It is that time of year when Janus-faced we look over the year just passed and towards the year about to start to get a sense of how much progress we have made, and what progress may lie before us. It would be true to say that over the last 12 months Artificial Intelligence (AI) has become a norm in our lives and is now part of the vernacular. People have now accepted that their lives interact multiple times a day with AI, and that such technology is becoming ubiquitous within their lives.

And looking forward to 2019, what does it hold for us? Well, if we pay heed to the predictions of Nostradamus, we should brace ourselves for flooding, wars and a strike by a meteor. Nothing there to bring cheer but, assuming we survive all of that, what does 2019 hold for technology trends? Well, this is what we at KantanMT are predicting for the new year ahead:

Artificial Intelligence

We’ve seen an explosion in the use of Artificial Intelligence in the delivery of Neural Machine Translation during 2018; expect this to continue into 2019 and beyond. AI is the catch-all term to cover Machine Learning and Deep Learning. Machine Learning is an over-arching term for the training of computers, using algorithms, to parse data, learn from it and make informed decisions based on the accrued learning. Examples of machine learning in action is Netflix showing you what you might want to watch next. Or Amazon suggesting books you might want to buy.

Within the localisation industry, the use of AI in the form of Machine Translation (in several forms) has significantly improved translation quality outputs, speeded up translation of huge quantities of data and reduced the price of translation to make it economically viable.

AI refers to computer systems built to mimic human intelligence (i.e. imitating human neural abilities) and to perform tasks such as image recognition, parsing speech forms, discerning patterns from complex data sets, and informing accurate decision making. What’s more, AI can do these tasks faster, cheaper and more accurately than humans. Although AI has been around since the 1950s, it can be truly said that it has now come of age. This maturity has been propelled by the ever-increasing computational power now available in the Cloud.

According to Forbes, five out of six people use AI technology each day. These services include such things as navigation apps, streaming services (Amazon Alexa, Netflix etc), smartphone personal assistants, dating apps and even smart home devices (e.g. remote-activated home security systems). Additionally, AI is used in recommendation engines used by eCommerce sites (Amazon, Netflix etc), to schedule trains, to predict maintenance cycles and for other mission-critical business tasks.

For the localisation industry, AI will become a highly-integrated component of Machine Translation (MT) systems. The role of the human translator will continue evolving to that of an editor of MT texts, rather than translator of raw texts. In addition, pricing models will continue to move from the traditional price per word based on word volumes, to pricing on a time-measured rate.  MT will become an integral part of the standard workflow. The reality of real-time translation – driven by such technology as the Internet of Things (IOT) – will see project managers/editors managing workflows of projects required by customers who need a constant flow of updated information as needed by the IOT. MT will become part of the translation process just as much as CAT did in the past. And, as ever, evolving technology will bring with it a desire for speedier and cost-effective solutions.

Machine Learning

Machine Learning (ML) will continue to grow as a tool used by most localisation departments as the requirement for the speedy translations of large datasets continues to be a driver in the industry.

ML is a subset of Artificial Intelligence: with ML, computers are automated to learn to do something that they are not initially programmed to do. So, ML is an over-arching term for the training of computers to use smart algorithms to automate actions, to parse complex data and to learn patterns from this learning thus enabling the computer to make informed decisions based on this accrued knowledge. Machine Leaning can be broadly broken down into two types of learning – supervised and non-supervised learning.

For supervised machine learning, the training data is pre-labelled and consists of an aligned input data set and desired output data set. For example, an input data set could be a translation memory. An ML algorithm analyses the training data and maps how to convert future inputs to match the learned, desired output data sets.

Unsupervised machine learning is like supervised machine learning; however, the input data sets are not pre-classified or labelled. The goal of unsupervised machine learning is to find hidden structures in the unlabelled data.

So how does this impact the localisation industry? Well, suppose you want to build a translation system to translate from Zulu to French, without any Zulu-French training data? The answer is, you can combine both supervised and unsupervised approaches to achieve this. You can use an English-Zulu data set in combination with an English-French data set and using unsupervised machine learning, the system can learn how to translate from Zulu into French.

