The Evolution of Media: Visualizing a Data-Driven Future
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The Evolution of Media: Visualizing a Data-Driven Future

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The Evolution of Media: Visualizing a Data-Driven Future

In today’s highly-connected and instantaneous world, we have access to a massive amount of information at our fingertips.

Historically, however, this hasn’t always been the case.

Time travel back just 20 years ago to 2002, and you’d notice the vast majority of people were still waiting on the daily paper or the evening news to help fill the information void.

In fact, for most of 2002, Google was trailing in search engine market share behind Yahoo! and MSN. Meanwhile, early social media incarnations (MySpace, Friendster, etc.) were just starting to come online, and all of Facebook, YouTube, Twitter, and the iPhone did not yet exist.

The Waves of Media So Far

Every so often, the dominant form of communication is upended by new technological developments and changing societal preferences.

These transitions seem to be happening faster over time, aligning with the accelerated progress of technology.

  • Proto-Media (50,000+ years)
    Humans could only spread their message through human activity. Speech, oral tradition, and manually written text were most common mediums to pass on a message.
  • Analog and Early Digital Media (1430-2004)
    The invention of the printing press, and later the radio, television, and computer unlock powerful forms of one-way and cheap communication to the masses.
  • Connected Media (2004-current)
    The birth of Web 2.0 and social media enables participation and content creation for everyone. One tweet, blog post, or TikTok video by anyone can go viral, reaching the whole world.

Each new wave of media comes with its own pros and cons.

For example, Connected Media was a huge step forward in that it enabled everyone to be a part of the conversation. On the other hand, algorithms and the sheer amount of content to sift through has created a lot of downsides as well. To name just a few problems with media today: filter bubbles, sensationalism, clickbait, and so on.

Before we dive into what we think is the next wave of media, let’s first break down the common attributes and problems with prior waves.

Wave Zero: Proto-Media

Before the first wave of media, amplifying a message took devotion and a lifetime.

Add in the fact that even by the year 1500, only 4% of global citizens lived in cities, and you can see how hard it would be to communicate effectively with the masses during this era.

Or, to paint a more vivid picture of what proto-media was like: information could only travel as fast as the speed of a horse.

Wave 1: Analog and Early Digital Media

In this first wave, new technological advancements enabled widescale communication for the first time in history.

Newspapers, books, magazines, radios, televisions, movies, and early websites all fit within this framework, enabling the owners of these assets to broadcast their message at scale.

With large amounts of infrastructure required to print books or broadcast television news programs, it took capital or connections to gain access. For this reason, large corporations and governments were usually the gatekeepers, and ordinary citizens had limited influence.

AttributeDescription
📡 Information FlowOne-way
💰 Barriers to EntryVery high
📰 DistributionControlled by mass media companies and government
🏆 IncentiveTo cast a wide net, and to not alienate viewers or advertisers

Importantly, these mediums only allowed one-way communication—meaning that they could broadcast a message, but the general public was restricted in how they could respond (i.e. a letter to the editor, or a phone call to a radio station).

Wave 2: Connected Media

Innovations like Web 2.0 and social media changed the game.

Starting in the mid-2000s, barriers to entry began to drop, and it eventually became free and easy for anyone to broadcast their opinion online. As the internet exploded with content, sorting through it became the number one problem to solve.

For better or worse, algorithms began to feed people what they loved, so they could consume even more. The ripple effect of this was that everyone competing for eyeballs suddenly found themselves optimizing content to try and “win” the algorithm game to get virality.

AttributeDescription
📡 Information FlowTwo-way
💰 Barriers to EntryVery low
📰 DistributionControlled by technology companies and algorithms
🏆 IncentiveTo cast a narrow net, to engage and mobilize a specific audience

Viral content is often engaging and interesting, but it comes with tradeoffs. Content can be made artificially engaging by sensationalizing, using clickbait, or playing loose with the facts. It can be ultra-targeted to resonate emotionally within one particular filter bubble. It can be designed to enrage a certain group, and mobilize them towards action—even if it is extreme.

