Should we fear the rise of robots? A look at the future of AI

Written for HUMAN Protocol

The fear of AI is really the fear of losing our jobs; it is the fear of losing our humanity, or having it taken away. While some fears could result in reasonable caution, it is important not to overestimate the dangers of AI, and instead to begin the narrative promoting a collaborative machine-human future. This is not an article to say that everything will inevitably be brilliant, and that, one day, we will live in a world of abundance and luxury, served by a robot class. Neither is it to say that AI will lead us to a dystopian future. There are no guarantees in AI; all we can say is that the future of machines is in our hands. 

Artificial, but not that intelligent

AI products are currently well short of justifying the fears of the past; the conversation around AI began in the 1950s, with Alan Turing, and most visionaries of the 1990s, or early 2000s, would be slightly disappointed at the rate of progress. It seems AI has been 20 years away for the last 60 years. 

Most AI products today, like Google’s AlphaGo, which plays Go, or IBM’s Watson, which plays Jeopardy!, are examples of specialized intelligence. Similarly, translation, GPS, chatbots, and personal assistant systems are good at fulfilling a single function; even Kiva robots simply relay Amazon products to and from workers. These systems lack the reason, perception, imagination, and basic faculties to pose any real threat to humans, for now.

The excitement of AI is associated with the development of generalized AI. Generalized intelligence is a human quality, reflective of reason, creativity, common sense, and adaptability. But most experts think general AI is still many decades away.

Employment

We know which jobs may be lost, but not which jobs could be gained – that is why many equate automation with job losses, and also why, far from pessimism, there should be great hope at the possibilities.

It seems reasonable to assume – given past trends – that advances in AI will create many jobs. An AI research paper by MIT states:

“Even two decades ago, when the dot-com boom was under way, few foresaw the emergence of social media, smart devices, and cloud computing—or the millions of jobs that have been created in connection with those new technologies.”

Over the long-term, given how many jobs will be automated, and how many created, it seems likely that the net jobs impact will be neutral. This is historically supported by the following graph from the Journal of Economic Perspectives:

Labor displacement reflects a drop in labor demand of about 0.48% per year, but labor reinstatement reflects an increase in labor demand of 0.47% per year. 

Automation and technological advances have always disturbed labor trends. In 1810 in the United States, 81% of the workforce was employed in agriculture; in 1960, it was 8%. Yet during the second half of the 20th century, agricultural output per worker increased 15x. There may well have been initial fear of job losses (as documented in The Grapes of Wrath), but no one could doubt the societal progress those technological advances have made, nor the quality of life they have supported.

Beyond employment

While employment is a significant factor, it is only a small reflection of the broader possibilities of prosperity. Just as the Internet increased global GDP, so will AI. In fact, PWC predict that global GDP could increase by 14% – the equivalent of $15.7 trillion – by 2030 as a direct result of AI. That is more than the combined output of China and India. The report puts it as such:

“Any job losses from automation are likely to be broadly offset in the long run by new jobs created as a result of the larger and wealthier economy made possible by these new technologies.”

A larger, wealthier economy. Beyond employment, the world will be more prosperous. When we think of loss of employment (which will not likely be a long-term issue), we must also consider the broader benefits of automation. World-wide problems such as low wages, poverty, high taxes, pollution, usage of non-renewable resources, and inequality are problems worth considering in the conversation around AI — because automation, robotics, NLP, and more can assist in tackling many of them.

That progress is by no means guaranteed. As the graphic below indicates, the jobs most likely to be automated are those done by low-skilled workers. Again, it is a short-term view to assume unemployment among lower skilled workers, but it must be considered by governments as they gear resources towards AI education, retraining, and adaptability to new, relevant skills.

Source: PWC: Will robots steal my job?

Automation without productivity

There is little reason to doubt that AI will provide a more prosperous future for everyone. However, along the way to that future (and likely waiting for us there) will be a proliferation of “so-so technologies” — those which automate jobs, but do not notably improve productivity. An example of such a technology includes self-checkout systems at supermarkets, which simply reapportion the work from the check-out worker to the customer. 