This approach is commonly referred to as ‘Zero-Shot’ machine learning – expect to hear more about this in 2019 for machine translation systems for long-tail languages.

Blockchain

I know what you’re thinking – why have we put Blockchain into this blog? Sure, isn’t that technology used only for Cryptocurrencies such as Bitcoin? Well you’re correct; while blockchain is most widely known as the technology behind Cryptocurrencies, it offers security that is useful in many other ways.

In simple terms, blockchain can be described as data you can add to, but not take away from or change. These ‘blocks’ of data can be ‘chained’ together to create incredible secure data repositories. Not being able to change any previous blocks is what makes it so secure.

This enhanced security is why blockchain is used for cryptocurrencies. It is also why it will play a significant role in localisation where it will used to protect information such as a client’s financial details, and to protect and preserve translation memories; especially in TMs used in distributed translation workflow scenarios.

Edge Computing

Cloud computing has now become mainstream: most of all global companies now rely on this centralised hosting structure for machine learning and powerful computational power. This Cloud market is dominated by just a few gigantic companies such as Amazon, Microsoft, Google, and IBM. However, now that we’ve been using Cloud Computing for some time, companies have realised that accessing all data from a central repository introduces a time-delay latency, which in turn slows down the delivery of services which can, in turn, increase costs. The “round trip” made by Cloud-based data is seen by many of today’s companies as a hindrance to their business growth.

Technology stands still for no man, and so, for many, the Cloud has reached its peak as a service for some technologies. The Cloud will continue to be used to analyse and process huge swathes of data, but the advent of the Internet of Things (e.g. connected security systems, electronic appliances, vending machines, automated lighting etc), where data processing needs to be high speed, if not real time, demands a different model. So, the logical, and necessary next move is to move this data processing to the Edge. The Edge simply means that data processing is moving from a centralised, far away location to a geographical site closer to the data source. The advent of powerful computer chips which allows such processing to be done locally has expedited this move to the Edge. Indeed, many of today’s Cloud setups automatically look to place the processing of data at the optimum Edge site for that data’s requirements.

So, Edge Computing solves the latency problem by simply moving the data processing closer to home. Closer to home means less time spent uploading and downloading data. Instead of the – centralised storage model, which has hitherto driven AI, companies are moving their data into the “local community” to be processed. This move will undoubtedly make data access much faster and facilitate the growing demand for real-time computing.

How will this impact on localisation: well, in 2019 we can expect to see the Edge model used in domain-adapted machine translation systems, and distributed translation workflows that are designed to meet the increasing demand for data distribution in real-time.

Summary

We are on the verge of an explosion in the use of AI. An explosion of the very thing that drives many of the vital cogs within the localisation business. This change will redefine many key roles and bring about the reconfiguration and automation of everyday workflow tasks. The inevitable growth of AI, and the implementation of such things as machine learning, will fundamentally re-shape how companies manage translation workflows; the very engine of their work process. Real-time translations will become the norm, where it is required.

We also predict that changes will happen at a human level; for example, the role of the translator will change from that of translator of raw text to that of editor of huge volumes of high-quality MT-produced text. We also believe this will be a beneficial change, allowing translators to increase their capacity and so increase their income. In 2019, we predict that the overall transformation effected by the advent of AI at all levels of the industry will bring with it an increased velocity of production, an improved efficiency in the delivery of translations, and a reduction in the cost of translating huge volumes of data.

We hope you all have a very successful 2019!

Tony O’Dowd CEO, KantanMT

This article first appeared in Multilingual in January 2019: https://multilingual.com/localization-tech-predictions-2019/

As the localization industry strives at a fast pace to integrate Machine Translation into mainstream workflows to increase productivity, reduce cost and gain a competitive advantage, it’s worthwhile taking time to consider which type of Neural MT provides the best results in terms of translation quality and cost.

This is a question that has been occupying our minds here at KantanMT and eBay over the past several months. The fact is, Neural MT comes in many variants – with the different models available yielding remarkably different quality results.