Despite the many benefits of Connected Media, we are seeing more polarization than ever before in society. Groups of people can’t relate to each other or discuss issues, because they can’t even agree on basic facts.

Perhaps most frustrating of all? Many people don’t know they are deep within their own bubble in which they are only fed information they agree with. They are unaware that other legitimate points of view exist. Everything is black and white, and grey thinking is rarer and rarer.

Wave 3: Data Media

Between 2015 and 2025, the amount of data captured, created, and replicated globally will increase by 1,600%.

For the first time ever, a significant quantity of data is becoming “open source” and available to anyone. There have been massive advancements in how to store and verify data, and even the ownership of information can now be tracked on the blockchain. Both media and the population are becoming more data literate, and they are also becoming aware of the societal drawbacks stemming from Connected Media.

As this new wave emerges, it’s worth examining some of its attributes and connecting concepts in more detail:

  • Transparency:
    Data literate users will begin to demand that data is transparent and originating from trustworthy, factual sources. Or if a source is not rock solid, users will demand that limitations of methodology or possible biases are openly revealed and discussed.
  • Verifiability and Trust:
    How do we know data shown is legitimate and bonafide? Platforms and media will increasingly want to prove to users that data has been verified, going all the way back to the original source.
  • Decentralization and Web3:
    Anyone can tap into large amounts of public data available today, which means that reporting, analysis, ideas, and insights can come from an increasingly growing set of actors. Web3 and decentralized ledgers will allow us to provide trust, attribution, accountability, and even ownership of content when necessary. This can remove the middleman, which is often large tech companies, and can allow users to monetize their content more directly.
  • Data Storytelling
    Growing data literacy, and the explosion of data storytelling is a key approach to making sense of vast amounts of data, by combining data visualization, narrative, and powerful insights.
  • Data Creator Economy:
    Democratized data and the rise of storytelling are intersecting to create a potential new ecosystem for data storytellers. This is increasingly what we are focused on at Visual Capitalist, and we encourage you to support our Kickstarter project on this (just 6 days left, as of publishing time)
  • Open-Ended Ecosystem:
    Just like open source has revolutionized the software industry, we will begin to see more and more data available broadly. Incentives may shift in some cases from keeping data proprietary, to getting it out in the open so that others can use, remix, and publish it, and attributing it back to the original source.
  • Data > Opinion:
    Data Media will have a bias towards facts over opinion. It’s less about punditry, bias, spin, and telling others what they should think, and more about allowing an increasingly data literate population to have access to the facts themselves, and to develop their own nuanced opinion on them.
  • Global Data Standards:
    As data continues to proliferate, it will be important to codify and unify it when possible. This will lead to global standards that will make communicating it even easier.

Early Pioneers of Data Media

The Data Media ecosystem is just beginning to emerge, but here are some early pioneers we like:

  • Our World in Data:
    Led by economist Max Roser, OWiD is doing an excellent job amalgamating global economic data in one place, and making it easy for others to remix and communicate those insights effectively.
  • USAFacts:
    Founded by Steve Ballmer of Microsoft fame to be a non-partisan source of U.S. government data.
  • FRED:
    This tool by the Federal Reserve Bank of St. Louis is just one example of many tools that have cropped up over the years to democratize data that were previously proprietary or hard to access. Other similar tools have been created by the IMF, World Bank, and so on.
  • FiveThirtyEight:
    FiveThirtyEight uses statistical analysis, data journalism, and predictions to cover politics, sports, and other topics in a unique way.
  • FlowingData:
    At FlowingData, data viz expert Nathan Yau explores a wide variety of data and visualization themes.
  • Data Journalists:
    There are incredible data journalists at publications like The Economist, The Washington Post, The New York Times, and Reuters that are tapping into the early beginnings of what is possible. Many of these publications also made their COVID-19 work freely available during the pandemic, which is certainly commendable.

