Category: AI

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AI Predicts Timeline for EVs to Capture 50% of US Car Sales

Introduction:

As electric vehicles (EVs) become more popular, experts and enthusiasts alike are trying to determine when they will capture a significant portion of the US automotive market. To shed light on this question, researchers at ApplyingAI.com have employed artificial intelligence (AI) to analyze historical data and predict when EVs will make up 50% of US car sales.

Methodology:

The AI model used for this prediction was trained on a dataset that includes historical EV sales data, government policies, technological advancements, and market trends. By analyzing these factors, the AI was able to identify patterns and correlations that influence EV adoption rates and market penetration.

Results:

Based on the AI’s analysis, EVs are predicted to account for 50% of US car sales by 2035. This projection takes into account the current growth rate of EV sales, as well as anticipated improvements in battery technology, charging infrastructure, and vehicle affordability. Additionally, the AI considered the impact of government policies, such as the recent ambitious EU and US programs – Europe’s Fit for 55 package and the US’s Inflation Reduction Act, which include new proposed EPA emissions rules. These policies are expected to drive significant growth in EV sales over the next decade.

Regional differences:

The AI model also identified regional differences in EV adoption rates across the United States. States with more progressive environmental policies and higher incentives for EV adoption are likely to reach the 50% milestone sooner than states with less supportive policies. Furthermore, urban areas with better charging infrastructure and higher population density are predicted to adopt EVs more quickly than rural areas.

Market implications:

The AI’s prediction of EVs capturing 50% of US car sales by 2035 has significant implications for the automotive industry, the oil industry, and the environment. Automakers will need to adapt their production lines and supply chains to meet the increasing demand for EVs. The oil industry will face a decline in demand, as EVs displace internal combustion engine vehicles, potentially reducing the need for at least 5 million barrels of oil per day by 2030, according to the International Energy Agency (IEA). Lastly, the widespread adoption of EVs will contribute to a reduction in greenhouse gas emissions, helping the US meet its climate change goals.

Conclusion:

The AI-powered prediction provides valuable insights into the future of EV adoption in the United States. While the timeline for reaching 50% of car sales is only an estimate, it underscores the importance of continued investment in EV technology, infrastructure, and policy to accelerate the transition to cleaner and more sustainable transportation.

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Unveiling the Untold Story of Tesla’s Q1 2023 Earnings Report: A Deeper Dive into Share Price Implications

Tesla’s Q1 2023 earnings report has captured the attention of investors and market enthusiasts alike, showcasing impressive revenue growth and a solid financial position. However, a closer inspection reveals some underlying concerns that might not be as clear on the surface. In this analysis, I will explore the hidden truths that may impact the company’s future success and share price, while offering a unique perspective on the unfolding situation.

Part 1: Financials and Profitability

The Balancing Act of Tesla’s Profitability: At first glance, Tesla’s Q1 2023 report reveals an 11.4% operating margin and $2.7B in GAAP operating income. However, this figure is down YoY, primarily due to reduced average selling prices (ASPs), higher raw material costs, and increased logistics and warranty expenses. As the company continues to expand production and launch new products, these costs may continue to escalate, potentially putting a strain on profitability.

Share Price Implications: A decline in profitability could dampen investor sentiment and lead to downward pressure on Tesla’s share price. As the electric vehicle (EV) market becomes increasingly competitive, Tesla may need to continue cutting prices to maintain its market share. This price war, along with the rising costs of production, could significantly impact the company’s profit margins and, consequently, its stock valuation.

Part 2: Product Development and Challenges

Navigating the Cybertruck Waters: Tesla’s Cybertruck has generated a significant buzz, and its production is set to begin later this year at Gigafactory Texas. However, the unconventional design and features of the Cybertruck may not resonate with traditional truck buyers. The pickup truck market is fiercely competitive, and Tesla’s entrance into this space comes with a considerable risk that may not be fully reflected in their earnings report.

