Category Archives: In The News

Deep Dreaming with Google

You may have seen some pretty weird and crazy images floating around the interwebs recently. You may have even heard about an emerging new artist who is producing them. You see something like this:


… and you go and think about someone having a go at some new tools in photoshop and you look at it more closely and you start thinking about the meaning of life and what must be going on in this person’s head.

And then … through a bit of Googling – you realize that actually… it was Google that created them. And by this I don’t mean its founders or some technician on acid, I mean some algorithm came up with this.

Now what is actually going on here is that a fraction of Google employees are working on machine-learning algorithms and the “emerging art” that you see is a part of their testing of this algorithm. I am not going to go into the science of machine-learning (Google it if you like!), but what it comes down to is letting a computer “teach” itself or “learn” how to perform certain tasks by simulating the human brain. This is done using the artificial neural networks. I will try and explain how this works in simple terms. Check out this discussion for more info. In fact, I learned a lot from here and draw some examples from comments in this thread.

Those of you out there who have ever tried to program anything (or have first done so at the Python course) know how hard it can be to instruct a computer to do something. Or maybe a better way of putting it is you know you have to give it a lot of information in order to perform a very simple task, because all this machine understands is numbers. Now imagine trying to teach this machine to recognize a picture of a dog, and KNOW that this is a dog. This is an extremely hard thing to do. To you and me this is a no-brainer – we all know what a dog looks like, but a computer doesn’t. The computer doesn’t even have a concept of a dog. It doesn’t have a concept of a concept.

Imagine for a second that you have met a kid who has never seen a cake in their lifetime (I know, this is cruel, but for the sake of the kid imagine this for me). So this kid doesn’t know what cake is, what it is for, what you do with it, what it smells, tastes or feels like and it has never seen any variant of a cake. Now you have the task of teaching this kid what a cake IS. So you sit this kid at a table and you present it with a few cakes – a brownie, a birthday cake with a candle on top, a pavlova. And you tell this kid that these are all cakes. Mind you, these are all very different cakes. You also point to a birthday cake and you tell this kid “This is a birthday cake”. And the kid goes “ok, I get it”. But you are not so sure – you want to see if this kid really understands you. So you give it a blank piece of paper and a pencil and you ask him to draw you a cake.

Now the kid is at a loss a bit, he has just seen a few cakes and he is still kind of wrestling with this concept and he can’t really just draw you a cake. He doesn’t know where to start. So you make it a tad easier for the kid and you give him one of those connect-the-dots type puzzles (nevermind what this should actually show once you connect the dots!) and you ask the kid to recognize a cake within those dots and outline it for you. And he sets off to the task, identifies 4 or 5 dots that in his mind kind of resemble a cake and he draws a very vague outline around those dots. Now you look at it, you don’t quite see it yet but you want to keep going. So you hide that piece of paper behind your back and pretend to get a new piece of paper but you actually give the kid the same one he just scribbled on. You ask the kid to outline the cake again on this paper. The kid has not only never experienced cake but also doesn’t recognize (for the sake of the argument) the piece of paper in front of him. So now he sees his own scribbles in this puzzle and they vaguely remind him of a cake, so now he adds more lines, more scribbles to the existing ones to make them more cake-like. Now you, being a patient teacher, repeat this a several dozen times, the kid keeps drawing over his own drawings and finally in front of you there is indeed a drawing of something that you might recognize as a cake.

Now you repeat this experiment and ask the kid to draw you a birthday cake. After some repetition (of the above-described kind) you have a drawing of a cake with the candle on top! But hold on, you see something weird in this picture. You see that right next to a birthday cake there is this long, slender shape. And then you look at the birthday cake on the table and you see that there is a cutting knife right next to it. Your kid has successfully put an image of a candle into his mind when picturing birthday cakes, but he also thinks that a knife is a part of this cake. If you ask the kid to identify a birthday cake among many other cakes he will always choose the cake with a candle and a knife! And the candle, as far as he is concerned, is a part of what constitutes a “birthday cake”. And so is the knife.

