The rise of the machines: not so doom and gloom

As technology perforates every aspect of our professional and personal lives, it is clear that a robotic revolution is upon society. While robots have been used in industries such as manufacturing and automotive for years, today’s systems are ever-improving and, in some cases, are now exceeding human limitations.

For instance, Google’s artificial intelligence (AI), DeepMind AlphaGo, is beating the world’s number one player in tournaments of Go (an incredibly complex Chinese grid game). While this represents the higher end of robots’ current capabilities, there is a growing, but unfounded, fear that with the rate of development, it’s only a matter of time before all jobs are completed by machines.

Losing jobs to technology is not simply a modern day worry. For example, the early days of the industrial revolution saw everyone believing that their jobs were at risk. In the 1930s, economist John Maynard Keynes coined the term ‘technological unemployment’, believing that the rise of technology would lead to a permanent decline in the number of jobs. In both cases, the impact was not quite as dramatic. Technology helped employees to do their jobs better, it didn’t necessarily replace them. It even created new positions.

It’s a similar story today. An accountant, for instance, is likely to use tools that automate certain aspects of their role. Data collection and report creation are both arduous time-consuming activities to complete manually, and the ever-growing deluge of information only increases the chances that something vital will be omitted. Automating the processes provides accountants with the data they need to analyse, enabling them to increase accuracy and productivity.

Many will argue that automated production lines are a testament to the fact that machines can do some jobs better, and businesses do indeed invest heavily in order to have work floors almost free of humans.

>See also: VR and machine predictions for 2017

But the belief that robots will take all jobs is wrong. Semi-skilled positions will bear the biggest brunt, while low- and high-skilled jobs (such as caretakers and data scientists respectively) will be impacted less, due to cost or the complexity of roles. In fact, the rise of machines will also help stimulate employment figures by creating new roles.

Despite having all the ‘bells and whistles’, robots can’t design and service themselves. Humans can try and create another robot to do the job, but then what happens if that one breaks down too?

The truth is, the cost of developing a machine to do such a role will always outweigh paying a few humans a salary; meaning the number of positions will increase in proportion to the number of robots. Furthermore, as machines become even more intelligent there are simply more things that can go wrong, exacerbating the need for skilled workers.

Some humans will be working for or following orders from robots. In warehouses and other logistics operations, for example, machines already tell humans what to do, including which items to select off shelves and where to process them. Robots can more quickly analyse orders and delegate the relevant responsibilities to ensure that they are fulfilled in almost real-time.

While it may seem incredibly ‘overlord’, it’s worth noting that people all already work for machines in some capacity. Every time someone uses Facebook or Google, they are providing AI systems with data and they pay people back in services. People also take their driving directions from apps on their smartphones. So, just as an employee generates value for an employer, humans are all worth something to those machines.

There will also be humans who own the machines, a role that is not just going to be filled by the billionaires developing machines today. Today’s independent lorry-driver who can operate just one lorry today will, in just a few years, be able to invest in and operate several autonomous lorries.

Ultimately, while reports will continue to conclude that robots are coming for people’s jobs, it’s often forgotten just how intensively competitive humans are. Elon Musk argues that humans will have to become cyborgs to beat machines, but people compete with other people, they don’t compete with machines. Humans will always find a way to win, even if they adopt technology from machines in order to compete with each other. Even if some jobs are replaced by robots, those affected will simply re-skill themselves, and perhaps upgrade themselves, to find another profession.

Source: Information Age-The rise of the machines: not so doom and gloom


Automation doesn’t have to be a dirty word…

Without a doubt, the impact of automation on the IT Services industry is a topic of much debate and contention. The challenge is that speculation drives much of the discussion, rather than quality data and analysis.


While the subject of automation has been discussed a few times on the blog, I feel compelled to add my experiences and those of the IT professionals I met on my travels to the discussion.

Not long before joining HfS, I spent several months presenting research on automation at events and conferences across the UK. While the research covered a broad range of topics, automation in IT services was by far the most popular. After a few presentations discussing the increased adoption of automation and the growing capability of the tooling, it became apparent where the popularity of the topic originated – fear. After each session, a small gathering of IT professionals would question me on job security, headcount decreases and how automation augered a bleak future for the industry.

It’s not difficult to see why the audience felt this way. The mainstream media and even some analyst firms have been stoking the climate of fear with considerable vigor.

So I went back to the drawing board and changed my presentation. I took a fresh look at the data to examine what was happening in the industry – did we genuinely need to worry? Beginning with an impactful quote most media outlets were running with – something along the lines of “be terrified, the robots are coming” – I started to dismantle these theories with my research data on employment trends, headcount increases, and industry perception.

While many argued that automation would lead to job cuts, my data showed the opposite. Organizations recognized the importance of technology to their businesses and were investing in the services needed to support it. The data revealed that in organizations with higher levels of automation, workers were not disappearing, they were moving to higher value areas of the support structure – taking on strategic projects or developing services.

At the end of the presentation, I concluded that the reality of automation’s impact on modern IT services was far from the bleak picture painted by other analysts and consultants.

Nevertheless, a few minutes after the session ended the same horror stories started to emerge: IT leaders facing a backlash from staff as automation projects ramp up and professionals working themselves into a frenzy over their job security if projects continued. It was frightening stuff.

