Category: General

 
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AI process automation offers benefits, but challenges remain

Enterprises are starting to employ machine learning tools as part of their AI automation strategies, but several key challenges stand in the way of effective usage.

Thanks to rapid improvements in machine learning tools, AI applications are just now starting to make inroads in industrial processes, promising to improve older industrial automation protocols built around expert systems.

AI process automation tools that simplify the workflow of front-line employees present a big opportunity for businesses, but several challenges remain. Enterprises are still grappling with making vast stores of existing data available to AI platforms. There are also several challenges when bringing agility to AI application development and improving training data quality for machine learning algorithms.

Speakers at the Re-Work AI in Industrial Automation Summit in San Francisco discussed how enterprises are taking on these challenges.

Focus on pain points

Broken systems and downtime are among the biggest drivers of AI adoption in industrial automation.

“One of the keys for our customers is that they have experienced some incident, like a failure,” said Drew Conway, CEO of Alluvium, a company that makes machine learning tools for analyzing the performance of industrial equipment.

In many cases, data that could predict large-scale equipment failures in industrial settings is available prior to the failure, but the human experts viewing it don’t recognize key signals indicating it is likely.

“All of this data is falling to the ground,” Conway said. “A big problem is figuring out how to build tools that work with that data in a way that is valuable.”

Building a better algorithm to detect problems involves more than simply analyzing sensor data. It’s important to capture expert operator feedback and institutional knowledge to identify potential issues and alert the operators when a problem occurs. Conway said industrial automation is in need of better ways to blend existing approaches to machine learning with expert-driven systems that can provide operators with more actionable feedback.

“We realized that if we could get people in the control room to use software they trusted, it would grow usage,” Conway said.

This involves not just predicting problems, but relating these predictions to operators’ understanding of how machines work and their different potential failures so operators can take preventative measures.

Streamlining manufacturing processes

The core principles behind Agile development started in the manufacturing sector as part of lean production processes. For the most part, this has been driven by people identifying sources of waste in manufacturing processes.

Now, enterprises are starting to use AI applications and Agile software development practices to develop AI process automation strategies, said Greg Kinsey, vice president at Hitachi’s Insight Group. This is being driven in large part by the rise of industrial IoT and better data management practices.

Traditional lean manufacturing processes work well to optimize highly standardized production lines that don’t change much. But they can suffer problems when a production line is constantly adapting in response to market pressures, said Kinsey.

For example, Hitachi has been working with one company that produces polymers. The marketing department found it could significantly increase sales by making custom blends for a variety of uses. The problem was yield would drop by 30-40% each time the production line changed the blend it made.

Hitachi worked with the company to use machine learning algorithms to figure out how to adjust the settings for the equipment for each new production run, which reduced the drop in yield to less than 10%.

Agile machine learning for new data sets

The hard part of this AI process automation wasn’t not finding the right data; Hitachi worked with the polymer company to identify almost 300 different data streams that might relate to yield. But it wasn’t as simple as compiling all these data sets to train algorithms. Each data set had to be cleaned, calibrated and synchronized with other data sources to produce useful results.

Hitachi worked with the company on an Agile development process that started with the minimum viable data sets, Kinsey said. In the discovery phase, they assessed the predictive value of a few critical variables.

“Once you have a hypothesis, you can think about the data you need and then do the hard work of cleansing, labeling, ingesting and aligning that with the tasks that engineers need to do,” Kinsey said.

Hitachi representatives typically spend a month or two on the discovery phase, during which they deliberately try to avoid talking about applications. In the second phase, they begin to look at specific applications. This is done to formulate a hypothesis and create a minimum viable data set for a potentially larger AI process automation use case.

One of the biggest challenges is making sure you have the right mix of personalities on your team to tackle the different aspects of process automation problems. Highly innovative people are creative, and even though they may make some mistakes, they frequently bring new perspectives to problems. Solution-oriented people look for a stable process. The creative types play a stronger role in the discovery phase, while the solution-oriented types play a stronger role in the deployment phase.

