I. Introduction to AI Applications and Impact
- Over 100,000 people die annually from snake venom; AI-designed antivenoms can be cheaply produced in silico.
- AI contributes beyond medicine: material science innovation, decoding animal languages, and astrophysics (e.g., black hole imaging).
- AI helps uncover hidden historical texts (Vesuvius challenge) and animal communication (Earth Species Project).

II. Origin and Evolution of AI
- AI concept began in 1955 with efforts to enable machines to think like humans.
- Modern AI advances rely on massive data to improve performance in image and text recognition.
- Large language models predict words based on extensive internet text.
- AI now processes various data types: audio, video, medical images, 3D signals.
- Some AI models surpass human capabilities in specific tasks.

III. Current Research & Challenges
- Agentic AI can outperform humans in persuasion and economic incentives.
- Current AI benchmarks are largely inadequate; a new "humanity last exam" is assessing models with 2,500 scientific questions—top models still score low (~20% correct).
- Cultural biases exist in AI language models, which mostly reflect Anglo-Saxon linguistic patterns—need for multilingual and multicultural model development.

IV. Personal Experience & UN Work
- Speaker rooted in AI and neural networks, with experience at NASA and the UN.
- Worked on humanitarian AI applications: emergency mapping via satellite images, augmenting human analysts with AI for precision.
- Led COVID digital twin simulation for a 700,000-person refugee camp to test emergency strategies.

V. Societal and Ethical Considerations
- AI presents both opportunities and risks akin to nuclear technology.
- Public perception about AI is split (~50% excited, 50% nervous).
- Important considerations include AI’s carbon footprint and whether AI models should be centralized in the cloud or distributed locally.

VI. Future of AI and Generative Engineering
- Shift from deterministic mathematical models to probabilistic AI models.
- Future trends include generative engineering to design new machines and tools.
- Human-created content may gain new value amidst widespread AI-generated material.

VII. AI in Employment and Collaboration
- Job automation expected to impact cognition-based work; augmentation likely for 15-30% of jobs.
- Humans collaborating with AI ("centaurs" or "cyborgs") show improved performance.
- Studies indicate workers combined with AI outperform teams without AI.
- Key future skills include formulating the right questions rather than providing all answers.

VIII. Educational Transformation
- AI offers new learning methods, such as the Socratic approach via AI questioning.
- Encouragement to engage with AI for fast, deep learning in various topics.

IX. AI for Medicine: SpotLab Example
- AI used to analyze bone marrow images to diagnose blood cancers, improving precision beyond human capability.
- Combines patient DNA data and medical images to tailor treatments.
- Developed AI "filters" that work with smartphone microscopes for accessibility in low-resource settings (e.g., Ethiopia, Colombia).
- Interdisciplinary teams of medical doctors and AI experts are vital for effective AI applications.

X. Broader Reflections and Call to Action
- Future projects should be anti-disciplinary or AI-disciplinary, blending ideas across fields.
- AI will become foundational infrastructure globally; need to preserve cultural diversity.
- Leaders and individuals must act as "orchestra directors" of algorithms, ensuring AI serves human values.
- Emphasis on using AI to enhance humanity and address meaningful challenges, especially for future generations.

Actionable Items and Tasks:
- Develop culturally and linguistically diverse AI foundational models.
- Design robust AI benchmarks beyond current standards like the "humanity last exam."
- Integrate human-AI collaboration frameworks to augment jobs, especially cognitive work.
- Promote interdisciplinary teams combining domain experts and AI specialists.
- Deploy AI medical diagnostic tools accessible in resource-limited regions using smartphone-based solutions.
- Incorporate AI literacy and engagement in education systems embracing new learning paradigms.
- Assess and optimize AI models for environmental impact and energy consumption.
- Foster ethical AI deployment prioritizing positive societal impact and human augmentation.