This process essentially describes how an algorithm learns and comes up with those trippy images you have seen around the web lately.

The engineers at Google have been playing with algorithms in the past few years in such a way so these algorithms can recognize a picture of, say, a dog. So the way they do it is they pass pictures of dogs to a complex algorithm and they tell it that each picture shows a dog. They have “trained” its algorithm to recognize objects this way. In fact you can test this yourself. Upload an image, any simple image of a simple object (dog, house, a bird, a tree…) to Google images (just literally drag and drop your image to its search box) and hit “search”. Go ahead, do it. Google will come up with similar images to that one. If you grabbed this photo off the internet chances are it will find the same one. The algorithm has “learned” to recognize objects – much in the similar way of how people do it. This is why if you go and read about this topic more you will find expressions like machine learning, artificial neural networks or deep neural networks .

So once these engineers have trained their algorithms to recognize stuff and successfully employed it in their search engines, they wanted to see what actually this algorithm “thinks” (? Careful now, it doesn’t actually think – you will have noticed I’m using a lot of terms in the post loosely for explanation purposes… that’s why they are in quotes, I’m sure there’s heaps of people online who would scrutinize me for being incorrect. If you notice an error in reasoning, or have questions, please post it in the comments) when it thinks of, say, bananas. So they gave it a picture of random noise (this is our connect-the-dots puzzle in the above example), which looks like this:


That’s right – it’s essentially your dead tv channel. And now they told it to find a banana in this image. After several thousands of iterations (working always on its own output, like the kid that keeps drawing over his own drawings) , this is what it came up with:


Yeah, you can vaguely see a banana there, right? Several of them.

Here are some other examples of feeding the random noise to this algorithm and telling it to find specific things.



Look what happens if they ask for a dumbbell:


There is a hand attached to it! Much like the knife in that birthday cake scenario. The algorithm has thought itself that a dumbbell also constitutes a hand, probably because most of the images of dumbbell it has “seen” were of a muscly hand holding one.

These brilliant people have also come up with one final test. They fed a random noise into the algorithm and basically said “tell me what you see”. So they didn’t say that there is a banana in there, or a dog or a parachute. They just instructed it to outline anything it sees there. And THAT’S how you get those trippy images. Through thousands and thousands of iterations and hundreds of different layers of image enhancement (there are layers and there are iterations in this process – separate things – there is a number of iterations on each layer) the algorithm takes some dots and to it these dots look more like a dog, those dots look more like a tree and so on… and you end up with something resembling that starting image. Why is it always dogs and pagodas and eyes you might ask?

This tells you what type of images the algorithm has been trained on. It has been trained on images of dogs and pagodas for example. It has also learned that pagodas appear on the horizons (because pictures it has “seen” showed pagodas mostly on horizons) so if you pass it an image of a tree, chances are it will turn into a pagoda somewhere on the horizon. Eyes? Dogs and all living creatures have eyes. So if it “sees” any creature in random noise it will most likely have eyes.

The swirly images of regular things and/or nature that you can find online like this one:


are created using the same process but to a lower layer – meaning the algorithm is at the stage of outlining some edges and geometrical shapes that it can recognize in the image. It hasn’t gone any further (it’s not looking for dogs, cakes or pagodas).

So if you now Google “deep dream” you will see all sorts of these images. You will recognize Google’s dream algorithm style pretty quickly. As to why it’s called “dreaming” – there are ongoing debates online whether this is an appropriate name or not, but this post is already too long. Suffices to say that the algorithm builds an image on its previous “experience” of certain objects – which is similar to what our brains do when we are dreaming.

You can have some fun too and create some of your own images like that. In fact a lot of people are doing it and the results range from meh, to beautiful to thanks-I-didn’t-want-to-sleep-ever-again creepy. I have personally uploaded three of my photos to this site and will be waiting for about a week for them to get processed. Once the results are out – I will show them in a new post, so we can all have a good laugh. Or no sleep.