Filling in the data gaps

In many cases, the data required to identify rare but expensive failures does not exist, said Dragos Margineantu, AI chief technologist at Boeing. Airplanes and the maintenance crews that service them collect vast troves of data. But airplanes are rarely grounded or breakdown in flight, so there is not much recorded data about what to look for related to edge cases that might cause a plane to break.

“No matter how much data you collect from real-world processes, it is typically incomplete,” Margineantu said. “We have data sets from customers that operate that have not had a single rejected takeoff in four years. This is an event that happens rarely.”

Building better algorithms for industrial automation sometimes requires finding ways to make sense of data stored in manuals and tapping into the knowledge of experts. It frequently demands a broad survey of potential sources of knowledge rather than simply building a bigger data set.

AI architecture required

Going forward, Margineantu believes AI process automation will require the development of special application architectures designed for other types of enterprise applications. These could be built using components that can be switched out, like microservices running on Docker containers. The beginnings of these kinds of architectures are already being used in domains like autonomous cars that use the Robotic Operating System framework.

An AI architecture can make it easy to develop and deploy a machine learning algorithm and then quickly switch it out when a better algorithm comes along. Today, Margineantu finds Boeing spends a lot of time developing the application infrastructure that wraps around each new machine learning algorithm.

Robustness is important

It’s also important to focus on robustness rather than just accuracy. Systems should be designed to alert humans when an algorithm has trouble reaching a conclusive prediction or recommendation, especially when AI decisions impact industrial equipment.

For example, if an algorithm is trained to identify cats and dogs in pictures, it may struggle with an edge case that includes a picture of a bear. AI systems will have to know how to respond when challenged by new classes.

“If you see a bear, you would like the systems to respond, ‘I don’t know,’ or ‘give me more information,'” Margineantu said.

In the long run, this kind of robustness is likely to be built by groups of algorithms that work together.

“I want to remind you that all competitions in machine learning are won by ensembles, since they provide for more robust outputs,” Margineantu said.

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Everything You Need to Know About Automation for Small Business

So, you want to implement automation into your business model? Every morning an article flashes across your iPhone about the benefits of automation and RPA, or as it’s commonly known, robotic process automation.
Automation is the process is giving artificial intelligence repeated tasks that usually involve huge volume and turnover. Giving a large part of the workload sounds great in theory, but so did many other technologies that are not used anymore. For business owners who are new to automation, there are a lot of question.
Which roles will become automated? Is full or partial automation the best fit for your business? How do you ensure that a smooth transition into automation with current employees? Will you need to expand or evolve the roles as employees work with AI?
All of these questions are valid. Having doubts is a healthy sign of not having Shiny Object Syndrome. Shiny Object Syndrome affects small business owners across the world. Symptoms may include wanting the latest and greatest technology without a proper strategy, lack of training, and research into the realities of automation.

Thankfully, there is a cure. The treatment options listed below should be taken together, if possible. Skipping one step may result in further discomfort for you and your business.

  1. Automation Academy: Automation has many benefits and some risks. The only way to know whether your business should be investing in new technology, is to learn more. A software company called WorkFusion runs an automation course, and although they are selling the product, they offer free sign ups for anyone is curious about RPA, or repeated process automation.
  2. Open Source: Anyone can start building AI and automation frameworks with open source tools. These DIY projects probably won’t be as good as something created by a company that specializes in automation, but they are a start. Tech Beacon pointed to nine open source automation frameworks. Even if you choose not to develop the structure yourself, poking around in open source can help you understand this field better and be more confident in choosing an automation partner.
  3. Read the Reviews: Almost every business is on some kind of review site, and automation is no different. Captera is a website that helps enterprises find software. The site features customer reviews and rankings of each product. There are also case studies on software company websites. Although they are edited to make the company look good, they are still a useful resource.