These technologies introduce a previously unmentioned variable in the equation: the wage bill. Even if these technologies offer no productivity gain, they save costs on labor. Two MIT professors write, in The National Bureau of Economic Research:

“It is not the “brilliant” automation technologies that threaten employment and wages […] productivity gains from automation […]  are not a consequence of the fact that capital and labor are becoming more productive in the tasks they are performing, but follow from the ability of firms to use cheaper capital in tasks previously performed by labor.”

And, importantly:

“Because the productivity gains of automation depend on the wage, the net impact of automation on labor demand will depend on the broader labor market context.”

In other words, if labor is cheap, there will be less incentive to automate, and visa-versa. Any conversation around employment must also look at market demands; western corporations have been outsourcing production for centuries to lower its cost, and, in some sense, “so-so technologies” simply reflect another opportunity to do that. Nonetheless, their market role and impact are worth understanding.

These so-so technologies could signal a broader long-term strategy to begin automation, to gather data, and start on the way towards a more productive and prosperous future. However, one of the professors that coined the term “so-so technologies”, Acemoglu warns in a separate interview:

“There’s a lot of hype, and that hype means that companies are overstating or overestimating the benefits they’re going to get from some of these technologies […] And as a result they are over-automating.”

As exemplified by Elon Musk, who, reflecting upon fully automating a Tesla assembly plant, admitted on Twitter:

“Excessive automation at Tesla was a mistake. To be precise, my mistake. Humans are underrated.”

Collaboration

“We should move away from thinking about putting humans in the loop to putting computers in the group” – Thomas W. Malone, MIT Sloan.

Most AI today is specialized; while there are many things machines can do with ease, there are simple tasks they fail to complete. It would make sense, then, to begin thinking about how to bring machines into our lives, businesses, and governments in a way that utilizes those specializations, while augmenting and complementing human skills.

Specialization has been a cornerstone of economic prosperity for centuries. It is a concept that has stood through the ages – perhaps testament to its simple truth. Cited by Aristotle, and later expounded upon by the forefather of economics, Adam Smith, who noted division of labor as the reason for “the greatest improvements in the productive powers of labor.” 

Bringing machines in the group is simply an evolution of this principle. Of course, jobs will be lost, and jobs will be gained, but the resulting future can be one in which we develop human-machine groups with unprecedented capability for achieving goals.

Supermind

MIT Sloan’s Malone champions the “supermind”, which he defines as “a group of individuals acting together in ways that seem intelligent.” Superminds are businesses, societies, and governments.

There are many different roles machines can take — from tool, assistant, peer, to manager — but the underlying motivator is for machines to do that which they do well, and for humans to do that which they do well. Examples of machines and humans working together include a food processor, Uber, or AI Jim, Lemonade’s automated insurance assistant. Beyond the obvious physical interaction with machines — whether Excel, or a hoover — another domain to consider is Malone’s following interpretation of human-machine collaboration:

“… we’ve created the most massively connected groups the world has ever known […] while we often overestimate the potential of AI, I think we often underestimate the potential power of this kind of hyperconnectivity among the seven billion or so amazingly powerful information processors called human brains that are already on our planet.”

The question that remains is how we unlock that information. HUMAN Protocol is itself a tool to facilitate the creation and collaboration of superminds. And, furthermore, it is the ultimate realization of Smith’s observation, as specialization becomes achievable on a global scale, with knowledge the contributed resource.

Today, on the Protocol, Workers earn HMT for labeling data for AI, but that is only the beginning. Any kind of fungible human task can be brought onto the Protocol. One could manage the collaboration of a distributed team of Workers towards any kind of goal. It could be an encyclopedia entry, where each section is distributed to individual workers who either know — or can find out — what they need to. Or it could be a company report, in which the graphic design would be sent to a designer, the numbers to an accountant, the text written by an NLP programme, and the overview to a lawyer, each paid for their role, and no one needing to know who they are working with.

Why caution is required

Despite good reason for a positive outlook, caution must be exercised. A truly comprehensive overview of risk — and how to manage it — can be read in this McKinsey report.

In terms of the immediate dangers of AI, the problem is not that robots will rebel — nor will they have the capacity to — but that they will do precisely what we tell them to. Far from fearing human-like machines, we should be cautious of inhuman machines; part of the downside of specialized, robot intelligence is that it is extremely literal and linear. It lacks the reason, adaptability, and common sense to behave in a sensible manner. 

