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Stop waiting for perfection

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These days, people should stop waiting for perfection and instead experiment, find errors and turn them into innovation, writes WERNER VOGELS, the CTO of Amazon.com.

“Man errs as long as he doth strive.” Goethe, the German prince of poets, knew that already more than 200 years ago. His words still ring true today, but with a crucial difference: Striving alone is not enough. You have to strive faster than the rest. And while there’s nothing wrong with striving for perfection, in today’s digital world you can no longer wait until your products are near perfection before offering them to your customers. If so, you will fall behind in your market.

So if you can’t wait for perfection, what should you do instead? I believe the answer is to experiment aggressively with your product development, accepting the possibility that some of your experiments will fail.

Anyone who has listened to, or worked with, management gurus know their mantra: Failure is a necessary part of progress. That’s true, but there’s often a big gap between the management theory and the reality on the ground. People want to experiment and learn from things that go wrong. But in the flurry of day-to-day business, they’re not given enough time to really reflect on the cause of an error and what to do differently next time.

The solution is to find a systematic approach that prevents errors from repeating themselves.

From perfection to anti-fragility

In finding such a systematic way, you first need to distinguish between two types of errors that can happen in your company: those of technology and those of human decision-making. The nice thing is: if you know how to deal effectively with the first, you might end up being better in the second, making better decisions. The financial mathematician and essayist Nassim Taleb offers an interesting take on this issue. He has argued that errors are incredibly valuable because they lead to innovation. He uses the term ‘anti-fragility’ to make his point. Today’s digital business models require smaller, frequent releases to reduce risk. That means the technologies underpinning these new business models must be more than just robust. They must be ‘anti-fragile’. The main feature of anti-fragile technology is that it can ‘err’ without falling apart. In fact, a crisis can make it even better.

At Amazon, we also require our systems and customer solutions to be anti-fragile, and we do that by designing our systems to stand the test of time. Our systems must be able to evolve and become more resilient to failure. They must become more powerful and more feature-rich over time as a result of learning from customer feedback and any failure modes they may encounter while operating the systems.

An example of a German company that has become ‘anti-fragile’ is HARTING, the world’s leading provider of heavy pluggable connectors for machines and plants. HARTING shows how to think a step ahead about the meaning of quality standards in the digital world. Quality and trust are the most important values for this traditional company, and Industry 4.0 and the digital transformation have already been important focus areas for them since 2011. Even though it was hard to accept at first, HARTING has meanwhile realized that errors are inevitable. For that reason, its development switched to agile methods. It also uses the “minimum-viable-product” approach and relies on microservices for its software. Working this way, HARTING can discard things and create new things more easily. All in all, HARTING has become faster.

That can be seen with HARTING MICA, an edge computing solution that enables older machines and plants to get a digital retrofit. The body and hardware still reflect HARTING’s standard of perfection. But for the software, the goal is “good enough”, because a microservice is neither ever finished nor perfect. As a result, wrong decisions and mistakes can be corrected very quickly and systems can mature faster, approaching the state of antifragility. If the requirements change or better software technologies become available, each microservice can be thrown out and a new one created. That’s how you gain speed and quickly digitize old machines and connect them to the cloud within a manageable cost framework.

Taking the dread out of mistakes

If you want to become anti-fragile, more than robust, like HARTING and other companies, you need to proactively look for the weak spots in a system as you experiment. In a system that should evolve, all sorts of errors will happen that you weren’t able to predict, especially when systems need to scale into unknown territories. So subject your system to continuous failures and make subsystems artificially fail using tools like Netflix’s Chaos Monkey.

If you do all of this, you will start to objectify errors at your company and make dealing with errors a matter of normality. And when errors become ‘business as usual’, no one will be afraid of taking a risk, trying out a new idea, a new product or a new service and seeing what happens when customers interact with it. That’s how you quickly find solutions that really work in the future.

