Feb 25, 2021

UK intelligence agency GCHQ sets out AI strategy and ethics

GCHQ
intelligence
AI
Cybersecurity
William Smith
2 min
GCHQ has published a paper, known as “Ethics of AI: Pioneering a New National Security”, in which it set out examples of how it could use AI
GCHQ has published a paper, known as “Ethics of AI: Pioneering a New National Security”, in which it set out examples of how it could use AI...

 

British intelligence agency GCHQ has laid out its plan to put artificial intelligence in national security.

GCHQ is the signals intelligence arm of the UK,and is responsible for gathering information as well as securing UK communications.

The organisation has published a paper, known as “Ethics of AI: Pioneering a New National Security”, in which it set out examples of how it could use AI going forwards.

AI in national security

Potential uses include fact checking and the detection of deepfake media, which has been mooted as a threat to democracy. Alongside that is mapping international trafficking networks, analysing chat rooms for evidence of child grooming and identifying potentially malicious software for cybersecurity purposes.

Director GCHQ Jeremy Fleming said: “AI, like so many technologies, offers great promise for society, prosperity and security. It’s impact on GCHQ is equally profound. AI is already invaluable in many of our missions as we protect the country, its people and way of life. It allows our brilliant analysts to manage vast volumes of complex data and improves decision-making in the face of increasingly complex threats – from protecting children to improving cyber security.”

An eye on ethics

The paper also saw the organisation consider how it would use AI ethically, fairly and transparently, alongside supporting the UK’s AI sector with an AI lab in its Manchester office, mentoring AI startups through accelerator schemes and supporting the creation of the UK’s national institute for data science and artificial intelligence, the Alan Turing institute, in 2015.

“While this unprecedented technological evolution comes with great opportunity, it also poses significant ethical challenges for all of society, including GCHQ,” said Fleming. “Today we are setting out our plan and commitment to the ethical use of AI in our mission. I hope it will inspire further thinking at home and abroad about how we can ensure fairness, transparency and accountability to underpin the use of AI.”

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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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