The Odyssey of 4680 Cell Production: Tesla’s 4680 battery cells are crucial for their future success, as they promise increased energy density, reduced cost, and better performance. However, ramping up production for these cells has proved challenging, contributing to the decrease in operating income. If Tesla encounters additional setbacks, it could significantly delay product launches and hinder their ability to meet the 50% compound annual growth rate (CAGR) target.

Share Price Implications: Tesla’s share price is heavily influenced by investor sentiment and expectations of future growth. If the Cybertruck fails to capture a significant portion of the pickup truck market, or if the 4680 cell production encounters further delays, it could lead to a negative impact on the stock price. Furthermore, as Tesla’s valuation is based on future growth potential, any delays in product development could result in a revaluation of the company’s worth by the market.

Part 3: Energy Storage Expansion and Market Position

Tesla’s Energy Storage Ambitions: Tesla’s energy storage business showed promising growth in Q1, with the company planning to increase production capacity at their Megafactories in Lathrop and Shanghai. Despite the positive outlook, the energy storage market is becoming increasingly crowded, and Tesla may face stiff competition from both new and established players. This competition may put pressure on margins and make it more difficult for Tesla to maintain its position as a market leader.

Share Price Implications: As a significant portion of Tesla’s valuation is tied to its position as a market leader in the EV and energy storage sectors, increased competition could negatively affect investor sentiment and the company’s stock price. The energy storage market is evolving rapidly, and new technologies could emerge that challenge Tesla’s dominance. If Tesla fails to maintain its competitive edge, the market may reevaluate the company’s growth prospects, leading to potential share price

Part 4: Tesla’s Long-Term Growth Strategy and Share Price Implications

The Roadmap to Tesla’s Growth: In the earnings report, Tesla outlines plans to grow production in alignment with their 50% CAGR target, aiming to produce around 1.8 million cars in 2023. However, the automotive industry is known for its unpredictability, and Tesla’s ambitious growth plans may not be feasible in the long run. The company’s aggressive expansion may leave them vulnerable to unforeseen challenges, such as supply chain disruptions, regulatory hurdles, or shifts in consumer preferences.

Share Price Implications: Investors have high expectations for Tesla’s growth, which is reflected in the company’s stock price. Any signs of faltering growth or the inability to meet their ambitious targets may cause a loss of investor confidence and result in a decline in Tesla’s share price. The market is sensitive to changes in growth projections, and if Tesla’s growth falters, even temporarily, it could cause significant volatility in the stock price.

Part 5: Tesla’s Long-Term Plans and Share Price Dynamics

Tesla’s Vision for the Future: Tesla has ambitious long-term plans that focus on rapid growth, expansion of its product lineup, and continuous investment in autonomy and vehicle software. The company’s strategy includes an emphasis on Full Self-Driving (FSD) technology, which could potentially revolutionize the automotive industry and create new revenue streams through ride-sharing and other applications. Tesla’s commitment to innovation and growth is part of what has propelled its share price to around $170 per share.

Cybertruck Manufacturing Optimism: Cybertruck has the potential to disrupt the pickup truck market with its unique design and advanced technology. Tesla’s Gigafactory Texas, where the Cybertruck will be produced, is expected to feature cutting-edge manufacturing techniques and innovations that could help streamline the production process. If Tesla can successfully ramp up Cybertruck production and gain a foothold in the competitive pickup truck market, it could further solidify its position as a leader in the EV industry and create a positive impact on its share price.

Full Self-Driving (FSD) Prospects: Tesla’s FSD technology is one of the key pillars of the company’s long-term strategy. The company has made significant progress in recent years, with multiple iterations of their Autopilot and FSD software being released to customers. However, the path to true autonomous driving is complex, with regulatory and technical challenges still to be overcome. If Tesla can successfully navigate these hurdles and deliver a fully functional FSD system, it could potentially unlock substantial value for the company and its shareholders.

