Curious Kids: why do spiders need so many eyes but we only need two?

Jumping spiders, like this one, usually have eight eyes: two very large front eyes to get a clear, colour image and judge distance, and extra side eyes to detect when something is moving.
Flickr/Thomas Shahan, CC BY-NC-ND

Samantha Nixon, The University of Queensland and Andrew Walker, The University of Queensland

Curious Kids is a series for children. If you have a question you’d like an expert to answer, send it to You might also like the podcast Imagine This, a co-production between ABC KIDS listen and The Conversation, based on Curious Kids.

Can you find out why spiders need six eyes but we only need two? – Amos, age 3, Newcastle.

Hi, Amos. Thanks for your excellent question.

The first thing we should say is that while it’s true that some spiders have six eyes, most actually have eight.

The short answer to your question is that animals have evolved different eyes that best suit the lives they lead.

Read more:
Curious Kids: What are spider webs made from and how strong are they?

Humans have two eyes that face forward. Our eyes are very good at seeing colours and shapes. Having two big eyes in the front of our head means they can work together to guess how far away something is (we call this “judging distance”). That makes it easier for us to catch another animal so we can eat it.

Spiders are also hunters and they need eyes that help them find and catch their food. In fact, most spiders can’t see very well, and use touch and taste to explore the world. But the kind of eyes they have tells us something about the food they eat and the lives they live.

Spider eyes for spider lives

Jumping spiders are active hunters, like tiny lions chasing down their prey (bugs). They usually have eight eyes: two very large front eyes to get a clear, colour image and judge distance, and extra side eyes to detect when something is moving. Here’s a picture of an Australian jumping spider.

Jumping spiders need two big eyes on the front so they can guess how far away their prey is.
Michael Duncan., Author provided

Some spiders make nets to catch their prey. These net-casting spiders also need to see clearly and judge distances. Some have developed huge, scary-looking black eyes that stare straight ahead, so they are nicknamed ogre spiders! These gigantic eyes help the spider to see a wide area and accurately throw down its spider web net to catch its prey. Here’s a picture of a net-casting spider.

This net-casting spider is from the Deinopis family. The little dots that look like nostrils are actually eyes!
Michael Duncan, Author provided

Some spiders live in caves that are completely dark, where eyes are no use at all. They have to rely on other senses to find their food in the dark. To save energy making eyes, these spiders lost their eyes during evolution, so now some of them have no eyes at all. You can see a picture of a spider like that here.

So why did most spiders end up with so many eyes?

Both human and spider eyes are the result of slowly evolving to help us survive in our different environments. One reason our human eyes are different from spiders is because our bodies and brains are also built differently.

For example, spiders don’t have necks. So they can’t turn their heads to look at things like we can. Having extra eyes around their heads is one way that spiders see more of the world around them, helping them to quickly spot prey or a potential predator.

Human eyes and spider eyes also do different jobs. Our two eyes are very complex and are good at doing many jobs at once, while spiders have different sorts of eyes that do different jobs.

For example, the large central eyes of jumping spiders are best for seeing shapes, but the simple side eyes have the important job of watching out for predators.

So a two-eyed spider or even an eight-eyed human isn’t impossible. But the two eyes we have and the eight eyes most spiders have are perfectly suited to help each of us live our lives just the way they are.

Read more:
Curious Kids: why do spiders have hairy legs?

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Samantha Nixon, PhD, The University of Queensland and Andrew Walker, Postdoctoral Research Fellow, The University of Queensland

This article is republished from The Conversation under a Creative Commons license. Read the original article.

Google’s artificial intelligence finds two new exoplanets missed by human eyes

File 20171215 17878 rik7zz.jpg?ixlib=rb 1.1
Artist impression of Kepler-90i, the eighth planet discovered orbiting around Kepler-90.

Jake Clark, University of Southern Queensland

Two new exoplanets have been discovered thanks to NASA’s collaboration with Google’s artificial intelligence (AI). One of those in today’s announcement is an eighth planet – Kepler-90i – found orbiting the Sun-like star Kepler-90. This makes it the first system discovered with an equal number of planets to our own Solar system.

A mere road trip away, at 2,545 light-years from Earth, Kepler-90i orbits its host star every 14.4 Earth days, with a sizzling surface temperature similar to Venus of 426°C.

The new exoplanets are added to the growing list of known worlds found orbiting other stars.