Take the following example: when we ask a robot to assemble itself and to get from A to B, we expect it to assemble itself in a (bipedal) human shape and walk from A to B.

Source: TED talk on AI 

In practice, the robot tends to stack itself up and topple from A to B:

Source: TED talk on AI 

In other words, we need to be extremely careful and clear about the instructions we give our machines. We also have to be careful about the data we train them with; unrepresentative, biased data will create biased machines. Bias is more damaging than exacerbating social inequalities and prejudices; bias can have more catastrophic consequences (read our basic overview of bias, or our in depth piece.) Providing data scientists with access to the quality, detailed data they require is essential to mitigating these risks; it is this very problem to which HUMAN Protocol was first applied.

The bigger picture – cause for hope

Finally, it is worth noting that, although lower skilled jobs may be automated, there is also the possibility that highly skilled individuals could lose out as machines allow lower skilled workers to do their job cheaper. Whichever way we look at it, there will be sizable changes to the employment landscape, and the evolution may be rough at times. It is up to societies to educate themselves; and for governments to encourage that education, to provide retraining, and to do what they can to protect those who initially lose out.

That initial loss, however, must not blind us to the simple fact that technological progress has, historically, raised productivity and wages to the benefit of the vast majority. Automation in general will lead to higher standards of living, higher standards of products, the development of more intelligent computers that can do jobs humans cannot, and the provision of consumer goods that many could not previously afford. It could begin to solve many of the world-wide issues mentioned above; for example, if a robot can clean the streets, drive the bus, and build motorways, then we don’t need to pay taxes for those things – or tax funds can be reapportioned to other areas in need of attention. Yes, the robot may put some workers out of a job one day, but it will not lead to a world of destitution and homelessness — and can, with stewardship, lead not only to many new jobs, but to a greater quality of life. 

Instead of fear, let us begin to think about how we can use machine specializations to free humans of inhuman labor, and to create global knowledge banks: the global “superminds” that can help society achieve a more prosperous future. Superminds are already at work to tackle depression in the creation of CareNet, which uses AI to interpret social media usage — among other variables — to detect depression, and propose corresponding solutions or treatments. Given the complex emotional, mental, and spiritual nature of an illness such as depression, it would be presumptive to say these technologies decisively “work” or improve people’s lives. However, under the correct stewardship, and when tasked to augment rather than replace human wisdom, these technologies could support a greater number of individuals. 

The key to progress is reasonable governance that balances the protection of those most vulnerable, while incentivizing market productivity and shared human goals. Fear should not prevent advances that can bring enormous long-term benefits to all. We did not, thankfully, reject the combine harvester to keep the workforce in the fields. That turned out well for most of us; let’s hope the same will be said in a few centuries’ time.

For the latest updates on HUMAN Protocol, follow us on Twitter or join our Discord. Alternatively, to enquire about integrations, usage, or to learn more about how HUMAN Protocol supports machine-learning technologies, get in contact with the HUMAN team.

Legal Disclaimer

The HUMAN Protocol Foundation makes no representation, warranty, or undertaking, express or implied, as to the accuracy, reliability, completeness, or reasonableness of the information contained here. Any assumptions, opinions, and estimations expressed constitute the HUMAN Protocol Foundation’s judgment as of the time of publishing and are subject to change without notice. Any projection contained within the information presented here is based on a number of assumptions, and there can be no guarantee that any projected outcomes will be achieved.

What is machine learning?

Article written for: HUMAN Protocol

Predictive text, voice recognition technology, and navigation systems; face identification, spam email filters, and product recommendations. Machine learning is used to build products we interact with every day. But what is it?

An algorithm is the set of rules that a machine is programmed to follow in order to complete a task. Machine learning is the practice of training a computer to develop its own algorithm, so that it can complete increasingly difficult tasks. The computer creates an algorithm from samples of data, often called ‘training data’, which it uses to generate predictions of appropriate answers and actions. The computer can, therefore, make decisions on its own, without being explicitly programmed in the unique context of that decision. 

