AI – The control problem

When designing a system to be more intelligent, faster or even responsible for activities which we would traditionally give to a human, we need to establish rules and control mechanisms to ensure that the AI is safe and does what we intend for it to do.

Even systems which we wouldn’t typically regard as AI, like Amazon’s recommendations engine, can have profound effects if not properly controlled.  This system looks at items you have bought or are looking to buy. It then suggests other items it thinks you are likely to additionally purchase which can result in some pretty surprising things – like this:

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Looking to buy a length of cotton rope?  Amazon might just recommend that you buy a wooden stool alongside it.  As a human, we would not suggest these two items alongside each other.  However Amazon’s algorithm has seen a correlation between people who bought cotton rope and those that also bought wooden stools. It’s suggesting to someone buying the rope that they might want a stool too with the hope of raking in an extra £17.42.  At best, this seems like an unfortunate mistake.  At worst, it’s prompting extremely vulnerable people and saying ‘why not?  This happens all the time?  Why don’t you add the stool to your basket?’.

If this can happen with a recommendation algorithm, designed to upsell products to us, clearly the problem is profound.  We need to find a reliable means to guarantee that the actions taken by AI or an automated system achieve a positive outcome.

Solutions?

Terminal value loading

So, why don’t we just tell an AI to protect human life?  That’s what Isaac Asimov proposed in ‘I Robot’.  Here are the three laws;

  1. A robot may not injure a human being or, through inaction, allow a human being to come to harm.
  2. A robot must obey orders given it by human beings except where such orders would conflict with the First Law.
  3. A robot must protect its own existence as long as such protection does not conflict with the First or Second Law.

They sound pretty watertight.  Adding in no injury through action or inaction seems to avoid a dystopia where AI takes over and lets the human race finish itself off.

Despite how good these laws sound, they don’t work.  Asimov wrote these laws for use in novels, and the novels were much more interesting when things went wrong.  Otherwise we might have ended up with a book of ‘Once upon a time, the end’.

There’s a 4th law, the ‘Zeroth Law’ added by Asimov . This extra rule was supposed to fix the flaws of the other three, the ones that gave Will Smith a bad day. I confess, I’ve not read the book, but I understand that one didn’t go so well either.

The rules don’t even have to refer to people to be a risk.  They could be about something really mundane.  Take the idea of a paperclip maximiser, an idea put forth by Nick Bostrom. This would be a machine made by a hypothetical future human race to manage paperclip creation. Paperclips are just a simple resource and seemingly don’t need a ton of consideration to make them safe, if we tell the AI that it’s purpose is to make paperclips, and that’s just what it does.

But what if we end up with a super intelligent system, beyond our control, with the power to rally the resources of the universe making paperclips? If this system, whose priority is turning everything it around it into paperclips, sees its creators attempts to prevent it reaching this goal, the best bet is to eradicate them.  Even if it doesn’t decide to eradicate them, those humans are still made out of valuable matter which would look much nicer if it was turned into a few paperclips, so turn them into paperclips it shall.

How do we change that terminal value?  Tell the machine to make 1,000 paperclips instead of turning the entire universe into paperclips? Unfortunately, it’s not much better.  That same AI could make 1,000 paperclips, then proceed to use all the resources in the observable universe (our cosmic endowment) to make sure that it’s made exactly 1,000 paperclips, not 999 or 1,001, and that those paperclips are what its creator intended for it to make, and all of the perfect quality to satisfy their desire.

It might not even be fair to give a super intelligent machine such a mundane terminal value– assuming we find a way to make its value remain constant despite becoming extremely intelligent.

Here I am with a brain the size of a planet and they ask me to pick up a piece of paper. Call that job satisfaction? I don’t.

Marvin – Hitchhiker’s Guide to the Galaxy, by Douglas Adam

 

TL;DR – Terminal values don’t seem to work well.

Indirect normativity

Instead of giving a machine a terminal value, could we instead indirectly hint towards what we want it to do?