Crucially, my research revealed that the cause of this panic doesn’t come directly from the automation itself – there were almost no real-life examples of automation leading to sweeping changes in any of the organizations I was working with. Without a doubt, much of the fear was generated by analysts and media outlets whipping up this distorted perception, but surely there must have been another force at work.

After a bit of digging around the real cause of the hysteria became clear. In organizations with little or no perception issues, it was clear that the leadership team had taken the time to communicate with their teams. Conversely, those with stressed and worried staff had not.

When I questioned an executive who sought advice on soothing fears in his team if he had clearly explained his vision, and what the outcome of the project would be, he replied that it was obvious what he was trying to achieve. If that were true, the perception crisis in his organization would not be there.

Successful automation projects have an engaged team working behind them. The most effective I have seen understand what will be automated and why. They know what impact it will have and, for the most part, agree it was an area of manual work they found repetitive, boring and unfulfilling anyway. They eagerly anticipated a time when they could dedicate their efforts to more meaningful and valuable work.

Under different circumstances, this committed group would be dealing with the same fear and stress as their peers in organizations with less effective communication.

In the noisy information age we now live in, it’s easy to get caught up in the hype. Business leaders have an obligation to provide clear, effective communication that outlines the vision and journey of automation projects. Without the context and understanding they provide, an engaged team can quickly turn into a stressed one. And a stressed team will undoubtedly hold your project back. It’s not hard to understand why an individual afraid of becoming obsolete may not be working towards your goals with total enthusiasm.

Source: HFS-Automation doesn’t have to be a dirty word…

RPA and AI – the same but different

For a conference run by the Institute of Robotic Process Automation (IRPA), there sure was a lot of talk about Artificial Intelligence (AI). Unfortunately, most of that talk only seemed to confuse people about this latest, and most-hyped of, technologies. There were frameworks presented which showed RPA and AI as a ‘continuum’, there were models that seemed to suggest that there was a natural ‘journey’ from RPA to AI, whilst others talked about AI being a ‘must have’ if RPA was to realise its full value. Some presenters talked about a ‘choice’ between RPA or AI. None of which really helped educate the conference attendees on the benefits of either technology. Let’s unravel each of these points so that everyone can be clear on the relationship between RPA and AI.

The RPA/AI Continuum – whilst it can be argued that RPA is the relatively simpler of the two types of technologies, they are very different beasts indeed. The key difference is that the robots of RPA are ‘dumb’ whilst the AI is ‘self—learning’. The robots will do exactly what you tell them to do, and they will do it exactly the same way again and again and again. Which is perfect when you have rules-based processes where compliance and accuracy are critical. However, where there is any ambiguity, usually when the inputs into a process are unstructured (such as customer emails) or where there are very large amounts of data, then AI is the appropriate technology to use because it can manage that variability and, most importantly, get better at it over time through its own experiences. So, if you do want think of a technology continuum, make sure you put a large gap between RPA and AI.

The RPA to AI Journey – there are a number of case studies where companies have implemented RPA and then implemented AI, but only because RPA is a more mature technology than AI. There are far more examples of companies implementing RPA and not implementing AI at all because they actually don’t need the AI. RPA does a fantastic job of delivering labour arbitrage, accuracy and compliance without AI coming anywhere near it. And, of course, some companies implement AI without RPA. It’s not a journey, just a set of choices based on specific demands.

The RPA Dependency on AI – another view that was put forward was that RPA is only valuable when it has AI in support. This is clearly a self-fulfilling view put forward by the vendors that are able to offer both technologies, but it is simply not correct. As mentioned above, many (in fact, most) companies implement RPA without any consideration or need for AI. If you want compliant, repeatable processes, and can feed the robots with structured data, then why complicate and confuse matters by introducing AI?

The RPA/AI Choice – There was yet another the view put forward (which actually conflicts with much of the above) that companies need to make a choice between RPA and AI – in other words which is the best one for them to implement that will deliver their objectives? As should be clear by now, the two technologies actually complement each other very well, for example by using AI to structure unstructured data at the beginning of the process, by using the robots to process the transactions, and then potentially using AI for decision making and/or data analytics at the end.

So, why all this confusion and mis-information? Part of it is obviously self-interest from vendors and providers to create frameworks and models that align with their own capabilities and marketing messages. And, although RPA is now pretty well defined (with that badge of maturity: its own acronym) some of the confusion surely arises from the multiple terms used to describe artificial intelligence; AI, cognitive computing, machine learning, NLP, etc. For now, it is much the best approach to think of AI in terms of how it can help your business, without worrying about what to call it. As the technology develops though a more robust approach is required, which is why at Symphony Ventures we are working on an ‘AI taxonomy’ that will clarify the different types of AI, and therefore help to explain the practical opportunities and uses for AI in our clients. We look forward to sharing this with you and de-bunking much of the confusion around RPA and AI that we have seen over the past few months.

Source: and AI – the same but different 

The Key to AI is Structured Data, BNY Says

The promise of artificial intelligence around streamlining and enhancing banking processes has enticed most, if not all, financial institutions at this point.

But before jumping to AI, FIs need to get their data straight, according to Jon Theuerkauf, managing director and group head of performance excellence at BNY Mellon.

“Forget AI, I don’t even know what it means,” he said at BluePrism World event today. “Why are we jumping on it, if we haven’t done the basics?”