Machine learning is most useful when the relationships within data are complex, or what is often called high-dimensional data, as is seen, for example, in Formula 1. The secret to winning a race is a complex balance of choices based on innumerable variables – from tire pressure, wind speed, to track temperature, and air humidity; each car, with 120 sensors to relay race data, will send and receive about 750 million data points over a two-hour race. No human could process the relationships between the data. A machine, however, can.

It is important to note that however smart a computer may seem, it fundamentally does not know anything. At best, it can make highly accurate predictions based on what it has already seen. That is why practitioners have to be careful about the data they show machines – more on that below.

How does it begin?

Machine learning techniques start with a training dataset. In the case of your computer’s predictive text, this may be tens of thousands of sentences that are used to detect similarity and, thereby, meaning between specific data points. By looking at ten thousand people typing an email to their boss, the machine can make a fairly accurate prediction that, at the end of an email, the word ‘Best’ will likely be followed by ‘wishes’. 

What is data labeling?

Data labeling is the process of taking raw data and ‘labeling’ it to make it useful for machines to learn from. A raw piece of data is an image of a road; data labeling, or data annotation, is the practice of labeling the car, the fire hydrant, and the crosswalk. The labels create the association between a word and an object, which is the foundation of making intelligent machines. Once the machine can recognize a crosswalk, it can learn the appropriate actions to take.

More often than not, data labeling requires a human to label the images. Sometimes, software built with machine learning can undertake the labeling process.

Types of ML

There are three key machine learning techniques: supervised learning, unsupervised learning, and reinforcement.

Supervised

The machine learns from labeled data. Over time, and through the sheer volume of datasets, the computer can become more accurate. This is the most common type of machine learning used today.

Example: Humans label images of fire hydrants, traffic cones, and crosswalks. These labeled images are fed to a machine to create an algorithm for a driverless car. 

Unsupervised

The machine detects patterns in unlabeled data. Instead of supervising the computer, the computer is left to identify patterns in data on its own, and thereby creates ‘clusters’ of data based on similar qualities across the raw data.

Example: Feeding a computer with a thousand news stories from across the web, it can, on its own, begin to find patterns in the text and thereby create categories of news. Articles that mention the Superbowl, football, and Full Time 34-7 can be tagged under ‘Sports’, just as those referencing the Senate and Joe Biden can be tagged under ‘Politics.’ 

Reinforcement

The machine equivalent of human conditioning: a machine learns through trial and error, with a predetermined reward system.

Example: to train models to play games or drive vehicles, by letting a machine know when the right decision was made. Over time, it uses this information to determine the actions it should take. 

Data challenges in ML and the HUMAN solution

The problems with data are numerous. Data can be scarce, low quality, incomprehensive, or noisy, which simply means it is unstable, unpredictable, and hard to glean useful information from. Below are some details of the problems faced in data, and the solution HUMAN Protocol provides.

Data shortage

Data labeling services have not been accessible or affordable to most practitioners of machine learning. The problem is such that data labeling has been left to graduate students and engineers, creating a problem of bias

HUMAN solution

Applications running on HUMAN Protocol already access hundreds of millions of data labelers. The Protocol is designed to fulfil jobs of many different scales; from large BPO (business process outsourcing) of major ML practitioners, university researchers who want a data set annotated, feedback given to a model, or AI startups looking to have small, or specific, data labeled. 

Bias

Bias in machine learning is different to regular human bias, as we discuss in this article. Bias in data would be, as described above, having only graduate students, researchers, and PhDs labeling data. It is one thing if they are labeling images of a dog, but another if they are creating an emotion-recognition technology to distinguish a happy face from a sad face. A small pool of labelers will likely imprint a bias – reflecting their specific world-view – onto the data. Given that emotion recognition varies from culture to culture, it would be appropriate to have a representative labeling workforce of many cultures to label the data.

That is why good data is representative data. To create globally appropriate AI products, the data used needs to be representative of different cultures, societies, and backgrounds. 

HUMAN solution

Bias can occur at both ends of the process. Limited access to data labeling services limits the voices used to create AI products; limited data-labeling services do not necessarily provide representative workpools. HUMAN Protocol helps to democratize access to data within a permissionless system, meaning anyone can buy the data they need to create the products they want. Most importantly, however, the data they use comes from a global source: applications running on the Protocol are accessed by hundreds of millions of data labelers, across 247 countries and territories. 