And just to top it off here’s this process animated:

<p><a href=”″>Inside an artificial brain</a> from <a href=””>Johan Nordberg</a> on <a href=””>Vimeo</a&gt;.</p>



“Let me tell you about my trouble with girls…”

By Jess

“Let me tell you about my trouble with girls. Three things happen when they are in the lab; you fall in love with them, they fall in love with you, and when you criticize them, they cry!”

These are the words of Nobel Prize winner and Royal Society member Sir Tim Hunt at the World Conference of Science Journalists in South Korea earlier this week; at a lunch hosted by women scientists, no less.

Of course, being in a room full of journalists, just because his speech was unrecorded did not mean it went unreported…

He must have just been joking… Well apparently not

As a female who spends a fair amount of time in a lab I feel a bit insulted. And I feel insulted on behalf of all women scientists, who I know to be a lot more capable than fulfilling Tim Hunt’s three expectations of what ‘girls’ do. Who knows, maybe on top of those things women could also do some science?

Although let’s not forget Hunt did also admit to his own faults, apparently he too is incapable of being in a lab with someone of the opposite sex without falling in love.

Which I guess is why he’s “in favour of single-sex labs”.

Wow. Just how are we getting anything done at RSES with all the falling in love and crying that must be going on in our labs where men and women are allowed to work side by side?

It’s disappointing to hear these words coming from the mouth of a prominent and respected scientist, especially when they are voiced so publicly. And it’s a sad reminder that though things have clearly come a long way, gender inequality still continues to exist in academia.

But finally, more than anything I can’t help but wonder what the reaction of his wife was, herself a successful scientist and professor…

The tooth, the whole tooth and nothing but the tooth

By Hannah

In a research school dedicated mainly to studying rocks and the environment, I like to think studying human remains is a bit of a novelty. I am part of the Archaeogeochemistry group, (introduced a few weeks ago by Kelsie), who work on using scientific methods for archaeological research.

My research focuses on using stable isotopes, mainly oxygen (δ18O) and strontium (87Sr/86Sr), in human teeth to answer questions about past human migrations and ancient diets **

The kind of people I like working with

The kind of people I like working with.

By measuring isotopes in human teeth we can roughly identify the geographical or geological location of the food an individual was consuming during the time the teeth were forming. This sounds like witchcraft I know, but it’s true! Let me explain.

Oxygen isotopes differ in rain, due to the temperature and the amount of rain in a particular region which creates this beautiful geographical distribution. Oxygen in all it’s isotopic forms, is incorporated in all human tissues as carbonates, phosphates, hydroxls etc.

Global values of oxygen isotopes (δ18O)  in precipitation (Figure from, Bowen 2015)

Global values of oxygen isotopes (δ18O) in precipitation (Figure from, Bowen 2015).

Strontium isotopes differ in bedrock, due to the age and composition of the rock which becomes the bioavailable strontium in the plants and animals of that area. Strontium then substitutes in the place of calcium in human tissues.

Map fo France showing strontium (87Sr/86Sr) values (IRHUM database, Willmes 2015)

Map fo France showing strontium (87Sr/86Sr) values (IRHUM database, Willmes 2015).

The oxygen and strontium isotopes in the food you consume are worked into the teeth and as teeth are not remodelled throughout life, the childhood values are retained into adulthood.

By looking at modern maps, like those shown above, we can compare the values in human tissues to those predicted in that region. Matching values indicate an individual has either not migrated or lived in an area with the similar values in childhood. If values don’t match then perhaps the individual has migrated and that’s where it gets exciting.

Like many other scientific methods though, isotope analysis does not give us definites, rather a variety of possibilities. By interpreting this data alongside archaeological, historical and cultural evidence, isotope analysis can help fill in the blanks and shine light on archaeological questions which were pretty hard to answer previously.

In most archaeological settings people would have had little chance to eat non-local food, but in a modern age where the majority of your supermarket options are foreign, our isotopic signatures are getting messy. So to assist future archaeologists please eat local produce and drink local rainwater, that way you will have a local isotopic signature and your geographic origin will be identifiable!