Growth in data journalism and the emergence of these pioneers helps give you a sense of the beginnings of Data Media, but we believe they are only scratching the surface of what is possible.

What Data Media is Not

In a sense, it’s easier to define what Data Media isn’t.

Data Media is not partisan pundits arguing over each other on a newscast, and it’s not fake news, misinformation, or clickbait that is engineered to drive easy clicks. Data media is not an echo chamber that only reinforces existing biases. Because data is also less subjective, it’s less likely to be censored in the way we see today.

Data is not perfect, but it can help change the conversations we are having as a society to be more constructive and inclusive. We hope you agree!

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Technology

Infographic: Generative AI Explained by AI

What exactly is generative AI and how does it work? This infographic, created using generative AI tools such as Midjourney and ChatGPT, explains it all.

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Generative AI Explained by AI

After years of research, it appears that artificial intelligence (AI) is reaching a sort of tipping point, capturing the imaginations of everyone from students saving time on their essay writing to leaders at the world’s largest tech companies. Excitement is building around the possibilities that AI tools unlock, but what exactly these tools are capable of and how they work is still not widely understood.

We could write about this in detail, but given how advanced tools like ChatGPT have become, it only seems right to see what generative AI has to say about itself.

Everything in the infographic above – from illustrations and icons to the text descriptions⁠—was created using generative AI tools such as Midjourney. Everything that follows in this article was generated using ChatGPT based on specific prompts.

Without further ado, generative AI as explained by generative AI.

Generative AI: An Introduction

Generative AI refers to a category of artificial intelligence (AI) algorithms that generate new outputs based on the data they have been trained on. Unlike traditional AI systems that are designed to recognize patterns and make predictions, generative AI creates new content in the form of images, text, audio, and more.

Generative AI uses a type of deep learning called generative adversarial networks (GANs) to create new content. A GAN consists of two neural networks: a generator that creates new data and a discriminator that evaluates the data. The generator and discriminator work together, with the generator improving its outputs based on the feedback it receives from the discriminator until it generates content that is indistinguishable from real data.

Generative AI has a wide range of applications, including:

  • Images: Generative AI can create new images based on existing ones, such as creating a new portrait based on a person’s face or a new landscape based on existing scenery
  • Text: Generative AI can be used to write news articles, poetry, and even scripts. It can also be used to translate text from one language to another
  • Audio: Generative AI can generate new music tracks, sound effects, and even voice acting

Disrupting Industries

People have concerns that generative AI and automation will lead to job displacement and unemployment, as machines become capable of performing tasks that were previously done by humans. They worry that the increasing use of AI will lead to a shrinking job market, particularly in industries such as manufacturing, customer service, and data entry.

Generative AI has the potential to disrupt several industries, including:

  • Advertising: Generative AI can create new advertisements based on existing ones, making it easier for companies to reach new audiences
  • Art and Design: Generative AI can help artists and designers create new works by generating new ideas and concepts
  • Entertainment: Generative AI can create new video games, movies, and TV shows, making it easier for content creators to reach new audiences

Overall, while there are valid concerns about the impact of AI on the job market, there are also many potential benefits that could positively impact workers and the economy.

In the short term, generative AI tools can have positive impacts on the job market as well. For example, AI can automate repetitive and time-consuming tasks, and help humans make faster and more informed decisions by processing and analyzing large amounts of data. AI tools can free up time for humans to focus on more creative and value-adding work.

How This Article Was Created

This article was created using a language model AI trained by OpenAI. The AI was trained on a large dataset of text and was able to generate a new article based on the prompt given. In simple terms, the AI was fed information about what to write about and then generated the article based on that information.

In conclusion, generative AI is a powerful tool that has the potential to revolutionize several industries. With its ability to create new content based on existing data, generative AI has the potential to change the way we create and consume content in the future.

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