May 17, 2020

The top three robotic process automation use cases for SMEs

Robotic process automation
Harry Menear
5 min
Our breakdown of the past, present and future of robotic process automation, and three of the technology's most evocative use cases for SMEs.
Alongside machine learning (ML) and natural language processing (NLP), robotic process automation (RPA) has moved over the last year “from fledgling s...

Alongside machine learning (ML) and natural language processing (NLP), robotic process automation (RPA) has moved over the last year “from fledgling siloed capabilities to tenets of strategy” with “profound potential for business and society,” according to Deloitte Insights’ breakdown of technology trends for 2019. Gartner predicted in June that the global RPA market, after growing 63.1% last year, will continue its meteoric expansion, reaching $1.3bn in 2019. By 2025, McKinsey & Co believes that automation technologies (of which RPA is expected to be a leading element) could have a global financial impact of around $6.7trn. The experts are united: RPA is going to leave behind a dramatically different world to the one that preceded it. 

RPA is often mentioned in the same breath as artificial intelligence (AI), deep learning, ML and NLP. Here’s our breakdown of the difference, and three potentially game-changing use cases for RPA in the world of small businesses. 

First coined by AI pioneer Arthur Samuel, RPA is a child of ML endeavors taking place at the end of the 1950s, thought to be stepping stones along the way to creating more and more sophisticated AI. While Samuel, his colleagues at IBM at the time, and the world’s IT community as a whole were decades away from anything approaching the success they sought, the first stepping stone had been crossed. 

As it was then, so it is now; AI, ML and RPA have always been closely entwined. An ML pioneer coined the term and computer scientists today still put a lot of stock in the interplay between the apparently similar, yet distinctly different technologies. 

In 2017, when it was starting to become apparent that RPA was stepping out of its theoretical and experimental stages and would be a fundamentally vital business tool in a short while, a report by the Institute of Electrical and Electronics Engineers (IEEE) furnished us with a working definition of RPA and, more importantly, a distinction between it and AI. 

AI: “the combination of cognitive automation, machine learning (ML), reasoning, hypothesis generation and analysis, natural language processing and intentional algorithm mutation producing insights and analytics at or above human capability.” 

RPA: “the use of a preconfigured software instance that uses business rules and predefined activity choreography to complete the autonomous execution of a combination of processes, activities, transactions, and tasks in one or more unrelated software systems to deliver a result or service with human exception management.”

In simple terms, AI mimics the way humans think and RPA mimics the way humans act. In both cases, the results are often significantly faster and more accurate, but limited in scope compared to human efforts. 


While the first references to intelligent objects can be found as far back as Homer’s Iliad, and science fiction writers like Isaac Asimov have long entertained the possibility for AI to simulate and even exceed human capabilities, RPA has - perhaps fittingly - let a humber existence. That isn’t to say that it’s less likely to change the world. 

While the major adopters of RPA so far have been large tech companies and other early adopters with economies of scale that can justify cutting edge tech investment, RPA has the ability to change the game for SMEs as well. Here are three use cases for RPA that could solve help staffing issues and support time management at smaller firms: 

Data Validation 

Speaking at the Gartner Data and Analytics Conference in London this year, Michael Corcoran, CMO at Information Builders acknowledged that: “One of the biggest challenges in our field is that people don’t trust their data.” Turning to a room full of around 60 thought leaders in the space he asked: “Who here’s got perfect data? Anyone? Thank you for being honest. None of us do.” 

The act of cross referencing databases against publically available data is becoming more and more essential to companies that work with Big Data. RPA can not only perform these tasks significantly faster than a human (freeing up personnel for higher cognition activities), but the knock on positive effects of companies being able to trust their data speak for themselves. 


A salesperson’s work is, at its heart, about relationship building. Yet, the majority of salespeople find that their time is mostly spent writing reports and inputting data. RPA can quite easily remove that pain point by automating administrative tasks. 

Sales and marketing automation company, Thoughtonomy, believes that RPA will “give you more time to spend on building your pipeline and engaging with your customers. Using our Virtual Workforce, you’ll be able to focus on nurturing your leads as we automate repetitive processes.”

L1 Tech Support

The number of people working daily with electronic devices is only going to increase over the next decade. According to AIMultiple,“without increasing automation capabilities, IT support teams can find themselves overwhelmed with simple yet time consuming queries. This not only results in slow service but also demotivates most support personnel who do not enjoy repetitive tasks that do not challenge them intellectually.”

By using RPA powered bots as front line support staff, companies not only automate simple processes and return time for more involved problems to their human workers, but also shield those workers from the personal abuse and resulting stress as a result of dealing with irate customers on a minute-by-minute basis. 


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Jun 11, 2021

Google AI Designs Next-Gen Chips In Under 6 Hours

3 min
Google AI’s deep reinforcement learning algorithms can optimise chip floor plans exponentially faster than their human counterparts

In a Google-Nature paper published on Wednesday, the company announced that AI will be able to design chips in less than six hours. Humans currently take months to design and layout the intricate chip wiring. Although the tech giant has been working in silence on the technology for years, this is the first time that AI-optimised chips have hit the mainstream—and that the company will sell the result as a commercial product. 


“Our method has been used in production to design the next generation of Google TPU (tensor processing unit chips)”, the paper’s authors, Azalea Mirhoseini and Anna Goldie wrote. The TPU v4 chips are the fastest Google system ever launched. “If you’re trying to train a large AI/ML system, and you’re using Google’s TensorFlow, this will be a big deal”, said Jack Gold, President and Principal Analyst at J.Gold Associates


Training the Algorithm 

In a process called reinforcement learning, Google engineers used a set of 10,000 chip floor plans to train the AI. Each example chip was assigned a score of sorts based on its efficiency and power usage, which the algorithm then used to distinguish between “good” and “bad” layouts. The more layouts it examines, the better it can generate versions of its own. 


Designing floor plans, or the optimal layouts for a chip’s sub-systems, takes intense human effort. Yet floorplanning is similar to an elaborate game. It has rules, patterns, and logic. In fact, just like chess or Go, it’s the ideal task for machine learning. Machines, after all, don’t follow the same constraints or in-built conditions that humans do; they follow logic, not preconception of what a chip should look like. And this has allowed AI to optimise the latest chips in a way we never could. 


As a result, AI-generated layouts look quite different to what a human would design. Instead of being neat and ordered, they look slightly more haphazard. Blurred photos of the carefully guarded chip designs show a slightly more chaotic wiring layout—but no one is questioning its efficiency. In fact, Google is starting to evaluate how it could use AI in architecture exploration and other cognitively intense tasks. 


Major Implications for the Semiconductor Sector 

Part of what’s impressive about Google’s breakthrough is that it could throw Moore’s Law, the axion that the number of transistors on a chip doubles every five years, out the window. The physical difficulty of squeezing more CPUs, GPUs, and memory on tiny silicon die will still exist, but AI optimisation may help speed up chip performance.


Any chance that AI can help speed up current chip production is welcome news. Though the U.S. Senate recently passed a US$52bn bill to supercharge domestic semiconductor supply chains, its largest tech firms remain far behind. According to Holger Mueller, principal analyst at Constellation Research, “the faster and cheaper AI will win in business and government, including with the military”. 


All in all, AI chip optimisation could allow Google to pull ahead of its competitors such as AWS and Microsoft. And if we can speed up workflows, design better chips, and use humans to solve more complex, fluid, wicked problems, that’s a win—for the tech world and for society. 



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