"...A promising approach is “literature-based discovery” (LBD) which, as its name suggests, aims to make new discoveries by analyzing scientific literature...such language-based inference methods can become an entirely new field of research at the intersection between natural-language processing and science...A paper...published this year in Nature Human Behaviour, extends this approach in a novel way. It starts with the observation that LBD systems tend to focus on concepts within papers and ignore their authors. So they trained an LBD system to take account of both. The resulting system was twice as good at forecasting new discoveries in materials science...and could also predict the actual discoverers with more than 40% accuracy....In future, researchers might come to rely on such systems to monitor the deluge of new scientific papers, highlight relevant results, suggest novel hypotheses for research—and even link them up with potential research partners, like a scientific matchmaking service. AI tools could thus extend and transform the existing, centuries-old infrastructure of scientific publishing... If LBD promises to supercharge the journal with AI, “robot scientists”, or “self-driving labs”, promise to do the same for the laboratory. These machines go beyond existing forms of laboratory automation, such as drug-screening platforms. Instead, they are given background knowledge about a particular area of research, in the form of data, research papers, and patents. They then use AI to form hypotheses, carry out experiments using robots, assess the results, modify their hypotheses, and repeat the cycle..." https://www.economist.com/science-and-technology/2023/09/13/how-scientists-are-using-artificial-intelligence
The article recently published in The Economist (excerpt above), presents an optimistic perspective on AI's potential to significantly augment and potentially revolutionize the field of science. However, it is crucial to contemplate the potential unintended consequences of these advancements, which may exacerbate the high economic inequalities between rich countries and poor countries (the 10th goal of the UN SDGs). Ultimately worsening the escalating issue of illegal emigration from the latter to the former, which today motivated EU Chief Ursula von der Leyen's trip to the island of Lampedusa, where an impressive number of 199 boats carrying around 8,500 migrants arrived between September 11th and 13th. https://www.dw.com/en/eu-and-italian-leaders-visit-lampedusa-amid-migrant-spike/a-66837104
When you ask ChatGPT, “How can AI be used to reduce illegal immigration from Africa to Europe?” he gives advice on border surveillance, predictive analytics, risk assessment, biometric Identification etc etc etc. However, much more interesting and important, is when you ask ChatGPT about "How can AI be used to reduce the economic gap between Europe and African countries ?". His response is reproduced below:
Reducing the economic gap between Europe and African countries is a complex and multifaceted challenge that involves a combination of economic, social, and political strategies. AI can play a role in this effort by supporting various initiatives and addressing specific aspects of the problem. Here are several ways AI can be utilized:
- Agricultural Optimization: Agriculture is a major sector in many African economies. AI can be used for precision agriculture, helping farmers optimize crop yields, manage resources efficiently, and adapt to changing weather conditions. This can increase agricultural productivity and income for rural communities.
- Financial Inclusion: AI-powered fintech solutions can expand financial services to unbanked and underbanked populations in Africa. Mobile banking, digital lending, and blockchain-based systems can improve access to credit and savings, fostering economic growth.
- Education and Skills Development: AI-driven personalized learning platforms can provide quality education to remote and underserved areas. These platforms can adapt to individual learning needs, improving literacy and job skills, and ultimately enhancing employability.
- Healthcare Access: AI can support telemedicine initiatives, making healthcare more accessible in remote regions. AI can also assist in early disease detection and epidemiological analysis, helping to prevent and manage health crises.
- Agricultural and Market Data: AI can analyze data on crop yields, market trends, and pricing information to empower farmers and businesses in Africa to make informed decisions and access broader markets.
- Infrastructure Development: AI can assist in the planning and maintenance of infrastructure projects, optimizing resource allocation and ensuring long-term sustainability.
- Entrepreneurship and Startups: AI-powered tools and platforms can support the growth of African startups and small businesses, helping them access funding, develop innovative solutions, and expand their reach.
- Trade and Supply Chain Optimization: AI can improve logistics, supply chain management, and trade facilitation, reducing transaction costs and barriers to trade between African countries and Europe.
- Energy Efficiency: AI can enhance energy management systems, making energy production and consumption more efficient and sustainable. This can reduce energy costs and promote economic growth.
- Policy Analysis: AI can help governments and international organizations analyze policy outcomes and assess the impact of various economic policies and initiatives, guiding evidence-based decision-making.
- Economic Forecasting: AI can provide more accurate economic forecasting and risk assessment, helping businesses and governments make strategic decisions to promote economic growth.
- Public Services Improvement: AI can enhance the delivery of public services, such as transportation, education, and healthcare, leading to more efficient and equitable access to these services.
- Promote Innovation Ecosystems: AI can support the development of innovation hubs, technology parks, and incubators, fostering entrepreneurship and technology transfer.