Overview of Neural Network Types

 The main models of Neural MT are:

• Recurrent Neural Networks (RNNs) – these have been designed to recognize sequential characteristics of data and use the detected patterns to predict the next most likely sequence. Training happens in a both forward and backward direction; hence, the use of the descriptor recurrent. RNNs have been the predominant neural network of choice by most MT providers.

Fig 1: Image Courtesy of Jeremy Jordon

• Convolutional Neural Networks (CNNs) – these are the main type of networks used in computer image processing (e.g., facial recognition and image searching) but can also be used for machine translation purposes. The model exploits the 2D-structure of input data. Training process is simplified and CNNs require less computational overhead to compute models.

Fig 1: Image Courtesy of Jeremy Jordon

• Transformer Neural Networks (TNNs) – The predominant approach to MT is based on the recurrent/convolutional neural networks model of connecting the encoder and decoder through an attention mechanism. However, the Transformer Neural Networks model uses only the attention mechanisms aspect (e.g., contextual characteristics of input data). This completely avoids using the recurrence and convolution structures of the other models. This has the effect of simplifying the training process and reducing the computational requirements for TNN modelling.

Fig 3: Image Courtesy of “The Illustrated Transformer” by Jay Alammar

The eBay NMT Experiment

To determine which model yields the best translation outcomes, eBay and KantanMT collaborated and set up a controlled experiment using the KantanMT platform, which supports all three types of Neural Models.

The language arc English => Italian was chosen, and the domain defined as eBay’s Customer Support content. Each Kantan model variant was trained on identical training data sets which consisted of:

• eBay’s in-domain translation memory
• eBay’s glossaries and lists of brand names
• Supplementary KantanLibrary training corpora

The Test Reference Set was created by the eBay MT Linguistic Team by sampling the eBay Translation Memory to mirror its segment length distribution (e.g., 10% short segments, 30% medium and 60% long).

To provide a comprehensive comparison and ranking of the performance of different models, the translation outputs from the following systems were included in our joint experiment:

• Kantan TNN (Transformer Neural Network, customized)
• Kantan CNN (Convolutional Neural Network, customized)
• Kantan RNN (Recurrent Neural Network, customized)
• Bing Translate (Transformer Neural Network, generic)
• Google Translate (Transformer Neural Network, generic)

Human Translation (HT) was also included in this comparison and ranking to determine how neural machine translation outputs compare to translations provided by Professional Translators.

The evaluator was an eBay Italian MT language specialist with domain expertise and experience in ranking and assessing the quality of machine translation outputs.

The following Key Performance Indicators (KPIs) were chosen to determine the comparative fluency and adequacy of each system:

• Fluency = Fluency determines the translation follows common grammatical rules and contains expected word collocation. This KPI measures whether the machine translation segment is formed in the same way a human translation would
• Adequacy = Adequacy measures how much meaning is expressed in the machine translation segment. It measures whether the machine translation segment contains as much of the meaning as if it were translated by a human

Each KPI was rated on a 5-star scale, with 1 star being the lowest rating (i.e., No Fluency) and 5 stars being the highest rating (i.e., Human-Level Fluency).

KantanLQR was used to manage the assessment, randomise and anonymise the Test Reference Set, score the translation outputs, and collate the feedback from the eBay MT linguist.

The Results

Our Conclusions

The Custom Kantan Transformer Neural Network (Kantan TNN) performed the best in terms of Fluency and Adequacy. It outperformed RNNs in terms of Fluency by 9 percentage points (which is statistically significant), and 11 percentage points in terms of Adequacy. While there is still some way to go to achieve near-human-level quality (as depicted by the HT graphs), Transformer Neural Networks provide significant improvements in MT quality in terms of Fluency and Adequacy, and they offer the best-bang-for-your-buck in terms of training time and process simplification.

Since this blog was first published, comparative analysis was also carried out for English=>German, English=>Spanish and English=>French language combinations and in all cases Kantan TNNs out-performed CNNs, RNNs, Google and Bing Translate.

As the localization industry strives at a fast pace to integrate Machine Translation into mainstream workflows to increase productivity, reduce cost and gain a competitive advantage, it’s worthwhile taking time to consider which type of Neural MT provides the best results in terms of translation quality and cost.

