Apr 28, 2021

IBM and Dronehub partner to provide AI solutions for drones

Tilly Kenyon
2 min
IBM and Dronehub have joined forces to manage the data volumes and build next-generation AI-powered technology for drone operations.
IBM and Dronehub have joined forces to manage data volumes and build next-generation AI-powered technology for drone operations...

IBM has partnered with Dronehub, a Poland-based manufacturer of drone-in-a-box systems, to automate drone inspection missions with machine learning and AI solutions.

Dronehub explained that while they focus on developing autonomous docking stations for drones they will collaborate with IBM to utilise IBM Power Systems Accelerated AI servers to help improve ground infrastructure and to improve inspection missions. ‘As a result of this IBM-Dronehub cooperation, we will strengthen our abilities to help our clients and partners with large infrastructure to reduce costs of monitoring and to optimise the process of getting real-time aerial data.’

“By working with IBM, we can implement new concepts and algorithms necessary for the further development of our drone systems,” said Vadym Melnyk, Dronehub founder, and CEO.

Leveraging IBM’s AI solutions will allow the drone startup to develop new artificial intelligence algorithms, expand its applications, and open up its offerings to new clients and industries. The ultimate goal is to reduce the costs of monitoring and optimise the process of getting real-time aerial data for clients with large infrastructure footprints.

“Next-generation AI-powered technology will considerably boost further development of aviation industry. With the support of IBM solutions, it will be possible to carry out even more advanced drone use cases across various sectors – from agriculture to railways,” said Sebastian Jeliński, IBM Senior Server Solutions Consultant.

Dronehub and Urban Air Technologies

The Uspace4UAM consortium, which includes Dronehub, was selected earlier this year to demonstrate Urban Air Technologies in four European countries to enable the safe integration of Urban Air Mobility in the European airspace.

The role of Dronehub in the project is to perform approximately 160 flights with drones in the city of Rzeszów for these three uses:

  • Transport of the AED to the spot of the accident
  • Aerial video monitoring of the scene of an incident or accident, along with providing the image for public services such as the police or fire brigade
  • Creating orthophotos and photogrammetry for the needs of public services

The first flights are planned for September 2021 and the last for October 2022. Alongside Dronehub other companies include Air Navigation Services of the Czech Republic, Altitude Angel, Austro Control, CATEC, CRIDA, DLR, ENAIRE, Lilium, TECNALIA, UpVision, and Vertical Aerospace. 

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Jun 15, 2021

The advantages and disadvantages of AI in cloud computing

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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