If we managed to perfectly sum up in terminal value what morality meant to the human race in Viking times, we might have an AI which prizes physical strength very highly.  We might think we’ve reached a higher ethical standard today but that’s not to say 1,000 years from now we will not look back on the actions we are taking were ignorant.  Past atrocities happened on human timescales, with only human level intelligence to make them happen.  Doing it orders of magnitude faster with a machine may well be worse and irreversible.

With indirect normativity we don’t even try to sum up that terminal value; instead we ask a machine to figure out what we want it to do.  Using something like Eliezer Yudkowski’s ‘Coherent Extrapolated Volition’ which asks that an AI predict what we would want it to do if “if we knew more, thought faster, were more the people we wished we were, had grown up farther together”

Rather than following whatever ethical code we have at the time of releasing the AI, we create something which grows and changes as we do, and creates the future which we’re likely to want rather than a far more extreme version of what we have today.

There’s perhaps still some overlap between this system and terminal value loading, and contradictions that the systems would find.  If a machine is asked to do whatever is most valuable to us, and prizes making that correct decision over anything else, perhaps its decision will be to take our brains out, put them on a petri dish and figure out exactly what we meant for it to do.  A clause like ‘do the intended meaning of this statement’ would seem to lessen the concern, but again, to know what we intend the machine needs to be able to predict out behaviour.

A perfect prediction system would look a lot like a ‘Black Mirror’ episode.  Using an application without a second thought to manage your home automation or to find your next date. Not knowing that the machine is simulating thousands of thinking and feeling human minds to make an accurate prediction of your desires and behaviours, including all the pain that those sentient simulations feel when being torn apart from one another on thousands of simulated dates to gauge how likely you are to stay together against all odds.

The control problem is extremely tricky, and looks for answers to questions which philosophers have failed to reach a consensus on over thousands of years of research.  It is imperative  that we find answers to these questions, not just before creating as Super Intelligent AI, but in any system that we automate.  Currently the vast majority of our resources and effort is put into making these systems faster and more intelligent, with just a fraction focused towards the control problem or the societal impact of AI and automation.

Let’s redress the balance.

 

Bridging the gap: how Fintechs and ‘big business’ can work together

by Colin Carmichael, UK Fintech Director

Everyone’s talking about Fintechs – but what does ‘Fintech’ really mean?  It’s a generic term that loosely groups a number of innovative technical organisations within Financial Services.

As the Fintech director for Sopra Steria, I believe I know all about Fintech. To me, Fintech is all about change – introducing new, fresh ideas and ways of working – and making them happen. I’ve worked in financial services across the UK, Europe and further afield for many years – and organisations of all sizes find it hard to change; the bigger the organisation – the greater the challenge. Change means that organisations have to think and act differently to introduce brand new ways of working to deliver desirable services to their customers.  The customer really is king and new products and services need to be built to their wishes (rather than the ‘old fashioned’ way of creating a product and selling it hard). What’s more, new, faster technology and access to huge amounts of data have made this issue more acute as it’s raised customer expectations. Put simply – there’s so much to think about and to do to get ahead and stay ahead.

Organisations need to keep up with the very latest ideas – and still deliver a reliable and robust service. And it’s a fact that incorporating new technology is how they will do it. So why is it so challenging for Fintechs and big players to work together? All too often, Fintechs struggle to get their ideas to the right decision makers – and established businesses are nervous of too much change.

The biggest hurdles are often company politics, internal structures, old processes and course – the difficulty of incorporating brand new ideas into ‘old’ systems. For Fintech’s, it’s tricky to get the right contacts at the right level – and to also ensure their ideas are brought to life safely and securely.  For banks and insurers, introducing new, untried and tested ideas is hugely risky and it can take a long time – as well as effort and money to get it right.