Since Ford’s assembly lines, we have been steadily marching towards a future where machines do tasks for people. Automation is the next step in that process, but that does not mean every business needs to buy into it without the proper research. By doing a little human legwork, small companies can make a smooth transition into automation.

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Three benefits of AI and automation for consulting businesses

Artificial Intelligence (AI) and automation are disrupting businesses across the globe. As these new technologies develop, many companies are increasingly thinking about integrating AI or at least automation into their operations. According to a study by Narrative Science. the number of companies implementing AI, within the space of one year, nearly doubled with an increase from 38% to 61%. Of these companies, a quarter use AI for predictive analytics, and 22% use it for machine learning.

When considering whether to implement AI or automation technology, it’s important to understand the difference between the two. This is certainly not an easy task as AI is often mistaken for automation due to the fact that AI often involves an element of automation and vice versa. As a result, it can be difficult for the average lay person to clearly identify the differences between these technologies. Machine learning is an algorithm that allows devices to “learn” based on data, i.e. using artificial intelligence to improve algorithms.

How can consultancy firms benefit from AI and automation?
While AI and automation have significantly disrupted some industries, most notably manufacturing and customer service, they have also undeniably had an impact on consultancy firms. Generally, the effects of these new technologies on consultancy firms are positive, and if harnessed correctly, AI and automation could significantly enhance how these firms operate as well as the services they provide to clients.

Data collection
AI technology can process, handle, and analyse massive amounts of data far more efficiently and faster than the average human. As such, it can provide more accurate insights into many areas of business including sales, operations, supply chain and more. For consultants, such information can be used to augment their offerings and services to clients, enhance clients’ ROIs. When used for consultancy purposes, information on for instance sales channels, customer journeys and client behaviour can help marketeers and consultants tasked with sales identify new opportunities and develop more effective strategies for advertising campaigns. For partners, AI can be tapped to streamline the delivery of projects, on the back of more effective resourcing and prioritisation.Three benefits of AI and automation for consulting businesses

Streamline admin tasks
Probably one of the most tedious aspects of any consultant’s job is processing routine paperwork. Whether it relates to manually creating client invoices, processing payroll or creating progress reports for clients, admin tasks can slow down a consulting business. A recent report by Sage revealed that the average small business, including smaller consultancies, spends 120 days per year on admin – time which would otherwise be spent growing their client base and creating new opportunities.

Robots are much more efficient at handling routine admin tasks than humans. Robotic process automation, also known as RPA, devices can help companies with a wide range of admin tasks; for example, creating and delivering invoices, matching incoming payments with the correct invoice, record-keeping, and much more. Some, more high-end AI devices, can even decision-makers make more logical and consistent business decisions or to ensure regulatory forms are completed to avoid non-compliance fines.

Improved productivity
Automating routine admin tasks can also increase consultancy firms’ productivity. Consultants can streamline mundane processes with financial software or schedule meetings, record conversations, and make restaurant reservations with a virtual personal assistant like Zoom.ai. Removing these responsibilities from junior staff members’ workloads means that they can focus on more rewarding work resulting in higher engagement and productivity levels across the whole consulting firm.

Consultants that want to use automation to boost their operations and performance can tap into a host of tools and software solutions. Using If This Then That (IFTTT) for instance, professionals can create processes to automate almost any task including logging time spent for time tracking or saving email attachments to Google Drive. The latest information sharing and collaboration platforms such as GoogleDocs or GSuite support automated document sharing, while a tool such as infusionsoft allows for automating emails to relations and staff.

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Automation, AI, and Machine Learning Are Changing Business Operations

Machine learning the concept that, once data is introduced to a computer, it can make decisions based on the input, has grown by leaps and bounds in the past few years. Machine learning enables predictions based on large quantities of data. The more data, the better the predictions. Add in AI and robotic automation, and the future of business looks very interesting.