At Amazon, our approach for systematically and constructively dealing with errors is called the “cause of error” method. It refrains from seeking “culprits”. Instead it documents learning experiences and derives actions that ultimately improve the availability of our systems.

From root cause to innovation

The method first calls for fixing an error by analyzing its immediate root cause and taking steps to mitigate the damage and restore the initial running state as quickly as possible.But we are not content with that result. We go further, trying to extract the maximum amount of insight from the incident. And this process begins as soon everything is working again for the client.

A key element of our cause-of-error method is asking 5 ‘Why?’ questions (a technique that originated in quality control in manufacturing). This is important because it determines the fundamental root of the problem.

Take the case of a website: Why was it down last Friday? The web servers reported timeouts. Why were there timeouts? Because our web services are overloaded and couldn’t cope with the high traffic. Why were the web servers overloaded? Because we don’t have enough web servers to handle all requests at peak times. Why don’t we have enough web servers? Because we didn’t consider possible peaks in demand in our planning. Why didn’t we take peaks in demand into account in our planning? By the end of this process, we know exactly what happened and which clients were affected. Then we’re in a position to distill an action plan that ensures that specific error doesn’t happen again.

Quite often, applying this cause-of-error approach allows us to find breakthrough innovations, in the spirit of Nassim Taleb. That’s how the solution Auto Scaling was created, after a certain client segment was fighting with strongly fluctuating hits on their website. When the load increases for a website, Auto Scaling automatically spins up an additional web server to service the rising number of requests. Conversely, when the load subsides, Auto Scaling turns off web servers that are not needed in order to save cost.

What it reveals is: Organizations need to look beyond superficial success. This is true for the development of systems as well as business models. If you want to remain agile in a complex environment, you must follow this path, even if it means leaving the comfort zone. If we transfer these ideas into an organizational context, three aspects might be worth considering:

1. Embrace error as a matter of fact

Jeff Bezos once said about Amazon: “I believe we are the best place in the world to fail.” That inspires a lot of our people to experiment, find errors and turn them into something innovative. A statement like this encourages your people to actively look for errors, and to turn them into pieces of innovation. And: reward employees when they find errors. What we have learned from our development work at Amazon is that you need to always look beyond the surface of an error. Some of our best products have been born from errors.

2. Make due with incomplete information

German companies have a tradition of being thorough and perfectionist. In the digital world, however, you need to loosen those principles a bit. Technology is changing so fast; you need to be fast too. Make decisions even if the information you have is not as complete as you would like.Jeff Bezos put his finger on that when he wrote in his most recent letter to shareholders that “most decisions should probably be made with somewhere around 70% of the information you wish you had. If you wait for 90%, in most cases, you’re probably being slow. Plus, either way, you need to be good at quickly recognizing and correcting bad decisions. If you’re good at course correcting, being wrong may be less costly than you think, whereas being slow is going to be expensive for sure.”

3. Praise the value of learning

I’ve stressed the need for companies to have a systematic approach to how they deal with errors. But your approach will only work if it’s part of your overall culture. Make sure you understand your DNAandknow what people are thinking and talking about on the work floor. Openly praising experimentation in product development and encouraging people to find errors will come across as empty rhetoric if your employees really do have reason to fear repercussions for themselves personally if they make mistakes.

It is a matter of leadership to foster and shape a culture of experimentation that is practiced day in, day out.

Whatever companies come up with in order to systematically learn from mistakes, it will make them better in competing in the digital world. And it will give them the freedom and courage to take their systems, solutions and business models to a higher level.

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Prepare your cam to capture the Blood Moon

On 27 July 2018, South Africans can witness a total lunar eclipse, as the earth’s shadow completely covers the moon.

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Also known as a blood or red moon, a total lunar eclipse is the most dramatic of all lunar eclipses and presents an exciting photographic opportunity for any aspiring photographer or would-be astronomers.

“A lunar eclipse is a rare cosmic sight. For centuries these events have inspired wonder, interest and sometimes fear amongst observers. Of course, if you are lucky to be around when one occurs, you would want to capture it all on camera,” says Dana Eitzen, Corporate and Marketing Communications Executive at Canon South Africa.