** When I mention ancient diets, people often assume I am talking about identifying the Paleo Diet, that diet craze which basically promotes eating like a caveman. My work has nothing to do with the Paleo Diet, but I have ranted about the Paleo Diet to the Archaeogeochem kids over coffee, my major issue with it being the archaeological inaccuracies, nerdy I know.

The diet is based on what is believed to be the diet of humans during the Palaeolithic time period. This gigantic time period encompasses the first appearance of modern human ancestors, the evolution of homo sapiens and then the development and expansion of modern humans.

My issues are:

  • Human diets (and humans themselves) would have changed during this time period. Identifying the diet of past populations is not straight forward and identifying particular resources is sometimes as hard as milking a bull.
  • Available resources would differ across the globe, do you pick a particular region and eat only foods available in that region, or do you cheat and eat like a Palaeolithic global traveller?
  • Modern domesticated plants and animals differ from their Palaeolithic versions, after years of selective breeding. Take Brassica oleracea, a native species to coastal southern and western Europe, which has been selectively bred to become cabbage, kale, cauliflower, broccoli, kohlrabi, brussel sprouts and broccolini.

Animals have also gone extinct both during and after the Palaeolithic, which would have been utilised by modern humans and their ancestors. If you were hungry enough you would probably eat most of the animals which went extinct during this time period, see the link below for recipe ideas. (Quaternary extinctions; animals once available for eating)

HJ 4

Crowd-funding and meteorite hunting – a success story!

By Eleanor

My former research group has just done something amazing…  and today, ended up on ABC news!


The photo above is my friend Alastair holding a particularly nice looking meteorite. Alastair and I both spent our honours year researching meteorites under the guidance of Dr Andy Tomkins at Monash University. Associated with that work, we were lucky enough to be part of several meteorite-hunting field trips to the Nullarbor Plain. These trips were not only an amazing experience (desert, flies, heat, dingoes, camels, and finding pieces of rock that record information about the earliest solar system), but they have been very successful. Over 20% of Australia’s meteorite collection has been found on the Nullarbor by the team from Monash.


A meteorite that I found on the Nullarbor Plain in 2013. This is a chip off an asteroid that has made its way through the solar system and fallen to Earth. The small round spots on the surface are ‘chondrules’, which formed before the planet Earth even existed.

However, with limited funding, for a while it looked liked they wouldn’t be able to run the trip at all this year. So they tried something different: crowd-funding.

They launched a Pozible campaign asking for $4000 which would be enough to run one trip. Within ten days they reached that target. The campaign still has two weeks to go, and so far they have raised close to ten thousand dollars.

Here is a short update about what they plan to do with the extra funds:

I am absolutely blown away by the response and so proud of my friends for their enthusiasm and hard work in putting together such a successful campaign. I wish them the best of luck and hope they find loads of interesting meteorites!!

Check out the Pozbile campaign… and please help support space science in Australia!

The Kardashian Index: A not so scientific measure

By Kate

How might you measure a scientist’s ‘scientific worth’?

Today I will cover three indices developed to rank just how effective scientists are! In alphabetical and best to last order.

The h-index

This index attempts to capture both the productivity and citation impact of a researcher by measuring the number of papers and number of citations these papers have. To calculate, ‘h’ is the number of papers h that have at least h citations (the other papers (total papers – ) have no more than h citations). See figure below.


The h-index

The i10-index

This is an index created by Google Scholar for Google Scholar – only they use this measurement. It is simple: ‘i10’ equals the number of papers with at least 10 citations.

An example of these first two indices in use can be seen in a screen shot of the Google Scholar Alfred Wegener – who had a mighty nice theory about continental drift (which forms a substantial basis of today’s understanding of plate tectonics). Unfortunately Alfred never made it to see how he would rank on the last index.


Alfred Wegeners i10- and h-indices

The k-index

Here ‘k’ stands for Kardashian.1 An index defined as “a measure of discrepancy between a scientist’s social media profile and publication record based on the direct comparison of numbers of citations and Twitter followers”. To find ‘k’, divide twitter followers by total citations. Kim Kardashian is the highest ranked in the system for being one of the most followed people on twitter (with very few scientific publications).


