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3 Positive Use Cases of Deepfakes

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3 Positive Use Cases of Deepfakes


The negative applications of deepfakes can be controlled through blockchain and other deep learning-based image forgery detection tools.

However, there is more to deepfakes than just negative applications.

Ever since the emergence of deepfakes, they have been normally associated with pranks or cybercrimes. Accordingly, there are several pieces that discuss the ways in which deepfake-related problems can be resolved through blockchain or deep learning-based image forgery detection. The concept of deepfakes, also known as synthetic media, is one of the more irreverent applications of AI and computer vision. It involves creating media that is completely computer-generated, with deep learning algorithms used to create fascinating and utterly believable fake videos and images. For example, here are deepfake videos of Nicholas Cage as Amy Adams and Kim Jong-un talking about democracy collapsing in the US

However, despite cybercriminals using deepfakes for digital forgery, creating superimposed pornographic videos of celebrities and other negative actions, synthetic media can also influence productive and positive actions.

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1) Deepfakes Are Beneficial For Educational Purposes

Deepfakes can superimpose another person’s face on an individual’s body. However, unlike tacky photoshopped media, a deepfake can replicate a person’s facial expressions and movement to make the made-up videos and pictures look photorealistic. This characteristic can be used to perfection in schools. Professors could show deepfake videos of historical speeches or scientific discoveries to help learners understand historical and scientific concepts in detail.

Needless to say, the usage of deepfakes in education can be harmful too. For instance, teachers or institutions with a specific political agenda or inclination towards a negative philosophy can show incorrect history to students. For example, using doctored speeches of dictators, there is a possibility of educators promoting or justifying acts of fascism in the past.

2) Some Deepfakes Spread Awareness About Dangerous Issues

One of the most popular use cases of deepfakes is creating dubbed videos of people appearing to be speaking several different languages. One such example is soccer player David Beckham speaking up to nine different languages to spread awareness about malaria and how the disease can be brought under control. In the video, the soccer star’s facial movements were manipulated to make them look as authentic as possible. This application can be invaluable as it makes people from different parts of the world pay attention and follow their favorite idol’s advice.

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3) Deepfakes Can Provide Anonymity Against Oppressors

In recent times, several countries have witnessed the rise of oppressive, dictatorial leadership in power. Unsurprisingly, the number of protestors and demonstrations have also visibly risen in such countries to oppose the draconian ways in which such leaders and governments run their respective constituencies. In this current internet age, protestors can mobilize rallies and meetings through various social media platforms. Deepfake tech can help mask the identity of such leaders from their governments, enabling protests to go on. 

Issues caused by deepfakes can be easily dealt with via various deep learning-based image forgery detection applications. However, users need to look beyond the evils of deepfakes to find incredible ways in which the technology can be used for a multitude of good causes.  



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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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