Category: Artificial Intelligence

The Future of Digital Transformation: 2023 and Beyond

Post author By Scottie Todd
Post date March 2, 2023

As organizations begin to move full throttle into enhancing internal and external business outcomes, the term ‘digital transformation’ has gained supreme status into the particular tech lexicon. Digital transformation has become an important strategy for organizations for years and is predicted to be a crucial factor in the competition of who remains in the business.

The term digital transformation is defined as the particular integration of technology into all areas of the business, fundamentally changing how it operates and delivers value to its customers. Digital transformation is also a cultural change that requires organizations to continually challenge the status quo, experiment, and get comfortable with failure even if that happens.

Research analysts believe that when it comes to a timeframe, 85% of key decision makers feel they have only 2 years to get in order to grips with digital transformation. So, while the past few years have seen some movement in digital transformation, there’s now an urgency, as time becomes the new benchmark associated with which businesses stay in the race and which ones drop out.

Important Change For Every Business

Digital transformation has increasingly become very important for every business, from small businesses to large enterprises. This is quickly becoming widely accepted with the increasing number of panel discussions, articles, and published studies related to how businesses can remain relevant as the operations and jobs become increasingly digital.

Many business leaders are still not clear with what digital transformation brings to the table, while many believe that it is all about the business moving towards the cloud. Business leaders in the C-Suite are still in two minds of the changes they have to take into their strategies and forward way of thinking. Numerous believe that they should be hiring an external agency for change implementation, whilst many still question about the costs involved in the particular process.

As every organization is different, so are their digital change requirements. Digital transformation has the long legacy and extends much beyond 2023. It is a change which requires businesses to experiment often, get comfortable with failing, and continually problem the status quo. It also means that companies have to move beyond the age-old processes and look out for new challenges and changes.

Here is what the essence of digital transformation brings to the table:

• Customer experience

• Culture and leadership

• Digital technology integration

• Operational Agility

• Workforce enablement

Digital transformation can be predominantly used within a business context, bringing change into the organizational structure, impacting governments, public sector agencies and enterprises which are involved in tackling societal challenges such as tracking pollution, the sustenance levels and so on by leveraging one or more of these existing plus emerging technologies.

Digital Transformation 2023 and Beyond

Because digital transformation techniques mature, and its status as an innovation driver becomes a new standard, leading IT professionals are usually asking – what’s next? If the particular lesson from the last decade was the power of digital flexibility, how can it create a more efficient and productive workforce moving forward?

Today’s businesses are as diverse as the clients they serve. From the cloud-native startup to the legacy enterprise, as companies have embraced the value of electronic flexibility, an overwhelming majority have embarked on digital modification journeys.

One critical aspect of the approach to digital transformation is that IT departments are progressively expected to take the greater role in driving overall company goals.

As technology gets more advanced, the human element becomes significantly vital. The digital transformation saw a seismic shift in the way IT leaders strategy their infrastructure, but workplace transformation requires a deep understanding associated with the unique way’s individuals approach productivity.

In essence, many businesses have begun their journey, and have started making changes in their strategies within the business’s large digital programs adapting to AI initiatives and modern technologies. In most cases, it is simply a humble beginning and a whole lot more needs to be achieved.

Technologies are evolving and changing, challenging the particular fundamental strategic and operational processes that have defined organizations up until now.

In the times to come, enterprises will no longer have separate digital and AI strategies but instead will have to integrate corporate strategies deeply infused with changing technologies.

Is AI Changing the 80/20 Rule of Data Science?

Post author By Scottie Todd
Post date February 15, 2023

AI technology brain background digital transformation concept

Cleaning and optimizing data is one of the biggest challenges that data scientists encounter. The ongoing concern about the amount of time that goes into such work is embodied by the 80/20 Rule of Data Science. In this case, the 80 represents the 80% of the time that data scientists expend getting data ready for use and the 20 refers to the mere 20% of their time that goes into actual analysis and reporting.

Much like many other 80/20 rules inspired by the Pareto principle, it’s far from an ironclad law. This leaves room for data scientists to overcome the rules, and one of the tools they’re using to do it is AI. Let’s take a look at why this is an important opportunity and how it might change your process when you’re working with data.