The k-index: Twitter followers versus number of scientific citations for a sort-of-random sample of researcher-tweeters

The inventor of the k-index (Neil Hall) declares “those people whose K-index is greater than 5 can be considered ‘Science Kardashians’”. These so called Kardashians are highlighted on the graph below. Hall advises that these Kardashians should get off twitter and get back to paper writing!!

So who are some of the greatest Science Kardashians?


The Science-Kardashians

This k-index portrays science communication as a negative. But some would think, is it not the ultimate goal of science to be able to communicate it with peers and the broader community? I mean how else will you get those citations up? Social media and science can (and should) overlap in a Venn diagram’s space. The exchange of ideas and communication of results should be the goal!

What’s your K-index?


1 For the full k-index article ‘The Kardashian index: a measure of discrepant social media profile for scientists‘ visit:


Pushing an empty envelope

By Michael

Some of you may have heard this embarrassing story from back in October. The incoming Chief of the Commonwealth Scientific and Industrial Research Organisation (CSIRO), Dr. Larry Marshall, was being interviewed by ABC Rural about what to expect at CSIRO under his leadership from the start of 2015. The interview covered his vision of how CSIRO’s scientists will continue to deal with the challenges that face the agricultural industry in Australia.

On top of that list of challenges is water scarcity, and it has been ever since there has been an agricultural industry in Australia. It was during this part of the interview that Dr. Marshall brings up water-dowsing by saying, “I’ve seen people do this with close to 80 per cent accuracy and I’ve no idea how they do it.”

Before I go on to what I took from this interview and the reaction to it, I want to briefly describe what water-dowsing (or water-divining) is.

The basic concept works like this: a stick or metal rods (sometimes just a pendulum swinging over a map) in the hands of a dowser will be attracted to areas where groundwater is present. The dowser then uses the movements of their instrument to suggest where their client should drill for water. Dowsing has also been used to find mineral deposits and archaeological sites.


Generic water-dowsing photo #1

So how does water-dowsing supposedly work? The website of the Dowsers Society of New South Wales (unfortunately it’s a thing) isn’t that helpful in explaining how but they do mention things like “energy lines”, “energy fields” and “chakras”. So in a nutshell: magic.

The United States Geological Survey explains it much better here: “the natural explanation of ‘successful’ water dowsing is that in many areas underground water is so prevalent close to the land surface that it would be hard to drill a well and not find water.”

So Dr. Marshall should really be surprised that water-dowsers are unsuccessful 20% of the time.

The Dowsers Society of New South Wales give the second part of the explanation on their website: “pendulums are subject to suggestion.” The movements of the stick/rods/pendulum in the dowser’s hands have been found to be caused by phenomenon known as the ideomotor response. This effect causes the dowser to subconsciously move their body without consciously deciding to. Much like when people scare themselves with Ouija boards.

Now that I have covered the main points on what water-dowsing is, I’ll explain why I think the Dr. Marshall interview on ABC Rural is such bad press coverage for groundwater science in Australia and for CSIRO.

Firstly, water-dowsing is junk science that should have died off long ago. Unfortunately, other junk sciences like homeopathy, astrology and fortune telling have lived longer lives than they should have as well. One of the main reasons for their continued existence is people in highly regarded positions, like the Chief of CSIRO, sometimes giving great public endorsements.

Look at the Dr. Oz fiasco in the USA for a very recent example of an apparently credible person misleading the public. Unfortunately, it hasn’t taken too long for Dr. Marshall’s statements to be used as a shining endorsement for water-dowsing in a recent column published in Fairfax newspapers (see here). This article will no doubt give the false impression to the public that water-dowsing is still something worth investigating.

If I haven’t been clear already – it is not.