Canon ambassador and acclaimed landscape photographer David Noton has provided his top tips to keep in mind when photographing this occasion.   In South Africa, the eclipse will be visible from about 19h14 on Friday, 27 July until 01h28 on the Saturday morning. The lunar eclipse will see the light from the sun blocked by the earth as it passes in front of the moon. The moon will turn red because of an effect known as Rayleigh Scattering, where bands of green and violet light become filtered through the atmosphere.

A partial eclipse will begin at 20h24 when the moon will start to turn red. The total eclipse begins at about 21h30 when the moon is completely red. The eclipse reaches its maximum at 22h21 when the moon is closest to the centre of the shadow.

David Noton advises:

  1. Download the right apps to be in-the-know

The sun’s position in the sky at any given time of day varies massively with latitude and season. That is not the case with the moon as its passage through the heavens is governed by its complex elliptical orbit of the earth. That orbit results in monthly, rather than seasonal variations, as the moon moves through its lunar cycle. The result is big differences in the timing of its appearance and its trajectory through the sky. Luckily, we no longer need to rely on weight tables to consult the behaviour of the moon, we can simply download an app on to our phone. The Photographer’s Ephemeris is useful for giving moonrise and moonset times, bearings and phases; while the Photopills app gives comprehensive information on the position of the moon in our sky.  Armed with these two apps, I’m planning to shoot the Blood Moon rising in Dorset, England. I’m aiming to capture the moon within the first fifteen minutes of moonrise so I can catch it low in the sky and juxtapose it against an object on the horizon line for scale – this could be as simple as a tree on a hill.

 

  1. Invest in a lens with optimal zoom  

On the 27th July, one of the key challenges we’ll face is shooting the moon large in the frame so we can see every crater on the asteroid pockmarked surface. It’s a task normally reserved for astronomers with super powerful telescopes, but if you’ve got a long telephoto lens on a full frame DSLR with around 600 mm of focal length, it can be done, depending on the composition. I will be using the Canon EOS 5D Mark IV with an EF 200-400mm f/4L IS USM Ext. 1.4 x lens.

  1. Use a tripod to capture the intimate details

As you frame up your shot, one thing will become immediately apparent; lunar tracking is incredibly challenging as the moon moves through the sky surprisingly quickly. As you’ll be using a long lens for this shoot, it’s important to invest in a sturdy tripod to help capture the best possible image. Although it will be tempting to take the shot by hand, it’s important to remember that your subject is over 384,000km away from you and even with a high shutter speed, the slightest of movements will become exaggerated.

  1. Integrate the moon into your landscape

Whilst images of the moon large in the frame can be beautifully detailed, they are essentially astronomical in their appeal. Personally, I’m far more drawn to using the lunar allure as an element in my landscapes, or using the moonlight as a light source. The latter is difficult, as the amount of light the moon reflects is tiny, whilst the lunar surface is so bright by comparison. Up to now, night photography meant long, long exposures but with cameras such as the Canon EOS-1D X Mark II and the Canon EOS 5D Mark IV now capable of astonishing low light performance, a whole new nocturnal world of opportunities has been opened to photographers.

  1. Master the shutter speed for your subject 

The most evocative and genuine use of the moon in landscape portraits results from situations when the light on the moon balances with the twilight in the surrounding sky. Such images have a subtle appeal, mood and believability.  By definition, any scene incorporating a medium or wide-angle view is going to render the moon as a tiny pin prick of light, but its presence will still be felt. Our eyes naturally gravitate to it, however insignificant it may seem. Of course, the issue of shutter speed is always there; too slow an exposure and all we’ll see is an unsightly lunar streak, even with a wide-angle lens.