Share Price Implications: The share price of Tesla is closely tied to the company’s long-term plans and its ability to execute them successfully. The introduction of the Cybertruck and advancements in FSD technology could potentially lead to significant upside in Tesla’s share price. However, the market will closely monitor the company’s progress in these areas. Any setbacks or delays in production, FSD development, or regulatory approval could negatively impact investor sentiment and the stock price.

Conclusion: Tesla’s long-term plans, including the Cybertruck production and Full Self-Driving technology, play a crucial role in the company’s current share price of around $170 per share. While there are risks associated with these ambitious plans, a successful execution could propel Tesla to new heights in the automotive industry and lead to potential gains in its stock valuation. As the electric vehicle market continues to evolve, Tesla’s ability to navigate these challenges and capitalize on emerging opportunities will be key to maintaining its dominance and protecting its share price.

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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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Unleashing the Revolutionary Power of AI in Data Entry and Processing: Anticipating Unprecedented Advances Before 2025

Data entry and processing is one of the key areas where Artificial Intelligence (AI) is expected to have a major impact in the coming years. With the increasing amount of data being generated every day, the demand for faster and more efficient data processing has never been higher. Fortunately, AI technology is here to help meet this demand and take data entry and processing to the next level.

One of the main advantages of AI in data processing is its ability to automate manual data entry. This means that instead of relying on human data entry clerks, AI algorithms can process and categorize vast amounts of data much more efficiently and accurately. AI algorithms can also identify patterns and relationships within the data, allowing for more comprehensive data analysis.

Another key area where AI is expected to enhance data entry and processing is in natural language processing (NLP). NLP is a subfield of AI that focuses on the interactions between computers and humans in natural language. With advancements in NLP, AI will soon be able to understand and interpret written and spoken human language, making data entry and processing even more seamless.

Before 2025, we can expect to see significant advancements in AI’s ability to process and analyze unstructured data, such as images, videos, and audio. AI algorithms will be able to automatically identify and categorize information within these types of data, making data entry and processing much easier and more efficient. Additionally, AI will be able to process multiple languages, further expanding its reach and impact on data entry and processing.

Another exciting development in the field of AI and data entry and processing is the use of machine learning. Machine learning is a type of AI that allows algorithms to learn and improve over time through experience. With machine learning, AI algorithms can become more accurate and efficient at processing and analyzing data, reducing the risk of human error and improving the overall accuracy of the data.

In conclusion, the next few years will bring significant advancements in the field of AI and data entry and processing. From automating manual data entry to processing unstructured data and utilizing machine learning, AI has the potential to greatly enhance the accuracy and efficiency of data processing. By embracing these changes, we can look forward to a future where data entry and processing is seamless and accurate, providing valuable insights and helping organizations make better data-driven decisions.

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AI Job Automation: What to Expect in the Next 5 Years and How to Stay Ahead of the Game

As the world of technology evolves, Artificial Intelligence (AI) is becoming an increasingly influential force in our lives and careers. With the advent of new and innovative AI technologies, we can expect to see a major transformation of the job market over the next few years.

One of the most significant changes we can expect to see is the automation of a wide range of tasks that were once performed by humans. This shift may seem daunting at first, but it’s important to keep in mind that it will also lead to the creation of new job opportunities.

Here are just a few of the areas where AI is expected to have a major impact:

Data entry and processing: With the help of advanced AI algorithms, vast amounts of data can now be processed and categorized with incredible efficiency and accuracy. This will result in a significant reduction of manual data entry tasks.

Customer service: AI-powered chatbots and virtual assistants are already helping many companies handle customer inquiries and support. In the years to come, these systems are only going to become even more advanced and capable of handling even more complex tasks.

Manufacturing and logistics: AI is revolutionizing production processes and reducing the need for manual labor in manufacturing and logistics. This technology can be used to optimize production runs, streamline supply chains, and minimize waste, ultimately improving efficiency and cost-effectiveness.

Sales and marketing: By analyzing customer data, AI can predict which customers are most likely to make a purchase, allowing companies to tailor their sales and marketing efforts with greater precision. AI can also automate tasks such as lead generation and email campaigns, freeing up valuable time and resources for sales and marketing teams.