The Scale of the Problem

At its core, the problem is that no one wants to be paying data scientists to prep data anymore than is necessary. Likewise, most folks who went into data science did so because deriving insights from data can be an exciting process. As important as diligence is to mathematical and scientific processes, anything that allows you to do more diligence and to get the job done faster is always a win.

IBM published a report in 2017 that outlined the job market challenges that companies are facing when hiring data scientists. Growth in a whole host of data science, machine learning, testing, and visualization fields was in the double digits year-over-year. Further, it cited a McKinsey report that shows that, if current trends continue, the demand for data scientists will outstrip the job market’s supply sometime in the coming years.

In other words, the world is close to arriving at the point where simply hiring more data scientists isn’t going to get the job done. Fortunately, data science provides us with a very useful tool to address the problem without depleting our supply of human capital.

Is AI the Solution?

It’s reasonable to say that AI represents a solution, not The Solution. With that in mind, though, chipping away at the alleged 80% of the time that goes into prepping data for use is always going to be a win so long as standards for diligence are maintained.

Data waiting to be prepped often follow patterns that can be detected. The logic is fairly straightforward, and it goes as follows:

Have individuals prepare a representative portion of a data set using programming tools and direct inspections.

Build a training model from the prepared data.

Execute and refine the training model until it reaches an acceptable performance threshold.

Apply the training model and continue working on refinements and defect detection.

Profit! (Profit here meaning to take back the time you were spending on preparing data.)

There are a few factors worth considering. First, depending on the size of the task and its overall value, it has to be large enough that a representative sample can be extracted from the larger dataset. Preferably, you don’t want it to be 50% of the overall dataset, otherwise, you might be better off just powering through with a human/programmatic solution.

Second, some evidence needs to exist that shows the issues with each dataset lend themselves to AI training. While the power of AI can certainly surprise data scientists in terms of improving processes such as cleaning data as well as finding patterns, you don’t want to be on it without knowing that upfront. Otherwise, you may spend more time working with the AI than you gain for doing analysis.

Conclusion

The human and programming elements of cleaning and optimizing data will never go away completely. Both are essential to maintaining appropriate levels of diligence. Moving the needle away from 80% and toward or below 50%, however, is critical to fostering continued growth in the industry.

Without a massive influx of data scientists into the field in the coming decade, something that does not appear to be on the horizon, AI is one of the best hopes for turning back the time spent on preparing datasets for analysis. That makes it an option that all projects that rely on data scientists should be looking at closely.

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Growth Hacking Your Business Processes with Artificial Intelligence

Post author By Scottie Todd
Post date June 7, 2021

Along with data and analytics, the focus on continuous improvement remains a constant in the business world. Recent market disruptions have only emphasized the need for businesses to optimize their processes and core functions moving forward. Artificial intelligence is one tool companies are turning to when achieving greater efficiency within their operations. Let’s explore how AI is transforming the way we do business, from data cleaning to the customer experience.

Using AI to Cleanup Dirty Data

Let’s get straight to the point. Dirty data costs businesses money, regardless of if they heavily rely on or prioritize data in their operations. The average cost of dirty data sings to the tune of around 15% to 25% of revenue each year. While this percentage doesn’t appear to be an overwhelming amount, consider the overarching estimate from IBM that bad data costs the U.S. $3.1 trillion each year. This high cost is mainly due to the complexities associated with cleaning and maintaining an organization’s data quality.

There’s no question that data cleaning is a lengthy and time-consuming process. As a result of this, less time is able to be devoted to high-level goals. Decision-makers have long wait times when it comes to converting raw data into actionable insights. AI, though, is able to automate this process so businesses can focus their efforts elsewhere. AI learns from each data set and can detect columns in need of cleaning, all while simultaneously updating the data model. The productivity of your data science team is improved, saving hundreds of hours that would have been spent on cleaning tasks.

