Breaking Code: How Programmers and AI are Shaping the Internet of Tomorrow

Eoin Pierce our consultant managing the role
Posting date: 9/13/2018 9:10 AM
Data. It’s what we do. But, before the data is read and analysed, before the engineers lay the foundation of infrastructure, it is the programmers who create the code – the building blocks upon which our tomorrow is built. And once a year, we celebrate the wizards behind the curtain. 

In a nod to 8-bit systems, on the 256th day of the year, we celebrate Programmers’ Day. Innovators from around the world gather to share knowledge with leading experts from a variety of disciplines, such as privacy and trust, artificial intelligence, and discovery and identification. Together they will discuss the internet of tomorrow. 

The Next Generation of Internet


At the Next Generation Internet (NGI), users are empowered to make choices in the control and use of their data. Each field from artificial intelligent agents to distributed ledger technologies support highly secure, transparent, and resilient internet infrastructures.

A variety of businesses are able to decide how best to evaluate their data through the use of social models, high accessibility, and language transparency. Seamless interaction of an individual’s environment regardless of age or physical condition will drive the next generation of the internet. But, like all things which progress, practically at the speed of light, there is an element of ‘buyer beware’, or in this case, from ‘coder to user beware’.

Caveat Emptor or rather, Caveat Coder


The understanding, creation, and use of algorithms has revolutionised technology in ways we couldn’t possibly have imagined a few decades ago. Digital and Quantitative Analysts aim to, with enough data, be able to predict some action or outcome. However, as algorithms learn, there can be severe consequences of unpredictable code

We create technology to improve our quality of life and to make our tasks more efficient. Through our efforts, we’ve made great strides in medicine, transportation, the sciences, and communication. But, what happens when the algorithms on which the technology is run surpasses the human at the helm? What happens when it builds upon itself faster than we can teach it? Or predict the infinite variable outcomes? Predictive analytics can become useless, or worse dangerous. 

Balance is Key


Electro-mechanical systems we could test and verify before implementation are a thing of the past, and the role of Machine Learning takes front and centre. Unfortunately, without the ability to test algorithms exhaustively, we must walk a tightrope of test and hope.

Faith in systems is a fine balance of Machine Learning and the idea that it is possible to update or rewrite a host of programs, essentially ‘teaching’ the machine how to correct itself. But, who is ultimately responsible? These, and other questions, may balance out in the long run, but until then, basic laws regarding intention or negligence will need to be rethought.

Searching for a solution 


In every evolution there are growing pains. But, there are also solutions. In the world of tech, it’s important to put the health of society first and profit second, a fine balancing act in itself.

Though solutions remain elusive, there are precautions technology companies can employ. One such precaution is to make tech companies responsible for the actions of their products, whether it is lines of rogue code or keeping a close eye on avoiding the tangled mass of ‘spaghetti’ code which can endanger us or our environment.

Want to weigh in on the debate and learn how you can help shape the internet of tomorrow? If you’re interested in Big Data and Analytics, we may have a role for you.

Check out our current vacancies. To learn more, contact our UK team at +44 20 8408 6070 or email us at info@harnham.com.

Related blog & news

With over 10 years experience working solely in the Data & Analytics sector our consultants are able to offer detailed insights into the industry.

Visit our Blogs & News portal or check out the related posts below.

