The Maasai are a proud people with a rich cultural tradition. They continue to live in some of the harshest savannah conditions with poor access to education, medicine and other benefits of the more developed areas. Our communities are based around cattle and the semi-nomadic pastoralist way of life. We have been romanticised in much Western media, and the iconic spear-wielding warriors with their herds on the vast plains, and beautifully be-jewelled women, are classic safari images. And these images are not without some credibility. We are tough and have adapted well for life in these harsh conditions, (particularly the women who are tasked with the majority of the domestic work), and we’ve maintained many of our customs in the face of continued development and change in Kenya.

In a small Maasai village 30 kms from Narok in Kenya. I grew up in the late 1970s as the second youngest of nine children. As a girl I quickly learnt my position within the family and wider community. Formal education was not valued by the Maasai in those days. Tending to the livestock was seen as more important. Education was reserved for males especially those who are regarded as  unskilled in looking after the livestock as it is a very physically demanding job. Even then going to school was seen as a form of punishment. A woman’s role from a very early age in the village would be to learn how to maintain the house, taking care of other siblings and to a lesser extent herding livestock.

masaai tribe

masaai tribe

Within our society women have a subservient position. Girls are often married-off for dowry, from 12 years of age, to much older men, many of whom have several other wives. In a society where the virtues of a Western style education are not rated highly (even for boys), educating girls is hardly considered at all. The law, as it is, states that all children should receive primary education but in practice, in the remote rural areas, girls receive very little, and those that do get a few years are withdrawn from education to be married or work at home when they are 11 or 12 years.


One day the chief came to our village and collected us all to go the school,  i remember being very excited as my dream was to be a ranger with Kenya Wildlife Service. I liked the uniforms. But my grandmother was against me doing anything that meant holding a gun, and advised me to become a teacher. I went to the class and I was taught in Maasai, Swahili and English. I loved school; it was interesting learning something new. Having passed my exams with very high marks,i was offered a place at a National School in Nairobi.  So I trained as a teacher and when I passed my certificate I taught for 5 years in the government Sekenani primary school in the Maasai Mara. It was here, in 1997, that I met my future husband, Chris. A British tour guide bringing tourists to our school. Chris and I moved to the UK in 2001 and now I work part time for the Development Education Centre in Sheffield. I spend a lot of time in South Yorkshire schools talking to children about the reality of life in Kenya.

kids4The small Maasai community of Oldanyati in southern Kenya has many young children with no nearby school. Older kids walk the 7 kms to their primary school but the youngsters stay at home. In 2013 two pupils lost their lives tragically commuting to school. One was drowned in the floods and another was attacked by a lion.

In 2013, my aim was to raise enough money to build, equip and maintain a small learning centre. It will be used as a school for the infant children, and a community-learning centre for the Osotua Women Group. The building will consist of 2 classrooms, a teacher’s room/office, and a kitchen all set in the legal minimum 5 acres of land. The building will also function as a learning centre for the Osotua Women Group. The Women Group will use the school building as a social/community centre for these purposes. It would also be used for adult literacy classes and marketing advice/training for work on income generating projects such as making and marketing their traditional beadwork jewellery and making fly-traps from recycled materials.

The community has already donated the 5 acres of land required for the school and its grounds.
Move forward to 2015, we successfully fundraised in the UK and USA, the 2 classrooms are now built with over 120 children attending the school in the morning and in the afternoon are classes for the women. Now the men are asking when they can learn as they don’t want to be left behind…

This is my story, which is shared by only a few Maasai girls who have been lucky to make it in one way or another.
My determination in making a difference, even for a few women and children, is the main reason behind this Alton Maasai Project. It just shows with determination you can achieve what you aim to do and more.
Keep on persuing your passion and eventually what you thought was a dream piece by piece comes together.

Suma olayioni nisum oltung’ani obo. Suma entito nisum oloosho….

Educate a man, you educate an individual. Educate a woman and you educate a community…

Rev Austin Inceptionism

Rev Austin Inceptionism

You’ve probably seen these crazy psychedelic pictures all over the internet over the past month or so. If not then prepare to trip, but don’t worry you won’t need any hallucinagenic substances or yogic meditation for this one.

So what is it, how does it work, and how can I do it to my own photos? I’m presuming those are the questions you’re asking anyway.

I first came across this effect via Google’s Research Blog and it is created using Artificial Neural Networks which mimic biological neural networks such as the brain or the central nervous system. An ANN is typically made up of around 10-30 layers of artificial neurons, images are fed through the input layer and then pass through each subsequent layer before finally reaching the output layer. Each layer tries to pick out certain attributes from basic lines and edges to basic shapes such as squares and circles all the way to complex structures such as dogs, buildings and birds. These ANNs “learn” what to look for in an image by being sent millions of images and being told what each image shows. The network then tries to learn the “essence” of each object it is shown and learns the specific features that belong to each type of object.

Inceptionism via Google

Inceptionism via Google

Each layer of artificial neurons looks for more and more detail as an image is passed through, so the earlier layers only check for edges, lines and corners, whereas the middle layers will check for more complex structures like basic shapes or components and then the final layers put this information together to try and work out what it is that the image is showing.