The “basics,” according to Theuerkauf, is having a structured data ecosystem in place.

“We are now in a transitional phase, and are still three to five years away from integrating operating automated environment,” he said. “For example, it takes a long time to train Watson. Why? Because the data does not land itself easily to allow Watson to learn. So, there needs to be an order around that data, and we are now starting to put things together and taking the chaos out of it.”

Currently, BNY is “consciously incompetent” in terms of adopting AI and automation, and “we are really proud of that,” Theuerkauf added. “We now know what we don’t know, we know that we need ways to structure data, and are still working on how.”

BNY began experimenting with bots and automation last year. In less than 10 months, the bank went from 0 to 200 bots, according to Theuerkauf, and has thus far automated more than 100 processes, with 50 more underway.

“We have $3 trillion in payments a day that run through our shop, would you let a robot handle that process? This was actually the second process we had automated,” he said.

The efforts around structure and RPA — robotic process automation — will, eventually, lead the bank to AI and machine learning integrations. “We are figuring stuff out, putting teams together, and hopefully this will lead us to maturing into a machine learning and AI-enabled world,” Theuerkauf said. “RPA is the ‘doing’ part of automation, AI is the ‘thinking’ part.”

Source: Key to AI is Structured Data, BNY Says

Robotic Process Automation & Artificial Intelligence – Disruption

Robotic Process Automation & Artificial Intelligence. Two technologies for your business – great alone, better combined

Right now there is plenty of excitement around the huge potential of automation in businesses, particularly regarding Robotic Process Automation (RPA) and Artificial Intelligence (AI). These two technologies have the capability to drive significant, step-change efficiencies as well as generating completely new sources of value for organisations.

But, as businesses look to adopt RPA and AI, and seek to get the most value from these disruptive technologies, they need to have a clear picture as to what they do and don’t do, and how they can work together to deliver even more value.

The first thing to understand is that RPA and AI are very different types of technology, but they complement each other very well. One can use RPA without AI, and AI without RPA, but the combination of the two together is extremely powerful.

So, first to explain what RPA and AI actually are, starting with RPA as it is the easiest to define. Robotic Process Automation is a class of software that replicates the actions of humans operating computer systems in order to run business processes. Because the software ‘robots’ mimic exactly what the human operators do (by logging into a system, entering data, clicking on ‘OK’, copying and pasting data between systems, etc.) the underlying systems, such as ERP systems, CRM systems and Office applications, work exactly as they always have done without any changes required. And because the licenses for the robots are a fraction of the price of employing someone, as well as being able to work 24×7 if need be, the business case from a cost point of view only is very strong.

As well as cost savings, RPA also delivers other important benefits such as accuracy and compliance (the robots will always carry out the process in exactly the same way every time) and improved responsiveness (they are generally faster than humans, and can work all hours). They are also very agile – a single robot can do any rules-based process that you train it on, whether it is in finance, customer services or operations.

Processes that can be automated through RPA need to be rules-based and repetitive, and will generally involve processes running across a number of different systems. Customer on-boarding is a good example of a candidate RPA process since it involves a number of different steps and systems, but all can be defined and mapped. High volume processes are preferable as the business case will be stronger, but low volume processes can be automated if accuracy is crucial.

The important thing to remember though is that RPA robots are dumb. The software may be really clever in terms of what it can achieve, but the robots will do exactly what you have trained them to do each time, every time. This is both their greatest strength and their greatest weakness. A strength because you need to be sure that the robot will carry out the process compliantly and accurately, but a weakness because it precludes any self-learning capability.

This inability to self-learn leads to two distinct constraints for RPA, both of which, luckily, can be addressed by AI capabilities. The first is that the robots require structured data as their input, whether this be from a spreadsheet, a database, a webform or an API. When the input data is unstructured, such as a customer email, or semi-structured where there is generally the same information available but in variable formats (such as invoices) then artificial intelligence can be introduced to turn it into a structured format.

This type of AI capability uses a number of different AI technologies, including Natural Language Processing, to extract the relevant data from the available text, even if the text is written in free-from language, or if the information on a form looks quite different each time. For example, if you wrote an email to an online retailer complaining that the dress that was delivered was the wrong colour to the one you ordered, then the AI would be able to tell that this was a complaint, that the complaint concerned a dress, and the problem being it was the wrong colour. If the order information was not included in the original email then the AI could potentially work out which order it related to by triangulating the information it already has. Once it has gathered everything together, it can then route that query to the right person within the organisation, along with all of the supporting data. Of course, the ‘right person’ could actually be a robot who could reorder the correct colour dress and send an appropriate email to the customer.

For semi-structured data, the AI is able to extract the data from a form, even when that data is in different places on the document, in a different format or only appears sometimes. For an invoice, for example, the date might be in the top left hand corner sometimes, and other times in the top right. It might also be written longhand, or shortened. The invoice may or may not include a VAT amount, and this may be written above the Total Value or below it. Once trained, the AI is able to cope with all of this variability to a high degree of confidence. If it doesn’t know (i.e. its confidence level is below a certain threshold) then it can escalate to a human being, who can answer the question, and the AI will then learn from that interaction in order to do its job better in the future.