Healthcare: AI is having a major impact on healthcare by automating many tasks and improving patient outcomes. For example, AI algorithms can be used to process medical images, diagnose illnesses, and develop personalized treatment plans.

As AI continues to shape the job market, it’s important for individuals to embrace new opportunities and develop new skills. Fields such as AI development, data analysis, and cybersecurity will likely be in high demand, and those who invest in these areas will be well positioned for success in the years to come.

In conclusion, AI is not something to be feared, but rather an exciting opportunity to be embraced and utilized to its full potential. The next five years are going to be an incredible time for AI, and we can’t wait to see the impact it will have on the job market and beyond!

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Maximizing Your Earnings with ChatGPT: A Guide for Aspiring AI Experts

Are you interested in making money with AI but not sure where to start? OpenAI’s ChatGPT is a powerful tool that has the potential to provide new opportunities for monetization and help you turn your AI expertise into a profitable venture. In this blog post, we will explore how you can use ChatGPT to earn money online, even if you are new to the field of AI.

  1. Offer ChatGPT-powered Customer Service: One of the easiest ways to get started with earning money using AI is by offering ChatGPT-powered customer service. Customer service is a critical component of any business, and many companies struggle to keep up with the volume of inquiries they receive. That’s where ChatGPT comes in – it can provide quick and personalized responses to customers 24/7, freeing up human customer service representatives to focus on more complex inquiries. By offering ChatGPT-powered customer service to businesses, you can earn a recurring income stream by charging a monthly fee for your services.
  2. Develop AI-powered Chatbots for E-commerce: Another way to earn money with ChatGPT is by developing AI-powered chatbots for e-commerce websites. Chatbots are becoming increasingly popular for online retailers as they provide instant support and recommendations to customers. With ChatGPT, you can create custom chatbots that can help e-commerce websites improve the customer experience. By developing chatbots for e-commerce websites, you can earn a one-time fee for your services and potentially earn recurring revenue through ongoing maintenance and updates.
  3. Create ChatGPT-powered Virtual Assistants: Virtual assistants are becoming more and more common in both personal and professional settings. With ChatGPT, you can create virtual assistants that can perform a range of tasks, such as scheduling appointments, answering frequently asked questions, and even making recommendations. As demand for virtual assistants continues to grow, there is a huge opportunity for aspiring AI experts to develop and sell these systems to businesses and individuals. You can earn money by charging a fee for your virtual assistant services or by selling the software outright.
  4. Offer ChatGPT Training and Consultation Services: Finally, you can monetize your AI expertise by offering ChatGPT training and consultation services to businesses and individuals. As ChatGPT continues to grow in popularity, there will be an increasing demand for experts who can help organizations and individuals understand and effectively utilize this technology. By offering training and consultation services, you can earn a fee for your expertise and help others take advantage of the potential of ChatGPT.

In conclusion, there are many ways for aspiring AI experts to use ChatGPT to earn money online. Whether you are interested in offering customer service, developing chatbots, creating virtual assistants, or offering training and consultation services, the potential for monetization is significant. Keep in mind that the key to success is staying up-to-date with the latest advancements in AI technology and marketing your services effectively. Don’t be intimidated by the fact that you are new to AI – with the right tools and resources, you can quickly become an expert and start earning money with ChatGPT.

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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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How Data Will Be Used To Decide Your Future

Welcome to 2019! Let’s start the year off by discussing how your ability to exercise your free will could be directly impacted by who you follow on social media, or who you pass by on the street.

