Jan 27, 2021

US commission recommends pressing ahead with AI weapons

AI
weapons
Defence
US
William Smith
2 min
The National Security Commission on Artificial Intelligence report and its non-binding recommendations is due to be submitted in March
The National Security Commission on Artificial Intelligence report and its non-binding recommendations is due to be submitted in March...

A US government-appointed panel led by former Google Chief Executive Officer Eric Schmidt has said the US should resist a ban on AI weapons.

The National Security Commission on Artificial Intelligence was established in 2018 with the goal “to consider the methods and means necessary to advance the development of artificial intelligence, machine learning, and associated technologies to comprehensively address the national security and defense needs of the United States."

Removing human mistakes

It has now produced a draft final report for Congress, which claimed that AI weapons should not be discounted, and that they are expected to make fewer mistakes than human combatants. The report and its non-binding recommendations is due to be submitted in March.

The report’s introduction states that “The AI revolution is not a strategic surprise. We are experiencing its impact in our daily lives and can anticipate how research progress will translate into real world applications before we have to confront the full national security ramifications. This commission can warn of national security challenges and articulate the benefits, rather than explain why previous warnings were ignored and opportunities were missed. We still have a window to make the changes to build a safer and better future.”

An ethical quandary

While autonomous weapons are nothing new (consider a land mine for instance), the introduction of AI in recent times has brought to the fore ethical questions, such as with whom the responsibility of decisions made by AI ultimately resides. Popular culture is certainly full of tales of rogue AI weapons systems, the most famous of which might be Skynet from the Terminator franchise. According to Reuters, a coalition of around thirty countries wants an outright ban on AI weapons.

Nevertheless, the report envisions a future where: “AI applications will help militaries prepare, sense and understand, decide, and execute faster and more efficiently. Numerous weapons systems will leverage one or more AI technologies. AI-systems will generate options for commanders and create battle networks connecting systems across all domains.”

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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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