The second constraint for RPA is that it can’t make complex decisions, i.e. it can’t use judgement in a process. Some decisions are relatively straightforward and can certainly be handled by RPA, especially if they involve applying rules-based scores to a small number of specific criteria. For example, you may only offer a loan to someone who is over 18, is employed and owns a house – if they satisfy all of these criteria (the data for which would be available on your internal or external systems) then they pass the test. You could even apply some weightings, for example, so that they score better as they get older and earn more money. A simple calculation could decide whether the customer scores over a certain threshold or not.

But what about when the judgement required is more complex? There might be 20, or 50, different criteria to consider, all with different weightings. Some could be more relevant for some customers, and for others certain criteria could be completely irrelevant. This is where another type of AI, usually called ‘cognitive reasoning’, can be used to support and augment the RPA process.

Cognitive reasoning engines work by mapping all of the knowledge and experience that a subject matter expert may have about a process into a model. That model, a knowledge map, can then be interrogated by other humans or by robots, to find the optimal answer. In my earlier loan example, a cognitive reasoning engine would be able to consider many different variables, each with its own influence, or weighting, in order to decide whether the loan should be approved or not. This ‘decision’ would be expressed as a confidence level; if it was not confident enough it could request additional information (through a chatbot interface if dealing with a human, or by using RPA to access other systems where the data might be held) to help it increase its confidence level.

Of course AI does many more things than the two capabilities I have described here. I’ve already mentioned chatbots which can be used to interface between humans and other systems through typing in natural language, but there is also speech recognition which is used for similar purposes through the telephone. As well as understanding natural language, AI can also generate it, creating coherent passages of text from data and information that it is given. Through ’predictive analytics’ data created and collated by RPA can be used to help predict future behaviours. AI can also recognise images, such as faces, and can learn and plan scenarios for new problems that it encounters.

The crucial thing to remember about AI capabilities are that they are very narrow in what they can do. Each of the examples I have given are very distinct, so an AI that can recognise faces, for example, can’t generate text. The AI system that Deepmind created last year to beat the best player in the world at the Chinese game of Go would lose to you at a simple game of noughts and crosses. Therefore, AI needs to be considered in terms of its specific capabilities and how these might be combined to create a full solution.

As we have seen, RPA can deliver some significant benefits all by itself, but the real magic comes when the two work together. AI opens up many more processes for robotic process automation, and allows much more of the process to be automated, including where decisions have to be made.

And it goes beyond simply automating processes. Using RPA and AI, the whole process can be re-engineered. Parts of the process that may originally have been expensive to execute suddenly become much easier and cheaper to run – they could therefore potentially be done right at the start, rather than waiting to the end. Credit checks, for example, are only usually carried out once other steps and checks in a process are completed so as to minimise the amount of times they have to be done. But if it is automated, and therefore only at a marginal cost, why not do it straight away at the beginning for every case?

Some existing processes are held until late in the day, because it is easier for the staff to process them in bulk, especially if it means logging into multiple systems to extract information from them for each case. This means that turnaround times for cases that arrive in the morning are longer than they need to be. An automated solution on the other hand can log into the relevant systems many times a day to extract the information as soon as it is available. The relevant decisions, made through AI, can then be made sooner and more effectively, improving turnaround times and customer satisfaction.

As I mentioned at the beginning of this piece, there is certainly plenty of excitement around automation right now, but it is very important to have a solid and sober understanding of what the different automations capabilities are. As you start your automation journey it is therefore crucial to consider all types of automation in your strategy, and how they can support and augment each other to achieve your business objectives.

Source: Process Automation & Artificial Intelligence – Disruption

How to Capitalize on Robotics: Savings Drivers with Digital Labor

For many of today’s organizations, moving forward with digital labor is no longer a question of if, but when.

For many of today’s organizations, moving forward with digital labor is no longer a question of if, but when. Companies know they need to jump on this trend as a differentiator, which encompasses robotic process automation and is the application of software technology to automate business processes ranging from transactional swivel-chair activities to more complex strategic undertakings.

However, like any other business decision, a business case needs to be made for digital labor and robotics efforts, which is built first by understanding the investment and financial savings opportunities, says David B. Kirk, PhD, Managing Director, Digital Labor / Robotic Process Automation – Shared Services and Outsourcing Advisory at KPMG. A case can’t be created, he explains, without understanding both the “cost to achieve” and the anticipated benefits – which includes direct cost savings as well as more qualitative benefits, such as improved customer satisfaction.

Digital labor: A financial puzzle

Understanding the investments and expected returns for digital labor is complicated by the fact that no two automation opportunities are the same — that is, your mileage will vary. In addition, digital labor can be categorized into three different classes that require different investments and that provide returns varying not only in magnitude, but also in the drivers that impact those savings. Basic Robotics Process Automation (RPA) leverages capabilities such as workflow, rules engines, and screen scraping/data capture to automate existing manual processes. Enhanced Process Automation leverages additional capabilities to address automation of processes that are less structured and often more specialized. Finally, Cognitive Automation combines advanced technologies such as natural language processing, artificial intelligence, machine learning, and data analytics to mimic human activities.

There are challenges in several of these areas, says Kirk. On the robotic process automation (RPA) end of the spectrum, one is “the simplicity of the deployment which can result in its adoption across the enterprise being explosive and disjointed, resulting in unnecessary expense and missed opportunities,” he says. On the cognitive side, the journey to get there is more complex, requiring proper guidance. “Predicting both the investment and anticipated outcomes is more of an art than a science,” he adds.