This effect works by sending an image into one of these networks, picking a layer and getting it to enhance whatever it thinks it has seen. The enhanced version of the original image is then fed back through the network and is asked once again to enhance what it thinks it has seen. As each layer detects different levels of detail, the complexity of the final image depends on which layer is selected to enhance, so if you pick one of the first layers that recognises edges and lines, it will enhance those edges and lines until after a few iterations you get this kind of brush stroke effect.

DreamDeep via Google

DreamDeep via Google

Seurat Dream Deep via Google

Seurat Dream Deep via Google

As the image is enhanced and fed back through the network, the network will start to pick up more and more on the parts of the image that it has already enhanced, so if you keep using the same layer then the features that are enhanced on the initial iteration will be more and more prominent after each iteration. Therefore if you select one of the final layers that detects more complex structures such as dogs, birds, buildings etc. then if the network identifies part of a cloud as part of a bird on first inspection, it will enhance the bit that it thinks looks like a bird so that it looks more like a bird. Then each time the image is fed through it will become more and more convinced that it’s looking at a bird rather than part of a cloud, so that part of the cloud will start to look more and more like a bird. This is how we end up with these kind of surreal images with strange dog-slugs and bird-rats appearing as if from nowhere.

Dog Knight via Google

Dog Knight via Google

Phew! Got that? I hope so. So now we know what’s going on we can look at some of, what I think, are the best examples of this technique in use. A great example, even if kind of obvious in hindsight, is this Github user’s demonstration of how to run video through one of these neural networks. He used a scene from Fear And Loathing In Las Vegas, and if the original film was far too trippy for you then probably best not to watch this. If however you’re like me and absolutely loved the film, then this is quite the treat. The constant shifting and changing of the shapes and animals is mesmerising. It also helps that he used one of my favourite scenes from the film.

Another great idea, but one that I’m not gonna post here, was to run pornography through an ANN. Just a heads up this is definitely NSFW so  DO NOT CLICK HERE if you don’t want to see filth. You’ve been warned.

This video has been made by running an image of just some standard white noise through the neural network and then showing the output at various stages through the layers, pretty interesting considering it starts out just white noise and then the rest is completely created by the computer’s imagination.

And here’s another creepy, trippy, weird video I enjoyed that used this technique.

If you want any more information then check out Google’s Research Blog HERE, they’ve broken it down with lots of the imagery that we stole for this post, but more.

Also I found this chap’s blog post about it and his interpretations pretty fascinating too, he looks further at how similarly these artificial neural networks work compared to our own brains. Essentially our brains do exactly the same thing, examine the basic shapes and structures of what our eyes and sending it, check them against everything it has ever seen before now and tries to work out what it is we’re looking at. At the very least this video he made using this technique should be enough to convince you of it’s radness.

There are a couple of websites already set up that you can upload your own pictures to such as DreamDeeply but there’s usually quite a queue and it can take a while for them to get back to you. If you want to delve deeper and you know a bit about coding you can have a go yourself (I’ve not tried it as it looked way to daunting for a basic like me) using this link HERE!

2014 was an extremely interesting year for Stance. It’s a product of old members from which started in April. In less than a year Stance has accumulated roughly 20,000 subscribers. To commemorate this achievement,
I (ChazB) am going to share what I believe are 6 of the top videos we’ve produced. In no particular order:

1. Thieu and Pac Pac vs Lussy Sky and Drud



Raw Circles was our first European event that we filmed that year. And this was by far the best battle of the night. For a while this was our most viewed video on Stance. In particular, the first round of Thieu’s (tall dancer on left) actually went viral on Facebook!

2. Lil’ Zoo vs Roxy

There’s something special about this video. I believe it’s the energy that the crowd is giving this battle. Anyway, this video got a lot of attention for a number of reasons. One of them being the poses Roxy would do during the battle.

3. Silverback Open Champs “Top Sets” Highlights

The name of the video gives away why I love this video. Small clips of the most amazing dance sets from the Silverback Open Champs. The event gained a lot of attention because it’s the first big event of its kind – giving away prize money to people who make Top32 onwards. So for a first event like this, it was extremely lucky that we were there to capture everything.

4. Lil’ Zoo in Seoul

Our first video to break 100,000 views! I’m going to let the video speak for itself. The moves are amazing; the mix of competition shots and outdoor shots are sweet too.

5. Lilou vs Alkolil at Red Bull BC One 2014

I believe we captured a moment that’ll be remembered for a long time to come. Not only did Lilou throw his hat at Alkolil, but also disrespected the judges. It’s the first video online to show how Lilou handled himself around the judges after the event – hence the number of views. But still, the battle was a good one!

6. Freestyle Session 2014 “Top Sets” Highlights

Currently our most viewed video. Similar to Silverback Open we uploaded just about everything we could from the event. And luckily we did. We saw some historic moments! Squadron winning 2 years in a row; Kujo from Soul Control with a memorable set and so much more.

There are so many videos that I wish to include in here. However these are 6 videos that have helped build our reputation in such a short time scale. One more honourable mention though to a video we put together at the beginning of the new year – “Elevate”.

During the days we would put together a portfolio video every year recapping what had been happening in the Breaking scene at that time. While some of this footage is actually ours, I feel this should be given a special mention since Stance edited the video.

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