Automation | Definition of Automation at

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OTHER WORDS FROM automationproautomation, adjective UnabridgedBased on the Random House Unabridged Dictionary, Random House, Inc. 2020

Heroin blocks this automation so that when you fall asleep, you stop breathing.

Beyond doubt, the steady advance of automation on airplane flight decks has greatly helped to reduce accidents.

Automation or not, Leoh thought smilingly, there were certain human values that transcended mere efficiency.

We must modernize our unemployment insurance and establish a high-level commission on automation.

Automation, the second industrial revolution, has eliminated for all practical purposes the need for their labor.

Strong emphasis was placed on the introduction of automation in both production and management processes.

Automation rationalized away the literate component of many activities.


/ (tmen) /

the use of methods for controlling industrial processes automatically, esp by electronically controlled systems, often reducing manpower

the extent to which a process is so controlled

Collins English Dictionary - Complete & Unabridged 2012 Digital Edition William Collins Sons & Co. Ltd. 1979, 1986 HarperCollins Publishers 1998, 2000, 2003, 2005, 2006, 2007, 2009, 2012

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Automation | Definition of Automation at

Automation – Advantages and disadvantages of automation …

Advantages commonly attributed to automation include higher production rates and increased productivity, more efficient use of materials, better product quality, improved safety, shorter workweeks for labour, and reduced factory lead times. Higher output and increased productivity have been two of the biggest reasons in justifying the use of automation. Despite the claims of high quality from good workmanship by humans, automated systems typically perform the manufacturing process with less variability than human workers, resulting in greater control and consistency of product quality. Also, increased process control makes more efficient use of materials, resulting in less scrap.

Worker safety is an important reason for automating an industrial operation. Automated systems often remove workers from the workplace, thus safeguarding them against the hazards of the factory environment. In the United States the Occupational Safety and Health Act of 1970 (OSHA) was enacted with the national objective of making work safer and protecting the physical well-being of the worker. OSHA has had the effect of promoting the use of automation and robotics in the factory.

Another benefit of automation is the reduction in the number of hours worked on average per week by factory workers. About 1900 the average workweek was approximately 70 hours. This has gradually been reduced to a standard workweek in the United States of about 40 hours. Mechanization and automation have played a significant role in this reduction. Finally, the time required to process a typical production order through the factory is generally reduced with automation.

A main disadvantage often associated with automation, worker displacement, has been discussed above. Despite the social benefits that might result from retraining displaced workers for other jobs, in almost all cases the worker whose job has been taken over by a machine undergoes a period of emotional stress. In addition to displacement from work, the worker may be displaced geographically. In order to find other work, an individual may have to relocate, which is another source of stress.

Other disadvantages of automated equipment include the high capital expenditure required to invest in automation (an automated system can cost millions of dollars to design, fabricate, and install), a higher level of maintenance needed than with a manually operated machine, and a generally lower degree of flexibility in terms of the possible products as compared with a manual system (even flexible automation is less flexible than humans, the most versatile machines of all).

Also there are potential risks that automation technology will ultimately subjugate rather than serve humankind. The risks include the possibility that workers will become slaves to automated machines, that the privacy of humans will be invaded by vast computer data networks, that human error in the management of technology will somehow endanger civilization, and that society will become dependent on automation for its economic well-being.

These dangers aside, automation technology, if used wisely and effectively, can yield substantial opportunities for the future. There is an opportunity to relieve humans from repetitive, hazardous, and unpleasant labour in all forms. And there is an opportunity for future automation technologies to provide a growing social and economic environment in which humans can enjoy a higher standard of living and a better way of life.

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Automation - Advantages and disadvantages of automation ...

Automation – The Car Company Tycoon Game on Steam

We held back on launching Automation into Early Access until the game had a solid, fleshed-out core which the main tycoon part of the game will be based on. We also wanted to make sure we can offer enough content and polish to warrant presenting and selling the game to a larger audience.

Previously we offered an early access version of the game via our website, but this sales platform and distribution channel has been outgrown by the steadily increasing interest in the game, becoming complicated to manage for a small team like ours.

Finally launching the game on Steam Early Access makes possible to speed up development with any additional income, allowing for quicker content addition (car bodies, engines, etc.) than otherwise possible. It also allows us to get additional manpower to the team to tackle the huge job of game balancing and AI programming.

Last but not least, with the major milestones of completing the car designer and engine designer under our belt, the implementation of multiplayer features means using the Steam API for network communications, saving us a lot of double work associated with developing our own networking code first.

We're not known for being good with estimates, but always deliver and are good at avoiding feature creep. Our development process focuses on milestone builds that introduce new features every ~3-4 months and are both beta-tested and reasonably polished-up. Any major problems with these milestones are addressed quickly in hotfixes before we move on to the next milestone.

Quick Facts About Development:

Since Mid 2015, a portion of our team has been focused on porting Automation over to Unreal Engine 4, and currently all team members are focused on that version. Using UE4 as a basis is giving us the developers the tools to develop Automation better, faster, and maintain it far into the future.


Automation - The Car Company Tycoon Game on Steam

Bridging the Network Automation Skills Gap –

With the introduction of network programmability and APIs for everything from management systems to devices themselves, the modern network landscape has evolved to being managed like software. This shift from hardware-focused networks to software-centric functions has had a profound effect on network management techniques and skillsets required to keep pace with the changing ecosystem.

A recent EMA survey notes that skills gaps associated with network automation are an issue for 96% of enterprises. Even respondents with significant network automation initiatives in place felt their solutions stretched the abilities of their network teams. Network automation can expose skills gaps where network engineers might lack experience with new tools and software development. At the same time, application development teams, who likely would be included in building an in-house network or automation software solution, will also lack networking skills. Hybrid skills with both development and networking expertise are rare and available only at a premium.

This journey to network automation can be difficult, especially given the required technical skills that are not in abundance within many organizations. Simplifying network automation and empowering a broader set of participation through capabilities to easily create and manage automation workflows presents a key opportunity for organizations to not only mature efforts around network automation, but to also execute against broader business digital transformation objectives.

The key to democratizing network automation and bridging the skills gap is to provide NetOps teams with the ability to easily design and build network automation without having to re-tool, build custom code or learn specialized software skills. There are four key elements required to effectively democratize network automation in the enterprise.

Automation is typically accomplished through writing code in a particular programming language. For a person without any programming skills (or even minimal skills), they must first learn how to code before they can even attempt their first automation task. This is very difficult in terms of time and interest. Additionally, many network engineers see the amount of work it would take to learn to write code, and become disinterested. There has to be a new set of tools and methodology to empower these people to automate their work without the long road of learning to become programmers. Its important to invite as many people to the automation table as possible including IT, security, cloud and NetOps teams, to streamline business processes.

Capture Network Tribal Knowledge

Within the organization, there is already existing embedded tribal knowledge that is incredibly valuable. By harnessing and transferring that knowledge into an automation workflow without a lot of programming, it provides an accelerated path to network automation. The goal is to leverage traditional network automation and management practices (Scripts, CLI) for re-use across the organization.

Network engineers with years and years of experience have a valuable set of processes and procedures theyve built up inside their heads, and this wealth of information is a valuable asset. Unfortunately, this information is rarely documented and is normally passed on by activity and word of mouth. This is the type of tribal knowledge that needs to be captured, preferably through automation workflows that can be used in a task, documented in the workflow and modified over time to improve it or change it as the infrastructure changes.

Integrate DevOps Principles

Augment network automation with CI/CD pipelines and API services. These are well known solid principles that are already used in other domains outside of networking and pay big dividends when applied to the automation of complex networks. When we look along the horizon of the network automation journey, these same CI/CD practices will be adopted by future networking teams and allow them the same advantages of reliability and velocity within the network.

