The third and final part of the series “The Dawn of the New Age: How the AI Revolution Mirrors the Middle Ages and Not an Industrial Revolution” tackles the thorny subject of literacy versus AI-oriented digital culture in a not-too-deep way. it reminds me of the “Great Schism” event Let me explain in the following discussion.
The Great Schism: The Divide Between the AI-Savvy and the Rest
In the Middle Ages, the literacy divide created a chasm between the educated clergy and the illiterate masses. Today, a similar divide is emerging between those who understand AI and those who do not. This schism threatens to create a society where an AI-literate minority holds a disproportionate amount of power and influence, while the majority struggles to keep up. It’s a societal division that requires urgent redress through education and the democratization of AI technology.
What is AI literacy and why does it matter?
AI literacy is the ability to comprehend, use, and critically evaluate AI systems and applications in various contexts and domains. It is not only about technical skills, but also about cognitive, social, ethical, and emotional skills. It is not only about consuming AI, but also about creating, controlling, and communicating with AI.
AI literacy matters because AI is becoming ubiquitous and pervasive in our daily lives. From personal assistants to social media, from e-commerce to entertainment, from education to health care, and from transportation to security, AI is reshaping every aspect of human activity. It enables us to benefit from the opportunities and advantages that AI offers, such as enhancing productivity, improving quality of life, fostering creativity, and advancing knowledge. It also empowers us to cope with the challenges and risks that AI poses, such as undermining privacy, discriminating against certain groups, disrupting labour markets, eroding trust, and threatening human dignity.
AI literacy is not only a personal asset, but also a social necessity. It allows us to participate in the digital society as informed and responsible citizens. It enables us to engage in public debate and decision-making on AI issues that affect our rights and values. It fosters a culture of transparency and accountability for AI systems and actors. It promotes a vision of human-centric and ethical AI that respects human rights and values, and that serves the common good.
What is the AI literacy divide and what are its consequences?
The AI literacy divide is the gap between those who have access to and proficiency in AI systems and applications, and those who do not. It divide is influenced by various factors, such as education level, income level, gender, age, race, ethnicity, geography, and disability. It is also affected by the availability and quality of AI resources, such as data, infrastructure, tools, services, policies, and regulations.
The AI literacy divide has serious consequences for individuals and society. It divide creates a situation of inequality and injustice, where some people have more opportunities and advantages than others. It divide exacerbates existing social problems, such as poverty, unemployment, discrimination, polarization, and marginalization. It divide undermines social cohesion and trust, as people feel alienated or threatened by AI systems or actors. Its divide hampers social progress and innovation, as people lack the skills or motivation to contribute to or benefit from AI development and use.
The AI literacy divide is not inevitable or irreversible. It can be reduced or eliminated through concerted efforts from all stakeholders involved in the AI ecosystem, such as governments, civil society organizations, academia, industry, and users.
How can we bridge the AI literacy divide?
Bridging the AI literacy divide requires a comprehensive and holistic approach that addresses both the supply and demand sides of AI resources and skills.
On the supply side, this involves:
- Providing universal and affordable access to high-quality and reliable data, infrastructure, tools, services, policies, and regulations for AI development and use.
- Developing and implementing standards and best practices for ensuring the safety, lawfulness, ethics, and robustness of AI systems and applications.
- Establishing and enforcing mechanisms for monitoring and evaluating the compliance and performance of AI systems and actors.
- Creating and supporting platforms and forums for dialogue and collaboration among different stakeholder groups on AI issues.
On the demand side, this involves:
- Developing and delivering inclusive and adaptive curricula and programs for teaching and learning about AI at all levels of education, from formal to informal, from basic to advanced.
- Providing continuous and lifelong learning opportunities for enhancing and updating one’s knowledge and skills on AI in various contexts and domains.
- Raising awareness and interest among diverse audiences about the benefits and risks of AI, as well as their rights and responsibilities in relation to it.