What’s needed is a bridge between the Fintechs and the more traditional organisations – to help them to work productively together. Organisations like Sopra Steria have platforms that are at the heart of many of today’s large businesses – and they also understand existing processes, procurement and politics which often stand in the way of getting things done. By working together, Fintechs, established players and platform organisations can listen to and learn from each other, in order to fast track innovation and get the results they need – quickly and cost effectively.

So, my advice to banks and insurance companies as well as the Fintechs is to work and collaborate with a platform provider from the start. Fintechs can safely test and prove their worth in ‘virtual factories’ using real systems and data – and financial organisations can be confident about bringing the best and brightest ideas to market without huge risk. It puts new Fintechs in touch with established players – and accelerates change. And that’s what we all want.

So, maybe, we shouldn’t be using the term ‘fintech’ to refer to just new and upcoming technology companies. After all – aren’t we all Fintechs? Perhaps instead we should be focusing on partnerships and collaborations between new technology companies, established organisations and the role platform players have to accelerate change.

It really is true. It’s not what you know but who you know that makes all the difference.

Google Dupe-lex

Google unveiled an interesting new feature at their I/O conference last week – Duplex.  The concept is this: want to use your Google assistant to make bookings for you but the retailer doesn’t have an online booking system?  Looks like your going to be stuck making a phone call yourself.

Google wants to save you from that little interaction.  Ask the Google assistant to make a booking for you and Duplex will make a call to the place, let them know when you’re free, what you want to book, when, and talk the retailer through it…. With a SUPER convincing voice.

It’s incredibly convincing, and nothing like the Google assistant voice that we’re use to.  It uses seemingly perfect human intonations, pauses, umms and ahs at the right moments.  Knowing that it’s a machine, you feel like you can spot the moments where it sounds a little bit robotic, but if I’m being honest, if I didn’t know in advance I’d be hard pressed to notice anything out of the ordinary, and wouldn’t for a moment suspect it was anything but a human.

I think what they’re using here is likely a branch of the Tacotron 2 speech generation AI that was demoed last year.  It was a big leap up from the Google assistant voice we are used to, and it was difficult to tell the difference between it and a human voice.  If you want to see if you can tell the difference follow this link;

https://ai.googleblog.com/2017/12/tacotron-2-generating-human-like-speech.html

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So, what’s the problem?

The big problem is that people are going to feel tricked (or ‘duped’ as me and likely 100 other people will like to joke).  Google addressed this a little bit, saying that Duplex will introduce itself and tell the person on the other end of the phone is a robot, but I’m still not sure it’s right.

I can absolutely see the utility in making this voice seem more human.  If you receive a call from a robotic sounding voice, you put the phone down.  We expect the robot is going to try to be polite for just long enough to ask us for our credit card details for some obscure reason.  By making the voice sound like a person our behaviour changes to give that person time to speak – To give them the respect that we expect to receive from another person, rather than the bluntness that we will tend to address our digital assistants with.  After all – Alexa doesn’t really care if you ask her to turn the lights off ‘please’, or just angrily bark at her to turn the lights off.

Making the booking could be just a little bit of a painful interaction. The second example that Google shows has a person trying to make a booking for 4 at a restaurant.  It turns out that the restaurant doesn’t make bookings for groups less than 5, and that it’s in fact fine just to turn up as there will most likely be tables available.  Imagine this same interaction with a machine.  Imagine that conversation with one of those annoying digital IVR systems when you call a company and try to get through to the right person – Saying ‘I want to book a table’…. ‘I want to book a table’…. ‘TABLE BOOKING’…. ‘DINNER’.   Our patience will run thin much faster if we’re waiting for a machine than if we’re waiting for a robot.

Just because there is utility, doesn’t mean this deception is fair.  I can see three issues with this.

  1. Even if the assistant introduces it as a machine, the person won’t believe it

It might just seem like a completely left of field comment and make people think they’ve just mis-heard something.  They’ll either laugh it off for a second and continue to believe it’s a person, or think they just couldn’t quite make the word our right – Especially as this conversation is happening over the phone.