Enable Cloud Architect Participation

It is important to have networking people that can understand the networking constructs of the cloud, as well as cloud teams that understand how it integrates and operates within the network. Enabling cloud architect participation in network automation will allow for the real-time feedback and control of cloud-based applications and infrastructure critical to modern networks.

Cloud networking is increasing in complexity and will soon require the skills and experience of network engineers to help solve the problems of cloud networking. Traditionally, cloud and networking teams have been separated because their technology domains have been siloed and there was very little interaction required between the teams. As technologies like SD-WAN have been adopted, the line of responsibility between cloud and network teams has blurred. Cloud and networking teams will need an automation platform that can be used by both to create workflows and automate between both domains. Business technology decisions are forcing these two groups together, and they must have a way to use their own tools, but also have a platform to collaborate automation between them.

As applications and services are becoming more complex, distributed and require connectivity and policy enforcement across a diversity of domains (such as cloud, SD-WAN, data center, wireless and network applications), the management of these network concepts requires us to re-think how we have traditionally managed networks. As network automation plays an increasingly larger role in enterprise networks, a byproduct of the complexity is a skills gap that threatens to slow or stymie enterprise initiatives.

By making the onramp to network automation easier for non-developers through capturing tribal knowledge, integrating DevOps principles and enabling cloud architect participation, enterprises better ensure a successful migration to network automation across multiple domains.

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Bridging the Network Automation Skills Gap -

Yard work: Automation strolls out the warehouse door – ZDNet

Logistics and fulfillment centers have adopted automation at a blistering pace since Amazon acquired Kiva back in 2012. The inside of a modern fulfillment warehouse looks like a carefully orchestrated dance between human workers and a number of robots, including autonomous mobile carts and pick-and-place machines.

But the distribution yards outside logistics warehouses have been run largely the same way -- by humans -- for years. That's about to change as logistics robots head outside.

A company calledOutrider, which launched in 2017 and has been operating in stealth, recently closed $53 million in funding and deployed initial pilots of its Outrider System, a solution focused on autonomous yard operations for logistics hubs.

Think Tetris. Strategically, that's a bit what it's like constantly keeping semi-trailers coming and going with freight. The space between the warehouse doors and public roads, however, is a chokepoint at even the best-run logistics centers. Humans oversee the yard, and the processes are manual, inefficient, and often hazardous.

Robots can do better, or so argues Outrider.

"Logistics yards offer a confined, private-property environment and a set of discrete, repetitive tasks that make the ideal use case for autonomous technology. But today's yards are also complex, often chaotic settings, with lots of work that's performed manually," said Andrew Smith, founder and CEO of Outrider. "This is why an overarching systems approach with an autonomous truck at its center is key to automating every major operation in the yard."

Outrider's solution automates the repetitive, manual aspects of yard operations, including moving trailers around the yard, moving trailers to and from loading docks, hitching and unhitching trailers, connecting and disconnecting trailer brake lines, and monitoring trailer locations. At scale, Outrider will deliver yards that are more efficient, safer, and more sustainable.

"We're constantly looking for ways to transform our company and the way we get work done, especially making work safer and more efficient and productive," said Annant Patel, Vice President of Automation Transformation at Georgia-Pacific. "Yard operations has been one of our opportunities, and Outrider has been a great partner to help us automate our pilot site."

The automated system is made up of three components: Advanced yard management software, autonomous zero-emission yard trucks that feature vision-based robotics, and site infrastructure. The sales pitch is that the Outrider System integrates with existing supply chain software used by large enterprises. It's the same pitch that robotics companies like Fetch have made to warehouses. Companies can go live with automation without major retrofits and be online exceptionally quickly.

"Modern distribution yards won't be just autonomous, they'll be electric," continued Smith. "Electric yard trucks are easier to operate and maintain than their diesel counterparts, and they lend themselves to better computer control. Our mission is to work with customers and suppliers to rapidly retire the more than 50,000 diesel-polluting yard trucks currently operating at logistics hubs throughout the U.S."

Outrider has been active helping define the next-generation standard for Level 4 Autonomy System Design.

"Outrider is introducing the transformational technology required for large, logistics-dependent enterprises to keep pace," said Jake Medwell, Founding Partner at 8VC. "We consider hundreds of investment opportunities in the logistics space every year. Our decision to be an early investor in Outrider was an easy one. Andrew's vision and plan for the industry are highly compelling, and he's mobilized an unmatched team to execute."

The company is based in Golden, Colorado.

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Yard work: Automation strolls out the warehouse door - ZDNet

Five Steps To Get Started With Robotic Automation – Forbes

This past summer, McKinsey reported that 88% of businesses want to implement more robotic automation. However, its hard to know where to start, because planning and implementing that automation project can seem daunting. What processes in my plants should I automate first? How do I calculate my ROI? How do I handle high-mix, low-volume processes? How will my workers react?

If you are an enterprise or conglomerate, things are even more complex. Which parts of my business are most conducive to automation? How do I replicate successes across my entire organization?

These are the questions that many business owners and stakeholders have asked me, everyone from tier-one automotive suppliers to 10-person machine shops, from small businesses to Fortune 100 enterprises. Here are the five steps I share with them to make their automation rollout smooth, cost-effective and repeatable.

1. Find The Low-Hanging Fruit

When looking for opportunities to automate, companies often gravitate to the most difficult tasks. The rationale behind this is simple: Automation can be complex and time-consuming; therefore, companies look for the highest-reward applications that are worth the effort. What I tell my clients is that the processes they should look to automate:

Are low in complexity.

Underuse people.

Cause bottlenecks in production.

Involve dull, dirty or dangerous work.

Whenever I visit a factory, I say the same thing: Show me a relatively simple process where a worker is spending more than half their time waiting for another process to finish, or where production is being bottlenecked by the availability of labor, or where that worker is constantly in reach of heavy machinery, and we should talk about automation.

2. Automate Incrementally

There is value in the large-scale automation of processes, but usually, a production process is a mix of menial, relatively simple, repetitive tasks and complex tasks that require significant worker knowledge and expertise. It is a mistake to try to automate this entire process because automating the tasks that require significant expertise will inevitably require a huge investment in time and money.

Instead, a better approach is to find the easy-to-automate tasks of that larger process and start with those. If after you have succeeded in those initial tasks you expand the scope of automation, you can do so incrementally.

3. Focus On Your People

Whenever I visit a factory, I ask the same questions about each process I see: How long does the process take, and how much time does a worker spend doing that process?

Take machine tending as an example, where a worker takes a few seconds to place raw material in a machine that then takes time to process that material (through material removal, heat, pressure, etc.). When the machine is done, the worker removes the finished part. Now, if that process takes 10 minutes, then chances are that worker is tending many of those machines or has a different task to do during the intervening time.

However, if the process takes less time, then the worker may not be able to leave that work station. This means that with automation, there may be an opportunity for that worker to do more value-add tasks that leverages other skills of that worker.

4. Aim For 80%, And Iterate

Being agile is not an excuse for delivering something that doesnt work. The 80% solution has to provide value and an acceptable ROI, but it also doesnt have to be perfect. The mantra here needs to be My automation solution must provide an acceptable amount of value over the nonautomated process, and then I can improve that over time. This allows you to extract value out of your automation system immediately before it is perfect, and then reap greater rewards as that project goes through continuous improvement.

5. Advertise Your Success

Once youve been successful at automating your first process, it becomes easier to automate your next task. In my experience, most low-hanging-fruit tasks are not unique, and by automating one instance of that task, youve likely created a template for automating more tasks throughout your factory or even your company.