In order to solve the digital labor puzzle and glean the right understanding, organizations need to have a plan. “Understand that you need alignment between your opportunity, your appetite for both change and technology, and the skillsets you either have internally or are willing to purchase,” says Kirk. Also, organizations must recognize from the very beginning that digital labor is an enterprise-wide opportunity and is worth an enterprise-wide strategy.

Opportunities and capabilities of digital labor and automation

One of the biggest opportunities for RPA, which automates repetitive, routine explicit steps, is providing a “quick hit” automation fix for connecting disparate legacy systems together, where a human takes data from one system and then uses that data to perform activities in another system.

Enhanced process automation is similar, but it adds on other capabilities such as the ability to handle unstructured data, or built-in automations (such as an out-of-the-box knowledge library), as well as capabilities to assist in capturing new knowledge to add to the knowledge base (such as watch and record).  It is most applicable in automating activities in a specific functional area, in which the built-in knowledge can be leveraged, such as in finance or IT.

Cognitive tools are substantially different, he adds. “Those need to be taught about the work they will do, as opposed to programmed, and their future success depends greatly on the success of this training,” he explains.

Foundational and specific savings drivers

There are certainly some common foundational drivers that will impact the overall success and financial returns of digital labor investment. An important one is executive support, Kirk points out, in order to build an enterprise-wide plan that avoids duplication of investments and promotes best practices to maximize savings.

In addition, governance is critical. “Governance insures participants deliver on the business case and associated savings, leverage the agreed upon tools and methodologies, and follow the risk, compliance and security policies to avoid unnecessary risk and expense downstream,” says Kirk.

There are also specific savings drivers for each class of digital labor, which have “triggers” that identify opportunities for automation and degree of the associated savings impact. For example, in the RPA space, processes that follow well-defined steps, that are prone to human error, suffer from inconsistent execution, have a high execution frequency and require significant human effort to accomplish are likely to provide the most significant impact when automated.

Next, enhanced process automation tends to be more expensive than basic process automation, but as a result of built-in learning support, savings also tend to increase more greatly over time. Its biggest savings drivers are the availability of industry/process-specific starting knowledge; complex processes; automation expertise and rapidly evolving processes.

Cognitive process automation also is more expensive, but also provides enhanced savings capabilities and can be truly transformative, with savings drivers such as natural language; automation experience; highly regulated domains; and quality source documents.

Preparing for the digital labor journey to capitalize on savings

How can organizations best prepare for their digital labor journey in terms of capitalizing on savings?  Starting small is key, says Kirk. “We advise our clients to identify an executive sponsor and understand what that means from an enterprise deployment perspective,” he explains, “as that helps define required roadmap activities and challenges.” It can help to pinpoint a handful, or fewer, of processes that are good RPA candidates and canvass the associated automation tools for a good fit for your opportunity and your organization.

Also, companies should understand, and document, the mission statement for the automation of each process – “It’s not always about pure cost savings,” explains Kirk  – and use these processes as a proof of concept with a well-defined business case.  “As business units see the results of the proof of concept automations, prepare for the onslaught of requests by leveraging a well-defined intake process and centralized governance,” he says.

Source: to Capitalize on Robotics: Savings Drivers with Digital Labor

Rise of the machines – The future of robotics and automation

So many of the tasks that we now take for granted once had to be done manually. Washing a load of laundry no longer takes all day; our phone calls are directed to the correct departments by automated recordings; and many of our online orders are now selected and packed by robots.

Developments in this area are accelerating at an incredible rate. But as exciting as these new discoveries may be, they raise question after question around whether the research needed to deliver such innovations is viable, both from an economical and an ethical point of view.

As expert manufacturers of engineering parts that help to keep hundreds of different automated processes up and running, electronic repair specialists Neutronic Technologies are understandably very interested in where the future is going to take us. Is it going to take hundreds, if not thousands, of years for us to reach the kinds of automation that are lodged in the imaginations of sci-fi enthusiasts? Or are we a great deal closer to a machine takeover than we think?

According to the International Federation of Robotics, there are five countries in the developed world that manufacture at least 70 per cent of our entire robotics supply: Germany, the United States, South Korea, China and Japan.

By 2018, the Federation of Robotics predicts that there will be approximately 1.3 million industrial robots working in factories around the world. That’s less than two years away.

The development of automation has received a great deal more attention over the past few years. And undoubtedly what has brought it to people’s attention is the popularisation of the subject following the explosion of science fiction books and movies such as Isaac Asimov’s ‘i, Robot’ and ‘The Bicentennial Man’. And this has continued to emerge throughout the decades and has likely only heightened our curiosity about the world of robots.

Why are we even exploring robotics?

Developing robotics is the next stage in our search for automation. We already have automation integrated into so many aspects of our daily lives, from doors that open due to motion sensors to assembly lines and automobile production, robotics is simply the next step along that path.

I predict that the biggest developments in the automation world will come from the automobile industry – so the likes of self-driving cars that are already being tested – and the internet.

Another area of development within automation is likely to come from the growth of the internet. The concept of the ‘Internet of Things’ has been gaining momentum for some years now, even decades amongst technology companies, but the idea has only recently started to break into a mainstream conversation.