 

On a clear night, mastering the shutter speed of your camera is integral to capturing the moon – exposing at 1/250 sec @ f8 ISO 100 (depending on focal length) is what you’ll need to stop the motion from blurring and if you are to get the technique right, with the high quality of cameras such as the Canon EOS 5DS R, you might even be able to see the twelve cameras that were left up there by NASA in the 60’s!

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How Africa can embrace AI

Currently, no African country is among the top 10 countries expected to benefit most from AI and automation. But, the continent has the potential to catch up with the rest of world if we act fast, says ZOAIB HOOSEN, Microsoft Managing Director.

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To play catch up, we must take advantage of our best and most powerful resource – our human capital. According to a report by the World Economic Forum (WEF), more than 60 percent of the population in sub-Saharan Africa is under the age of 25.

These are the people who are poised to create a future where humans and AI can work together for the good of society. In fact, the most recent WEF Global Shapers survey found that almost 80 percent of youth believe technology like AI is creating jobs rather than destroying them.

Staying ahead of the trends to stay employed

AI developments are expected to impact existing jobs, as AI can replicate certain activities at greater speed and scale. In some areas, AI could learn faster than humans, if not yet as deeply.

According to Gartner, while AI will improve the productivity of many jobs and create millions more new positions, it could impact many others. The simpler and less creative the job, the earlier, a bot for example, could replace it.

It’s important to stay ahead of the trends and find opportunities to expand our knowledge and skills while learning how to work more closely and symbiotically with technology.

Another global study by Accenture, found that the adoption of AI will create several new job categories requiring important and yet surprising skills. These include trainers, who are tasked with teaching AI systems how to perform; explainers, who bridge the gap between technologist and business leader; and sustainers, who ensure that AI systems are operating as designed.

It’s clear that successfully integrating human intelligence with AI, so they co-exist in a two-way learning relationship, will become more critical than ever.

Combining STEM with the arts

Young people have a leg up on those already in the working world because they can easily develop the necessary skills for these new roles. It’s therefore essential that our education system constantly evolves to equip youth with the right skills and way of thinking to be successful in jobs that may not even exist yet.

As the division of tasks between man and machine changes, we must re-evaluate the type of knowledge and skills imparted to future generations.

For example, technical skills will be required to design and implement AI systems, but interpersonal skills, creativity and emotional intelligence will also become crucial in giving humans an advantage over machines.

“At one level, AI will require that even more people specialise in digital skills and data science. But skilling-up for an AI-powered world involves more than science, technology, engineering and math. As computers behave more like humans, the social sciences and humanities will become even more important. Languages, art, history, economics, ethics, philosophy, psychology and human development courses can teach critical, philosophical and ethics-based skills that will be instrumental in the development and management of AI solutions.” This is according to Microsoft president, Brad Smith, and EVP of AI and research, Harry Shum, who recently authored the book “The Future Computed”, which primarily deals with AI and its role in society.

Interestingly, institutions like Stanford University are already implementing this forward-thinking approach. The university offers a programme called CS+X, which integrates its computer science degree with humanities degrees, resulting in a Bachelor of Arts and Science qualification.

Revisiting laws and regulation

For this type of evolution to happen, the onus is on policy makers to revisit current laws and even bring in new regulations. Policy makers need to identify the groups most at risk of losing their jobs and create strategies to reintegrate them into the economy.

Simultaneously, though AI could be hugely beneficial in areas such as curbing poor access to healthcare and improving diagnoses for example, physicians may avoid using this technology for fear of malpractice. To avoid this, we need regulation that closes the gap between the pace of technological change and that of regulatory response. It will also become essential to develop a code of ethics for this new ecosystem.

Preparing for the future

With the recent convergence of a transformative set of technologies, economies are entering a period in which AI has the potential overcome physical limitations and open up new sources of value and growth.

To avoid missing out on this opportunity, policy makers and business leaders must prepare for, and work toward, a future with AI. We must do so not with the idea that AI is simply another productivity enhancer. Rather, we must see AI as the tool that can transform our thinking about how growth is created.

It comes down to a choice of our people and economies being part of the technological disruption, or being left behind.

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