The key, though, is to advertise your success to the rest of your organization so the automation benefits can be replicated. Once you create buy-in around a solution, with concrete evidence about its positive effects in the business, the rest of your organization will be chomping at the bit to copy it.

Dont Wait; Dive In

The hardest part of most projects is taking that first step. I have used these five steps to help many manufacturers begin their automation journey, starting from their first cell and building up to entire automated lines. Dont wait until you have a master plan be agile. Kick things off today by looking for that first low-hanging project, and use it as a quick win to act as the foundation for your widespread automation success.

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Five Steps To Get Started With Robotic Automation - Forbes

Bosses speed up automation as virus keeps workers home – The Guardian

Almost half of company bosses in 45 countries are speeding up plans to automate their businesses as workers are forced to stay at home during the coronavirus outbreak.

Some 41% of respondents in a survey by the auditing firm EY said they were investing in accelerating automation as businesses prepared for a post-crisis world.

The news comes just days after figures showing that 3.3 million people have filed for unemployment in the US. That is by far the highest number ever recorded, and a jump from less than 300,000 the week before. In the UK, 477,000 people applied for universal credit in just nine days.

The human cost is the most tragic aspect of this crisis, not only in terms of the lives lost, but also the number of livelihoods at risk, said Steve Krouskos at EY.

As business leaders respond with urgency to the unprecedented impact that Covid-19 is having globally, workforce welfare and job preservation will be at the top of their minds.

In the UK, the government said it would guarantee 80% of salaries for workers who are unable to do their jobs because of the outbreak.

Meanwhile, most American adults are expected to receive a cheque of up to $1,200 (950) after politicians approved a $2tn stimulus package to breathe life into the economy.

Once the situation stabilises, executives will have to make faster moves to reimagine, reshape and reinvent their business and create long-term value

Of the 2,900 executives who were surveyed by EY, 43% said they expected normality to return by the third quarter of this year. But until then, 73% said, Covid-19 would have a severe impact on the global economy.

A majority of companies said they were already planning major transformation before the pandemic hit. Once normality returns, they would focus on new investment in digital and technology, the survey found.

Business leaders are seeing their transformation plans paused or slowed currently. With these plans set to restart, possibly with added energy, once the situation stabilises, executives will have to make faster moves to reimagine, reshape and reinvent their business and create long-term value, Krouskos said.

About 1.5 million workers in Britain are at high risk of losing their jobs to automation, according to government estimates. Supermarket checkout assistants have already borne the brunt of the phenomenon, the Office for National Statistics said last year, with 25.3% of jobs disappearing between 2011 and 2017.

Some technology groups are already experimenting with retail outlets that will not require human-run checkouts or cashiers. Amazon, which has expanded into grocery selling, has a supermarket in Seattle with no checkout assistants, relying instead on sensors to track what shoppers removed from shelves, using just walk out technology to bill customers and end queues. McDonalds is shifting to self-ordering kiosks in its restaurants, removing the need for customers to speak to workers at the counter.

Other jobs where automation has taken its toll include laundry workers, farm workers and tyre fitters, among which numbers have dropped by 15% or more, said the ONS, as machines have replaced labour.

Women are most likely to lose out, the ONS said. The analysis showed a higher proportion of roles currently filled by women are at risk of automation. In 2017, 70.2% of high-risk jobs were held by women.


Bosses speed up automation as virus keeps workers home - The Guardian

ValueLink drives efficiency in the valuation process through a combination of automation, AI and analytics – HousingWire

Valuations are one of the most important parts of the loanprocess, and often the most time-consuming as well. Appraisals that containerrors or arent delivered on time can cause delayed closings or, in somecases, result in the loan falling through altogether.

In an increasingly digital world customer expectations are on the rise. Streamlining the mortgage application process and reducing the time to closehave become paramount. The industry is ripe for disruption and ValueLink is at the cusp of driving the next wave of innovation.

ValueLink offers customized valuationmanagement solutions designed for lenders, appraisal management companies(AMCs) and appraisers. The solutions are designed tosimplify order management by automating the process and reducing thetouchpoints between various stakeholders, while ensuring regulatory compliance.

Seamless integrations with the leading LOS platforms ensure that lenders can work in systems they are already familiar with, while Connect acts as a unified platform for valuation professionals who can respond to client requirements in real-time using the mobile apps.

With a network of 100-plus AMCs already using the ValueLink platform to manage their entire order workflow, lenders can engage them within minutes and start sending out orders.

Automation is the key driver for efficiency and ValueLink ensures that lenders can customize the platform to completely automate the valuation workflow.

Each step requires minimal human intervention and orders can be automatically assigned to the best vendor based on availability and geographic competence. Follow-ups are automated using the SmartAssist engine that also provides workflows designed to identify when to escalate an order and get a human involved.

Our direct integrations with the leading LOS platformsprovide lenders real-time visibility into the valuation process and thepowerful reporting and analytics tools put important data at their fingertips.said Aqil Ahmed, SVP Operations at ValueLink.

Underwriters can take full control of their review processusing the proprietary CrossCheck tool and augment it with integrated offeringsfrom partners. Real-time data validation ensures underwriters spend minimaltime doing stare and compare reviews.

Built-in data analytics tools make it easy to take a deepdive into the order data and make faster decisions. Dissect the valuation databy geography, vendors or workflow stage and quickly identify what needsimprovement. The reporting engine allows building real-time reports that can bescheduled for automated delivery.

The available APIs allow controlling order workflows fromproprietary systems and use the data in ways best suited to your organization.

ValueLink has built the most powerful portfolio of valuationmanagement tools and tied them together with industry leading platforms tobring a unified and frictionless experience to the valuation process. Withinnovation at its core, the company will continue to drive progress in thevaluation space.

Our company has been driving valuation innovation for a decade now and moving forward we will be utilizing AI and machine learning to speed up the valuation process while reducing costs and increasing operational efficiencies for our customers, added Farrukh Omar, chief operating officer at ValueLink Software.

Farrukh Omar, COO

Farrukh Omar graduated from the University of Houston in2002 and founded ValueLink Software in 2009. At ValueLink, Farrukh oversees allaspects of the operations, including product development, technology andinfrastructure, with a passion to build products that are loved by ValueLinkcustomers.

Aqil Ahmed, SVP Operations

Aqil Ahmed joined the ValueLink team in 2015. As senior vicepresident of operations, Aqil oversees the day-to-day operations and managesthe sales and operations teams. He also oversees relationships with ValueLinkslargest customers and is directly involved in formulating the sales strategyintegral to building the customer base at ValueLink.

Bill Omar, SVP Client Relations

Bill Omar joined ValueLink in early 2012. Since then, he hasbeen involved with all aspects of operations at the company, from customerrelations to the technology behind ValueLink products. As the senior vicepresident of client relations, Omar oversees the companys support andonboarding teams.

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ValueLink drives efficiency in the valuation process through a combination of automation, AI and analytics - HousingWire

What is Feature Engineering and Why Does It Need To Be Automated? – Datanami


Artificial intelligence is becoming more ubiquitous and necessary these days. From preventing fraud, real-time anomaly detection to predicting customer churn, enterprise customers are finding new applications of machine learning (ML) every day. What lies under the hood of ML, how does this technology make predictions and which secret ingredient makes the AI magic work?

In the data science community, the focus is typically on algorithm selection and model training, and indeed those are important, but the most critical piece in the AI/ML workflow is not how we select or tune algorithms but what we input to AI/ML, i.e., feature engineering.

Feature engineering is the holy grail of data science and the most critical step that determines the quality of AI/ML outcomes. Irrespective of the algorithm used, feature engineering drives model performance, governs the ability of machine learning to generate meaningful insights, and ultimately solve business problems.

Feature engineering is the process of applying domain knowledge to extract analytical representations from raw data, making it ready for machine learning. It is the first step in developing a machine learning model for prediction.