For a comprehensive account of how HUMAN Protocol mitigates bias, read our in depth article on the subject.

Data quality

When it comes to data labeling, poor labels are a problem. For applications like CVAT, where the labeler is required to draw a bounding box around, for example, a truck, it is easy for the box to be too small, too big, or simply inaccurate. This is a problem for the machines trained to find the truck. Such data creates ‘noise’ in the dataset, and can lead to inaccuracies in the learning.

HUMAN solution

The Protocol is designed to support an independent and, therefore, decentralized network of oracles to manage job quality. Each component can be incentivized to catch human errors, to reward quality work, and to disincentivize any malicious behaviour. Workers who perform well can build their reputation, and then be prioritised for future jobs.

For the latest updates on HUMAN Protocol, follow us on Twitter or join our community Telegram channel. Alternatively, to enquire about integrations, usage, or to learn more about how HUMAN Protocol supports machine-learning technologies, get in contact with the HUMAN team.

Legal Disclaimer

The HUMAN Protocol Foundation makes no representation, warranty, or undertaking, express or implied, as to the accuracy, reliability, completeness, or reasonableness of the information contained here. Any assumptions, opinions, and estimations expressed constitute the HUMAN Protocol Foundation’s judgment as of the time of publishing and are subject to change without notice. Any projection contained within the information presented here is based on a number of assumptions, and there can be no guarantee that any projected outcomes will be achieved.

Democratizing Data: Why HUMAN Protocol is Important to the World

“Software is simply the encoding of human thought” — Chris Dixon

You may not realize it, but every time you interact with an Artificial Intelligence product — such as photo tagging suggestions — you are benefitting from the unseen labor of hundreds of thousands, or even millions, of data labellers.

Traditionally, AI products are created by Google, Apple, Facebook, and Amazon (GAFA), and the data they use to train their products is consolidated within the company silos. GAFA create centralized services for billions of users, and harvest the generated data to train AI products. For other Machine Learning practitioners, it is difficult to access sufficient data for both training and quality control.

Read more here.

How Loss Aversion Affects Market Research and Decision Making

You’re offered a gamble on a coin toss:

If it’s heads, you lose £100.

If it’s tails, you win £150.

Would you take the offer?

Despite the fact that you stand to win more than you can lose, and your chances of either outcome are equal, you probably don’t like the offer.

This is because of loss aversion.

Read more at Attest.

The Availability Heuristic, Green Tees & Decision Making

In a study, participants listen to either:

A list of 19 famous women and 20 less famous men

A list of 20 famous men and 19 less famous woman

Afterwards, some were asked to recall the names they could remember, and then if the list they had heard contained more men or women.

Unsurprisingly, the famous names were more readily recalled; but, interestingly, the vast majority of participants then incorrectly assumed that the gender of the more famous people were the majority gender in the list they heard.

This is an example of the Availability Heuristic.

Read more at Attest.

Denominator Neglect: What You Need to Know

Picture two urns stood on a table in front of you.

You’re given the opportunity to pick a marble from one of them, and drawing a red marble wins a prize.

The first urn has 10 marbles in it, 1 of which is red.

The second urn has 100 marbles in it, 8 of which are red.

Which urn would you choose? It doesn’t seem a tricky decision: your chances of drawing a red marble out of the first urn are greater (10%) than your chances of drawing a red marble out of the second urn (8%).

Read more at Attest.

Catalogue Essay for JGM Gallery, Art of the Ömie.

There is nothing quite like these barkcloths anywhere in the world. What differentiates the Ömie nioge from Pacific and South American barkcloth is the simplicity of its creation: Ömie women work individually, and only by hand. The lines wobble and weave along the uneven, untreated surface, and avoid symmetry or mimesis; but the Ömie aren’t interested in contriving their art. Their art reflects nature in its freeness, and in its composition. Everything used in the making of nioge comes from nature. There is an integrity to nature in this work.

In 2004, David Baker came across the barkcloths while exploring the isolated regions of Papua New Guinea. Since then, Ömie have exhibited their work in galleries in Sydney, Brisbane, Melbourne, and San Francisco. In 2010, the first exhibition came to the Osborne Samuel gallery in London, and then, in 2013, the Ikon Gallery in Birmingham, which took its collection from the Museum of Archaeology and Anthropology at the University of Cambridge. Among this collection is the work of Brenda Kesi, whose nioge are rare and unique, coloured by the mud-dying techniques she learnt from her grandmother, replicating the first nioge created by the first woman, Sujo. We are privileged to have some of her work exhibited at JGM Gallery.