  1. They know it’s a robot, but they still behave like it’s a human

Maybe we have people who hear it’s a robot, know that robots are now able to speak like a human, but still react as though it’s a person.  This is a bit like the uncanny valley.  They know it’s a machine, and the rational part of their mind is telling them it’s a machine, but the emotional or more instinctive part of their mind hears it as a human, and they still offer much the same kind of emotion and time to it that they would a human.

  1. They know it’s a machine and treat it like a machine.

This is interesting, because I think it’s exactly not what Google want people to do.  If there wasn’t some additional utility in making this system sound ‘human like’, they wouldn’t have spent the time or money on the new voice model and would have shipped the feature out with the old voice model long ago.  If people treat it like a machine, we may assume that the chance of making a booking, or the right kind of booking would be reduced.

If you believe the argument I’ve made here, then Duplex introducing itself as a machine is irrelevant.  Google’s intention is still for it to be treated like a human – And is this OK?

I’m not entirely sure it is.  When people make these conversations, they’re putting a bit of themselves into the relationship.  It reminds me of Jean-Paul Sartre talking about his trip to the café.  He was expecting to meet his friend Pierre, and left his house with all the expectations of the conversation he would have with Pierre, but when he arrives Pierre is not there.  Despite the café being full, it feels empty to Sartre.  I imagine a lot of people will feel the same when they realize that they’ve been speaking to a machine.  As superficial as the relationships might be when you are making a booking over the phone, they are still relationships.  When the person arrives for their meal, or their haircut, and they realise that person they spoke to before doesn’t really exist – that it has no conscious experience –  and they’ll feel empty.

They’ll feel kinda… duped…

Light at the end of the financial tunnel?

In March 2018 the government reached a significant economic milestone. It eliminated the deficit on its day-to-day budget. Tax revenues will exceed public spending. Public sector net debt will fall for the first time since 2001-02. It took eight years rather than five. But the primary target set by government in 2010, as the UK struggled to recover from the financial crisis, had finally been met.

The chancellor declared the nation was at a turning point in its recovery. He could see ‘light at the end of the tunnel’. Some commentators suggested the light might be an oncoming train, pointing out that economic forecasts rely on the government following through on ambitious plans for further spending cuts.

What does the economic evidence tell us?

We recently asked the National Institute for Economic and Social Research (NIESR) to help us understand if the upbeat economic message was justified.

They told us that if government is to achieve its ambitious target of a balanced budget by the middle of the next decade, public spending as a share of Gross Domestic Product (GDP) would need to fall to 36.6% in 2022-23. The post-war average is 39.3% of GDP.

Since 2010, significant savings have been made by transforming how government works, through commercial reforms and reducing the costs of major projects. However, the evidence is that public sector pay restraint and relatively low levels of public spending, rather than widespread productivity gains, explain the vast majority of deficit reduction.

What future do public services face?

We then asked NIESR to suggest how sustainable these savings might be. They told us government faces a daunting challenge. Why?

First, continued pay restraint is unlikely to have the same impact. The across-the-board 1% pay cap has already been lifted. And there are signs in health and education of recruitment and retention problems. Second, there are signs that low levels of public spending might be affecting the quality of public services. For example, the Institute for Government have noted a marked deterioration in key government targets for health and public safety. And third, most significantly, the pressure from further ageing of the population is gradually building. Significant extra resources will be needed to cover raising health and care costs and serve a larger number of pensioners.

NIESR used a series of scenarios to reveal the economic strain that government might experience over the next seven years. They warned that a public spending gap of up to £300bn could emerge, created by the cumulative impact of an ageing population and the cost of easing austerity.

Of course, it is possible that the economy will improve significantly, lifting tax revenues and providing an opportunity to increase public spending and reduce debt (the government still owe more than £1.6 trillion). But this cannot be counted on and it would be better to consider other options.

What options are open to government?