This is a question that has been occupying our minds here at KantanMT and eBay over the past several months. The fact is, Neural MT comes in many variants – with the different models available yielding remarkably different quality results.

Overview of Neural Network Types

 The main models of Neural MT are:

• Recurrent Neural Networks (RNNs) – these have been designed to recognize sequential characteristics of data and use the detected patterns to predict the next most likely sequence. Training happens in a both forward and backward direction; hence, the use of the descriptor recurrent. RNNs have been the predominant neural network of choice by most MT providers.

Fig 1: Image Courtesy of Jeremy Jordon

• Convolutional Neural Networks (CNNs) – these are the main type of networks used in computer image processing (e.g., facial recognition and image searching) but can also be used for machine translation purposes. The model exploits the 2D-structure of input data. Training process is simplified and CNNs require less computational overhead to compute models.

Fig 1: Image Courtesy of Jeremy Jordon

• Transformer Neural Networks (TNNs) – The predominant approach to MT is based on the recurrent/convolutional neural networks model of connecting the encoder and decoder through an attention mechanism. However, the Transformer Neural Networks model uses only the attention mechanisms aspect (e.g., contextual characteristics of input data). This completely avoids using the recurrence and convolution structures of the other models. This has the effect of simplifying the training process and reducing the computational requirements for TNN modelling.

Fig 3: Image Courtesy of “The Illustrated Transformer” by Jay Alammar

The eBay NMT Experiment

To determine which model yields the best translation outcomes, eBay and KantanMT collaborated and set up a controlled experiment using the KantanMT platform, which supports all three types of Neural Models.

The language arc English => Italian was chosen, and the domain defined as eBay’s Customer Support content. Each Kantan model variant was trained on identical training data sets which consisted of:

• eBay’s in-domain translation memory
• eBay’s glossaries and lists of brand names
• Supplementary KantanLibrary training corpora

The Test Reference Set was created by the eBay MT Linguistic Team by sampling the eBay Translation Memory to mirror its segment length distribution (e.g., 10% short segments, 30% medium and 60% long).

To provide a comprehensive comparison and ranking of the performance of different models, the translation outputs from the following systems were included in our joint experiment:

• Kantan TNN (Transformer Neural Network, customized)
• Kantan CNN (Convolutional Neural Network, customized)
• Kantan RNN (Recurrent Neural Network, customized)
• Bing Translate (Transformer Neural Network, generic)
• Google Translate (Transformer Neural Network, generic)

Human Translation (HT) was also included in this comparison and ranking to determine how neural machine translation outputs compare to translations provided by Professional Translators.

The evaluator was an eBay Italian MT language specialist with domain expertise and experience in ranking and assessing the quality of machine translation outputs.

The following Key Performance Indicators (KPIs) were chosen to determine the comparative fluency and adequacy of each system:

• Fluency = Fluency determines the translation follows common grammatical rules and contains expected word collocation. This KPI measures whether the machine translation segment is formed in the same way a human translation would
• Adequacy = Adequacy measures how much meaning is expressed in the machine translation segment. It measures whether the machine translation segment contains as much of the meaning as if it were translated by a human

Each KPI was rated on a 5-star scale, with 1 star being the lowest rating (i.e., No Fluency) and 5 stars being the highest rating (i.e., Human-Level Fluency).

KantanLQR was used to manage the assessment, randomise and anonymise the Test Reference Set, score the translation outputs, and collate the feedback from the eBay MT linguist.

The Results

Our Conclusions

The Custom Kantan Transformer Neural Network (Kantan TNN) performed the best in terms of Fluency and Adequacy. It outperformed RNNs in terms of Fluency by 9 percentage points (which is statistically significant), and 11 percentage points in terms of Adequacy. While there is still some way to go to achieve near-human-level quality (as depicted by the HT graphs), Transformer Neural Networks provide significant improvements in MT quality in terms of Fluency and Adequacy, and they offer the best-bang-for-your-buck in terms of training time and process simplification.