AI and Machine Learning
While machine learning and AI are often conflated, they are two different concepts. It’s not quite the same. AI is meant to simulate intelligent thought, while machine learning is more about using data for prediction. AI like IBM’s Watson can use machine learning for analyzing big data and sharing its insight across a company, utilizing then-unheard of amounts of data to draw conclusions.
“The data that is used in these algorithms can include everything from customer spreadsheets, past buyer information, murder rates, loaner information, census information, survey information, diabetes rates, website visiting rates and much more,” according to the University of California, Riverside. “Machine learning can not only reveal trends about this information, but can also give insight toward predicting things about future behavior, such as who is likely to pay back their loans or what customer base a specific marketing campaign should target.”
Here’s a relatable example: You are searching for something to watch on Netflix. There’s a “recommended for you” section based on previous movies and shows you have watch. Algorithms have used the data — what you have watched — to predict other movies and show you might be interested in watching.

Natural Language Processing
Another major application that is still evolving is speech recognition, and natural language processing in particular. Think of one of your smart home devices, such as Amazon’s Alexa or a Google Home. Machine learning can learn how you phrase a particular request, parse the idea into one of its normal commands, and execute the command. By the same token, automated systems are using NLP to help route callers to the correct department and customer service representative. For example, calling an insurance company will pose an automated prompt of which kind of insurance you are seeking and route your call accordingly.
AI and machine learning in tandem are also changing the face of marketing. In the search engine optimization world, there are tools using NLP to create stories that read as if a real person wrote them, rather than being computer-generated, all aimed at ranking higher on Google. Even news agencies such as the Associated Press use NLP tools to create articles quickly, such as business earnings reports and localized election coverage.
Robotic Process Automation.


RPA is a technology where software robots, like Watson, perform routine and repetitive tasks normally done by humans.
Because it’s a robot, it doesn’t have to take breaks or go home at 5 p.m. It can be extremely efficient, but complex tasks may be out of its purview. For example, it can provide lesser IT support but may not be able to solve a complicated problem and elevate to a human IT specialist. A global company might have multiple large offices but a small IT department. These lesser problems can be filtered out, saving time and money on having to have a large IT team.
The same system can be used to update user preferences or obtain billing data. It can be done as a chatbot on the company’s website, with a database of questions and answers to pull from.
RPA can even standing in for parts of an HR department, partly automating the hiring and firing process while also managing payroll. It can filter out resumes lacking certain keywords, and when a decision is made, automatically fill out and file paperwork. There are additional benefits, such as promoting anti-discriminatory hiring practices, taking much of the human bias out of the hiring process.
Combine these concepts, and we can see the technology is evolving quickly. Putting them together creates an exciting future outlook. Imagine a future where AI algorithms can predict the outcomes of CRISPR-Cas9 gene editing, carry out the gene edits, and perform other minor surgery without humans needed for anything more than oversight. The AI analyzes the data, uses tools, and does the surgery. There have already been more than 3 million robot-assisted surgeries in the past two decades.
AI, machine learning, and robotic process automation are all current but evolving technologies. They are shaping how businesses will operate in the future, and hold promise to improve a company’s efficiency in anything from HR to marketing, data analysis to customer service.

Machine learning the concept that, once data is introduced to a computer, it can make decisions based on the input, has grown by leaps and bounds in the past few years. Machine learning enables predictions based on large quantities of data. The more data, the better the predictions. Add in AI and robotic automation, and the future of business looks very interesting.

AI and Machine Learning

While machine learning and AI are often conflated, they are two different concepts. It’s not quite the same. AI is meant to simulate intelligent thought, while machine learning is more about using data for prediction. AI uses machine learning for analyzing big data and sharing its insight across a company, utilizing then-unheard of amounts of data to draw conclusions.