Feature engineering involves the application of business knowledge, mathematics, and statistics to transform data into a format that can be directly consumed by machine learning models. It starts from many tables spread across disparate databases that are then joined, aggregated, and combined into a single flat table using statistical transformations and/or relational operations.


For example, predicting customers likely to churn in any given quarter implies having to identify potential customers who have the highest probability of no longer doing business with the company. How do you go about making such a prediction? We make predictions about the churn rate by looking at the underlying causes. The process is based on analyzing customer behavior and then creating hypotheses. For example, customer A contacted customer support five times in the last month implying customer A has complaints and is likely to churn. In another scenario, customer As product usage might have dropped by 30% in the previous two months, again, implying that customer A has a high probability of churning. Looking at the historical behavior, extracting some hypothesis patterns, testing those hypotheses is the process of feature engineering.

Feature engineering is about extracting the business hypothesis from historical data. A business problem that involves predictions such as customer churn is a classification problem.

There are several ML algorithms that you can use, such as classical logistic regression, decision tree, support vector machine, boosting, neural network. Although all these algorithms require a single flat matrix as their inputs, raw business data is stored in disparate tables (e.g., transactional, temporal, geo-locational, etc.) with complex relationships.


We may join two tables first and perform temporal aggregation on the joined table to extract temporal user behavior patterns. Practical FE is far more complicated than simple transformation exercises such as One-Hot Encoding (transform categorical values into binary indicators so that ML algorithms can utilize). To implement FE, we are writing hundreds or even thousands of SQL-like queries, performing a lot of data manipulation, as well as a multitude of statistical transformations.

In the machine learning context, if we know the historical pattern, we can create a hypothesis. Based on the hypothesis, we can predict the likely outcome like which customers are likely to churn in a given time period. And FE is all about finding the optimal combination of hypotheses.

Feature Engineering is critical because if we provide wrong hypotheses as an input, ML cannot make accurate predictions. The quality of any provided hypothesis is vital for the success of an ML model. Quality of feature is critically important from accuracy and interpretability.

Feature engineering is the most iterative, time-consuming, and resource-intensive process, involving interdisciplinary expertise. It requires technical knowledge but, more importantly, domain knowledge.

The data science team builds features by working with domain experts, testing hypotheses, building and evaluating ML models, and repeating the process until the results become acceptable for businesses. Because in-depth domain knowledge is required to generate high-quality features, feature engineering is widely considered the black-arts of experts, and not possible to automate even when a team often spends 80% of their effort on developing a high-quality feature table from raw business data.

Feature engineering automation has vast potential to change the traditional data science process. It significantly lowers skill barriers beyond ML automation alone, eliminating hundreds or even thousands of manually-crafted SQL queries, and ramps up the speed of the data science project even without a full light of domain knowledge. It also augments our data insights and delivers unknown- unknowns based on the ability to explore millions of feature hypotheses just in hours.

Recently, ML automation (a.k.a. AutoML) has received large attention. AutoML is tackling one of the critical challenges that organizations struggle with: the sheer length of the AI and ML project, which usually takes months to complete, and the incredible lack of qualified talent available to handle it.

While current AutoML products have undoubtedly made significant inroads in accelerating the AI and machine learning process, they fail to address the most significant step, the process to prepare the input of machine learning from raw business data, in other words, feature engineering.

To create a genuine shift in how modern organizations leverage AI and machine learning, the full cycle of data science development must involve automation. If the problems at the heart of data science automation are due to lack of data scientists, poor understanding of ML from business users, and difficulties in migrating to production environments, then these are the challenges that AutoML must also resolve.

AutoML 2.0, which automates the data and feature engineering, is emerging streamlining FE automation and ML automation as a single pipeline and one-stop-shop. With AutoML 2.0, the full-cycle from raw data through data and feature engineering through ML model development takes days, not months, and a team can deliver 10x more projects.

Feature engineering helps reveal the hidden patterns in the data and powers the predictive analytics based on machine learning. Algorithms need high-quality input data containing relevant business hypotheses and historical patterns and feature engineering provides this data. However, it is the most human-dependent and time-consuming part of AI/ML workflow.

AutoML 2.0, streamlines feature engineering automation and ML automation, is a new technology breakthrough to accelerate and simplify AI/ML for enterprises. It enables more people, such as BI engineers or data engineers to execute AI/ML projects and makes enterprise AI/ML more scalable and agile.

About the author: Ryohei Fujimaki, Ph.D., is the founder and CEO of dotData. Prior to founding dotData, he was the youngest research fellow ever in NEC Corporations 119-year history, the title was honored for only six individuals among 1000+ researchers. During his tenure at NEC, Ryohei was heavily involved in developing many cutting-edge data science solutions with NECs global business clients, and was instrumental in the successful delivery of several high-profile analytical solutions that are now widely used in industry. Ryohei received his Ph.D. degree from the University of Tokyo in the field of machine learning and artificial intelligence.

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What is Feature Engineering and Why Does It Need To Be Automated? - Datanami

Parascript and Le Mans Tech Partner to Offer Integrated Automation Solutions to Small and Midsize Banks – Yahoo Finance

Longmont, CO, April 02, 2020 (GLOBE NEWSWIRE) -- Parascript, which supplies the digital workforce with automated data entry solutions that process over 100 billion documents annually, announced today its new partnership with Le Mans Tech, LLC to offer affordable check and document automation solutions to small and midsize businesses. Le Mans Tech, LLC specializes in process automation, networking and document management to improve client operations while maintaining compliance and reducing costs.

Le Mans Tech has a tightknit team that is growing rapidly due to the success of its Daytona document solution, which now leverages Parascript CheckPlus and CheckUltra.

Initially, we were looking for a higher accuracy read on checks and found it with Parascript, said Tom Boser, Co-Founder of Le Mans Tech. Our new partnership provides a lot more flexibility in how we manage licenses. We also discovered that Parascripts SDKs are easy to integrate and took less than a day to get up and running so we thoroughly tested them. Besides, the customer support is phenomenal.

The document automation, which Le Mans Tech provides small and midsize financial institutions, is in high demand because many banks are looking to scale up their existing automation and reduce their data entry. They need to rapidly extract key data automatically from their paper and born-digital documents to store in their systems. Also, that data must be extremely accurate. Le Mans Tech eliminates manual keying where possible so Daytona users can focus on the broader process and only perform data validation, which there is less need for due to the higher accuracy and ease-of-use offered by Parascript software.

Le Mans Tech is a terrific partner for us because they understand their clients challenges, have successful, landmark implementations and extensive experience integrating intelligent capture into their custom solutions, said Ati Azemoun, Vice President of Business Development at Parascript. Small and midsize companies are often still manually doing their document processing because its too expensive to adopt new and updated automation. Together, we offer formidable check processing, form recognition and invoice capture automation solutions within a unique system that is Daytona.

Often the automation that companies have is almost a decade old, limited to a department or not implemented fully because of the expense to implement, maintain and sustain those legacy systems. By leveraging Parascripts proprietary deep learning algorithms, checks and other documents are processed in a significantly smarter, more human-like way.

This is a word-of-mouth environment where we get referrals based on our previous successes. There are a lot of areas we are expanding into with intelligent document processing, said Mr. Boser. We are building small business solutions to automate a wide range of processes that are highly capable offered at the right price-point. Since we are all about excellent service, its nice to have a partner like Parascript that is looking out for us.

About Le Mans Tech, LLC

From networking, document management to process flow automation, Le Mans Tech, LLC improves the clients operations while lowering costs, improving productivity and maintaining compliance. Le Mans Tech utilizes Enterprise Content Management (ECM) technologies and software to manage documents and create workflows to automate processes. In addition to ECM, Le Mans Tech also specializes in network design, workflow optimization, business process automation, document management and records management. Visit Le Mans Tech.