Analyzing Business Data for Forecasting and Prediction

The use of business data to identify patterns and make predictions is well established. Using AI-powered tools and solutions, any business user can generate insights quickly without advanced programming or data science skills. The ease of use makes this process faster, more accessible, and efficient across business units. This reduces miscommunications between the analytics team and eliminates wait times on reports, query requests, and dashboard delivery.

Additionally, exploding data volumes have made effective use of an organization’s data difficult to manage. Artificial intelligence helps to quickly analyze these large volumes in record time, allowing for faster insights along with higher quality forecasting. Actionable business data is becoming accelerated with the use of AI, helping business leaders make decisions with greater accuracy.

Improving Sales and Customer Success

AI-powered analytics is helping companies gain insights into their prospects as well as current customers. For instance, companies can use AI in conjunction with their CRM data to predict which customers are most likely to cancel their subscriptions or which new sales accounts are more likely to close. These predictions can be flagged to alert the customer success team or sales staff, highlighting where they should be maximizing their time. This acceleration can also result in a more efficient and effective customer lifecycle.

On the customer experience side of things, process improvement can also be established through automatic support lines and AI-powered chatbots. AI systems can monitor customer support calls and detect detailed elements as minuscule as tone to continually keep an eye on quality. Chatbots also offer additional availability for immediate support. Problems are identified and resolved faster to increase revenue along with customer retention.

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Press Release: Inzata Analytics: Expanding its Reach to the DoD with Nobletech Solutions

Post author By Scottie Todd
Post date January 18, 2021

Inzata Analytics, a data analytics software company, has announced its partnership with Nobletech Solutions Inc., a provider of technology and engineering services. This partnership will address the DoD’s full spectrum of data challenges ranging from prognostic & predictive maintenance (PPMx), logistics, and sustainment to intelligence, human resources, and fiscal management without the complexity and delays that existing DoD data analytics tools provide.

Inzata provides the software to bring data analytics to end-users within hours and days at all levels within DoD organizations, regardless of user-level experience. Nobletech provides the proper secure network, DoD experience, and contract vehicles that will put Inzata’s AI and data analytics solutions into the hands of those front-line users when the data is needed to make those critical decisions.

Inzata’s ability to assemble large disparate data sources without coding enables data analysis to occur in significantly shorter times than any of its competitors. The current political climate cannot afford to wait months and years for analysts and data architects to develop products for decision makers while Inzata’s AI/ML and no-code solution can have it done in days and weeks, vs. months and years that the competition requires.

Nobletech Solutions will bring Inzata into the DoD by leveraging their GSA Schedule while pursuing a DoD Enterprise Software Agreement (ESA). This will be done by demonstrating to the DoD and its industry partners the power of Inzata’s Artificial Intelligence/Machine Learning (AI/ML) capability to perform rapid assembly and analysis of data and visual dashboards through its unique no-code solutions.

It is certain that Nobletech Solutions with the ability to host on the DoD secret cloud, without the required add-ins and extras required by those other data analytics platforms, will surely be a significant mission enabler to special operations and other organizations within the DoD and OGA.

Nobletech Sr. Analyst: “Providing an edge to the DoD to quickly assemble large data with the help of AI/ML will certainly be a game changer. We envision Inzata as the tool to provide that big picture view of people, equipment, parts, location, training, intelligence, risk, environmental conditions, and other data needed by the most senior level commanders down to the small unit leader.”

Nobletech CEO: “We are excited to have this partnership and look forward to being a key player in meeting the needs of DoD’s data analytics pertaining to human resource, material, training/qualification, prognostic maintenance, and intelligence.”

For more information, please contact the following:
Jim Scala at jscala@nobletechsolutions.com
Luke Whittington at lwhittington@nobletechsolutions.com
You can also learn more by visiting https://www.nobletechsolutions.com/data-analytics

This press release was originally featured on PRWeb, find the press release here: http://www.prweb.com/releases/inzata_analytics_expanding_its_reach_to_the_dod_with_nobletech_solutions

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Big Data on the Big Screen: Top 5 Movies on Big Data & AI

Post author By Scottie Todd
Post date October 19, 2020

Movies serve as a medium to convey and bring life to complex topics in a new way. Big Data and artificial intelligence have long been favorites of Hollywood and remain the focus of many feature films. While the big screen tends to exaggerate certain aspects of technology for its cinematic value, some truth remains behind the elements of AI and data science within these films. Let’s examine 5 top movies that explore the topics of Big Data and AI to add to your watchlist.

