity.

Basically, this is a way for me to show off my knowledge to the world… 

Every day, I’m going to summarize what I’ve learned. The source of the material will either be from my CFA prep material, my own business, InOrbis Intercity, my MIT and Udacity AI courses,  or just something I picked up along the way.

You’ll get a deep dive into the inner workings of my mind. The mind of a person who is probably trying to do too many things at once, but who is going to try to do them anyways, because, What the heck! Right?

Not only will you be learning alongside me, you will be joining me as I wade neck-deep into two of the most confusing and challenging spaces that the 21st century has to explore: artificial intelligence, and financial analysis.

A lot of what I discuss will be sourced from my other courses, so I’ll always try to provide links and images for reference to the source material. While I’ve always considered myself to be relatively creative, my propensity for original thought is limited by my expertise, so where I share something that isn’t my own work, I will try to give credit where credit is due.

Here we go! Let’s dive right in and get started. Today’s topic is Understanding Machine Learning. An undoubtedly simple subject. Let’s see how it goes 😀