Leaked AI plans show proposal for new regulations in the EU
The European Commission is set to unveil new regulations on AI products. A leaked draft of the legislation, obtained by , shows how the Commission is planning on achieving these regulations in upcoming legislation this month.
The draft includes bans for certain uses of AI systems and will only allow risky AI technology into the EU’s internal market if it has been vetted.
As part of the draft regulation on ‘European approach for artificial intelligence’, have reported that the EU executive proposes to ban AI technologies that are used for “indiscriminate surveillance applied in a generalised manner to all natural persons without differentiation.” This includes surveillance such as those “monitoring and tracking of natural persons in digital or physical environments, as well as automated aggregation and analysis of personal data from various sources”.
Companies that don’t comply could be hit with a fine of up to €20 million or 4 per cent of their total worldwide annual turnover.
According to the draft none of the bans will apply if the use of them are authorised by law and are carried out by authorities to enforce laws and fight crime. For example, the use of facial recognition in public places could be allowed if it is limited in time and geography, and necessary to fight terroism.
The EU has made a previous statement which : “This proposal will aim to safeguard fundamental EU values and rights and user safety by obliging high-risk AI systems to meet mandatory requirements related to their trustworthiness. For example, ensuring there is human oversight, and clear information on the capabilities and limitations of AI.”
Even with the leak of the draft there is still little information available as to how the EU plans to enforce the regulations or how systems will be vetted. Once the Commission has published the new regulations, EU lawmakers will be able to voice their views about AI technologies and more information could be released.
Are the EU using AI?
AI can be key in economic development and can provide solutions to a range of different challenges such as treating diseases to dealing with complaints.
A study found that in 2020, 7 per cent of enterprises in the EU with at least 10 people employed used AI applications. While 2 per cent of the enterprises used machine learning to analyse big data internally, 1 per cent analysed big data internally with the help of natural language processing, natural language generation or speech recognition. A chat service, where a chatbot or virtual agent generated natural language replies to customers, was used in 2 per cent of the enterprises. The same proportion of enterprises, 2 per cent, used service robots, which are characterised with some degree of autonomy, for example to carry out cleaning.
Among the EU Member States, Ireland recorded the highest share of enterprises (23 per cent) that used any of the four considered AI applications in 2020. Other countries with widespread uptake of AI technologies were Malta (19 per cent), Finland (12 per cent) and Denmark (11 per cent).
The future of certain aspects of AI in the EU look uncertain at the moment and will be dependent on the decisions and outcome that the Commission makes.
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.