Moneyball

Brad Pitt leads this film as the general manager for the Oakland A’s, a baseball team with the lowest budget for players in the league. With the salary constraints to acquiring new players, the team looks to gain a competitive edge through statistical analysis. Data is at the core of management’s decision-making process when it comes to choosing key players and maximizing their budget. This film emphasizes the real-world application of predictive analytics and statistics when it comes to decision-making.

Blade Runner

This classic film takes place in a world where artificially intelligent robots are created to serve society and work in off-world colonies. It is difficult to distinguish these robots, also referred to as “replicants,” from real humans. They are ruled illegal on Earth due to their lack of emotion, overpowering strength, and the danger they pose towards society. When four replicants manage to sneak onto Earth, they are hunted down by Rick Deckard, a resident Blade Runner. AI plays an integral role in this film as it tackles difficult conversations around humanity’s relationship with artificial intelligence and the ethical dilemmas that come with creating such machines.

I, Robot

In a futuristic world, robots are engrained in the daily lives of humans, working as their assistants and serving their every need. The robots are programmed to follow the “Three Laws of Robotics” which are meant to protect society. However, this harmony is challenged when a supercomputer named VIKI (Virtual Interactive Kinetic Intelligence) violates these laws. VIKI sources and collects data from around the world in an effort to gain control of all robots. Here we have another representation of the age-old man vs. machine, but the uncertainty surrounding our ability to control the power of AI, even with rules in place, is highlighted.

Minority Report

Here data science is used by PreCogs, a team of “Data Scientists” operating in conjunction with the police, to predict precisely when and how future crimes will occur. Based on this, the police are able to arrest individuals before they’ve even committed a crime. Tom Cruise’s character, an officer in the PreCrime unit himself, is accused of a future murder and must prove he’s being framed. This film represents the real-world use of data to create social good and help make society better as a whole.

Her

This movie follows the relationship between Theodore Twombly, a lonely writer, and his AI-powered virtual assistant named Samantha. As a highly sophisticated operating system, Samantha can master large volumes of information and complete daily tasks for Theodore simultaneously. Her conversation skills are indistinguishable from that of another human, the witty banter and humorous remarks eventually evolve into a romantic connection between the two. This film portrays the potential complexities of the relationship between humans and AI-powered assistants as they become more advanced.

How to Learn from these Films

Though the primary goal of these films is to entertain and stimulate discussion amongst the audience, they each can help us learn important messages from the world of data science.

In order to dive deeper into the underlying themes and messages of these films, try the following:

Read film analyses and discussion forums online
Take time to reflect on your experience
Discuss the film with a friend or coworker
Research ideas and theories presented in the film

Overall, there are many opportunities to learn from these films and gain a deeper perspective on the power of data science. From real-world applications of predictive analytics to tackling the ethics of AI, movies have an interesting way of bringing life to these topics. Add any of these films to your watchlist to see for yourself!

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AI vs. Automation: Key Differences & Operational Impacts

Post author By Scottie Todd
Post date July 20, 2020

One of the biggest challenges for companies trying to utilize big data, statistics, and programming capabilities is to use those tools effectively. In particular, there can be immense misunderstandings about how AI and automation work. The differences aren’t always readily apparent, but there are real operational impacts that come from knowing which jobs are meant for AI and which ones are better handled with automation.

What is Automation?

In the simplest form, there is the question of independence that distinguishes AI from automation. Programmable automated systems have existed for centuries, with the first re-programmable machines coming into operation in the weaving industry in 1801. The Jacquard loom automated processes by way of punch cards defined desirable patterns.

No one would confuse the Jacquard loom with anything approaching AI. Instead, the looms were automated by using a series of pre-defined patterns. A machine would read the holes punched into the card, and this triggered a series of tasks. In other words, automation is very good at doing jobs quickly and repeatedly.

How is AI Different from Automation?