How Ömie Nioge Began

Along the south-eastern end of Papua New Guinea, between the Solomon and the Coral seas, lies the Ömie territory. The villages cling to an active volcano, Huvaemo, a nine hour walk from the nearest road. Ömie isolation, and proximity to nature, has allowed them to develop a unique vision of life. Nature and ancestral forces are their guide and teacher; they live on the same mountain that the first Ömie were created.

Think of the Christian creation story. Now imagine that Adam sends Eve into exile, and orders her to paint a picture as a symbol and manifestation of her wisdom, and ability to bear children. That’s what happens in the Ömie creation story. In short:
The first man, Mina, and first woman, Suja, emerge from the waters of the Girua River, which flows down from Huvaemo. Suja, whose name means ‘I do not know’, cannot have children, and Mina sends her away until her first menstrual cycle is over. He then tells her, when it does end, that she should create a nioge (barkcloth). She cuts into the first tree, and creates the first barkcloth. She soaks it in red river mud to symbolise her blood, and her ability to bear children. The couple reunite, cut the barkcloth in two, and each wear a piece to cover themselves.

 

The importance of nioge seems apparent: Suja can bear children, and so she makes a nioge to symbolise her fertility.

But there is more going on here. It is suggested that Suja can only create nioge once she has grown out of her ignorance; it seems that the nioge is more than a symbol of fertility, and, in fact, a manifestation of wisdom. It is as if the creation of art reflects the creation of life.

And that is why, even today, only Ömie women are allowed to create nioge. It takes some training for a woman to be allowed to create nioge. Until a woman reaches that stage, she is allowed to practice her art by filling in the red and yellow paint between the black lines of the work. The black lines cannot be touched. They are the pathways, the essence, the energy and wisdom of the art, and a woman must be initiated as an artist before she can create the pathways. That such importance is placed on the training, and the wisdom, of these women is testament to the value the Ömie place on the artist, and their process.

The Process

It starts as a duvahe (chief) selects a tree from which to make the nioge. The chiefs are selected on merit, not birthright; one critical requirement is that the chief is deemed to possess uehorëro (wisdom), and understand the symbolic practices that govern the creation of nioge, which include a collection of rituals, rules, and magic cumulatively called jögore. It is the jögore that gives the nioge its spirit, or kinë’i. I use the word spirit cautiously, for our language is inadequate to describe things we do not culturally understand.

The selected trees are most commonly paper mulberry, fig, or hibiscus. Once a tree is chosen, the woman positions herself towards a stream coming off the mountain, then speaks words of ritual, and removes the outer bark from the tree. The incision runs from the top of the inner flesh, down to the bottom. From here, a long strip of bast, or inner bark, is produced.

The tree gives its ‘spirit’ to the infant nioge. The bast is then left to dry, and laid under sleeping mats for several nights to keep it flat. Then, it is beaten into a cloth using a hitaborota (flat stone), and then a kiveroi (broad mallet). During this process, the artist drips water on the cloth to stop it breaking.

There are three basic colours from which most nioge are made. The black paint is called barige, and is made from burnt leaves of omu hane (a small bamboo tree). The ash of these leaves is wrapped in tulif leaves, and chewed up, before being spat into the shell of a coconut. Sometimes, this black has a green hue. The shade is modified by the amount of fresh leaves that are included in the chewing process.

The red colour, called barire, is ingeniously procured. The skin of the biredihane tree, which grows by the rivers, is placed on ferns that line a bark container. Stones are heated in a fire, and added to the mixture, along with some ash. The fern lining is folded over to help to cook the mixture. Water is added, and the liquid turns red with the heat; the ferns are squeezed to release the paint. With varying applications of heat, the shade of red can be modified from a dark brown to oxblood.

The yellow colour, called are, is made from a guava-like fruit. It is green when young, and so has to ripen to the perfect shade of yellow. Then, it is cut open, and the flesh is scooped into a coconut shell. Water is added to make a paste, which is worked to make cadmium yellow.