“The data that is used in these algorithms can include everything from customer spreadsheets, past buyer information, murder rates, loaner information, census information, survey information, diabetes rates, website visiting rates and much more,” 

“Machine learning can not only reveal trends about this information, but can also give insight toward predicting things about future behavior, such as who is likely to pay back their loans or what customer base a specific marketing campaign should target.”

Here’s a relatable example: You are searching for something to watch on Netflix. There’s a “recommended for you” section based on previous movies and shows you have watch. Algorithms have used the data — what you have watched — to predict other movies and show you might be interested in watching.

Natural Language Processing

Another major application that is still evolving is speech recognition, and Natural Language Processing in particular. Think of one of your smart home devices, such as Amazon’s Alexa or a Google Home. Machine learning can learn how you phrase a particular request, parse the idea into one of its normal commands, and execute the command. By the same token, automated systems are using NLP to help route callers to the correct department and customer service representative. For example, calling an insurance company will pose an automated prompt of which kind of insurance you are seeking and route your call accordingly.

AI and machine learning in tandem are also changing the face of marketing. In the search engine optimization world,  that read as if a real person wrote them, rather than being computer-generated, all aimed at ranking higher on Google. Even news agencies such as the Associated Press use NLP tools to create articles quickly, such as business reports and localized election coverage.

Robotic Process Automation

RPA is a technology where software robots, like Watson, perform routine and repetitive tasks normally done by humans.

Because it’s a robot, it doesn’t have to take breaks or go home at 5 p.m. It can be extremely efficient, but complex tasks may be out of its purview. For example, it can provide lesser IT support but may not be able to solve a complicated problem and elevate to a human IT specialist. A global company might have multiple large offices but a small IT department. These lesser problems can be filtered out, saving time and money on having to have a large IT team.

The same system can be used to update user preferences or obtain billing data. It can be done as a chatbot on the company’s website, with a database of questions and answers to pull from.

RPA can even standing in for parts of an HR department, partly automating the hiring and firing process while also managing payroll. It can filter out resumes lacking certain keywords, and when a decision is made, automatically fill out and file paperwork. There are additional benefits, such as promoting anti-discriminatory hiring practices, taking much of the human bias out of the hiring process.

Combine these concepts, and we can see the technology is evolving quickly. Putting them together creates an exciting future outlook. Imagine a future where AI algorithms can predict the outcomes of CRISPR-Cas9 gene editing, carry out the gene edits, and perform other minor surgery without humans needed for anything more than oversight. The AI analyzes the data, uses tools, and does the surgery. 

AI, machine learning, and robotic process automation are all current but evolving technologies. They are shaping how businesses will operate in the future, and hold promise to improve a company’s efficiency in anything from HR to marketing, data analysis to customer service.


Another major application that is still evolving is speech recognition, and natural language processing in particular. Think of one of your smart home devices, such as Amazon’s Alexa or a Google Home. Machine learning can learn how you phrase a particular request, parse the idea into one of its normal commands, and execute the command. By the same token, automated systems are using NLP to help route callers to the correct department and customer service representative. For example, calling an insurance company will pose an automated prompt of which kind of insurance you are seeking and route your call accordingly.
AI and machine learning in tandem are also changing the face of marketing. In the search engine optimization world, there are tools using NLP to create stories that read as if a real person wrote them, rather than being computer-generated, all aimed at ranking higher on Google. Even news agencies such as the Associated Press use NLP tools to create articles quickly, such as business earnings reports and localized election coverage.
Robotic Process Automation
RPA is a technology where software robots, like Watson, perform routine and repetitive tasks normally done by humans.
Because it’s a robot, it doesn’t have to take breaks or go home at 5 p.m. It can be extremely efficient, but complex tasks may be out of its purview. For example, it can provide lesser IT support but may not be able to solve a complicated problem and elevate to a human IT specialist. A global company might have multiple large offices but a small IT department. These lesser problems can be filtered out, saving time and money on having to have a large IT team.
The same system can be used to update user preferences or obtain billing data. It can be done as a chatbot on the company’s website, with a database of questions and answers to pull from.
RPA can even standing in for parts of an HR department, partly automating the hiring and firing process while also managing payroll. It can filter out resumes lacking certain keywords, and when a decision is made, automatically fill out and file paperwork. There are additional benefits, such as promoting anti-discriminatory hiring practices, taking much of the human bias out of the hiring process.
Combine these concepts, and we can see the technology is evolving quickly. Putting them together creates an exciting future outlook. Imagine a future where AI algorithms can predict the outcomes of CRISPR-Cas9 gene editing, carry out the gene edits, and perform other minor surgery without humans needed for anything more than oversight. The AI analyzes the data, uses tools, and does the surgery. There have already been more than 3 million robot-assisted surgeries in the past two decades.
AI, machine learning, and robotic process automation are all current but evolving technologies. They are shaping how businesses will operate in the future, and hold promise to improve a company’s efficiency in anything from HR to marketing, data analysis to customer service.

