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The Different Challenges and Approaches to AI by Country

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With the complete transformation of society being imminent due to artificial intelligence (AI) technologies, it is important to look at the different approaches being taken by countries around the globe. Whether it is for reasons of prosperity or surveillance, there is no doubt that nations are increasingly investing in AI. 

China

China is taking a very strategic approach to artificial intelligence, with the Chinese government declaring its hopes for the nation to become the world’s leading AI innovator by 2030. The government has released a national AI strategy and plans on investing tens of billions of dollars in AI research and development. Cities are investing their own money as well, with Beijing’s US$ 2.1 billion AI technology park and Tianjin’s planned US$16 billion AI fund. 

The private sector is playing the other big role in China, with Chinese AI startups competing with the United States for AI venture funding. The country is second, behind the United States, when it comes to the number of AI companies. 

China’s advancement in AI has also brought along serious concerns about human rights abuses and surveillance. AI companies within China are exporting surveillance technology to countries like Kenya, Laos, Mongolia, Uganda, and Uzbekistan. The biggest concerns are stemming from the use of facial recognition technology to track individuals. 

United States

The United States has constantly been a leader in public and private AI research, with massive venture capital investment taking place in the industry. In 2012, AI initiatives received US$282 million from venture capitalists, and that number reached US$8 billion by 2018. 

The United States is facing big problems in areas like cybersecurity and the skills gap. Organizations are increasingly implementing big AI initiatives, which comes with an increased security risk. There is a big concern among executives about proprietary and sensitive data being stolen, as well as outside actors influencing training data and algorithms. As for the skills gap, it is being widened due to the increasing implementation of this technology. This will have big implications for the economy and could lead to massive unemployment if not addressed immediately. Companies are beginning to implement retraining and upskilling programs for their employees. 

Germany

Germany is accelerating the development of AI technologies, with plans to invest €3 billion in AI research by 2025. Their national strategy is called “AI Made in Germany,” and they hope that AI will expand the economy and improve the competitiveness of existing industries. According to a study commissioned by the German government, AI will add around €32 billion to Germany’s manufacturing output within 5 years. 

Germany has put an increased focus on the ethical issues surrounding AI. They have big concerns about disinformation and the technology being manipulated. Besides manipulation, there is a concern about the economic impact of the technology. Because of this, the country puts forth a strong effort to train workers in AI. They see it as a way of enhancing performance and enabling partnerships between humans and machines.

United Kingdom 

The United Kingdom has an impressive AI startup scene, along with £1 billion in government support going to industry and academia. There is a focus on large-scale initiatives, as well as implementing a comprehensive strategy for AI adoption. While there is concern among the government about legal liability and autonomous decision-making, the biggest challenges are seen by many as proving the business value for AI projects, as well as integrating AI into roles and functions. 

The UK could also see a big disruption of the workforce due to new technology. They have piloted retraining programs such as the National Retraining Scheme, which are likely to be expanded in the near future. It consists of various initiatives meant to prepare workers for the evolution of the workspace. 

France

Mathematician Cédric Villani was appointed by President Emmanuel Macron in 2017 to develop a national AI strategy. He came up with “AI for Humanity,” which was released in 2018 with €1.5 billion in funding. 

The plan focuses on the nation’s resources and talent, an open data ecosystem, research institutions, ethical issues, and implications for the economy. The government has a strong relationship with the European Union, but the nation also develops AI domestically. 

France has a larger number of small AI projects, not yet taking part in the large-scale ones. This could be due to competing priorities like the General Data Protection Regulation (GDPR) compliance. 

Some of the nation’s biggest challenges are integrating AI into organizations and obtaining talent. Because of the extreme skills gap, the government is working on developing a system that relies on graduates of the French education system. 

Canada

Canada is taking a slow approach to AI technology, which could hurt innovation and implementation. There is a lack of urgency, with only around 51 percent of executives believing AI will transform their company. 

As the rest of the world moves forward with AI technology, Canadian companies could fall behind. However, the government is taking steps to try to avoid that situation. They have implemented policies to make immigration easier for those with AI-related skill sets. Since they are not producing enough of the talent within their own borders, they are looking to bring it in. There is not a strong push for AI-training within the country, but that could change with partnerships with academic institutions. The University of Toronto is investing around CAN$100 million to support the work coming from individuals like AI scientists. 

Preparing for the Future

With AI technology set to take over many aspects of society within a decade, the impact in each country will depend on their current approach. It can be argued that none of the nations listed here are taking a drastic enough approach to what will be the Fourth Industrial Revolution, but a lot can be learned by studying them. Most of these initiatives will likely have to be upscaled very quickly to prepare for the future of AI.