From here, there are several directions for the progression of the art. Some artists have designs handed down to them by their family. Meanwhile, the duvahe are allowed to paint out their own uehorëro (wisdom), and visions. The men will contribute stories to the women, but it is up to the initiated women to interpret them, which they can do freely. The Ömie emphasis on interpretation reflects a certain openness, a freeness of mind, and an understanding of the importance of the individual – the artist – and what they see, rather than what they are prescribed. It leads to a more natural, open, and honest form of art that can be seen in every nioge, none of which are the same. This interpretative stance allows for one story to result in many different styles and forms, which again highlights something of Ömie wisdom – perhaps, that no one view can be deemed ‘right’, and no one deemed ‘wrong’.

The artist starts with the black paint. She holds a sharp stick between her first and third finger, and dips it into the mixture. She usually starts with the frame, lines of two or three, and then the ore sige (pathways), which vary in density and thickness, giving the life-force to the nioge. However, there is no prescribed rule for the order of painting. Some artists move methodically from one corner across the cloth, whilst others have several starting points, and move freely between them, letting the pathways meet. When the artists paint, they sing and dance, offering ritual to the proceedings, and a special kind of ancestral homage that awakens the art. When they sing, they sing ‘This place, our art’.

Ömie then, Ömie now

It has not been an easy recent history for the Ömie. That is why the rituals are so important; they have become increasingly sparse over the last century.

In 1942, war between the Australians and Japanese broke out on the Kokoda trail, causing devastation and loss of life to the Ömie people. That same year, an Australian patrol drove deep into Ömie territory, hoping to recruit labour for the war. When they entered, they interrupted a special Ömie tradition, which took place every seven to twelve years, known as sore bijiohe. Young boys were kept in underground cells for months at a time, before they were tattooed to signify and announce their manhood. The patrol halted the ritual, and the tradition started to break up with the ever progressing influence of Christian missionaries. The missionaries burnt nioge. Then, in January 1951, Mount Lamington erupted. Many of the Ömie, especially the lower villages near the abandoned airport in Asapa, believed that this was a sign from their ancestors, who reside in an invisible village on the volcano. They believed it was a sign that the old ways were being lost. Many of these villages gave up nioge creation, and sore bijiohe, and decided to join their Orokaivan neighbours, with whom they had previously warred, by adopting the evangelical teachings of the missions, in the hope that they would find monetary wealth. Marta Rohatynskyj, who worked in some of the areas that abandoned traditional practices, describes how she heard reports of ‘mass burnings of ritual paraphernalia’, and that the Ömie ‘literally gave themselves up to another configuration of power in the control of their lives’.

Meanwhile, the villages further up the mountain, and therefore less accessible to tahua (white people), felt differently about the eruption. They believed that it was caused by the wandering spirits of foreign soldiers, who could not rest because they had died on a land that was not their home. These villages strengthened their traditions, and decided to double up and use the nioge as a kind of second skin on which they could rekindle the tattoos that had once signified so much to them.

It is worth noting that the nioge you see here are not simply husks of tradition, or vain attempts to appease some ancestral spirit; this is the living culture of the Ömie people, an art that marks their vision of life. It is a beautiful vision, and one that, in many ways, we have moved far away from, as our society spends less time with nature; but in the pathways, in the lines and colours and shapes, we can hope to glimpse something of the world that transcends language or culture, to reach something essentially human.

The Art

“I paint from my observations of the mountains and forests and creatures. Dreams give me inspiration…”

  • Dapeni Jonevari (Mokokari), Chief of Ematé clan women.

Jennifer has selected a wide range of artists for this exhibition, each displaying different styles and techniques, echoing the freedom with which they work. While all artists take ‘observations’ from the natural world, their visions are unique and personal. They observe nature, and learn what it has to say. From this source, the artists reveal little truths, patterns, and ways of envisioning life, and the natural world.

