Sep 25, 2020

The Impossible Tattoo - Powered by 5G

5G
Technology
AI
Amber Naylor
2 min
T-Mobile reveals the power of 5G with the world’s first remote tattoo
T-Mobile reveals the power of 5G with the world’s first remote tattoo...

5G has become the next level of wireless technology, globally. Whilst its presence only continues to grow; the world embracing and preparing for its permanent arrival, many telecom providers are still trying to understand and translate the technological improvements that are relevant for consumer benefits. The advancements that 5G will provide are greater reliability, an enhanced capacity and overall being quicker. But the question remains, for what purpose. T-Mobile has a clear plan in mind. 

The Mill teamed up with the creative agency Anomaly Amsterdam, to bring 5G ever closer and built T-Mobile’s status as The Netherland’s most trusted network. From this pairing came ‘The Impossible Tattoo’: the world’s first remote tattoo, all powered by T-Mobile’s new 5G technology. What was originally perceived as impossible, became a reality through the use of a robotic arm. Controlled by the tattoo artist from a separate location, he proceeded to create a tattoo onto a human arm, through the use of real-time 5G. With the new benefits of the 5G network, there is virtually no delay in time, resulting in the ability to perform actions with millimeter accuracy, from a far off location. ‘The Impossible Tattoo’ project has greatly illustrated what larger reliability, speed and low latency can mean within the real world. 

Several months worth or research was put into action by The Mill’s Experience Team - led by Noel Drew. Thoroughly exploring and developing this cutting edge technology, the team built both the custom robotic tattoo arm, alongside the film that captured the ground-breaking experience. The dutch tattoo artist, Wes, was also deeply involved in the development of the remote tattooing technology, ensuring that a variety of factors were taken into consideration when designing and replicating the use of tattooing on a human arm. Multiple tests were performed before the final event, on varying vegetables and prosthetic skin samples. The final creation was applied to dutch actress and TV personality Stijn Fransen; successfully receiving the world’s first 5G tattoo. 

Footage of the entire process covers from the building, to the testing of the AI robotic arm. The final event being this last phase to be filmed, which will now be used in online communications and national broadcasts for T-Mobile. 

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Jun 15, 2021

The advantages and disadvantages of AI in cloud computing

AI
CloudComputing
Data
ML
3 min
AI is being used in cloud computing, which works by allowing client devices to access data over the internet remotely, but are there pros and cons?

Cloud computing offers businesses more flexibility, agility, and cost savings by hosting data and applications in the cloud. AI capabilities are now combining with cloud computing and helping companies manage their data, look for patterns and insights in information, deliver customer experiences, and optimise workflows.

We take a look at some of the benefits and drawbacks of AI in cloud computing. 
 

The benefits of AI in cloud computing

 

Lower costs

A major advantage of cloud computing is that it eliminates costs related to on-site data centers, such as hardware and maintenance. Those upfront costs can be restrictive with AI projects, but with cloud enterprises you can access these tools for a monthly fee, making research and development related costs more manageable. AI tools can also gain insights from the data and analyse it without human intervention, reducing staff costs.

Deeper insights 

AI is able to identify patterns and trends in large data sets. Using historical data, AI compares it to the most recent data, which provides IT teams with well-informed, data-backed intelligence. AI tools can also perform data analysis fast so enterprises can rapidly and efficiently address customer queries and issues. The observations and valuable advice gained from AI capabilities result in quicker and more accurate results.

Improved data management

AI enables extensive data management, and cloud computing maximises information security, making it possible to deal with massive amounts of data in a programmed manner to analyse them properly, allowing the business to leverage information that has been “mined” and filtered to meet each need. AI can also be used to transfer data between on-premises and cloud environments. 
 

Intelligent automation 

Businesses use AI-driven cloud computing to be more efficient and insight-driven. AI can automate repetitive tasks to boost productivity, and also perform data analysis without any human intervention. IT teams can also use AI to manage and monitor core workflows. IT teams can focus more on strategic operations while AI performs the mundane tasks. 

Increased security 

With businesses deploying more applications in the cloud, security is crucial in order to keep data safe. IT teams can use different AI-powered network security tools which can track network traffic, they can flag issues, such as finding an anomaly. 
 

The drawbacks of AI in cloud computing

 

Data privacy 

 Enterprises need to create privacy policies and secure all data when using AI in cloud computing. AI applications require a large amount of data, which can include consumer and vendor information. While some data can be anonymous and can't be tied to personally identifiable information, knowing who the data belongs to makes it more valuable. When sensitive information is used, data protection and compliance is a major concern.

Connectivity concerns 

IT teams use the internet to send raw data to the cloud service and recover processed data. Poor internet access can hinder the advantages of cloud-based machine learning algorithms, as cloud-based machine learning systems need consistent internet connectivity. 

While processing data in the cloud is quicker than conventional computing, there is a time lag between transmitting data to the cloud and receiving responses. This is a significant issue when using machine learning algorithms for cloud servers, where prediction speed is one of the primary concerns.

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