Since this blog was first published, comparative analysis has also been carried out for English=>German, English=>Spanish and English=>French language combinations and in all cases Kantan TNNs out-performed CNNs, RNNs, Google and Bing Translate.

To be called a veteran is a dubious honour, and as sure a sign of aging as greying hair. As I sit here looking back on my almost three decades in the translation/localisation industry there is little doubt that I am one of its veterans, something of which I am proud.

We are only days away from the United Nation’s International Translation Day on September 30^th. The UN describes this international day of recognition as:

“International Translation Day is meant as an opportunity to pay tribute to the work of language professionals, which plays an important role in bringing nations together, facilitating dialogue, understanding and cooperation, contributing to development and strengthening world peace and security.”

The date of 30 September was chosen because it is the feast of St. Jerome, the Bible translator who is considered the patron saint of translators. St. Jerome was a priest from North-eastern Italy, who is known mostly for his endeavour of translating most of the Bible into Latin from the Greek manuscripts of the New Testament. He also translated parts of the Hebrew Gospel into Greek.

The UN encourages all member states, international and local organisations and individuals to celebrate the occasion to raise the awareness of the importance professional translation has in this modern world. A world that has been shrunken by the advent of technology that now puts instant, worldwide access in the hands of many of the world’s citizens.

Given this call to celebrate and commemorate, I thought it a good moment to write a blog reflecting on the journey this industry has taken since I first joined a small company called Softrans (subsumed later by the newbie, Berlitz), all those years ago.

On reflection, the average translation office of yore was truly a low-tech, primitive environment compared to today’s office full of super technology and computational power. To give you an idea, here’s a list of technologies we didn’t have, way back then:

• CAT Tools
• Machine Translation (of any calibre)
• Email (although it came soon after)
• The Internet
• The Cloud
• Memory Sticks
• Windows (although it arrived soon after)
• Software development tools such as Catalyst
• HELP authoring tools
• Automated software testing tools
• Workflow systems

The modern localisation professional must look at this list and scratch their heads in wonderment as to how we managed to produce anything. Well, here’s a list of tools we did have at our disposal:

• Fax machines
• 5 ¼ inch 1.2 mb floppy disks (you read that correctly)
• DOS (do you remember C:>?)
• 24-hour delivery services (replaced by the internet!)
• Internal translation teams
• Internal desktop publishing teams
• Internal engineers (with few tools)
• Manual software and help testing
• Manual Software building
• A choice of maybe three DTP packages

And as if that was not bad enough, the machines at our disposal in those days were slower than a wet Sunday. In Softrans, the average translator had use of an IBM 286. A machine that had a whole 10 MB hard drive (that’s not a typo), 128K of RAM (ditto) and a four colour CGA screen. The RAM could be boosted to a massive 640K. Oh, and to move it you needed someone to lift all 80 lbs of it. The operation system was DOS. When you turned it on, instead of a shiny looking Windows GUI you saw C:>. And off you went typing your commands to get started.

But technology does not stand still, and soon we were able to upgrade some of our translators to the IBM 386 model. Yoo-hoo – now we had screaming fast computers. It had a 256K RAM and a huge 40 MB of hard disk space. They also came with the first version of Windows, a revolutionary development in GUI.

They were much fought over by the translators. And I say translators, as the early localisation office had basically two main production departments; the translation department and the desk top publishers. There was also a small project management team and a small engineering group. Project management was still a discipline in its early development, without any of the tools on today’s project manager’s desk.

The engineers were more IT support people than the software engineers that are so needed today. As the industry developed, customers saw that shipping translated documentation and non-localised software and Help did not make much sense. So, they started asking for the Help and software to be translated. Consequently, the engineering team began to grow and evolve in to the engineering discipline that we have today. The engineers then had few of the tools we have today. Software engineering tools such as Catalyst did not exist in any commercial sense. Some of the bigger software development companies had their own internal tools, but it was the mid-1990s before a commercially developed tool – Catalyst – was made available to the engineers in companies like Softrans.