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Men’s Ceremonial Initiation Tattoo

In some cases, this patterning is profound and dense. In Dapeni Jonevari’s piece, ‘Men’s Ceremonial Initiation Tattoo [Heads of men (with pattern of a leaf, jungle vines, siha’e fruit design of the belly button) and Ömie mountains]’, we have such an example. The minute details of the vines, and the tendrils that wave in untouched, negative space, give the barkcloth a rhythm and a motion. Ilma Savari offers a similarly dense detailing of nature in her work ‘Design of the bush snail, design of the bellybutton, spots of the wood boring grub, tattoos and beaks of the parrot’. But when you step back, and look at the piece in its entirety, the details amount to striking patterns. The energetic, zigzagging black and white triangles offer varying suggestions of depth as they spread from the centre to the corners of the bark.

 

In other cases, the patterning is less dense, and more abstract. Ilma Savari offers such a piece, demonstrating her range of styles, in ‘Tail feathers of the swift when sitting in the tree’. Here she uses thick, bold strips of black. The tail feathers of the swift are but the bones of inspiration – if we did not have the title, it is unlikely we would recognise the subject of the piece. While the subject is literally a tail feather, it seems that the artist is offering us a more broad comprehension of the pattern and unity of nature. The bold strips, which resemble the arching form of a swift’s tail, form into triangles, and the triangles form parts of larger triangles, and so on. The piece, as interested as it is in swift’s tails, is more about the artist’s ability, and vision to take the shapes of nature into a powerful, yet reticent, piece of work.

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Tail feathers of the swift when sitting in the tree

 

Brenda Kesi is perhaps the most renown for bold designs. We have on display several of her rare mud canvases: we have three named ‘Ancestral Design of the Mud’, a homage to Suja’s first nioge, which was dyed by being soaked in mud. These three pieces are unified by the small squares and rectangles that are laid over the bark, stitched in with 

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Ancestral Design of the Mud

the wing bone of a flying fox. One piece is a black background with white squares; one a white background with black squares; one a mud-red background with white squares. That Brenda Kesi has made designs of mud, without using mud in some instances, demonstrates the figurativeness in which the Ömie operate. She has interpreted the creation story, Suja’s first nioge, and how the mud relates to it. In the piece that actually has been soaked in mud, the overlaid white squares are generally smaller, and spaced further apart, than the others, as if to give some importance to the mud; the white squares, as they pop out and draw the eye, seem also to fade into the murky waters around.

Brenda Kesi’s design, ‘The Ground-burrowing Spider’, has one large, black spot in the middle of the bark, with thick black lines radiating out from it, like spider’s legs, or a spider’s web. The piece is incredibly familiar to my eye; it looks like something I’ve seen before, like something incredibly natural to do with a piece of paper and a pen. I think, in this, we reach something quite interesting about all the pieces – as strange as much of this work can appear, there is something quietly familiar in all of them.

As much as we could seek to understand in this art, whether it seems busy and in need of inspection, or abstract and in need of thought, there is a reticence in all of them. The beauty of these pieces is in their quiet energy, and the rhythm they create. The works are all complete and unified within. That is why nioge do not separate easily into foreground or background elements. Each piece deserves a holistic view; they are, after all, created through a holistic relationship with life and nature.

Through their art, the Ömie are offering us their perspective of the world. It is a perspective that seems at once strange, and yet so familiar. The art is borne from the essential human desire to know and understand life; the art itself echoes that, and in the wisdom they espouse, and the rhythms they create, there is something beautiful and powerful that reminds us that, although countries and cultures divide us, we are not so different after all.

I do not believe in the vain application of poetry. But I think that this art reverberates with something that the great Romantic poets half-understood, half revealed and tried to reflect in their poetry. As Wordsworth writes, as he recalls the power of nature in tranquility:

 

                                                     I have felt

A presence that disturbs me with the joy

Of elevated thoughts; a sense sublime

Of something far more deeply interfused,

Whose dwelling is the light of setting suns,

And the round ocean and the living air,

And the blue sky, and in the mind of man:

A motion and a spirit, that impels

All thinking things, all objects of all thought,

And rolls through all things. Therefore am I still

A lover of the meadows and the woods

And mountains

– Lines Composed a Few Miles above Tintern Abbey, William Wordsworth

Whereas Wordsworth can only trace strands of this phenomenon, of the spirit of nature and humanity in words, the Ömie achieve the thing itself. These pieces of art, borne from nature, crafted through nature and the language of wisdom, hold an energy and a power that can help us, too, see into the life of things.

See the gallery page here.