Humanity Amplified: The Fusion of Deep Learning and Human Insight to Shape the Future of Innovation

Abstract

A report for educational leaders and policymakers who want to understand the AI World and help our students prepare to thrive in it.

The Executive Summary is structured to provide an overview and then the highlights of each chapter. Readers can then read the full chapter for each topic if they are interested.

Chapter 1: Education's efforts to develop intelligence in humans

-Early efforts to develop intelligence in humans
-Developing intelligence through education in agriculture and first industrial eras (1765-1869)
-Developing Intelligence through Education for the Second Industrial Era (1870-1968)
-Developing Intelligence in the Third Industrial Era (1969-2000)
-Developing Intelligence in the Fourth Industrial Era (2000-Present)
-Developing Intelligence in the Fifth Industrial Era or a ‘Cambrian Explosion’?

Chapter 2: The development of intelligence in machines: from computers that learn to artificial superior intelligence (2010-2045)

-Beginnings of Deep Learning
-Chess, Othello, Diplomacy
-Language Models
-Industry Applications
-Investment and Acceleration
-Multimodal AI
-Development of Reasoning and Planning
-AI agents and "Capable" Intelligence
-AI and "general intelligence"
-AI and "super intelligence"
-Living in the Exponential
-We are Just Getting Started + Limitations
-Beyond Generative AI
-Models that run locally
-AI Wearables
-The focus on developing intelligence in machines instead of humans

Chapter 3: AI and other challenges to the educational system and society

-Instructional Challenges (plagiarism; dependence;
-Intelligent Tutoring Systems (ITS)/ "learning bots")
-Shiny objects
-Inequality and Inequity
-Challenges to What We Currently Teach
-AI literacy
-Ethics
-Participatory AI
-The Future of Work
-Preparing Teachers
-Rate of Change
-The Unknown
-Broad Administrative Challenges
-Challenges that came before AI

Chapter 4: Academic/Human deep learning as a response to our challenges

-Introduction
-Details
-Deep learning and cognitive flexibility
-Deep learning and emotional intelligence
-Deep learning applied: portfolios; debate; entrepreneurship education; STEM/STEAM; phenomenological approaches; games-based approaches
-Deep learning distributed
-Sam Ramon Valley ISD

Chapter 5: Amplifying human intelligence with machine intelligence

-HCI and amplifying humanity: reducing cognitive load and extending the mind
-Maintaining cognitive rigour
-Roles of AI: Ai-directed; AI-supported; AI-empowered
-Applying Mollick & Mollick
-Applying Generativism
-Digital twins
-The Human Teacher
- HCI in portfolios; debate; entrepreneurship education; STEM/STEAM; phenomenological approaches; games-based approaches
-A return to "deep learning"
-Assessing deep learning


Chapter 6: Overcoming education's challenges

-Engagement
-Student burnout
-Skill development
-AI literacy and augmentation
-Faculty burnout
-Implementation

Chapter 7: AI Policies & Guidance
-Introduction
-Major AI Guidance and Frameworks
-Common Issues across the Guidance
-Limitations of the Guidance
-Strengths of the Guidance
-Guidance for Teachers
-Additional Considerations When Drafting Policies
-Conclusion


Chapter 8: Practical implementation

-Facilitating Dialogue
-Design Thinking
-Head(s) of AI
-Teacher Professional Learning
-Changes in assessment
-Expand skill-based and after-school programs
-AI policy questions
-AI literacy
-AI writing standards
-Lobbying for new curriculum standards
-Lobbying for funding
-Regulatory compliance
-Strategic planning


Chapter 9: Broad frameworks for change and Design 39: A future beyond more technology

Chapter 10: Leading change in industrialization 4.0/5.0

Chapter 11: A Renaissance Beyond Intelligence: Ethics, Babies, Friends, Lovers, Joy, and Future of the Humanit(ies)

-Introductions
-The horrors of "intelligence"
-Developing human attributes beyond intelligence
-Renaissance 2.0
-Centering the human in the age of AI


Chapter 12: Conclusion

-Call for a Moonshot

Appendixes

-A sample of change from September and November
-Phenomenological approach
-Games in American history
-Proposed time-line
-Colleges and universities
-Quantum computing and brain computer interfaces
-Schools in action (samples)
-Key vocabulary


This report looks at the growing gap between the attention paid to the development of intelligence in machines and humans. It argues for placing a greater priority on the development of human intelligence through academic deep learning approaches, as well the need for humans to develop skills needed collaborate with machines in a way that both amplifies their own intelligence but also celebrates their humanity.

While computer scientists have made great strides in developing human intelligence capacities in machines using deep learning technologies, including the abilities of machines to learn on their own, a significant part of the education system has not kept up with developing the intelligence capabilities in people that will enable them to succeed in the 21st century. Instead of fully embracing pedagogical methods that place primary emphasis on building skills that nurture our humanity, such as empathy, collaboration, critical thinking, communication, creativity, and self-learning through experiential, interdisciplinary approaches, a substantial portion of the educational system continues to heavily rely on traditional instructional methods and goals. These methods and goals prioritize knowledge acquisition and organization, areas in which machines already perform substantially better than people.

The report emphasizes the need to prioritize academic programs that promote human deep learning as well as methods that integrate human deep learning approaches and AI tools. It suggests that this kind of human-computer interaction (HCI) will amplify all capacities of human intelligence, ensuring that people thrive even though machines will likely surpass human intellectual capabilities in all or nearly all domains over the next few decades or less, possibly by the time today’s first graders graduate from high school. We also make the case that schools should support developing many essential human attributes such as courage, kindness, love, and patience in students, traits that are not only difficult to develop in machines but that will be required to thrive regardless of any progress made in developing human intelligence and other capabilities in machines. Our approach does require a change to the underlying grammar of education, but without it, we risk employing AI tools to simply hyperscale approaches to education developed in the early twentieth century. These approaches are arguably not only inappropriate for today, but they will certainly be inadequate in a future where we live and work with multiple machines that possess superior intelligence to us in many aspects and where the massive rate of constant change will require individuals to learn on their own to thrive.

The report includes an overview of the radical and continued advances in AI that we are currently experiencing, including imminent developments such as autonomous AI agents that will be able to make their own choices and undertake their own actions when given goals. We conclude with a practical roadmap for schools to immediately begin implementing changes so that all students can succeed in the AI world. Such changes will require educators to embrace significant change, overcome entrenched grammars, and attract funding, but it is no different than the struggle deep learning computer scientists endured for decades: opponents who thought computers had to be fed hand-crafted instructions that would help models make decisions rather than allowing them to largely learn on their own. Eventually, the computer scientists who promoted deep learning approaches that allowed computers to largely learn on their own won out and changed the world forever. The same changes are possible in education, though they may require a moonshot, and they will certainly require immediate and impactful leadership.

Keywords: deep learning, artificial intelligence, critical thinking