To meet this challenge, the government will need to embark on a transformation programme on a scale unprecedented in the post-war era. The combination of fiscal consolidation and rising expectations for service delivery represents both an opportunity and an imperative to radically redesign the services government provides to the public.

Government have already started digitising front-end interfaces, processes and workflows to improve cost efficiency and user experience. The next step is to reduce duplication of structures and resources between and within levels of government. And this requires a change of mindset – from government and industry. Rather than simply implementing bits and pieces of technologies on their own, there is a need for equally necessary organisational and service design changes. And this means joining up and trying to de-silo processes, creating new processes that do things once rather than many times.

In launching a new strategy for the government business last week, Sopra Steria commits to using our understanding of the public sector and emerging technologies to introduce a series of platforms for government, focussed on core government activities, which are standardised and repeatable. They will enable government to re-engineer, streamline and automate policy processes (including those enabling the UK’s exit from the EU).

Further information on the strategy launch and the NIESR research can be found on the Sopra Steria website. As always, I would be grateful for your thoughts and suggestions on how public services should be reformed.

Going waste-free

This weekend is Earth Day – a time to highlight global support for improving environmental sustainability and bring together millions of people, cities, and organisations across the world.  As part of the sustainability committee at Sopra Steria, part of my role is to raise awareness of initiatives amongst our employees, I am also thinking about the importance of individual action, including my own.

Going into 2018, the images I saw in Blue Planet 2 such as the albatross parents unwittingly feeding their chicks plastic, and the proliferation of media stories about the impact of plastics on the environment prompted me to think more about the waste that arises from my own lifestyle choices.

What does Zero Waste mean?
Zero Waste is as simple as it sounds: it’s all about trying to live without waste.  Everything we use should be reused or recycled or composted; nothing should go to landfill; ideally more and more of what we use will contain materials that have already been used before. Everything that we produced or consumed should be returned back to society or nature – so products are either reused, recycled or biodegrade.

In reality it’s not so simple.  Look around your supermarket and you’ll see thousands of products in packaging that cannot be recycled, everything from our food and drink, to cosmectics, to cleaning products comes in single use plastic and the challenge is how can we elimate this. .

How to get started

Arming myself with a list of all the places that stock zero packing products. I was ready for the challenge and started to plan how to adjust my lifestyle. I planned to reuse first, using my newly acquired home composting bin second, then recycling and finally sending non-recyclables to charity.

The first step was to eliminate all single use plastic. From the morning cup of coffee in the plastic-coated, non-recyclable cardboard cup, to the disposable cutlery used at lunch and the unnecessary food packing at supper, most of us have a lot of waste in our day-to-day life.  For instance buying a coffee daily is 30 cups and lids a month, all which end up in landfill,  and may take hundreds of years to decompose.  More importantly, it is 30 cups worth of materials that had to be mined, shipped to factories, manufactured,  and then shipped to my local Starbucks.  Thinking about the life of a coffee cup, from origin to where it ends up, its environmental impacts become clear and throwing a cup away every day seems unconscionable.  Suddenly, using a reusable mug seems like no-brainer.

Secondly, was composting all my food scraps.  I found that this step alone eliminated about 50% of my waste. Living in a second floor flat with no garden made this more of a challenge, I found a friend willing to take my scraps in their home compost heap which has made things much easier. But there are plenty of local councils who have composting services and there are lots of alternative options such as indoor womeries here.

The final stage was to address the longer use plastic items, buying cosmetics that come unpackaged (such as solid shampoo from Lush) and finding suppliers who will refill your cleaning product, eco companies such as Ecover are more than happy to refill existing bottles – find a local one here.

The results

By making a concerted effort to eliminate waste from my life, I have been able to reduce my waste footprint by about 90%.  The remaining 10% was made up of parcels covered in plastic, make up where there isn’t alternative packaging and longer use items such as headphones, tupparware that do break down eventually. The best result of this month-long experiment is the simply way it has enabled me to change my habits and make a difference.  It showed me that I can reduce my waste by a huge proportion, without requiring me to spend more money or make much more effort, and I have continued to live a low-waste life.