Most forms of AI use statistical models to derive inferences from large data sets. Notably, this work often requires continuous adaptation as circumstances change.

For example, take how a spam filter might use AI to keep up with evolving techniques used by scammers. A spam filter might use some combination of techniques, many that are very time-consuming to execute, such as:

Word cloud analysis
Bayesian inference
Seq2Seq correlations
Neural networks
Sentiment analysis
Scoring

Every day, the filter is going to attain some level of success or failure. As end-users mark different emails in their inbox as spam, the AI powering the filter will run a new analysis to adapt.

It’s worth noting that this form of AI is playing against many intelligent opponents. In fact, nothing prevents spammers from using their own AI systems to assess their success and build more suitable emails. This means the AI has to go back to the lab every day to update its analysis of which emails should be let through and which ones need to be flagged.

They Are Not Mutually Exclusive

You should consider that, in many cases, there isn’t an inherent mutual exclusivity between AI and automation. Many AI functions are automated. In the previous example of a spam filter, most people running email servers will have some sort of cron job set up to trigger the next run of the AI’s analysis.

The flow of information can go the other way, too. A set of IoT sensors in a cornfield, for example, might collect data and send it to a central AI. Upon receipt of the new data, the AI goes to work analyzing it and producing insights.

Additionally, a self-perpetuating loop can also be created. The AI might send a fresh clone of a neural network to an edge device each day. Upon completion of its tasks, the edge device then ships relevant data back to the AI. The AI conducts new analysis, creates another neural network, and ships yet another clone of the NN downstream to the edge device. Rinse and repeat in perpetuity.

What Are the Operational Impacts?

A report from 2018 indicated that companies who achieved 20% or greater growth were functioning at 61% automation across their operations. Those producing less growth had only automated 35%.

Companies are also achieving significant improvements using AI. For example, 80% of customer support queries can now be handled solely by high-quality AI-based chatbots. This means human operators can focus their energy on the challenging cases that make up the other 20%, leading to greater attention to queries and improved customer satisfaction.

To say AI and automation are transformative for businesses is an understatement. Increasingly, the winners in the business world are those enterprises that can leverage both tools. Operations that haven’t automated need to get started yesterday, and the ones that are already invested need to keep pushing the envelope to stay competitive.

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The Future of Digital Transformation: 2019 and Beyond

Post author By Scottie Todd
Post date August 28, 2019

As organizations begin to move full throttle into enhancing internal and external business outcomes, the term ‘digital transformation’ has gained supreme status into the particular tech lexicon. Electronic transformation has become an important strategy for organizations for years and is predicted to be a crucial factor in the competition of who remains in the business.

The term digital transformation is defined as the particular integration of electronic technology into all areas of the business, fundamentally changing how it operates and delivers value to its customers. Digital transformation is also a cultural change that requires organizations to continually challenge the status quo, experiment, and get comfortable with failure even if that happens.

Important Change For Every Business

As every organization is different, so are their digital change requirements. Digital transformation has the long legacy and extends much beyond 2019. It is a change which requires businesses to experiment often, get comfortable with failing, and continually problem the status quo. It also means that companies have to move beyond the age-old processes and look out for new challenges and changes.

Here is what the essence of digital transformation brings to the table:

• Customer experience

• Culture and leadership

• Digital technology integration

• Operational Agility

• Workforce enablement

2019 and Beyond

One critical aspect of the approach to digital transformation is that IT departments are progressively expected to take the greater role in driving overall company goals.

Technologies are evolving and changing, challenging the particular fundamental strategic and operational processes that have defined organizations up until now.

In the times to come, enterprises will no longer have separate digital and AI strategies, but instead will have to integrate corporate strategies deeply infused with changing technologies.

Artificial Intelligence Big Data

A Data Monetization Strategy: It’s What Your Business Needs

Post author By Scottie Todd
Post date July 25, 2019

Collecting and monetizing data is a goal that many organizations now have. Setting out a goal, however, isn’t the same as actually employing data monetization strategies.

When we think about data monetization strategies, they can be broadly put into two main camps. These are strategies that are meant to be:

Cost-saving measures
Revenue-generating ones

Similarly, strategies tend to be either internally or externally facing. Let’s take a look at how each approach works and how they might fit your operation’s needs.