About Parascript, LLC

Parascript supplies the digital workforce with AI-based software that automates human tasks involved with document-based information. Parascript provides intelligent capture leveraging machine learning with real-time adaptability and auto-configuration. Our software offers easy-to-use, image-based analysis, classification, data location, extraction and verification. More than 100 billion documents for financial services, government organizations and the healthcare industry are analyzed annually by Parascript software. Parascript offers its technology both as software products and as software-enabled services to our partners. Our BPO, service provider, OEM and value-added reseller network partners leverage, integrate and distribute Parascript software in the U.S. and across the world. Visit Parascript.

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Parascript and Le Mans Tech Partner to Offer Integrated Automation Solutions to Small and Midsize Banks - Yahoo Finance Powers Digital Advertising Innovation and Automation on Pinterest – Business Wire

NEW YORK--(BUSINESS WIRE), the leading social advertising automation platform for creative and performance marketers, today announced new innovative capabilities to its Pinterest solution. In close partnership with Pinterest, advanced its offering on the platform to enable brands to innovate and connect with customers in entirely new ways.

Pinterest is increasingly becoming an attractive platform for advertisers, and with solutions such as Shopping Ads, Pinterest continues to evolve its ad offerings and expand its capabilities to move down the funnel.

Along with creative automation tools that enable brands to produce creative at scale, sleek campaign management and customized reporting capabilities, is now bringing its Automated Ads solution offered for other online channels to Pinterest. has been working with some of the largest and most advanced retail and CPG brands to target customers with customized offers to drive bottom-funnel metrics, such as online checkouts and bookings. This innovation is now available to a wider set of advertisers, including direct-to-consumer, travel, grocery and more. Brands are empowered to easily run hyper-targeted and localized campaigns on Pinterest that dynamically showcase real-time pricing, localized offers, flash promos, copy variations and even weather-based promos to consumers.

Smart marketers are those who test, adapt and move quickly -- and while most marketing teams are eager to try new channels -- they often lack the time, creative resources or best-practice knowledge, said Tuomo Riekki, Chief Product Officer and Co-Founder, Pinterest is a prime example of a channel that continues to evolve and outpace brand expectations. Our new solution helps them get there -- simplifying the process to run ads by letting brands leverage their assets used on Facebook and Instagram on Pinterest, all while reducing manual work with automation.

With high buyer intent on Pinterest, Smartly.ios solution now makes it easy for marketing teams to get started on this channel and scale their campaigns with:

"Pinterest is a natural platform for us to be on; consumers are seeking to discover new brands there and its a very visual channel in general, said Aubrie Richey, VP of Customer Acquisition & Media, TechStyle Fashion Group. Weve worked with previously on Facebook and Instagram campaigns, and they've now provided the path to unlocking incremental reach on Pinterest."

With, brands across industries from grocery and direct-to-consumer to travel and apps can now not only get started with Pinterest advertising, but begin innovating and testing different tactics to engage with consumers. Ultimately, embracing Pinterest as a performance channel, marketers across verticals can plug in any of their data sources or APIs to deliver more customized promotions.

Learn more about Smartly.ios approach to Pinterest social advertising and read our Pinterest eBook.


Powering beautifully effective ads, automates every step of social advertising to unlock greater performance and creativity. We combine creative production and ad buying automation with outstanding customer service to help 600+ brands scale their results not headcount on Facebook, Instagram and Pinterest. We are a fast-growing community of 350+ Smartlies with 16 offices around the world, managing over 2.5B in ad spend and growing rapidly and profitably. Visit to learn more.

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Overcoming the challenges of intelligent automation – ITProPortal

Robotic process automation (RPA) software is the fastest-growing segment of the global enterprise software market. Its easy to see why. Intelligent Automation (IA) and RPA tools automate repetitive and mundane tasks, freeing up employees to do more high value work. In turn, this helps organisations deliver better customer experiences, increase business agility and improve productivity. Yet, many companies are struggling to realise these benefits, or are not exploring them at all, due to the real and perceived challenges associated with the technology.

Although IA and RPA have applications in almost every industry, many of the logistical and technological challenges that businesses face, from making the business case to automate, to implementing and embedding the technology, are the same.

IA has many individual sensory capabilities; from using image recognition to scan photographs, to converting the spoken word to text, to predicting the future based on past actions. However, scaling this technology is essential to achieve true business transformation. Getting it right means marrying vision and strategy. This follows three stages:

With any technology, security absolutely needs to be a main priority. For IA the biggest security issue often arises at the point where human and machine interact. For example, human error during an automated financial reporting process can result in losing-man weeks not days and a delay on the reporting of the groups finances.

Other security issues that need to be considered include rogue access; data loss; hacking; privilege abuse; vulnerabilities and malware, which all show the centrality of security to IA implementations.

But like well backed up data all is not lost! Those looking to implement IA should heed security protocols like encrypting data and multiple layers of authentication, along with reducing access rights and requiring human validation on certain processes.

No discussion of IA would be complete without an examination of the organisational changes that are taking place. New technologies will create a number of different roles in the future.

When we think of the benefits of automation, we typically consider things such as time saving, headcount reduction and reducing processing times. But there are other significant benefits which dont typically appear in business cases:

IA impacts also AI. Faulty data or human error could affect AI and its smooth running. IA can overcome the roadblocks in AI implementation..

AI eats data. It needs a lot of correct data to be able to function. There are practical considerations things like enforcing two factor authentication but human considerations as well. Indeed, successfully implementing AI also meant incorporating fairness; reliability & safety; privacy and security; transparency and accountability.

Its also important to remember what AI is not. AI, contrary to popular belief, does not see output simply improve over time. Indeed, in the beginning, AI functionality is based on what it has been taught by humans. As soon as it is in use, it collects more data and thus becomes more and more precise.

But before AI is used, it is trained with pre-selected data. As soon as it stops being fed with training data, AI begins to classify new data in the same way it has been trained before. But AI does not learn anything new. If AI cannot categorise some of this new data, the accuracy of the output worsens (called data drift). In which case, AI has to be retrained.

This goes hand in hand with one of the biggest misconceptions of AI projects. The work does not stop after the initial roll out. AI models have to be continuously verified while they are in use.

And, as always, we must think about money too. While some form of AI applications do require the work of expensive data scientists and/or computational linguistics, an increasing number of AI software tools are becoming easily accessible to businesses and do not require big investments. While advanced AI technology does require deep understanding in programming languages, most enterprises will opt to leverage business applications developed on top of tools that were built by companies such as Google, Amazon or expert startups. Examples are Amazons Alexa or Google Home which provide multi-lingual voice recognition. By using these existing tools, the business value lies in configuring customer centric components tailored for specific needs and less in the application of data science.

IA and AI has already been shown to have profound and far-reaching benefits and, while it is difficult if not impossible to predict with complete certainty where the technology will go next, what is more certain is that its use will only proliferate.

Tom Leggett

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Overcoming the challenges of intelligent automation - ITProPortal

Will the COVID-19 Pandemic Promote Mass Automation? – Walter Bradley Center for Natural and Artificial Intelligence

Will business managers seize on the coronavirus (COVID-19) pandemic as a chance to automate away many workers jobs while they are ? That concern was recently voiced at The Guardian:

Almost half of company bosses in 45 countries are speeding up plans to automate their businesses as workers are forced to stay at home during the coronavirus outbreak.

Some 41% of respondents in a survey by the auditing firm EY said they were investing in accelerating automation as businesses prepared for a post-crisis world.