It is debatable whether it is possible to be truly zero-waste in modern society due to the complexity of our supply chains, but there are very easy ways to reduce the sheer amount that we as individuals get through.  What’s more, in the process, by changing the way we consume products – choosing products  with no packaging or recyclable packaging – we can have an influence the companies who sell them to us.

In any case, in sustainable living, it’s far more important to ensure we don’t make the perfect the enemy of the good.   I have put time into changing my routines to make how I live as sustainable as possible and have no intention of going back.   While I’m not going to stress if someone accidently puts a straw in a drink I order, I will continue to search for no-waste solutions to my everyday decisions.   If we all take small steps in our personal lives, and continue to campaign for companies and governments to affect larger change, we can make a difference.

 

 

 

 

Quantum Computers: A Beginner’s Guide

What they are, what they do, and what they mean for you

What if you could make a computer powerful enough to process all the information in the universe?

This might seem like something torn straight from fiction, and up until recently, it was. However with the arrival of quantum computing, we are about to make it reality. Recent breakthroughs by Intel and Google have catapulted the technology into the news. We now have lab prototypes, Silicon Valley start-ups and a multi-billion dollar research industry. Hype is on the rise, and we are seemingly on the cusp of a quantum revolution so powerful that it will completely transform our world.

On the back of this sensationalism trails confusion. What exactly are these machines and how do they work? And, most importantly, how will they change the world in which we live?

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At the most basic level, the difference between a standard computer and a quantum computer boils down to one thing: information storage. Information on standard computers is represented as bits– values of either 0 or 1, and these provide operational instructions for the computer.

This differs on quantum computers, as they store information on a physical level so microscopic that the normal laws of nature no longer apply. At this minuscule level, the laws of quantum mechanics take over and particles begin to behave in bizarre and unpredictable ways. As a result, these devices have an entirely different system of storing information: qubits, or rather, quantum bits.

Unlike the standard computer’s bit, which can have the value of either 0 or 1, a qubit can have the value of 0, 1 or both 0 and 1 at the same time. It can do this because of one of the fundamental (and most baffling) principles of quantum mechanics- quantum superposition, which is the idea that one particle can exist in multiple states at the same time. Put another way: imagine flipping a coin. In the world as we know it (and therefore the world of standard computing), you can only have one of two results: heads or tails. In the quantum world, the result can be heads and tails.

What does all of this this mean in practice? In short, the answer is speed. Because qubits can exist in multiple states at the same time, they are capable of running multiple calculations simultaneously. For example, a 1 qubit computer can conduct 2 calculations at the same time, a 2 qubit computer can conduct 4, and a 3 qubit computer can conduct 8- increasing exponentially. Operating under these rules, quantum computers bypass the “one-at-a-time” sequence of calculation that a classical computer is bound by. In the process, they become the ultimate multi-taskers.

To give you a taste of what that kind speed might look like in real terms, we can look back to 2015, when Google and Nasa partnered up to test an early prototype of a quantum computer called D-Wave 2X. Taking on a complex optimisation problem, D-Wave was able to work at a rate roughly 100 million times faster than a single core classical computer and produced a solution in seconds. Given the same problem, a standard laptop would have taken 10,000 years.

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Given their potential for speed, it is easy to imagine a staggering range of possibilities and use cases for these machines. The current reality is slightly less glamorous. It is inaccurate to think of quantum computers as simply being “better” versions of classical computers. They won’t simply speed up any task run through them (although they may do that in some instances). They are, in fact, only suited to solving highly specific problems in certain contexts- but there’s still a lot to be excited about.