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Artificial intelligence in business

Many businesses take up artificial intelligence (AI) technology to try to reduce operational costs, increase efficiency, grow revenue and improve customer experience.
For greatest benefits, businesses should look at putting the full range of smart technologies – including machine learning, natural language processing and more – into their processes and products. However, even businesses that are new to AI can reap major rewards.

Artificial intelligence impact on business
By deploying the right AI technology, your business may gain an ability to:
• save time and money by automating routine processes and tasks
• increase productivity and operational efficiencies
• make faster business decisions based on outputs from cognitive technologies
• avoid mistakes and ‘human error’, provided that smart systems are set up properly
• use insight to predict customer preferences and offer them better, personalised experience
• mine vast amount of data to generate quality leads and grow your customer base
• achieve cost savings, by optimising your business, your workforce or your products
• increase revenue by identifying and maximising sales opportunities
• grow expertise by enabling analysis and offering intelligent advice and support

According to a recent study, the main driving force for using AI in business was competitor advantage. After that, the incentive came from:
• an executive-led decision
• a particular business, operational or technical problem
• an internal experiment
• customer demand
• an unexpected solution to a problem
• an offshoot of another project

AI opportunities for business
Whatever your reason for considering AI, the potential is there for it to change the way your business operates. All it takes to start is an open-minded attitude and a willingness to embrace new opportunities wherever and whenever possible.

Keep in mind, however, that AI is an emerging technology. As such, it is changing at a fast pace and may present some unexpected challenges.

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Examples of artificial intelligence use in business

Artificial intelligence (AI) is all around us. You have likely used it on your daily commute, searching the web or checking your latest social media feed.

Whether you’re aware of it or not, AI has a massive effect on your life, as well as your business. Here are some examples of AI that you may already be using daily.

Artificial intelligence in business management

Applications of AI in business management include:

  • spam filters
  • smart email categorisation
  • voice to text features
  • smart personal assistants, such as Siri, Cortana and Google Now
  • automated responders and online customer support
  • process automation
  • sales and business forecasting
  • security surveillance
  • smart devices that adjust according to behaviour
  • automated insights, especially for data-driven industries (eg financial services or e-commerce)

Artificial intelligence in e-commerce

AI in e-commerce can be evident in:

  • smart searches and relevance features
  • personalisation as a service
  • product recommendations and purchase predictions
  • fraud detection and prevention for online transactions
  • dynamic price optimisation based on machine learning

Artificial intelligence in marketing

Examples of AI in marketing include:

  • recommendations and content curation
  • personalisation of news feeds
  • pattern and image recognition
  • language recognition – to digest unstructured data from customers and sales prospects
  • ad targeting and optimised, real-time bidding
  • data analysis and customer segmentation
  • social semantics and sentiment analysis
  • automated web design
  • predictive customer service

These are only some of the examples of AI uses in business. With the pace of development increasing, there will likely be much more to come in the near future.  Please share with us if you have another example and email info@winyourbrand.com

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