Generic water-dowsing photo #2

My second issue comes from Dr. Marshall’s and CSIRO’s response to criticism after the interview (see here, here and here). The critics aren’t being petty either. Water-dowsers can cost farmers thousands of dollars on badly placed wells, so it isn’t just harmless fun. However, Dr. Marshall’s only response so far has been to miss the point of the criticism and say that it’s CSIRO’s job to “push the envelope”. Apparently without any regard to how many people let him know that the envelope is empty.

The response from CSIRO’s twitter account during National Water Week was just as unimpressive: “Larry’s interested in helping farmers access water but wasn’t saying divining is the answer.” Again, they have missed the point of the criticism. We know he doesn’t think water-dowsing is the answer but he definitely gave it endorsement by giving the false impression that it actually works.

Lastly, the former Chief of CSIRO Land and Water, Dr. John Williams, pointed out another problem with the interview in a statement to Science Insider. He pointed out that Dr. Marshall’s focus on water scarcity was in the wrong direction. Dr. Marshall gave the impression that there is a problem with finding water in Australia. The problem isn’t finding water (scientists are aware of most of the productive aquifers), the problem is how we manage what we’ve got, and as Dr. Williams noted, “there isn’t much of it, and we don’t know how it’s replenished.”

What we do need is to develop new and improved methods of accurately estimating aquifer recharge rates, more robust modeling techniques for predicting catchment responses to water use and climate change, and better methods for managing and identifying water quality problems. Water-dowsing will never play a part in solving any of those problems.

The reason I put off writing this blog post until now was because I was interested in how Dr. Marshall might respond after such an obvious mistake. I was hoping he might finally listen to, and understand, some of the criticism that came after the interview. Unfortunately, we have now begun 2015 and he still hasn’t given a well thought out response. I guess we can only hope that farmers keep themselves up-to-date with all the great science that CSIRO Land and Water researchers are working on. I’m not doubting his sincerity about wanting to help farmers in areas where water is scarce. But if he really wants to make a constructive contribution to the discussion on water security in Australia he needs to set the record straight and understand that water-dowsing has no place even being mentioned.

Communication Breakdown

By Thomas

Over the last week space science got a lot of publicity thanks to Rosetta and its sidekick Philae. ESAs successful attempt to land a spacecraft on a comet was all over the news. Apart from the news coverage, which the mission got thanks to the landing, you could and can follow Rosetta on Twitter or on the Rosetta blog, ESA is providing detailed information about the mission on their website and last but not least the use of videos explaining Rosettas mission and the ingenious short-movie Ambition got a lot of people excited about the mission. A pathetic hysteria raging over a scientists sense of fashion aside, it was an excellent example for science communication well-done. Or was it?


Rosetta and Philae
Source: ESA

I followed the discussion of the mission in the comments on a German news website. As some people were nagging about the not so perfect landing, someone remarked that the mission is now going on for ten years, and in this time has provided a lot of data and insights and therefore the mission was already a success even before Philae attempted its landing. This was one of the comments1 that followed:

“The discussion here underpins my critique that we don`t get sensible information from the people in charge and the scientists about the actual results. We all have to speculate. After ten years that is a bit weird, if there really are already that many results. Not only Philae is in stand-by, apparently science as well. It is time that science comes down from the ivory tower and explains to us pity layman why this mission is so important and what insights it has really provided.2

Now, don`t get me wrong, I will not write a blog post every time I read an ignorant comment3 somewhere in the internet4. In this case it just wonderfully displays a dilemma I see for science communication and it has a connection to an “en vogue” topic, so I couldn`t resist.

But first, why do I think this comment is ignorant?

Well, the essence of the comment is that ESA is not communicating why the mission is important and what it has already achieved. This ignores, that if you go to the website about Rosetta provided by ESA you will find plenty of information on these issues.

For example, you want to know why it is important to investigate comets, read the four-part series on the history of comets.

You want to know about what has been achieved? This article gives you the overview. And if that grasped your attention and you want to know more, you can, amongst other things, read about the flybys of the asteroids Steins and Lutetia.


Rosetta Selfie
Source: ESA

And here we are in the middle of the dilemma:

On the one side we have a lot of science communication going on. On the other side we have the person at who it is aimed at, who nevertheless feels obviously not informed.

