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TECHNOLOGY

Artificial Intelligence in the 4th Industrial Revolution

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Artificial Intelligence in the 4th Industrial Revolution


Artificial intelligence is providing disruptive changes in the 4th industrial revolution (Industry 4.0) by increasing interconnectivity and smart automation.

Industry 4.0 is revolutionizing the way companies manufacture, improve and distribute their products. 

What Makes Artificial Intelligence Unique?

Artificial intelligence (AI) makes it possible for machines to learn from experience, adjust to new inputs and perform human-like tasks.

It allows computers to think and behave like humans, but at much faster speeds and with much more processing power than the human brain can produce.

AI offers advantages of new and innovative services, and the potential to improve scale, speed and accuracy. 

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There are 3 types of artificial intelligence:

  • Artificial narrow intelligence (ANI), which has a narrow range of abilities.

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  • Artificial general intelligence (AGI), which is on par with human capabilities.

  • Artificial superintelligence (ASI), which is more capable than a human.

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Artificial intelligence can also be classified as weak or strong. 

Weak AI refers to systems that are programmed to accomplish a wide range of problems but operate within a predetermined or pre-defined range of functions. Strong AI, on the other hand, refers to machines that exhibit human intelligence.

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Artificial intelligence has several subsets:

Most AI examples that you hear about today – from chess-playing computers to self-driving cars – rely heavily on deep learning and natural language processing.

What is the Fourth Industrial Revolution?

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The Fourth Industrial Revolution is the current and developing environment in which disruptive technologies and trends such as the Internet of Things (IoT), robotics, virtual reality (VR) and artificial intelligence (AI) are changing the way modern people live and work. The integration of these technologies into manufacturing practices is known as Industry 4.0. 

The first industrial revolution used water and steam power to mechanize production.

The second used electric power to create mass production.

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The third used electronics and information technology to automate production.

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The fourth Industrial revolution is characterized by a fusion of technologies that is blurring the lines between the physical, digital, and biological spheres, with rising emerging technologies, as real AI, Narrow AI/ML/DL, robotics, automation, materials science, energy storage, the Internet of Things, autonomous vehicles, 3-D printing, nanotechnology, biotechnology, neurotechnology, cognitive technology, and quantum computing. It implies radical disruptions to everything, industries, jobs, works, technologies, and old human conditions. In its scale, scope, complexity, and impact, the AI transformation will be unlike anything humankind has experienced before.

The Role of Artificial Intelligence in the 4th Industrial Revolution

Artificial intelligence is making companies make the best use of practical experience, even displacing traditional labor and becoming the productive factor itself. 

It offers entirely new paths towards growth for manufacturing, service, and other industries, reshaping the world economy and bringing new opportunities for our societal development.

As AI begins to impact the workforce and automation replaces some existing skills, we’re seeing an increased need for emotional intelligence, creativity, and critical thinking.

Zvika Krieger, co-leader of the World Economic Forum’s Center for the Fourth Industrial Revolution.

Deploying AI requires a kind of reboot in the way companies think about privacy and security, As data becomes the currency of our digital lives, companies must ensure the privacy and security of customer information.

Businesses will need to ensure they have the right mix of skills in their workforce to keep pace with changing technology. 

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TECHNOLOGY

How Businesses Can Automate Root Cause Analysis (RCA) With Machine Learning

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How Businesses Can Automate Root Cause Analysis (RCA) With Machine Learning


In the event of a severe incident for your business, you need to analyze what exactly changed (the root cause) to understand its impact.

Using machine learning for root cause analysis can help identify the event that caused the change quickly and easily.

Things can sometimes go wrong in your business’s daily operations. It can be a minor issue, such as a system outage lasting for a couple of minutes. Or it can be something severe as a cyberattack.

Generally, such events result from a chain of actions that eventually culminate in the event. Identifying the root cause is the best way to solve the issue. But manual root cause analysis takes time and often doesn’t provide the exact cause of a mishap. Using machine learning for root cause analysis can automate the process, helping identify the underlying cause quickly and with higher accuracy.

Power of Machine Learning for Root Cause Analysis

To understand why an issue occurred, you need to identify the root cause. But root cause analysis can often be complex and provide inaccurate results. Using machine learning for root cause analysis helps solve this issue.

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Log Analysis

Using machine learning for root cause analysis can help zero in on the exact location of the problem. You don’t have to scroll through infinite logs to identify which components were impacted and when. The machine learning program can automatically and quickly find the root cause by analyzing a given log data set. 

Moreover, the machine learning program can even predict future incidents as more and more data is available. The program compares real-time data with historical data to predict future outcomes and warns you of any unwanted incident beforehand. This will help improve your incident response, reduce downtime and improve productivity.

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Benefits of Using Machine Learning for Root Cause Analysis

There are many benefits of using machine learning for root cause analysis. It can help teams take the right action at the right time, minimizing your losses. Some of the benefits are discussed below.

Reduces Costs

The cost of solving the issue is reduced as your teams don’t have to guess and work around blind spots. Machine learning tools locate the exact line of code responsible for a performance issue, and your team can start working on fixing it right away.

Saves Time

The time spent fixing the issue is significantly reduced as it helps solve business pain faster by locating the cause quickly and accurately.

Provides Long-Lasting Solutions

Machine learning tools provide a permanent solution for your problems and foster a productive and proactive approach.

Grows Your Business

Using machine learning for root cause analysis helps improve the efficiency and productivity of your organization, which eventually leads to business growth.

 

No system is perfect. Incidents will happen, no matter what. But what you do afterward is in your control. Root cause analysis should be done as soon as possible. Using machine learning for root cause analysis not only improves your incident response, but over time, it can also help prevent incidents from happening in the first place.



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