We have already seen glimpses of the future starting to creep into reality, most notably with the introduction of Amazon Dash. Linked to the person’s account and programmed to a certain item, all you have to do is press the button and an order is placed and delivered. Of course, this process is currently only half automated; a button still has to be manually pressed and Amazon shippers still post and deliver the item, but it certainly shows the direction in which we are headed.

But ultimately the Internet of Things can go even further than creating smart homes. The term ‘smart cities’ has been coined that could theoretically include connected traffic lights to control vehicle flow, smart bins that inform the right people when they need to be emptied, to even the monitoring of crops growing in fields.

How do we reach these automation goals?

Ultimately, the end goal of any research into robotics or automation is to emulate the actions of humans. People across the world engage in heated debates about whether machines will ever have the ability to think like people – a subject known as A.I. or Artificial Intelligence which is worthy of its own exploration. Whether that will become a reality in the future we cannot currently tell for sure, but researchers are hard at work across the world trying to inch our way closer.

There are, of course, issues that arise when we try to develop machines to take over certain tasks from humans, most notably to do with quality control and the increased margin for error. Some question whether a machine, that doesn’t necessarily have the capacity to consider extenuating circumstances or raise certain questions or react in a way, would be able to perform these tasks.

Let’s look at self-driving cars for example. So much of driving depends on the person behind the wheel being able to react in seconds to any changes around them. It is, therefore, essential that machines are able to “think” as close to humans as possible. If artificial intelligence and technology alone cannot achieve this, it would be very difficult for such vehicles to become road legal. However, experts in the industry have suggested a very clever solution.

Are there any disadvantages to the research?

As with any major development, there are always going to be people who oppose it, or at the very least point out reasons why we should proceed with caution – and with good reason.

One of the biggest, and indeed most realistic, fears that many people express, is all to do with economics and jobs. It’s no secret that the UK’s economy, and indeed the world’s economy, has been somewhat shaky over the past few years. This has led to many people showing concern that the development of automated processes, which are able to perform certain tasks with precision and accuracy that surpasses humans and at a much faster speed, will mean that many people’s jobs will become redundant.

Where are we headed?

It is unlikely that we are going to see any robot uprisings anytime soon. But the potential threats that an increase in automation brings to our society should not be underestimated. With the economic state of the world already so fragile, any attempts to research areas that could result in unemployment should be very carefully considered before implementation.

That being said, we are living in exciting times where we are able to witness such developments taking place. So much has already occurred over the past few years that many people may not be aware of. We may not have reached the exciting level of developments as seen in the movies – not yet anyway – but with the amount of ideas and research taking place in the world, the sky really is the limit.

Source: – Rise of the machines – The future of robotics and automation

4 Unique Challenges Of Industrial Artificial Intelligence

Robots are probably the first thing you think of when asked to imagine AI applied to industrials and manufacturing. Indeed, many innovative companies like Rodney Brooks’ Rethink Robotics have developed friendly-looking robot factory workers who hustle alongside their human colleagues. Industrial robots have historically been designed to perform specific niche tasks, but modern-day robots can be taught new tasks and make real-time decisions.

As sexy and shiny as robots are, the bulk of the value of AI in industrials lies in transforming data from sensors and routine hardware into intelligent predictions for better and faster decision-making. 15 billion machines are currently connected to the Internet. By 2020, Cisco predicts the number will surpass 50 billion. Connecting machines together into intelligent automated systems in the cloud is the next major step in the evolution of manufacturing and industry.

In 2015, General Electric launched GE Digital to drive software innovation and cloud connectivity across all departments. Harel Kodesh, CTO of GE Digital, shares the unique challenges of applying AI to industrials that differ from consumer applications.

1. Industrial Data Is Often Inaccurate

“For machine learning to work properly, you need lots of data. Consumer data is harder to misunderstand, for example when you buy a pizza or click on an ad,” says Kodesh. “When looking at the industrial internet, however, 40% of the data coming in is spurious and isn’t useful”.

Let’s say you need to calculate how far a combine needs to drill and you stick a moisture sensor into the ground to take important measurements. The readings can be skewed by extreme temperatures, accidental man-handling, hardware malfunctions, or even a worm that’s been accidentally skewered by the device. “We are not generating data from the comfort and safety of a computer in your den,” Kodesh emphasizes.

2. AI Runs On The Edge, Not On The Cloud

Consumer data is processed in the cloud on computing clusters with seemingly infinite capacity. Amazon can luxuriously take their time to crunch your browsing and purchase history and show you new recommendations. “In consumer predictions, there’s low value to false negatives and to false positives. You’ll forget that Amazon recommended you a crappy book,” Kodesh points out.

On a deep sea oil rig, a riser is a conduit which transports oil from subsea wells to a surface facility. If a problem arises, several clamps must respond immediately to shut the valve. The sophisticated software that manages the actuators on those clamps tracks minute details in temperature and pressure. Any mistake could mean disaster.

The stakes and responsiveness are much higher for industrial applications where millions of dollars and human lives can be on the line. In these cases, industrial features cannot be trusted to run on the cloud and must be implemented on location, also known as “the edge.”

Industrial AI is built as an end-to-end system, described by Kodesh as a “round-trip ticket”, where data is generated by sensors on the edge, served to algorithms, modeled on the cloud, and then moved back to the edge for implementation. Between the edge and the cloud are supervisor gateways and multiple nodes of computer storage since the entire system must be able to run the the right load at the right places.