In the dystopian Netflix series Black Mirror, there is an episode called Nosedive where a person’s ability to ride a plane, work at their job or even be served food at a restaurant is decided by a crowd-sourced rating system. Much like you rate your Uber driver at the end of your trip (and your Uber driver rates you), in this episode, every social interaction is followed by both parties giving each other a star-rating with an app on their phone. Everyone’s rating is public and it seems like you can downvote someone at any time. People with higher overall scores have more influence on the scores of people that they interact with. Inevitably, the episode’s main character encounters a string of bad luck and unfortunate encounters that lead to her score tumbling to almost nothing. She ends up losing her job, her home and her ability to travel in a matter of days based on nothing more than a few left swipes from some strangers and a co-worker.

Image result for black mirror rating episode

Everyone has bad days, so it really hits home when you see the protagonist lose her life over a few social mishaps. She doesn’t actually die, but she is locked in a plastic box because of her low social score.

How far are we from experiencing this kind of scenario in our lives?

The Chinese government has announced plans to implement a Social Credit Score for its citizens, that tracks all online and in-person activity, including who your friends are, who you talk to and who your acquaintances talk to. This score will decide if you can get a loan on a house, similar to a credit score in the United States or Canada, but it will also decide what schools you can go to, what businesses you can shop at and even whether or not you can leave the country. Going live in 2020, literally next year, all 1.6 billion Chinese residents will be subject to this Social Credit System; a system that is eerily similar to the one described in the Black Mirror episode.

I live in North America. Calgary, Alberta, Canada, specifically, so this won’t affect me, right?

All around the world, people have free and easy access to instant global communication networks, the wealth of human knowledge at their fingertips, up-to-the-minute information from across the earth, and unlimited usage of the most remarkable software and technology, built by private companies, paid for by adverts. That was the deal that we made. Free technology in return for your data and the ability to use it to influence and profit from you. The best and worst of capitalism in one simple swap. We might decide we’re happy with that deal. And that’s perfectly fine. But if we do, it’s important to be aware of the dangers of collecting this data in the first place. We need to consider where these datasets could lead – even beyond the issues of privacy and the potential to undermine democracy (as if they weren’t bad enough). There is another twist in this dystopian tale. An application for these rich, interconnected datasets that belongs in the popular Netflix show Black Mirror, but exists in reality. It’s known as Sesame Credit, a citizen scoring system used by the Chinese government. Imagine every piece of information that a data broker might have on you collapsed down into a single score. Everything goes into it. Your credit history, your mobile phone number, your address – the usual stuff. But all your day-to-day behaviour, too. Your social media posts, the data from your ride-hailing app, even records from your online matchmaking service. The result is a single number between 350 and 950 points. Sesame Credit doesn’t disclose the details of its ‘complex’ scoring algorithm. But Li Yingyun, the company’s technology director, did share some examples of what might be inferred from its results in an interview with the Beijing-based Caixin Media. ‘Someone who plays video games for ten hours a day, for example, would be considered an idle person. Someone who frequently buys diapers would be considered as probably a parent, who on balance is more likely to have a sense of responsibility.’ If you’re Chinese, these scores matter. If your rating is over 600 points, you can take out a special credit card. Above 666 and you’ll be rewarded with a higher credit limit. Those with scores above 650 can hire a car without a deposit and use a VIP lane at Beijing airport. Anyone over 750 can apply for a fast-tracked visa to Europe. It’s all fun and games now while the scheme is voluntary. But when the citizen scoring system becomes mandatory in 2020, people with low scores stand to feel the repercussions in every aspect of their lives. The government’s own document on the system outlines examples of punishments that could be meted out to anyone deemed disobedient: ‘Restrictions on leaving the borders, restrictions on the purchase of . . . property, travelling on aircraft, on tourism and holidays or staying in star-ranked hotels.’ It also warns that in the case of ‘gravely trust breaking subjects’ it will ‘guide commercial banks . . . to limit their provision of loans, sales insurance and other such services’. Loyalty is praised. Breaking trust is punished.

Fry, Hannah. Hello World: Being Human in the Age of Algorithms (pp. 44-46). W. W. Norton & Company. Kindle Edition.

Science fiction mirrors reality and reality reflects science fiction. While this type of system is only going live in China, it is unclear how long we have before something similar arrives in the West.