HOW BRANDS USE DATA TO CREATE SUCCESSFUL CAMPAIGNS

Make no mistake: making minor adjustments to an ad or campaign that’s meant to appeal to the masses just won’t cut it. Customers crave creativity. They want to be understood. Which is why people respond best to brands that do their homework, doing their research into what appeals to different groups. How should businesses appeal to their chosen segments, then, considering how diverse people are? Data, of course. Why Data? For one thing, it drives results and creates improved outcomes. Data also helps to prove the value of marketing, providing a bargaining chip for future budget discussions. And, most rewarding of all, brands get valuable insights into their target market. Which, in turn, leads to more well-targeted, profitable campaigns.  And if you think Data doesn’t belong in the world of creative campaigns, think again. As OpenJaw Technologies Chief Marketing Officer Colin Lewis argues: “Creativity is not just compatible with being Data-driven – Data can drive better creative.” Psychological profiling Strategic communications consultancy, Verbalisation, researches and analyses language to form valuable insights. Using its Rapid Audience Insights Diagnostic system, the company’s team of psychologists and researchers work out how an audience thinks. They also learn the actual words an audience uses, which they then use as the basis of a marketing strategy.  Based on their unique research and insights, Verbalisation has created several successful campaigns for high-profile brands. These include the #NotAnotherBrother campaign for counter-terrorism organisation Quilliam, which looked at the motivations of jihadists.  The campaign is now used by the UN and schools across the UK, as well as the US Department of Defense. It is the most viewed counter-extremism campaign of all time, with more than half a billion global media impressions.  Location, location, location Out-of-home (OOH) advertising. Yes, it goes way back, but it’s actually the only traditional advertising channel posting rapid growth. In fact, thanks to mobile-location Data, brands can target audiences quicker and with a greater chance of success than ever before.  Great news for JCDecaux (JCD), a leading OOH company with ads reaching 410 million people in over 4,000 cities. JCD now works with location Data to define and segment audiences. Doing so helps it decide where to place media, improve campaigns and measure resulting store footfall and purchases.  Knowledge, so they say, is power. Particularly when that involves knowing the whereabouts of the most coveted customers. Newly teamed up with identity resolution company, Neustar, JCD’s insights look stronger than ever. JCD can now understand which of its locations rank higher for any brand’s most desired audiences. All thanks to location Data and real-time behaviour analysis.  Personalised employee training Data doesn’t just boost the results of B2C brands; it can also be a vital shot in the arm for internal security training campaigns. Training provider, CybeReady, for instance, uses a Data science-driven approach to deliver cyber awareness training with a difference: its anti-phishing platform helps security teams quickly roll out and tailor campaigns to individual employees. In big companies, getting employees up to speed is especially challenging. With many locations, languages and time zones to contend with, Information Security teams have their work cut out.  CybeReady eliminates these challenges by delivering 12 personalised, 60-second simulations to each employee. In their first language, every year. What’s more, the training provider uses machine learning to analyse performance on a daily basis. This enables it to provide the most appropriate simulations to each individual. The result? IT teams save 160 hours each month and employee resilience increases five-fold. There’s no limit to what Data can do. If you’re a fan, we may have a role for you. Take a look at our latest opportunities or get in touch with our expert consultants.  

The Evolution Of The Data Engineer

Every Data Science department worth its salt has at least one engineer on the team. Considered the “master builders,” Data Engineers design, implement and manage Data infrastructure. They lay down digital foundations and monitor performance. At least, that’s what they used to do.  Over the last few years, the role has shifted. Data Engineers have gone from mainly designing and building infrastructure, to a much more supportive and collaborative function.  Today, a key part of the engineer role is to help their Data Analyst and Data Scientist colleagues process and analyse data. In doing so, they are contributing to improved team productivity and, ultimately, the company’s bottom line. THE IMPACT OF THE CLOUD In the past, a Data Engineer would often move data to and from databases. They’d load it in a Data Warehouse, and create Data structures. Engineers would also be on hand to optimise Data while businesses upgraded or installed new servers.  And then along came the Cloud.  The rapid dominance of cloud computing meant that optimisation was no longer needed. And as the cloud made it easy for companies to scale up and down, there was less need for someone to manage the data infrastructure.   The collective adoption of the cloud has had a big impact on the function of Data Engineers. Because, provided a company has the funds, there is no longer the same demand for physical storage. Freed from endless scaling requests, engineers have more time to program and develop. They also spend more time curating data for better analytics.  AUTOMATING THE BORING BITS  Less than a decade ago, if start-ups wanted to run a sophisticated analytics program, they’d automatically hire a couple of Data Engineers. Without them, Data Analysts and Data Scientists wouldn’t have any Data. The engineers would extract it from operational systems, before giving analysts and business users access. They might also do some work to make the Data simpler to interpret.  In 2019, none of this extraction and transformation work is necessary. Companies can now buy off-the-shelf technology that does exactly what a Data Engineer used to do. As Tristan Handy, Founder and President of Fishtown Analytics, puts it: “Software is increasingly automating the boring parts of Data Engineering.”  STILL SOUGHT-AFTER  With automation hot on the Data Engineer’s tail, it can be tempting to ask whether they are still needed at all.  The answer is: yes, absolutely. When recruiting engineers, Data Strategist Michael Kaminsky says he looks for people “who are excited to partner with analysts and Data Scientists.” He wants a Data Engineer who knows when to pipe up with, “What you’re doing seems really inefficient, and I want to build something better.” Despite the rise in off-the-shelf solutions, engineers still play a key role in the Data Science team. The difference is simply that their priorities and tasks have shifted.  Today, innovation is the watchword. The best engineers are hugely collaborative, helping their teams go further, faster. It’s an exciting time to be a Data Engineer. If you’re interested in this field, we may have a job for you. Take a look at our latest opportunities or get in touch with our expert consultants.  

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