Source: Unique Challenges Of Industrial Artificial Intelligence

The Countries Most (and Least) Likely to be Affected by Automation

Today, about half the activities that people are paid to do in the global economy have the potential to be automated by adapting currently demonstrated technology. In all, 1.2 billion full time equivalents and $14.6 trillion in wages are associated with activities that are technically automatable with current technology. This automation potential differs among countries, with the range spanning from 40% to 55%. Four economies—China, India, Japan, and the United States—dominate the total, accounting for just over half of the wages and almost two-thirds the number of employees associated with activities that are technically automatable by adapting currently demonstrated technologies.


Around the world, automation is transforming work, business, and the economy. China is already the largest market for robots in the world, based on volume. All economies, from Brazil and Germany to India and Saudi Arabia, stand to gain from the hefty productivity boosts that robotics and artificial intelligence will bring. The pace and extent of adoption will vary from country to country, depending on factors including wage levels. But no geography and no sector will remain untouched.

In our research we took a detailed look at 46 countries, representing about 80% of the global workforce. We examined their automation potential today — what’s possible by adapting demonstrated technologies — as well as the potential similarities and differences in how automation could take hold in the future.

Today, about half the activities that people are paid to do in the global economy have the potential to be automated by adapting demonstrated technology. As we’ve described previously, our focus is on individual work activities, which we believe to be a more useful way to examine automation potential than looking at entire jobs, since most occupations consist of a number of activities with differing potential to be automated.

In all, 1.2 billion full-time equivalents and $14.6 trillion in wages are associated with activities that are automatable with current technology. This automation potential differs among countries, ranging from 40% to 55%.

The differences reflect variations in sector mix and, within sectors, the mix of jobs with larger or smaller automation potential. Sector differences among economies sometimes lead to striking variations, as is the case with Japan and the United States, two advanced economies. Japan has an overall automation potential of 55% of hours worked, compared with 46% in the United States. Much of the difference is due to Japan’s manufacturing sector, which has a particularly high automation potential, at 71% (versus 60% in the United States). Japanese manufacturing has a slightly larger concentration of work hours in production jobs (54% of hours versus the U.S.’s 50%) and office and administrative support jobs (16% versus 9%). Both of these job titles comprise activities with a relatively high automation potential. By comparison, the United States has a higher proportion of work hours in management, architecture, and engineering jobs, which have a lower automation potential since they require application of specific expertise such as high-value engineering, which computers and robots currently are not able to do.

On a global level, four economies — China, India, Japan, and the United States — dominate the total, accounting for just over half of the wages and almost two-thirds the number of employees associated with activities that are technically automatable by adapting demonstrated technologies. Together, China and India may account for the largest potential employment impact — more than 700 million workers between them — because of the relative size of their labor forces. Technical automation potential is also large in Europe: According to our analysis, more than 60 million full-time employee equivalents and more than $1.9 trillion in wages are associated with automatable activities in the five largest economies (France, Germany, Italy, Spain, and the United Kingdom).

We also expect to see large differences among countries in the pace and extent of automation adoption. Numerous factors will determine automation adoption, of which technical feasibility is only one. Many of the other factors are economic and social, and include the cost of hardware or software solutions needed to integrate technologies into the workplace, labor supply and demand dynamics, and regulatory and social acceptance. Some hardware solutions require significant capital expenditures and could be adopted faster in advanced economies than in emerging ones with lower wage levels, where it will be harder to make a business case for adoption because of low wages. But software solutions could be adopted rapidly around the world, particularly those deployed through the cloud, reducing the lag in adoption time. The pace of adoption will also depend on the benefits that countries expect automation to bring for things other than labor substitution, such as the potential to enhance productivity, raise throughput, and improve accuracy and regulatory and social acceptance.

Regardless of the timing, automation could be the shot in the arm that the global economy sorely needs in the decades ahead. Declining birthrates and the trend toward aging in countries from China to Germany mean that peak employment will occur in most countries within 50 years. The expected decline in the share of the working-age population will open an economic growth gap that automation could potentially fill. We estimate that automation could increase global GDP growth by 0.8% to 1.4% annually, assuming that people replaced by automation rejoin the workforce and remain as productive as they were in 2014. Considering the labor substitution effect alone, we calculate that, by 2065, the productivity growth that automation could add to the largest economies in the world (G19 plus Nigeria) is the equivalent of an additional 1.1 billion to 2.2 billion full-time workers.

The productivity growth enabled by automation can ensure continued prosperity in aging nations and could provide an additional boost to fast-growing ones. However, automation on its own will not be sufficient to achieve long-term economic growth aspirations across the world. For that, additional productivity-boosting measures will be needed, including reworking business processes or developing new products, services, and business models.