Due to the massive disruption of business, a record 3.3 million Americans filed for unemployment for the week ending March 21, 2020. Analysts have noted that the key difference between the global coronavirus impact and past periods of economic distress is that it is sudden and impacts virtually every industry and business model around (CNN, March 26, 2020).

How will automation help if there is hardly any business to automate? And if there is business out there, would automation help?

Machines have a significant advantage over human workers: You can turn them off. And, when you do, no one complains and no one files for unemployment. In fact, a business would likely save money due to reduce power consumption and worker benefits. Machines, provided they perform as promised, can enable a rapid response to market changes.

The coronavirus disruption, for example, has caused a historic drop in business demand. The week before coronavirus really hit, only 300,000 Americans filed for unemployment 90% fewer. Such rapid changes are hard for any business to absorb. Worse (from a strict dollars and cents perspective) ceasing to pay an employee does not mean that the expenses stop. Employees may have accrued vacation, sick leave, and other benefits owing. All these put a strain on a businesss cash flow just when the cash stops flowing.

Soagain, from a strict dollars and cents perspectiveincreasing automation as a hedge against a huge, sudden drop in demand makes sense for businesses.

But, does it make sense overall and will it work? Over the last year, Ive covered many occasions in which automation failed to live up to the hype. Here are a few:

The bottom line is, automation often works but it often fails too, often for unforeseen reasons. While it can help humans do better, it cannot replace them.

I understand the panic many business leaders experience as they try to stay solvent while customers evaporate. Panic, however, is a poor teacher: AI-based automation will not only not solve all their problems, it may very well add to them. AI is not a magic box into which we can stuff them and make them disappear.

We will get past SARS-Cov-2 (COVID-19) Life, and business, will return to something more normal (even if it is a new normal). And it may be that we, all together, will need to create safety measures to better protect local businesses and their employees from world class catastrophes.

Further reading: Robot-proofing your career, Peter Thiels way Jay Richards and Larry L. Linenschmidt continue their discussion of what has changedand what wont changewhen AI disrupts the workplace.


We will never go back to the pre-Covid-19 workplace (Jonathan Bartlett)

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Will the COVID-19 Pandemic Promote Mass Automation? - Walter Bradley Center for Natural and Artificial Intelligence

Automating with a solid foundation – ITProPortal

In an effort to get from point A to point B as quickly as possible, many companies jump into automation without considering the bigger picture. They adopt one tool to solve a problem, and then another one to handle a different set of challenges. The end result is that organisations use multiple tools and technologies many of which dont co-operate and collaborate with each other to handle different parts of a larger process.

What else causes friction in the business journey? Processes that exist but havent yet been automated. When asked about the untapped opportunity within their organisations, more than three-fourths of senior executives said 60 per cent or more of process work could be automated while nearly one in five said 80 per cent or more, according to a Forbes Global Insight survey.

Clearly, theres still much work to be done to connect the dots and cobbling together solutions wont cut it. Thats why a platform-centric approach is best. It eliminates the need to have humans fill the gaps in processes and saves the time and headaches associated with making multiple, disparate tools work together. Whats more, the flexibility of a single platform built on complementary technologies allows businesses to be more agile and meet the demands of customers, employees and suppliers today, next week and next year.

Of course, multiple technologies will always be needed to handle different parts of the business journey. Itd be great if this could be achieved with a single automation tool, but thats not the case. For example, executives and managers need access to advanced analytics, while back office workers in accounting and finance often benefit the most from Robotic Process Automation (RPA) and cognitive capture. Just about everyone wants support for mobile (including external vendors and customers).

A KPMG and HFS Research survey found companies are investing in a broad array of these intelligent automation capabilities, but only about 10 per cent say theyre leveraging an integrated solution approach. Yet its a platform-centric approach which enables organisations finally to stop cobbling together solutions and close the gaps.

A combination of smart technologies such as RPA, cognitive computing, process orchestration, mobility and engagement designed to work together and integrated on a single platform, will transform your business, making it more agile and competitive. The following steps will help you make a smooth transition to a platform-centric approach and achieve end-to-end automation faster.

Repetitive, manual tasks bog employees down as they navigate between systems and copy-and-paste data. Its inefficient and not a smart use of employees time. RPA uses software robots to automate manual, data-driven activities. These bots can easily integrate data from internal and external systems including websites, portals and enterprise applications without the need for coding or months of development time. You can deploy digital workers quickly and scale as needed, freeing up your human workers to focus on strategic tasks that add more value to the business. The right balance of digital and human workers gives your business the edge required to stay competitive and stand out in the marketplace.

Processing documents and electronic data is another bottleneck in the digital transformation journey for many businesses. Cognitive capture, however, transforms how documents and electronic data are captured and processed. Multichannel document capture is combined with optical character recognition (OCR), delivering cognitive document automation (CDA) technology which enables organisations to quickly process documents, images and unstructured data. Artificial intelligence takes it to the next level, using machine learning and natural language processing to identify, classify and extract content and data from documents and records. Employees know exactly what a given documents about and what information it contains, so they know what the next appropriate step is in the process. The application of these additional intelligent technologies also makes it possible to integrate CDA with downstream processes and make connections with other internal systems, such as CRM applications all of which contribute to complete end-to-end automation.

Its not enough to automate simple tasks. Truly successful organisations take automation to the next level, simplifying inefficient processes and creating a streamlined workflow that benefits employees, vendors and customers. You can apply process orchestration to a range of functions across the enterprise including supplier and customer self-service, claims processing, compliance and regulation checks and customer onboarding.

Capabilities such as omnichannel document capture and extraction, mobile access, workflow automation and optimisation can improve employee productivity, increase operational efficiency and lower costs. Meanwhile, integration options and analytics can accelerate the customer journey and help you uncover new opportunities.

Your customers want interaction with your business to be easy and on their terms. Mobile technology allows companies to engage with customers in their preferred communications channels and eliminates the need to enter information manually. Businesses can also leverage advanced analytics to optimise the customer experience and resolve issues faster at every stage of the process. A powerful mobile solution includes support for identity verification, document gathering, personalised omni-channel communications and e-signature within a secure end-to-end mobile experience.

With real-time information, you can make smart decisions and drive the business forward. Advanced analytics monitor and analyse information across business processes and systems. Youll gain an accurate and comprehensive view of how the business is doing and where it needs to adapt to the changing marketplace. Customer service and sales reps have the information they need to hold more engaging conversations with customers. Real-time data means problems are identified and addressed immediately, before they get out of hand.

To work like the digitally enabled company of tomorrow, organisations need processes not just tasks to be seamlessly integrated and automated end-to-end. A platform-based approach eliminates the need to have humans fill the gaps in processes and saves the time and headaches associated with making multiple, disparate tools work together.

Ultimately, companies can achieve their digital transformation objectives the easy or hard way. Businesses can continue to cobble together solutions or they can be more agile, dramatically accelerate time-to-value and improve ROI with a platform-based approach.

Chris Huff, CSO, Kofax


Automating with a solid foundation - ITProPortal

Attractive Opportunities in Automation – Morningstar

Holly Black: Welcome to Morningstar. I'm Holly Black. With me is Denise Molina. She is an equity analyst at Morningstar. Hello.

Denise Molina: Hi.

Black: So, a common phrase we hear in investing is that we should be greedy when everyone else is fearful. And I think people are quite fearful at the moment with stock markets falling, but you're seeing opportunities in the supply chain side of things. Can you tell us a bit more about that?

Molina: Yeah. During troubled times in the economy, when we have recessions, companies as like other people in the economy tend to hoard cash. So, the first thing that happens with supply chain and with capital goods companies is they see a drop in demand for orders that are called short cycles. So, there's a large part of cap goods that is ordered and delivered within a quarter. And if you're a company that's uncertain about your cash flow outlook, you're going to cut CapEx and within the CapEx budget, it's going to be all those capital goods products.