Translation too was done in a rudimentary way. The translator worked in MSWord, translating directly from the English hardcopy source document. They relied on dictionaries and self-created glossaries to keep them right. They would create the glossaries by laboriously identifying key terms in the English and then, in a table, translate the term in to their local language. Each translation team had an editor whose job was to ensure continuity of terminology across the project. The editor also had the task of keeping glossaries updated when the reviewed copy came back from the customer. Very few customers had their own glossaries. Indeed, most customers did not have a translation/localisation department. As a consequence, their translation would be managed by groups as diverse as marketing, internal training, or simply an individual tasked to run the project.

Because the only means of external communication was by fax, post or overnight courier it meant that translation companies had to keep a core team of translators in-house. Softrans, for example, had French, Italian, German and Spanish (FIGS) translators. It was like a mini UN. So too with DTP. Today, in the era of the home office a translator or DTP person can be in Timbuctoo but so long as they have access to the internet they can be usefully productive. Not then, everything had to be housed under one roof, which caused high overheads for companies as they needed to supply desk space, heating, lighting etc.

Today, jobs are delivered by the pressing of a key and off zips the project. Or, the work is done on the customer’s network without the files every having to moved from their control and monitoring. No project is too big as the Cloud offers an endless amount of storage space. In the Softrans’ day a project was delivered on a mass of 5.25-inch disks. Each had a capacity of 1.2 MB. So, a large project took a very large number of disks. These were usually shipped by overnight courier. On occasions, the disks got corrupted in transit meaning a new set would have to be sent, so causing a delay of days on something that was badly needed by companies with product ready to ship to meet a particularly tight marketing window.

The first time I realised that the industry was developing a tool to help speed up translation was when Softrans was chosen to trial a new product called a Computer Aided Translation tool. That tool was called Trados. It was about 1994 when I first came across it. I remember translators falling in to two categories; those who feared that their jobs would become redundant, and those who were cynical about the possibility of technology making their job easier. But as it was introduced over time, both groups began to see the power of Trados. It very soon became an essential part of the translation process. No translator today would even consider translating a large project without using a CAT tool. Even more so, no customer would accept you translating from scratch every time and charging them for new words in every project!

And here I am still loitering with intent inside the localisation industry. This time in the marketing department (previously, I managed translators, DTP groups and engineers) of one of the most innovative technology groups in the industry KantanMT.com. Kantan is at the cutting edge of machine translation technology. Kantan started with Statistical Machine Translation, before riding the wave that is Neural Machine Translation. MT is a technology that has an amazing exponential growth. The advent of the Cloud, super-computers and huge volumes of translated data has fed the industry like petrol to a fire. And, as with all ‘new’ technologies – for example CAT – the cynics are out there saying it cannot be done. The translators are decrying MT as a sorcery that is going to disappear their industry. But I am too long in this industry not to realise that MT is a technology that the translation/localisation needs to lift in to the next level of development. The industry profile is not longer one of small translation companies like Softrans with its $1 million turnover, but companies that are now valued in the billions.

The business world has contracted, and every major company sees its market as global. They need to overcome the challenges that language brings, and they want to do this in the speediest, most cost-effective way. Machine translation allows this to happen. The market will not allow MT to disappear. You only need look at the companies now committed to MT – Amazon, Google, eBay, Facebook; to name but a few, to see that MT is fast becoming the norm. The translation paradigm is shifting fast. But translators will not disappear; their jobs will just take on a different dimension. They will become more editors than translators. MT will become an integral part of the standard workflow. It will become part of the process just as much as CAT did in the past. We are seeing exciting new developments in translation/localisation. It is difficult to predict what is coming around the corner for the industry; but unlike the IBM 286, IBM 386 and floppy disks, it is not going to go away!

Enjoy International Translation Day!

Aidan Collins is a language industry veteran. He works in the marketing department at KantanMT.

The author of this book is Founder and Executive Chairman of the World Economic Forum. That is the forum where the movers and shakers of the world meet at the Swiss alpine location of Davos every year to discuss geo-global trends across a range of political, social and economic areas. In this slim book Schwab seeks to predict how impending technological changes will impact on our life; socio, political and economic.