Cost-Saving Data Monetization Strategies

Oftentimes, the simplest data monetization strategy is the one that leverages something a company already has. For example, a large home nursing agency has lots of data about the appointments it makes. That data can be leveraged to make determinations about when to schedule employees, how to handle travel and even what order clients should be visited in.

A cost-saving data monetization strategy is typically internally facing. This is because the easiest data to get your hands on is what your company has.

There are, however, businesses that have emerged that provide cost-saving data monetization strategies to others. Many consulting firms now help other companies make the most of their existing data in order to:

Improve processes
Spot fraud
Create better information-driven products
Develop better compliance measures
Anchor analysis

If a problem can be identified, there’s a good chance it can be monetized if you can find and exploit a data pool related to it.

Revenue-Generating Data Monetization Strategies

Streamlining a business is one thing, but at the end of the day, your organization needs to turn a profit. The best way to accomplish that sometimes is to employ a revenue-generating data monetization strategy.

A Data Monetization Strategy: It’s What Your Business Needs

A simple version of this approach is assembling collected data into products that can be sold to other parties. This is an especially good plan if you’re trying to monetize what is fundamentally dead data. For example, a healthcare company might not have much use for decades-old epidemiological data. Plenty of researchers, though, would pay to get their hands on that data. Bear in mind, however, that anonymization is often necessary when selling data products.

Turning information into a new product is another way to generate revenue. Your company might compile loads of data on trends in your industry, for example. Converting such information into reports that are sold to outside parties is one of the most time-honored data monetization strategies.

Creating whole new opportunities is another approach. A company might focus on culling existing data to determine where there are new markets to enter. For example, your organization might examine international sales and see that loads of folks in Australia are ordering your products online. This may suggest an opportunity to open up new stores in the country.

Inward vs. Outward

Another question is just how inward- or outward-facing you want your data monetization strategies to be. Naturally, some organizations are reluctant to put their data in places where competitors might take advantage of it. There also may be concerns about regulations limiting the transfer of data.

In some cases, an outward trajectory is the only viable approach. In the previous example involving dead data, there simply may be no other way to extract any more value from the data as a product.

The question of inward versus outward monetization sometimes hinges on building a business model. Inward-facing models generally are more sustainable because they don’t depend on outside parties continuing to pay for reports or subscriptions. Conversely, an inward-facing model frequently has an installed limit on how much value it can generate because its audience is capped.

Preparing a Data Monetization Strategy

Having a strategy isn’t enough. To put one to work, you need to prepare the data itself and to be prepared for several potential issues.

If your operation doesn’t already have a large data infrastructure and a data-centric culture, you’ll need to put that in place. This entails:

Developing a business case for the strategy
Establishing processes and a compliance structure
Hiring professionals who can handle data
Building up servers and networks for storage and processing
Refining processes
Maintaining the strategy long-term

In many instances, the strategy you choose will guide the decision-making process. For example, a company that’s building a model for selling anonymized customer data to third parties will need to build its processes around privacy and consent concerns. A solution will need to be in place for customers to deny the use of their information, and an audit system will need to be designed to ensure privacy concerns are addressed.

Data monetization is a huge opportunity, especially for organizations that are already accumulating loads of information. It does require a cultural commitment, though, to handling the data itself and treating it with an appropriate level of care. In time, your company can realize major savings and turn its data into a profit generator.

Leveraging the Power of AI Can Drastically Fuel the Manufacturing Industry

Post author By Scottie Todd
Post date July 18, 2019

AI-enabled machines are creating an easier path to the future of manufacturing by yielding a pool of advantages, including providing new opportunities, improving production efficiencies, bringing machine interaction closer to human interaction, etc.

According to the market’s facts and statistics, the global marketplace for artificial intelligence in manufacturing will be predicted to reach $15,237.7 million by 2025. The market was valued at $513.6 million within 2017 and is projected to rise at a CAGR of 55.2%.

