The Most Hyped Technology of Every Year From 2000-2018 - Visual Capitalist
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The Most Hyped Technology of Every Year From 2000-2018

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20 years of technology hype cycles

Visualizing Technology Hype Cycles (2000-2018)

Nothing captures our collective imagination quite like emerging technology.

In a short amount of time, technological innovations such as wireless internet and social networking have become a ubiquitous part of our everyday lives, quietly transforming the way we live, work, and communicate. Other promising technologies have their moment in the sun, only to fade into obscurity.

Gartner’s Hype Cycle charts the roller coaster ride of emerging tech, from the first stirrings of public awareness to the point of wider adoption and economic viability. Today’s graphic is a retrospective look at which trends scaled the summit of the Hype Cycle each year since 2000.

Reaching the Peak

As the media searches for the next big thing, certain technologies tend to dominate the headlines. Meanwhile, venture capital flows into the companies racing to bring the tech to market, valuations swell, marketing departments generate excitement, and the expectations of the general public begin to grow as well.

One example of this phenomenon at work is the adoption of microblogging. Today, we don’t think twice about posting a tweet or updating our status on Facebook, but a decade ago, the act of posting a short public message was major shift in the way people used technology to communicate with one another. The intense buzz that sent microblogging towards the top of the Hype Cycle is corroborated by Google Search data.

microblogging trend

Living Up to the Hype

A few technologies transcend the hype to transform entire industries. Here are some examples that lived up to their time in the spotlight.

Cloud Computing
Right from the beginning, the analogy of data breaking the shackles of folders and clunky external drives – instead zipping efficiently into the invisible cloud – generated a lot of excitement. It felt like the future of computing, and enterprises and individuals eagerly adopted the technology.

Today, Microsoft and Amazon’s cloud computing divisions each make $6-7 billion in revenue per quarter, and that number is still growing at a brisk pace.

NFC Payments
Near Field Communication – the technology that enables contactless payments – is transforming the way people pay for purchases around the world.

The global contactless payments market is expected to reach $138.4 billion by 2023. Here’s a look at where NFC payments are making the greatest in-roads:

NFC payment by country

The Ones That Underwhelmed

During the Christmas season of 2009, Kindle became the most gifted item in Amazon’s history. This watershed moment looked like the end of physical books as the public embraced the e-reader as the new way of consuming text.

Fast-forward to today, and only 19% of adults in the U.S. own an e-reader.

Of course, not every technology that grabs the headlines is going to become the next iPhone. Here are some others that didn’t immediately meet expectations after topping the Hype Cycle.

m-Commerce
Some concepts fail primarily because they’re ahead of their time. Such is the case with mobile commerce.

By 2001, more than half of Americans owned mobile phones, and this represented a huge opportunity. Unfortunately, early m-commerce was restricted by the limitations of mobile phones of that time period. It wasn’t until the introduction of smartphones that the concept really took off. Today, nearly half of all online transactions are made via mobile devices.

3D Printing
Few technologies reach the fever pitch that 3D printing did in 2012. From the $1.4 billion merger of the largest players in the sector to the reports of firearm blueprints circulating the web, you could forgive people for believing that the 3D printer was destined to become the next microwave. In the end, interest in 3D printing leveled off.

While it is getting used for prototyping in many different industries, it remains to be seen whether the technology will ever achieve the wide consumer-level adoption that was promised.

What’s Next?

When 2019’s Hype Cycle is released later this year, it remains to be seen which technology will rise to the top. Based on the trajectory from last year, search volume, and current news reports, 5G is a strong competitor.

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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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