One possibility that has attracted a lot of fanfare lies in the field of medicine. Last year, IBM made headlines when they used their quantum computer to successfully simulate the molecular structure of beryllium hydride, the most complex molecule ever simulated on a quantum machine. This is a field of research which classical computers usually have extreme difficulty with, and even supercomputers struggle to cope with the vast range of atomic (and sometimes quantum) complexities presented by complex molecular structures. Quantum computers, on the other hand, are able to read and predict the behaviour of such molecules with ease, even at a minuscule level. This ability is significant not just in an academic context; it is precisely this process of simulating molecules that is currently used to produce new drugs and treatments for disease. Harnessing the power of quantum computing for this kind of research could lead to a revolution in the development of new medicines.

But while quantum computers might set in motion a new wave of scientific innovation, they may also give rise to significant challenges. One such potentially hazardous use case is the quantum computer’s ability to factorise extremely large numbers. While this might seem relatively harmless at first sight, it is already stirring up anxieties in banks and governments around the world. Modern day cryptography, which ensures the security of the majority of data worldwide, relies on complex mathematical problems- tied to factorisation- that classical computers have insufficient power to solve. Such problems, however, are no match for quantum computers, and the arrival of these machines could render modern methods of cryptography meaningless, leaving everything from our passwords and bank details to even state secrets extremely vulnerable, able to be hacked, stolen or misused in the blink of an eye.

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Despite the rapid progress that has been made over the last few years, an extensive list of obstacles still remain, with hardware right at the top. Quantum computers are extremely delicate machines, and a highly specialised environment is required to produce the quantum state that gives qubits their special properties. For example, they must be cooled to near absolute zero (roughly the temperature of outer space) and are extremely sensitive to any kind of interference from electricity or temperature. As a result, today’s machines are highly unstable, and often only maintain their quantum states for just a few milliseconds before collapsing back into normality- hardly practical for regular use.

Alongside these hardware challenges marches an additional problem: a software deficit. Like a classical computer, quantum computers need software to function. However, this software has proved extremely challenging to create. We currently have very few effective algorithms for quantum computers, and without the right algorithms, they are essentially useless- like having a Mac without a power button or keyboard. There are some strides being made in this area (QuSoft, for example) but we would need to see vast advances in this field before widespread adoption becomes plausible. In other words, don’t expect to start “quoogling” any time soon.

So despite all the hype that has recently surrounded quantum computers, the reality is that now (and for the foreseeable future) they are nothing more than expensive corporate toys: glossy, futuristic and fascinating, but with limited practical applications and a hefty price tag attached. Is the quantum revolution just around the corner? Probably not. Does that mean you should forget about them? Absolutely not.

Our Apprentices Answer

by Nadia Shafqat, Junior Software Engineer

In an ongoing series, we hear from both our current and past apprentices about their experiences. Apprenticeships are offered to anyone at any level, from those at the start of their career to those who wish to change the direction of their career and learn new skills. Here we hear from Nadia.

Tell me about your journey in Sopra Steria.

I joined the Sopra Steria’s Apprenticeship Programme in October 2016, to get a more hands on, practical experience in the IT sector. My journey started of small, contributing to internal data projects where I built up my coding skills in JAVA and XQuery, before progressing on to a ‘leading edge’ client project developing an application, which will help them to continuously monitor, validate and communicate safety, clinical and product performance data. Initially, I focused on acquiring the necessary skills across various elements of the programme, including becoming proficient in Agile Scrum methodology and practice, however with the support of the team, and the resources available in Sopra Steria, I feel I am now at a level where I am a contributing member of the team.

What skills have you learnt?

I have been exposed to a variety of technical skills and languages, such as Java, Angular, Marklogic, etc. However, I have also picked up general work skills, e.g. Agile, and used this to enhance my already-present skills, such as teamwork, etc.

What has been your greatest achievement?

My greatest achievement was being nominated for the FDM: Women in IT award.

Would you recommend an apprenticeship?

Definitely! An apprenticeship is a great way to experience the working industry and find what career you would like to pursue in the future, without any debt.

 

To learn more about our opportunities visit our apprenticeship page or if you have any further questions, email the team at early.careers@soprasteria.com