The author argues that the fusing of the political, physical, digital and biological worlds will have a transformative impact on all facets of human existence. This will range from the way we live our lives, the manner in which we will work, the reconfiguration of economic models, the products we sell and even, how long we will choose to live.

The author outlines the drivers of this revolution and cautions the business readers to “get on board” as we are “already reaching an inflection point in [technological] development as they build on and amplify each other in a fusion of technologies”. In an appendix he conveniently lists what are termed 23 “Deep Shift” technologies; those must likely to impact the way we live. This list is headed up by implantable technologies, courtesy of nanotechnological developments; the widespread use of digital currencies such as Bitcoin, driven by Blockchain developments; and the surreal prediction that neurotechnology will allow for humans to have artificial memories implanted in their brains.

Of course, all of the above can not happen without society acquiescing (or one would hope so). The author does discuss how the revolution will throw up challenges on all fronts as to the ethics, morality and legality of many of the putative changes. He warns that society will be in a state of rapid change as the fusion of technologies will create an exponential growth that will make this revolution a much shorter and deeper period of impact that the societal revolutions than mankind has ever before experienced.

Throughout the book Schwab posits a benign view on the power of the Fourth Industrial Revolution. He argues that its power can be used for good if harnessed by careful, democratic control by “good leaders” and “decision makers”. Of course the obverse is also a possibility, although the author does not discuss that likelihood.

The challenge to societies’ leaders will be to learn how to harness for good the changes, but controlling and curtailing them when they venture in to possible unethical or illegal terrain  (as experienced  already with the whole hoovering up of people’s data for selling on to others).

Schwab sets this revolution in a historical context referring to the previous societal upheavals such as the industrial revolution. Each revolution did a lot to transform society, and not always for the better

The first revolution was 1760-1840 and was triggered by the construction of railroads and development of steam power. It heralded the beginning of the mechanical age. The second started in the late 19th century and was the beginning of mass production, factory workplaces, the production line and mass employment in often poor conditions. The third, and most recent, was caused by the Digital Age. The development of semiconductors, mainframe computing and the emergence of personal computing harnessing the internet.

The author concedes that there are those who argue that what he heralds as a fourth revolution is no more than the outworking of a more advanced part of the third industrial revolution. Schwab holds his ground and says the fourth industrial revolution began in 2000 when technologies began to converge, Artificial Intelligence became a reality and robotics made huge advancements. He also argues that the ubiquity of small, integrated technology available to all at low cost, plus the conquering of the language barrier through the use of machine translation, has made the global market available to all who choose to exploit it.

Disappointingly, for a book that deals with such a diverse range of concepts and technologies, it lacks an index. This was probably the result of the book having been the product of the compiling of a series of papers originally written for other forums. The book is heavy on jargon and management speak. It is, nonetheless,

a slim volume that is fairly accesible to the average reader. Schwab leaves you in no doubt that the Fourth Industrial Revolution is capable of creating a dystopian world of cloud power, AI, implanted brains and robots. The question – which Schwab hints at rather than elucidates on, is whether we as humans should meekly adapt to all technology, or whether as a society we say: “hold on; thus far and no further, thank you.”

Aidan Collins is a language industry veteran. He works in the marketing department at KantanMT.

Artificial Intelligence, Machine Learning, and Deep Learning are just some of the technologies that today are driving the expansion of what Klaus Schwab has dubbed in a recent book as the “Fourth Industrial Revolution” (see previous blog for a review of his book). This lexicon of terms populates the scientific papers people read in an effort to educate themselves about this galaxy of sophisticated concepts. The technology is sophisticated and understanding it can be daunting, but it is possible to become enlightened without burning up too much grey matter.

Firstly, let me explain a few key terms to help you differentiate between Artificial Intelligence, Machine Learning and Deep Learning. This graphic shows that they are inextricably linked, and are often referred to as cousins of each other:

What I would like to try to do in this blog is explain some of these terms, and in particular to try to demystify Machine Learning. In a few words, Machine Learning is the capability of a machine to learn from data patterns to estimate a predictive model of behaviour or wants. In other blogs I will address some of the other concepts and hopefully demystify those.

