For years, we've been aware that AI is set to be one of the world's biggest – if not the biggest – technological and economic game-changers. With PwC estimating that by 2030 AI will grow the global economy by nearly $16 trillion, we've become used to claims that it will be a transformative technology from the media. For those of us who actually work with AI though, it's clear that some of this optimism needs to be tempered. That's because right now many of the processes to develop, test, deploy, and monitor AI models are not as efficient as they could be. In practice, most people who've worked with AI or ML in industry know that the technology requires a great deal of manual intervention to be able to smoothly run in a production environment. To take one example, the data scientists who help develop and train models end up finding most of their time consumed on manual and repetitive tasks around data preparation – around 45% of their working hours.
Software testing teams analyse and correct thousands of code on a daily basis to ensure the final product is free of errors. However, the on-demand customer expects software to be comprehensive in functionality and delivered with precision and speed. Current software testing procedures are not scalable to meet these needs, nor are they cost- or time-efficient in the digital economy. As products become more complex to create, the code becomes more challenging to test accurately. Manual testing exposes development teams to many challenges--code changes causing errors elsewhere in the product, the considerable length of regression testing cycles, resourcing constraints of hiring skilled software testers to meet demand, and more.
Artificial Intelligence is turning out to be a game changer, with countless applications in nearly every domain. It is now making its way into the area of Production and Manufacturing, allowing it to harness the power of deep learning and in doing so, providing automation that is faster, cheaper and more superior. This article aims to give a brief understanding of automated visual assessment and how a deep learning approach can save significant time and effort. It involves the analysis of products on the production line for the purpose of quality control. Visual inspection can also be used for internal and external assessment of the various equipment in a production facility such as storage tanks, pressure vessels, piping, and other equipment.
Industries and businesses cutting across sectors are increasingly turning to RPA, or Robotic Process Automation, an emerging technology that codes sophisticated software systems or "bots" to handle high-volume, low-value, repetitive tasks, freeing human labor for high-value work. The advantages of adopting RPA are significant. By eliminating human error, the business makes a mark in quality assurance. Customer satisfaction increases several notches, and delivery systems become efficient. The cost of production climbs down substantially, and companies improve ROI. RPA employs artificial intelligence and deep machine learning protocols that enable "bots" to handle virtually any backend process from start to finish.
More and more enterprises are turning to a promising technology called RPA (robotic process automation) to become more productive and efficient. Successful implementation also helps to cut costs and reduce error rates. RPA can automate mundane and predictable tasks and processes leaving employees to focus more on high-value work. Other companies, see RPA as the next step before fully adopting intelligent automation technology such as machine learning and artificial intelligence. RPA is one of the fastest-growing sectors in the field of enterprise technology. In 2018 RPA software soared in value to $864 million, a growth of over 63%. In the course of this article, we clearly explain exactly what RPA really is and how it works. To help our understanding we will also explore the potential benefits and disadvantages of this technology. Finally, we will highlight some of the most powerful and exciting ways in which it is already transforming enterprises in a range of industries. Robotic Process Automation, or RPA for short, is a way of automating structured, repetitive, or rules-based tasks and processes. It has a number of different applications. Its tools can capture data, retrieve information, communicate with other digital systems and process transactions. Implementation can help to prevent human error, particularly when charged with completing long, repetitive tasks. It can also reduce labor costs. A report by Deloitte revealed that one large, commercial bank implemented RPA into 85 software bots. These were used to tackle 13 processes interacting with 1.5 million requests in a year.