Every Mission to Mars in One Visualization
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Every Mission to Mars in One Visualization

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chart showing ever mission to mars ever attempted

Timeline: A Historical Look at Every Mission to Mars

Within our Solar System, Mars is one of the most similar planets to Earth—both have rocky landscapes, solid outer crusts, and cores made of molten rock.

Because of its similarities to Earth and proximity, humanity has been fascinated by Mars for centuries. In fact, it’s one of the most explored objects in our Solar System.

But just how many missions to Mars have we embarked on, and which of these journeys have been successful? This graphic by Jonathan Letourneau shows a timeline of every mission to Mars since 1960 using NASA’s historical data.

A Timeline of Mars Explorations

According to a historical log from NASA, there have been 48 missions to Mars over the last 60 years. Here’s a breakdown of each mission, and whether or not they were successful:

#LaunchNameCountryResult
11960Korabl 4USSR (flyby)Failure
21960Korabl 5USSR (flyby)Failure
31962Korabl 11USSR (flyby)Failure
41962Mars 1USSR (flyby)Failure
51962Korabl 13USSR (flyby)Failure
61964Mariner 3US (flyby)Failure
71964Mariner 4US (flyby)Success
81964Zond 2USSR (flyby)Failure
91969Mars 1969AUSSRFailure
101969Mars 1969BUSSRFailure
111969Mariner 6US (flyby)Success
121969Mariner 7US (flyby)Success
131971Mariner 8USFailure
141971Kosmos 419USSRFailure
151971Mars 2 Orbiter/LanderUSSRFailure
161971Mars 3 Orbiter/LanderUSSRSuccess/Failure
171971Mariner 9USSuccess
181973Mars 4USSRFailure
191973Mars 5USSRSuccess
201973Mars 6 Orbiter/LanderUSSRSuccess/Failure
211973Mars 7 LanderUSSRFailure
221975Viking 1 Orbiter/LanderUSSuccess
231975Viking 2 Orbiter/LanderUSSuccess
241988Phobos 1 OrbiterUSSRFailure
251988Phobos 2 Orbiter/LanderUSSRFailure
261992Mars ObserverUSFailure
271996Mars Global SurveyorUSSuccess
281996Mars 96RussiaFailure
291996Mars PathfinderUSSuccess
301998NozomiJapanFailure
311998Mars Climate OrbiterUSFailure
321999Mars Polar LanderUSFailure
331999Deep Space 2 Probes (2)USFailure
342001Mars OdysseyUSSuccess
352003Mars Express Orbiter/Beagle 2 LanderESASuccess/Failure
362003Mars Exploration Rover - SpiritUSSuccess
372003Mars Exploration Rover - OpportunityUSSuccess
382005Mars Reconnaissance OrbiterUSSuccess
392007Phoenix Mars LanderUSSuccess
402011Mars Science LaboratoryUSSuccess
412011Phobos-Grunt/Yinghuo-1Russia/ChinaFailure
422013Mars Atmosphere and Volatile EvolutionUSSuccess
432013Mars Orbiter Mission (MOM)IndiaSuccess
442016ExoMars Orbiter/Schiaparelli EDL Demo LanderESA/RussiaSuccess/Failure
452018Mars InSight LanderUSSuccess
462020Hope OrbiterUAESuccess
472020Tianwen-1 Orbiter/Zhurong RoverChinaSuccess
482020Mars 2020 Perseverance RoverUSSuccess

The first mission to Mars was attempted by the Soviets in 1960, with the launch of Korabl 4, also known as Mars 1960A.

As the table above shows, the voyage was unsuccessful. The spacecraft made it 120 km into the air, but its third-stage pumps didn’t generate enough momentum for it to stay in Earth’s orbit.

For the next few years, several more unsuccessful Mars missions were attempted by the USSR and then NASA. Then, in 1964, history was made when NASA launched the Mariner 4 and completed the first-ever successful trip to Mars.

The Mariner 4 didn’t actually land on the planet, but the spacecraft flew by Mars and was able to capture photos, which gave us an up-close glimpse at the planet’s rocky surface.

Then on July 20, 1976, NASA made history again when its spacecraft called Viking 1 touched down on Mars’ surface, making it the first space agency to complete a successful Mars landing. Viking 1 captured panoramic images of the planet’s terrain, and also enabled scientists to monitor the planet’s weather.

Vacation to Mars, Anyone?

To date, all Mars landings have been done without crews, but NASA is planning to send humans to Mars by the late 2030s.

And it’s not just government agencies that are planning missions to Mars—a number of private companies are getting involved, too. Elon Musk’s aerospace company SpaceX has a long-term plan to build an entire city on Mars.

Two other aerospace startups, Impulse and Relativity, also announced an unmanned joint mission to Mars in July 2022, with hopes it could be ready as soon as 2024.

As more players are added to the mix, the pressure is on to be the first company or agency to truly make it to Mars. If (or when) we reach that point, what’s next is anyone’s guess.

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This article was published as a part of Visual Capitalist's Creator Program, which features data-driven visuals from some of our favorite Creators around the world.

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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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