Artificial intelligence, blockchain and the future of Europe
This report presents a case for allocating more resources to increase Europe’s innovation capacity, to look beyond immediate needs and to think long-term, to think strategically, and to think visionary. It shows that Europe needs to address an investment gap of up to €10 billion that is holding back the development and deployment of artificial intelligence (AI) and blockchain technologies in the EU.
The report found that Europe is lagging behind in particular on the financing front, where they only account for 7% of annual global equity investments in these technologies, and the United States and China together account for over 80% of the €25 billion of annual equity investments.
All major world economies, including the European Union, are racing to achieve a leading global position in the development and deployment of AI and blockchain. The European Commission has taken several measures to advance these technologies: the Horizon 2020 programme allocated €1.5 billion to AI in 2018–2020; the Digital Europe Programme (DEP), as part of the 2021–2027 Multiannual Financial Framework (MFF), will complement this by dedicating an additional €2.5 billion to investing in and opening up the use of AI by businesses and public administrations. On the investment front, the European Investment Bank (EIB) launched a €150 million co-investment facility to invest alongside fund managers and private investors backed by the European Investment Fund (EIF), while the EIF recently launched a pilot for a dedicated AI and blockchain investment scheme of €100 million.
"Companies and governments in Europe are substantially underinvesting in AI and blockchain compared to other leading regions and it has become clear that the European Union struggles to translate its scientific excellence into business application and economic success," the bank said in a report. Compared to the United States, the European Union has fewer venture capital investors specialising in AI and blockchain.
Improvements in Europe's AI and blockchain developments
The study identified three major areas needing improvement in Europe’s AI and blockchain landscape. The challenges identified span the development of AI and blockchain technologies, their deployment in the market, and the wider EU innovation ecosystem:
1. Development: Boost financing for AI and blockchain development and scale-up
2. Deployment: Support take-up of AI and blockchain technologies in the market
3. EU innovation ecosystem: Develop a European integrated innovation ecosystem
Most national banks have launched support schemes in the aftermath of COVID-19. The study suggests the EIB Group could deploy coordinated co-funding models with national promotional banks to ensure that AI/blockchain technology and startups become more central in local financing responses to recover from the crisis generated by COVID-19.
Since 2014, the European Union has implemented several regulations to facilitate and regulate the development of data-intensive industries such as AI and blockchain. As a major step in this regard, in April 2021, the European Commission laid out proposals for new rules and actions that aim to turn Europe into the global hub for trustworthy AI. The efforts include the first-ever legal framework on AI and a new Coordinated Plan with Member States that will guarantee the safety and fundamental rights of people and businesses, at the same time strengthening AI uptake, investment, and innovation across the European Union.
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.