So, what we're anticipating is a pretty steep drop in earnings for capital goods companies coming up in the second quarter because this particular go around is not just like a recessionary environment, it is literally the spigot being turned off. So, plants are being shut down, because of social distancing. So, that means that the supply is severely disrupted and orders are not getting through and they're not being produced. So, what we're going to see is something pretty dramatic in the second quarter. And it could be to some share prices declining to levels that we think could become more attractive than they are right now. So, with the caveat that we've got earnings volatility ahead of us and potential further declines in share prices, we see some really nice companies that haven't been cheap for a while, potentially coming on sale that we'd be looking at and watching for good opportunities to get into.

Black: So, when you talk about capital goods, what sorts of companies are you looking at? Where are we seeing those opportunities?

Molina: Well, right now, we've seen some of the companies that are exposed to automotive end markets and to the oil price end markets. And so, one of the things that we look at it is, diversified like ABB and Schneider, these companies supply into refineries, into utilities, into general manufacturing and those orders are going to be cut severely, as we all know that refiners, the integrated oil companies have already announced CapEx budget declines. So, we know that 2019 is going to be pretty horrific in terms of earnings. But those companies will see those orders come back. And if you look at the Great Recession, if you look at the pattern of capital goods spend, the drop-off in 2009 was pretty dramatic. But then that same volume of demand came back within two years' time. So, capital goods are things you can hold back on in the short term but not in the long term because equipment ages that basically runs your plants or refineries. So, you need to spend on it eventually. You can only hold off so long.

So, ABB we like because it not only has a really strong portfolio of moaty products, but it also has the number two robotics company in the world. So, they are the number two supplier of robotics. Robotics has a long, long runway of growth. It's really only used heavily in automotive. So, we like that long-term in terms of the automation trends and adoption rates for robotics to offer promise long term. It also has exposure along with Schneider to ESG theme. So, if you think about building automation and building automation, you can think about like how to control energy costs, make sure that the lights off when nobody is in the building, make sure that you're not powering up equipment that's not being used. So, that kind of spend is something that we see as also a long-term trend. And ABB and Schneider both supply a lot of software and components that help companies become more ESG-compliant.

Black: So, you said you're expecting a bit of a horrific 2020 before a bounce back? How do investors handle this? Do they buy in now? Or do they wait, or should they already be invested and just waiting for the recovery?

Molina: Well, we're certainly not market timers. And we don't think we can be precise about where the bottom is. But we do think that there's potentially more volatility to come that the sector hasn't sold off as much as it did during the Global Financial Crisis. And it certainly was expensive before we went into this situation. So, we've seen some pretty decent drops in some of the companies and we think that there could be further declines as the second quarter comes out. As you said, there's going to be probably some pretty dramatic earnings declines from these shutdowns and that's maybe not fully appreciated yet by the market, especially when the US starts to put out their numbers.

Black: Denise, thank you so much for your time. For Morningstar, I'm Holly Black.

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Attractive Opportunities in Automation - Morningstar

Industry could fast-track automation amid COVID-19 fallout –

Editor's Note: Mining Journal is making some of its most important coverage of the COVID-19 pandemic freely available to readers. For more coverage, please see ourCOVID-19 hub. To subscribe to Mining Journal, clickhere

While the outbreak of COVID-19 has made the immediate future of some mines uncertain, the appeal of technologies and approaches that decrease site workforce numbers and travel is no doubt growing as the industry seeks to chart a more sustainable course.

"The uptake of automated mine solutions including self-driving haul trucks and remote operations centers has been slow but steady," said Ahmed in a sector commentary.

"Whilst it is not possible to predict how COVID-19 will further disrupt the mining industry, what is certain is that the mining industry must reconfigure and prepare itself to operate under a new normal, one in which it can operate and sustain itself under the new constraints and challenges that such pandemics bring with them."

Ahmed pointed to one of the earliest movers into automation as an example of how the coronavirus outbreak could force miners to modernise around the globe.

Mining giant Rio Tinto's Mine of the Future initiative in 2008 really got first-generation automated mine fleets moving. Today many of its Pilbara mining, ore handling, processing and logistics operations are remotely supervised and operated from a large central control centre more than 1,200km away in Western Australia state capital, Perth. Today, about one-third of the mine haul truck fleet at Rio Tinto's Pilbara mines is automated.

Another, fresher example is Resolute Mining's Syama underground gold operation in Mali, which Resolute claims is the world's first fully-automated mine. Designed in partnership with Sweden's Sandvik, the mine operates with automated trucks, loaders and drills. Resolute says the mine can operate 24 hours a day, with all operations overseen from a site operations centre.

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Industry could fast-track automation amid COVID-19 fallout -

Building automation to generate $44bn in revenue per annum – Smart Energy

The global revenue for commercial building automation systems (BAS) is anticipated to increase by 3.2% between 2020 and 2029.

Navigant Research predicts more than $44.2 billion in revenue will be generated by market players by 2029.

Daniel Talero, a research analyst with Navigant Research, said: The development of the intelligent buildings (IBs) market through Internet of Things (IoT) deployments in buildings has introduced customers to new tools for data acquisition and analysis.

The emerging building IoT market is projected to grow at almost triple the commercial BAS rate. Although the automation systems are seeing increased adoption throughout the global building stock, particularly in retrofit and new construction, large-scale trends are expected to significantly affect the markets 10-year growth outlook.

Market entrants are competing with traditional building automation systems offerings, while major vendors recognise the market evolution and are introducing new versions of core BAS offerings as well as complementary IB solutions.

Traditional commercial BAS products are anticipated to reach $66 billion by 2029, growing steadily from 2020 to 2029.

Click here for more information about the report.

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Building automation to generate $44bn in revenue per annum - Smart Energy

The Idea Behind Automating Your Commissions – Yahoo Finance

NEW YORK, NY / ACCESSWIRE / April 2, 2020 / Automating Your Commissions was founded by Matt Tommy and Michael Dallas-Petersen. When they found out about the reality of the real estate industry, spending billions of money over nothing, they knew they needed to do something.

Today, $10 billion dollars worth of real estate leads are generated annually and that 45-55% do not receive an initial first contact. This goes to show that despite how good the quality of the leads is, a lot of these leads go down the drain because realtors, brokers, and lenders don't have the time to reach out to their customers. However, it is worthwhile to note how this happens. These very same people wear different hats on the daily, from marketing to sales to branding, that they don't have the time to focus on what matters most anymore.

Come to Matt and Michael, who have found a way to automate success through Automating Your Commissions. With expertise in marketing, they know too well that there is so much they can do to address how the real estate industry can do better at providing services like no other. This means not only optimizing the lead-generation but also on aspects such as sales coaching and automating tasks that can be automated.

In order to do so, Automating Your Commission created a four-pillar system for closing transactions, saving time, automating the processes, which allows realtors, brokers, and lenders to focus on key income-generating activities that drive their business forward.

The first pillar is a lead generation process that is unlike no other. They are able to innovate outdated strategies that still exist in the industry.

The second pillar is the actual follow up and speed to lead to see how quick the realtor, agent, or lender is at following up and contacting the lead.

The third pillar, on the other hand, is a step-by-step course on how to follow up, book appointments, close transactions and how to work the system effectively saving time, increasing appointments, overall pipeline management, closing more deals and even how to build a personal brand where you can also acquire organic leads.

And the fourth pillar is by far, the most important, coaching. The coaching provides accountability, feedback on sales calls, detailed understanding on how to navigate market conditions, how to coach realtors on how to close clients, how to nurture leads (through automation) and scale your business. The goal is to allow you to work on your business and be an owner rather than just become a day to day operator who is in reactive mode.