AI is Essential for the Next-Gen Software in the Manufacturing Industry

Since AI came into view over the last few years, it has managed to surmount an array of internal challenges that have haunted the manufacturing industry for decades. This ranges from the lack of expertise to complexity in decision making, issues related to integration, and overloaded information. With AI, manufacturers can completely transform their proceedings.

Unlike healthcare, finance, utilities, and e-commerce industry, AI-powered analytics along with real-time insights have already rolled out in the manufacturing field to assist businesses in upsurging their revenues and market shares faster than their competitors.

In a 2018, Manufacturer’s Annual Manufacturing report revealed 92% of senior manufacturing executives say that ‘Smart Factory’ digital technologies, which includes AI, will provide them access to escalate their productivity levels and enable staff to work “smarter”. In the same way, Forrester, a global research firm, emphasizes that 58% of business and technology professionals are exploring AI systems, while just 12% are actively using them.

AI Growth in Manufacturing

Advancements in manufacturing automation and the increase in demand for big data integration are fueling the AI growth in manufacturing market. Additionally, extensive utilization of machine vision cameras in manufacturing applications – such as machinery inspection, material movement, field service, and quality control – could also accelerate the AI growth inside manufacturing.

Leveraging AI within manufacturing plants may allow businesses to entirely transform their own proceedings, assisting the industry in accomplishing directed software, 24/7 production, reduced operational costs, safer operational environment, new opportunities for employees, etc. Additionally, bringing AI into the production industry would necessitate a huge capital investment that can significantly increase the return associated with investments. While intelligent machines start taking care of routine activities, manufacturers can enjoy significantly lower operating costs.

AI further enables machines to reap and extract data, recognize patterns, learn and adapt in order to new things or situations through device intelligence, and speech recognition.

Manufacturers, by utilizing AI, will have access to…

Produce data determined decisions swiftly
Facilitate improved production outcomes
Advance process effectiveness
Decrease operational costs
Improve scalability and product development

AI Trends in Manufacturing Businesses

Within the manufacturing industry, AI could be integrated with the particular Internet of Things (IoT) to deliver supplies and services in order to customers. IoT can also convey in-depth measurements back to manufacturers and distributors to analyze quality and factors that might impel fiascos.

According to a research report, AI technologies are approaching the increase of production by 40% or more through 2035. Also, this technology will fortify the economic growth by an average of 1.7% across 16 industries by 2035.

Let’s take a look at some AI trend examples…

Two-years back in October 2017, computer software giant Oracle launched new AI-driven apps for supply chain, manufacturing, and other professionals. Last year, IBM released an AI-optimized Watson Assistant for businesses, which usually is a smart enterprise assistant powered with AI features.

In brief, adoption of AI can assist in empowering manufacturers to effectively deploy predictive and preventive maintenance, flexible automation, automated quality control and demand-driven production.

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Anomaly Detection — Another Challenge for Artificial Intelligence

Post author By Scottie Todd
Post date June 28, 2019

It is true that the Industrial Internet of Things will change the world someday. So far, it is the abundance of data that makes the world spin faster. Piled in sometimes unmanageable datasets, big data turned from the Holy Grail into a problem pushing businesses and organizations to make faster decisions in real-time. One way to process data faster and more efficiently is to detect abnormal events, changes or shifts in datasets. Thus, anomaly detection, a technology that relies on Artificial Intelligence to identify abnormal behavior within the particular pool of collected data, has become one of the main objectives of the Industrial IoT.

Abnormality detection refers in order to the identification associated with items or occasions that do not conform to an expected pattern or to other items in a dataset that are usually undetectable by the human expert. Such anomalies can generally be translated into problems such as structural defects, errors or frauds.

Examples of potential anomalies

A leaking connection pipe that leads to the shutting down of the entire production line;
Multiple failed login attempts indicating the possibility of fishy cyber activity;
Fraud detection in financial transactions.

Why is it important?

Modern businesses are beginning to understand the particular importance of interconnected operations to get the full picture of their…

Using AI to Cleanup Dirty Data

Analyzing Business Data for Forecasting and Prediction

Improving Sales and Customer Success

Moneyball

Blade Runner

I, Robot

Minority Report

Her

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