How could automation play out among countries? We have divided our 46 focus nations into three groups, each of which could use automation to further national economic growth objectives, depending on its demographic trends and growth aspirations. The three groups are:

  • Advanced economies. These include Australia, Canada, France, Germany, Italy, Japan, South Korea, the United Kingdom, and the United States. They typically face an aging workforce, though the decline in working-age population growth is more immediate in some (Germany, Italy, and Japan) than in others. Automation can provide the productivity boost required to meet economic growth projections that they otherwise would struggle to attain. These economies thus have a major interest in pursuing rapid automation development and adoption.
  • Emerging economies with aging populations. This category includes Argentina, Brazil, China, and Russia, which face economic growth gaps as a result of projected declines in the growth of their working population. For these economies, automation can provide the productivity injection needed to maintain current GDP per capita. To achieve a faster growth trajectory that is more commensurate with their developmental aspirations, these countries would need to supplement automation with additional sources of productivity, such as process transformations, and would benefit from rapid adoption of automation.
  • Emerging economies with younger populations. These include India, Indonesia, Mexico, Nigeria, Saudi Arabia, South Africa, and Turkey. The continued growth of the working-age population in these countries could support maintaining current GDP per capita. However, given their high growth aspirations, and in order to remain competitive globally, automation plus additional productivity-raising measures will be necessary to sustain their economic development.

For all the differences between countries, many of automation’s challenges are universal. For business, the performance benefits are relatively clear, but the issues are more complicated for policy makers. They will need to find ways to embrace the opportunity for their economies to benefit from the productivity growth potential that automation offers, putting in place policies to encourage investment and market incentives to encourage innovation. At the same time, all countries will need to evolve and create policies that help workers and institutions adapt to the impact on employment.


Source: Harvard Business Review-The Countries Most (and Least) Likely to be Affected by Automation

Bill Gates Is Wrong: The Solution to AI Taking Jobs Is Training, Not Taxes

Let’s take a breath: Robots and artificial intelligence systems are nowhere near displacing the human workforce. Nevertheless, no less a voice than Bill Gates has asserted just the opposite and called for a counterintuitive, preemptive strike on these innovations. His proposed weapon of choice? Taxes on technology to compensate for losses that haven’t happened.

AI has massive potential. Taxing this promising field of innovation is not only reactionary and antithetical to progress, it would discourage the development of technologies and systems that can improve everyday life.

Imagine where we would be today if policy makers, fearing the unknown, had feverishly taxed personal computer software to protect the typewriter industry, or slapped imposts on digital cameras to preserve jobs for darkroom technicians. Taxes to insulate telephone switchboard operators from the march of progress could have trapped our ever-present mobile devices on a piece of paper in an inventor’s filing cabinet.

There simply is no proof that levying taxes on technology protects workers. In fact, as former US treasury secretary Lawrence Summers recently wrote, “Taxes on technology are more likely to drive production offshore than create jobs at home.”

Calls to tax AI are even more stunning because they represent a fundamental abandonment of any responsibility to prepare employees to work with AI systems. Those of us fortunate enough to influence policy in this space should demonstrate real faith in the ability of people to embrace and prepare for change. The right approach is to focus on training workers in the right skills, not taxing robots.

There are more than half a million open technology jobs in the United States, according to the Department of Labor, but our schools and universities are not producing enough graduates with the right skills to fill them. In many cases, these are “new collar jobs” that, rather than calling for a four-year college degree, require sought-after skills that can be learned through 21st century vocational training, innovative public education models like P-TECH (which IBM pioneered), coding camps, professional certification programs and more. These programs can prepare both students and mid-career professionals for new collar roles ranging from cybersecurity analyst to cloud infrastructure engineer.

At IBM, we have seen countless stories of motivated new collar professionals who have learned the skills to thrive in the digital economy. They are former teachers, fast food workers, and rappers who now fight cyber threats, operate cloud platforms and design digital experiences for mobile applications. WIRED has even reported how, with access to the right training, former coal miners have transitioned into new collar coding careers.

The nation needs a massive expansion of the number and reach of programs students and workers can access to build new skills. Closing the skills gap could fill an estimated 1 million US jobs by 2020, but only if large-scale public private partnerships can better connect many more workers to the training they need. This must be a national priority.

First, Congress should update and expand career-focused education to help more people, especially women and underrepresented minorities, learn in-demand skills at every stage. This should include programs to promote STEM careers among elementary students, which increase interest and enrollment in skills-focused courses later in their educational careers. Next, high-school vocational training programs should be reoriented around the skills needed in the labor market. And updating the Federal Work-Study program, something long overdue, would give college students meaningful, career-focused internships at companies rather than jobs in the school cafeteria or library. Together, high-school career training programs and college work study receive just over $2 billion in federal funding. At around 3 percent of total federal education spending, that’s a pittance. We can and must do more.

Second, Congress should create and fund a 21st century apprenticeship program to recruit and train or retrain workers to fill critical skills gaps in federal agencies and the private sector. Allowing block grants to fund these programs at the state level would boost their effectiveness and impact.

Third, Congress should support standards and certifications for new collar skills, just as it has done for other technical skills, from automotive technicians to welders. Formalizing these national credentials and accreditation programs will help employers recognize that candidates are sufficiently qualified, benefiting workers and employers alike.

Taking these steps now will establish a robust skills-training infrastructure that can address America’s immediate shortage of high-tech talent. Once this foundation is in place, it can evolve to focus on new categories of skills that will grow in priority as the deployment of AI moves forward.

AI should stand for augmented—not artificial—intelligence. It will help us make digital networks more secure, allow people to lead healthier lives, better protect our environment, and more. Like steam power, electricity, computers, and the internet before it, AI will create more jobs than it displaces. What workers really need in the era of AI are the skills to compete and win. Providing the architecture for 21st century skills training requires public policies based on confidence, not taxes based on fear.

Source: Wired-Bill Gates Is Wrong: The Solution to AI Taking Jobs Is Training, Not Taxes