- Encouraging participation and engagement in the co-creation and co-decision of digital policies and services that involve or affect them.
Bridging the AI literacy divide is not only a moral duty, but also a strategic opportunity. Bridging the AI literacy divide can create a more fair, inclusive, and sustainable digital society. Bridging the AI literacy divide can unleash the full potential and value of AI for humanity.
Some examples of AI applications that require high literacy are:
- Natural language processing (NLP): This is the branch of AI that deals with the analysis and generation of natural language, such as text or speech. NLP applications include chatbots, machine translation, sentiment analysis, text summarization, speech recognition, and natural language generation. To use NLP applications effectively, one needs to have a high level of literacy in the language(s) involved, as well as an understanding of the linguistic and semantic aspects of communication. One also needs to be aware of the limitations and biases of NLP systems, and how to evaluate their quality and reliability. For example, one needs to know how to distinguish between literal and figurative language, how to handle ambiguity and context, how to avoid plagiarism and misinformation, and how to respect ethical and cultural norms.
- Computer vision: This is the branch of AI that deals with the processing and understanding of visual information, such as images or videos. Computer vision applications include face recognition, object detection, scene segmentation, image enhancement, video analysis, and image generation. To use computer vision applications effectively, one needs to have a high level of literacy in the visual domain, as well as an understanding of the mathematical and computational aspects of image processing. One also needs to be aware of the challenges and risks of computer vision systems, and how to protect one’s privacy and security. For example, one needs to know how to interpret and manipulate visual data, how to deal with noise and distortion, how to prevent unauthorized access or misuse of personal images or videos, and how to respect ethical and legal standards.
- Machine learning: This is the branch of AI that deals with the creation and application of algorithms that can learn from data and improve their performance over time. Machine learning applications include recommendation systems, anomaly detection, fraud prevention, spam filtering, sentiment analysis, image recognition, natural language generation, and self-driving cars. To use machine learning applications effectively, one needs to have a high level of literacy in the data domain, as well as an understanding of the statistical and logical aspects of learning algorithms. One also needs to be aware of the implications and consequences of machine learning systems, and how to ensure their fairness and accountability. For example, one needs to know how to collect and analyze data, how to choose and evaluate appropriate models and parameters, how to avoid overfitting and underfitting, how to address data quality and bias issues, and how to explain and justify the outcomes and decisions of machine learning systems.
The Dawn of a New Renaissance?
Don’t forget …Just as the Middle Ages eventually gave way to the Renaissance, a period of great cultural and intellectual expansion, the AI revolution holds the promise of a new kind of Renaissance. With machines taking over routine tasks, humans could be freed up to explore higher-order creative and intellectual pursuits, leading to an explosion of innovation and discovery.
However, like the Middle Ages, the AI revolution also brings with it the potential for significant societal upheaval and conflict. How we navigate this transition will determine whether we end up with a new Renaissance or a new Dark Age. To steer towards the former, we must learn from the mistakes and successes of the past, and apply these lessons to our present situation. This involves creating inclusive policies, fostering transparency in AI, and promoting education for all in this new technological realm.
Let me add finally, this incipit…The Middle Ages were marked by a deep spiritual quest, a search for meaning, and the rise of influential religious movements. With the AI revolution, we are experiencing a similar existential grappling. As AI begins to take over tasks once thought uniquely human — from composing music to driving cars — we find ourselves questioning the very nature of what it means to be human. Can a machine possess creativity? Consciousness? A soul? Much like the theological debates of yore, these questions stir deep philosophical and ethical debates.
The AI revolution is a new dawn. It’s a time of great potential and peril, much like the Middle Ages. The challenge for us now is to ensure that this new age benefits all of humanity, not just a privileged few. The choices we make now will shape the course of our future, much like the events of the Middle Ages shaped the world we live in today.
You have reached the end, my affectionate thanks go to you, curious reader and thinker who goes beyond appearances…
As always I say: Stay tuned!