Let me begin with a few words of solace and encouragement; stripped down to the basics, the fundamentals of this technology can be understood by the average layperson. That’s not to say you can become an overnight expert in any of these fields, but you will be able to understand at least their design and purpose. And as I say in the heading on this piece, Machine Learning is just like pouring a pint of Guinness. Stick with me while I explain what I mean. Hopefully it will make sense.

In the 1980s, while at college, I worked as a barman. I was a pretty decent barman too. I liked the job and I enjoyed keeping my customers happy. Little did I realise that during this time in my efforts to keep the “punters” happy I was applying the basics of Machine Learning. As a term it was coined in 1959 by Arthur Samuel at IBM. Trust me, I’d never heard of it in the 1980s, but I was applying some of the concepts to my work with me, in this case, being the machine.

Every Sunday, without fail, one of my customers would come in after church for a few drinks before going home for Sunday lunch. He arrived same time. Took the same seat. Ordered the same drink. It never wavered. One Sunday I saw him through the window heading towards the bar. I anticipated his order and pulled a pint of Guinness. He had no sooner sat on the same stool as always when I slid the fresh creamy pint in front of him. He looked and smiled a big appreciative grin.

It became the norm from then on that every Sunday I would look out the window and on seeing him approach, put his pint on. That went on for years until I left the job. Throughout that time he was very happy customer. No queueing for him. A pint ready-made and set up. It was for him the acme of customer service. As a customer experience, it could not be bettered (other than getting it for free!).

But wait says you, what to heck has this got to do with Machine Learning? Good question: well, it has everything to do with it, I would argue. In doing what I did every Sunday for that customer I was applying the methodology of Machine Learning. To explain further: I was identifying someone; I was discerning their pattern of behaviour and calculating the probabilities of their taking a certain action; I was learning from that process, and based on all of this data I was processing and predicting what I should do to keep that customer happy. In short, I was building a little algorithm in my brain which kicked in to action every time I saw that gentleman approach the bar on a Sunday.

This fact was underlined to me when one Tuesday afternoon the same punter came in to the bar. My reaction was to stand there waiting for him to order his drink. A Tuesday visit was an anomaly. I had no experience of him visiting on a Tuesday. I could have guessed he wanted a pint of Guinness and set it up. But it would have been just that, a guess based on zero data with no pattern of behaviour to guide me. As it turned out, he ordered a coffee as he was taking a break from work. Of course, had he come in every Tuesday over a reasonable period I could have worked out a pattern of behaviour with certainty, and thus made sure to have his hot coffee waiting.

Just as in Machine Learning, I had a well-formed algorithm that allowed me to act with certainty on a Sunday, but the same algorithm could not be applied on a Tuesday. That is the core of Machine Learning. Machines can be trained to gather and assess data, allowing them to recognise patterns and predict peoples’ behaviours or wants. How do you think Netflix knows what films and box sets you like to watch? Or travel companies know which advertisements for sunny climes they should present to you in the middle of winter? Machines develop algorithms that can be fined-tuned to guess what you want to watch, where you want to go, what you are likely to buy, or what your tastes in music might be.

The foundation of all of this “magic” is the accumulation of personal data that can be sliced and diced, analysed and parsed and from it the machine can be taught (learns) how to keep you happy (Netflix), or how to get you to part with your money (the unplanned winter holiday). Of course, Machine Learning is everywhere now. It is even driving cars, and is a growing part of our world. Why has something that has its origins in the 1950s, and was available and used in a limited way during the 1980s and 1990s become so prevalent and growing?

Why? Big data. Within the last 10 years it has become possible for companies to harvest huge swathes of data, from multiple and varied online sources. The additional ascent of powerful computers with extraordinary computational power has allowed corporations to analyse and exploit this treasure trove of information. Whole industries have been built around the ability to gather, analyse and exploit big data. As a means for building algorithms, and propelling company sales, it is a powerful tool. It is unlikely to go away. One thing that can be said for certain, increasingly ways will be found to expand the use of Machine Learning over the coming years. As a technology, like a pint of Guinness, it is here to stay.

Aidan Collins is a language industry veteran. He works in the marketing department at KantanMT.