How can we bridge this gap?

Yes, in the first instance the responsibility lies within the field of science. Yes, scientists have to continuously work on their communication skills. Yes, scientists should use different media to distribute their message.

But we cannot bridge the gap completely from one side alone. No matter how much information scientists put out there and how nicely they wrap them up, they will unfortunately not always make the headlines. So how do we get science communicated in the cases when science doesn`t win the race to the top of the newspapers against wars, conflicts, politics and Kim Kardashians backside?

I think the answer might lie in the term itself:

“Science communication” – that`s two words.

While scientists have to do the “communication”, it requires the interested layman to do a little bit of “science”, namely looking up the communicated information and asses them.

The scientists in their respective fields can only build a bridge across the gap with the information they put out there. Sometimes (as in the case of Rosetta) it is a broad and stable stone bridge, sometimes it will only be a slippery rope bridge.

Either way, you`ll have to cross the bridge yourself.

1 Better: A translation of the comment that approximately reproduces the original meaning.

2 If you want to read the original: Comment 76 here.

3 Note: “Ignorant” solely refers to the comment itself, not to the commentator.

4 I wouldn`t be able to do anything else

Not-so-serious Sunday 55: Ambitious science communication

By Kelly

The European Space Agency shows the world what science communication can achieve (on a large budget). The making of below is also excellent.

Ambition is a collaboration between Platige Image and ESA. Directed by Tomek Bagiński and starring Aiden Gillen and Aisling Franciosi, Ambition was shot on location in Iceland, and screened on 24 October 2014 during the British Film Institute’s celebration of Sci-Fi: Days of Fear and Wonder, at the Southbank, London.

Scroll down for the making of….

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Meet the scientists…Who? Me?


One very good scientist and four dressing the part

By Kelly

Just recently I was given a healthy reminder that some stereotypes are really hard to break. I am very open about the fact that I was always interested in science, however when I hit 16 I was more interested in being cool. Unfortunately I had no role models that were cool scientists which led me to make some decisions that would lead me away from science* for over a decade**. And so during my time at the Research School of Earth Sciences I have gladly been involved with the university’s Equity and Diversity Unit, that most recently included participating in their ‘Who are scientists?’ workshop that was held for 14 year olds from regional school along the coast.

The 8 representative ‘scientists’ were jumbled in with other staff from our coastal campus, and when singled out the 120 kids were asked to stand if they thought that person was a scientist. Of 120, guess how many stood for me……

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Good News are Good News

By Thomas

We all know the saying “Bad News are Good News”, usually used by/for the media, referring to the phenomenon that “Bad News” normally get much more attention than “Good News”. Over the course of the last few weeks the plane disasters in Ukraine, Taiwan and Mali and the subsequent media coverage attest to this.

If our loved ones are on a trip, we might rather think of the saying “No News are Good News”, especially if they travel in region that doesn`t allow them to have 24/7 access to Facebook and Twitter.

When it comes to do a job, the principle is again a bit different. And while “Good News are Good News” is hardly a saying, it pretty much sums up the desired outcome that everyone hopes for when there is work in progress. Science is no different in this respect.

As a scientist you want to announce the discovery of the Higgs Boson, rather than explain to the citizens of several european countries that they paid 7.5 billion Euro for a machine that created a black hole that is now swallowing up Switzerland.1

As a scientist you want to announce, that the planet your curious rover is driving on has some interesting features. In the best case something that can be interpreted as possibly indicating that there was an environment on this planet that could in theory have hosted life. You don`t want to tell them that your orbiter crashed on the same planet, because someone thought “pound-seconds” is a sensible unit.2

As a scientist you want to tell your boss that you created a cure for Alzheimer, rather than a virus that will wipe out most of the human population, while at the same time creating highly intelligent apes, that will wipe the floor with the few human survivors. (Figure 1)


Figure 1: What is better than apes on horses? Apes on horses with guns!
Gee, how are they planning to top that in the next movie? Apes on motorbikes?
Source: Hollywoodreporter

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