Should Western countries block AI warfare?
AI warfare is an understandably risky proposal. Weaponising decisions that could be made without human intervention, and faster than humans can think, is a terrifying prospect in the wrong hands. So why is there resistance to the idea of banning AI warfare?
Who’s against AI warfare?
The Campaign to Stop Killer Robots, for one, who say AI warfare will lead to an “irresponsible” arms race. The campaign’s spokesman, Prof Noel Sharkey, said, “"This is a shocking and frightening report that could lead to the proliferation of AI weapons making decisions about who to kill. The most senior AI scientists on the planet have warned them about the consequences, and yet they continue. This will lead to grave violations of international law."
And who’s for AI warfare?
The National Security Commission on AI, which is headed by Eric Schmidt, formerly CEO of Google, and Robert Work, the former deputy secretary of defence in the US. Although it doesn’t condone AI warfare per se, a report it has issued recommends the US and its allies resist a ban, warning it could open up a strategic lead in warfare for countries who failed to sign up for the terms. China and Russia are of particular concern. It cautions that a ban on developing AI-led weaponry could lead to the US losing “its military-technical superiority in the coming years”. The report says, “"The DoD [Department of Defense] has long been hardware-oriented toward ships, planes, and tanks [and] is now trying to make the leap to a software-intensive enterprise. If our forces are not equipped with AI-enabled systems guided by new concepts that exceed those of their adversaries, they will be outmatched and paralysed by the complexity of battle."
What’s the solution?
The report suggests creating a guiding body for the White House, attracting talent from overseas, training civil servants in digital and speeding the adoption of new technologies by intelligence agencies. Another thought is to create an alliance that would prevent advanced hardware technologies that are made in the West from being exported to hostile markets.
The advantages and disadvantages of AI in cloud computing
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
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.
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.
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.
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
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.
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.