There's no question as to how Automating Your Commissions is automating success and making real estate marketing great again. However, for Matt and Michael, the blueprint needs as much as hard work as you do in business. Therefore, those who are willing to invest in the long-term to turn their lives and career around are the perfect people to work with them.

At the end of the day, Automating Your Commissions has a mission: To help people break through the barriers, own their time and freedom, focus on their strengths, and come out winning at the end. Want to know more? Send an email to

SOURCE: Automating Your Commissions

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The Idea Behind Automating Your Commissions - Yahoo Finance

Why Intelligent Automation is the Need of the Hour –

By: Tyler Suss

With the explosion of data and emerging automation technologies, organizations are looking at how they can optimize business processes to achieve greater operational efficiencies. Theyre likely to find the engine runs smoothly until unstructured data enters the mix. At that point, the process stalls or even stops dead in its tracks. This is a problem for companies that want to take full advantage of what robotic process automation (RPA) has to offer, including greater efficiency and a lower total cost of ownership (TCO) for their automation initiatives.

Documents and other unstructured data, such as PDFs, videos, photos, emails and websites, make full end-to-end automation of business operations difficult because they require a human to analyze, understand and make a decision based on the information contained within each. This creates bottlenecks and dramatically slows workflow quite the opposite of what organizations want to achieve with their automation initiatives.

This situation is far from uncommon, making it a significant threat to companies automation ambitions. As much as 60 percent of business processes contain some sort of unstructured data. That means 60 percent of the time robots have to stop their work until a human intervenes.

For example, in the claims processing world, nearly every aspect of the process remains paper-based. People mail or email physical or scanned documents to a system, where humans must then review and classify them by hand. For those with full automation dreams, this is a nightmare.

It also might explain why, despite two decades of business process management (BPM) implementations, full process automation is still the exception. According to AIIMs 2019 Emerging Technologies Market Report, two-thirds of organizations say that specific core back-end processes are less than 50% automated.

And while some industries are using RPA for records management, customer correspondence, check processing and other paper-intensive processes, fewer than one in five organizations have fully automated their core back-end processes, AIIM found.

In the meantime, the problem caused by unstructured data is only going to get worse. Half of respondents to the AIIM survey say 70% of the data in their organizations is unstructured. At the same time, organizations are anticipating massive data growth. According to the survey, 35% expect the amount of data to increase fivefold over the next two years. Its no wonder that 70% of organizations surveyed by AIIM say unstructured information is the Achilles Heel for many RPA implementations.

To achieve mature levels of automation, businesses will need to combine RPA with artificial intelligence a core capability of an Intelligent Automation platform. With advanced cognitive capture and entity extraction, analyzing and interpreting unstructured data becomes a reality. Intelligent automation enables organizations to digitally transform knowledge-based business processes, turning their nightmares into fulfilling dreams.

An Intelligent Automation platform can manage document separation, classification and routing, increasing the speed of processing and accuracy, while reducing the need for human involvement. Thus, routine tasks that previously derailed a robot are handled more efficiently.

Consider what happens when a customer trying to open an account via the banks mobile application uploads a photo of their drivers license. The image must be read, and the data extracted, or how RPA alone handles a patient email that includes important details about a recent claim. In both cases, the RPA bot cant handle this sophisticated data. A human must step in to read, understand and make a decision.

But an Intelligent Automation platform does that and more. Using cognitive document automation (CDA), the platform captures, reads and understands the information. Because CDA can read data in a variety of formats, it can transform the drivers license and the email into usable information. Using machine learning and natural language processing, the Intelligent Automation platform then interprets the data and determines what happens next.

An Intelligent Automation platform handles this job more effectively and at a lower cost than a bolt-on solution. This enables companies to create greater efficiencies, lower TCO and fully automate their business operations end-to-end.

For organizations that are struggling to achieve more mature levels of automation due to data limitations creating bottlenecks and slowdowns, a key consideration should be implementing a solution that integrates RPA with artificial intelligence. Rather than endure the nightmare, organizations can advance automaton initiatives from repetitive transactional use cases to more complex knowledge-based business processes enhancing customer experiences and operational excellence. With the combination of AI and intelligent automation technologies, your teams can begin working amd reach greater heights in automation.

(The author is Product Marketing Director at Kofax and the views expressed in this article are his own.)

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Why Intelligent Automation is the Need of the Hour -

Using RPA to automate internal audits where to start – TechHQ

Much of the repetitive reporting, number-crunching work of the accounting and finance department is the type of work best suited for a robotic takeover.

Use of RPA (Robotic Process Automation) software is rocketing its become a highly competitive market for those companies leading the charge. Gartner predicts the market was worth around US$1.3 billion last year.

Businesses with legacy systems are some of the biggest adopters. That includes banks, insurance companies, telcos and utility companies, where RPA can be put to task on routine, mundane tasks that would otherwise be assigned to a worker.

By using this technology, organizations can quickly accelerate their digital transformation initiatives, while unlocking the value associated with past technology investments, Fabrizio Biscotti, research vice president at Gartner, has said.

RPA has proven exceptionally beneficialfor the routine work of internal audits at SMEs, ensuring all the numbers on the books can be accounted for.

In fact, the applications available to both improve accuracy and expedite the process are such that executives overseeing the work are having a tough time working out where to apply it first.

Neil White, principal at Deloitte Risk & Financial Advisory of Deloitte & Touche LLP, believes that executives overseeing audits should first identify the automation opportunities that deliver strategic value that is in line with the rest of the organizations digital framework, while also managing cost expenditures.

Objective assessment of the complexity and benefits of automation can help internal audit leaders identify where to deploy RPA and develop a road map to plan further advancements.

Michael Schor, a partner at Deloitte Risk & Financial Advisory of Deloitte & Touche LLP, agreed with that assessment.A structured approach to evaluating RPA opportunities can help prioritize where to apply the technology to gain the greatest value, he said.

Besides performing rules-based, repetitive tasks that are time-consuming for people such as pawing through data from multiple systems RPA is also effective in monitoring aspects of the business that might be demanding (or expensive) to monitor independently.

Some audit departments deploy RPA processes for cost-benefit analyses on frequent datasets, being ideally suited to parse minute differences and between data sets that would appear similar to the human eye.

There have also been cases of capable internal audit departments combining automated processes with cognitive tools like natural language processing (NLP) to compile and produce full lists of vendor requirements, or spotting legal risks that arise outside of standard agreement clauses.

When combined with cognitive technologies, RPA can produce real-time reporting of fraud arising in financial systems, and it can test control effectiveness based on not just samples but entire populations of data, commented Deloittes White. It also can detect suspicious logs associated with IT systems.

The first step for internal auditors to consider when considering adopting RPA is to identify operations that could actually benefit from automation.

Consider processes that are standardized and rule-based, including tests or parts of tests that can be performed by analyzing and comparing large datasets, Deloitte Risk & Financial Advisory senior manager Martin Rogulja advised.

This might also include controls where full-population testing would be both feasible and insightful as well as tests that could benefit from increased scope.

Once a suite of control testing has been identified, the audit executives need to determine the intrinsic value of deploying RPA for each possible function, or set of functions.

The value is ascertained by several factors such as the potential cost savings, risks to the existing process flow, productivity improvement estimates, impact of risk mitigation, and potential customer/employee satisfaction.

Essentially, one must perform an audit of the existing system and define the parameters that automating parts of the system would affect.

Processes that would provide the greatest value and be the least complex to automate represent the highest-priority RPA projects, according to White. Conversely, processes that are of lower value and would be more complex to automate would fall into the lowest priority category.

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Using RPA to automate internal audits where to start - TechHQ