The Kepler-90 planets have a similar configuration to our solar system with small planets found orbiting close to their star, and the larger planets found farther away.
NASA/Ames Research Center/Wendy Stenzel

This new Solar system rival provides evidence that a similar process occurred within Kepler-90 that formed our very own planetary neighbourhood: small terrestrial worlds close to the host star, and larger gassy planets further away. But to say the system is a twin of our own Solar system is a stretch.

Read more: Exoplanet discovery by an amateur astronomer shows the power of citizen science

The entire Kepler-90 system of eight planets would easily fit within Earth’s orbit of the Sun. All eight planets, bar Kepler-90h, would be too hostile for life, lying outside the so-called habitable zone.

Evidence also suggests that planets within the Kepler-90 system started out farther apart, much like our own Solar system. Some form of migration occurred, dragging this system inwards, producing the orbits we see in Kepler-90 today.

Kepler-90 is a Sun-like star, but all of its eight planets are scrunched into the equivalent distance of Earth to the Sun.
NASA/Ames Research Center/Wendy Stenzel

Google’s collaboration with NASA’s space telescope Kepler mission has now opened up new and exciting opportunities into AI helping with scientific discoveries.

So how exactly did Google’s AI discover these planets? And what sort of future discoveries can this technology provide?

Training AI for exoplanet discoveries

Traditionally, software developers program computers to perform a particular task, from playing your favourite cat video, to determining exoplanetary signals from space based telescopes such as NASA’s Kepler Mission.

These programs are executed to serve a single purpose. Using code intended for cat videos to hunt exoplanets in light curves would lead to some very interesting, yet false, results.

Googles’s AI is programmed rather differently, using machine learning. In machine learning, AI is trained through artificial neural networks – somewhat replicating our brain’s biological neural networks – to perform tasks like reading this article. It then learns from its mistakes, becoming more efficient at its particular task.

Google’s DeepMind AI, AlphaGo, was trained previously to play Go, an extremely complex yet elegant Chinese board game. Last year, AlphaGo defeated Lee Sedol, the world’s best Go player, by four games to one. It simply trained itself by watching thousands of previously played games, then competing against itself.

In our exoplantary case, AI was trained to identify transiting exoplanets, sifting through 15,000 signals from the Kepler exoplanet catalogue. It learned what was and wasn’t a signal caused by an exoplanet eclipsing its host star. These 15,000 signals were previously vetted by NASA scientists prior to the AI’s training, guiding it to a 96% efficiency of detecting known exoplanets.

Researchers then directed their AI network to search in multiplanetary systems for weaker signals. This research culminated in today’s announcement of both Kepler-90i and another Earth-sized exoplanet, Kepler-80g, in a separate planetary system.

Hunting for more exoplanets using AI

Google’s AI has analysed only 10% of the 150,000 stars NASA’s Kepler Mission has been eyeing off across the Milky Way galaxy.

How AI helps in the hunt for other exoplanets.

There’s potential then for sifting through Kepler’s entire catalogue and finding other exoplanetary worlds that have either been skimmed by scientist or haven’t been checked yet, due to Kepler’s rich data set. And that’s exactly what Google’s researchers are planning to do.

Machine learning neural networks have been assisting astronomers for a few years now. But the potential for AI to assist in exoplanetary discoveries will only increase within the next decade.

Beyond Kepler

The Kepler mission has been running since 2009, with observations slowly coming to an end. Within the next 12 months, all of its on-board fuel will be fully depleted, ending what has been, one of the greatest scientific endeavours in modern times.

NASA’s new TESS mission will inundate astronomers with 20,000 exoplanetary candidates in the next two years.
Chet Beals/MIT Lincoln Lab

Kepler’s successor, the Transiting Exoplanet Survey Satellite (TESS) will be launching this coming March.

Read more: A machine astronomer could help us find the unknowns in the universe

TESS is predicted to find 20,000 exoplanet candidates during its two-year mission. To put that into perspective, in the past 25 years, we’ve managed to discover just over 3,500.

This unprecedented inundation of exoplanetary data needs to either be confirmed by other transiting observations or other methods such as ground-based radial velocity measurements.

The ConversationThere just isn’t enough people-power to sift through all of this data. That’s why these machine learning networks are needed, so they can aid astronomers in sifting through big data sets, ultimately assisting in more exoplanetary discoveries. Which begs the question, who exactly gets credit for such a discovery?

Jake Clark, PhD Student, University of Southern Queensland

This article was originally published on The Conversation. Read the original article.