PROF. FERNANDO GARIBAY

AI Just Formalized Scientific Taste References: [1] Tong, J., et al. (2026). arXiv:2603.14473 [2] Gong, Z., et al. (2026). arXiv:2603.16659 [3] Fudan University & SAIS. (2025). Nature Research Intelligence.

The Voice in Your DNA Ref: 1. Cole, S.W. (2014). Human Social Genomics. 2. Blackburn, E. (2012). Stress & Telomeres. 3. Fredrickson, B. (2015). Well-being & Gene Expression.

I had the pleasure of meeting Professor @fernandogaribay Fernando Garibay, founder of the Garibay Institute for Systems Diplomacy, as well as the institute’s president, Ronald Gannel. Fernando Garibay has made a significant impact on modern pop music. His work includes collaborations with world-renowned artists such as @ladygaga @whitneyhouston @britneyspears @shakira @snoopdogg @brunomars @enriqueiglesias @ricky_martin @theofficialsting @wizkhalifa Khalifa, and many others.

The Interconnected Brain Ref: -The Foundation: Jahn & Dunne (2005) The 28-year PEAR legacy. -The Global Scale: Nelson & Bancel (2011) Evidence of world events affecting the field. -The Scientific Skepticism: Bösch et al. (2006) A necessary balance regarding effect sizes. -The Modern Frontier: Plonka et al. (2026) Recent findings on group coherence and global RNG synchronization.

Gut Feelings: Memories from the Future Ref: -Rayne, E. (2025, November 26). Your consciousness can jump through time, meaning “gut feelings” are memories from the future, scientists say. Popular Mechanics. -Mossbridge, J., Tressoldi, P., & Utts, J. (2012). Predictive physiological anticipation preceding seemingly unpredictable stimuli: A meta-analysis. Frontiers in Psychology, 3, 390. -Bem, D. J. (2011). Feeling the future: Experimental evidence for anomalous retroactive influences on cognition and affect. Journal of Personality and Social Psychology, 100(3), 407–425. -Mumford, M. D., Rose, A. M., & Goslin, D. A. (1995, September 29). An evaluation of remote viewing: Research and applications (American Institutes for Research report prepared for the U.S. Central Intelligence Agency). American Institutes for Research.

Repost from @californiawesternschooloflaw • This past Thursday, Professor Fernando Garibay of ASU’s Thunderbird School of Management was a guest lecturer in CWSL’s AI and the Law course. The music super producer (and co-writer of Lady Gaga’s Born This Way album and SIA’s recent work) is a long-time collaborator and writing partner of CWSL Professor James Cooper. A globally recognized thought leader on Artificial Intelligence and sought-after advisor to governments, Professor Garibay works to promote the responsible use of technology in diplomacy, commerce, and human relations. He shared his vision of what AI can do with the class, leaving our students with a sense of hope and possible paths for their respective careers. He called for “hyperspecialization” and recommended that we all strive to be better curators and trust-builders. Thank you, Professor Fernando Garibay, for being there for our students again!

Deeper Humans: Reality “responds in reverse” Ref: -Ma, X.-S., Kofler, J., & Zeilinger, A. (2016). Delayed-choice gedanken experiments and their realizations. Reviews of Modern Physics, 88, 015005. -Jacques, V., et al. (2007). Experimental Realization of Wheeler’s Delayed-Choice Gedanken Experiment. Science. -Scully, M. O., & Drühl, K. (1982). Quantum eraser: A proposed photon correlation experiment concerning observation and “delayed choice” in quantum mechanics. Physical Review A, 25, 2208. -Kim, Y.-H., Yu, R., Kulik, S. P., Shih, Y., & Scully, M. O. (2000). Delayed “Choice” Quantum Eraser. Physical Review Letters, 84, 1.

I was deeply honored to be received last week at the official residence of Hon. Chief Minister Devendra Fadnavis, alongside Prof. Ronald C. Gunnell, President of the Garibay Institute. What made the moment especially meaningful was not only the caliber of leadership present, but the shared commitment to exploring new pathways for cultural and academic diplomacy, where soft power becomes a practical, measurable force for long-horizon collaboration. At the heart of our discussions was India’s youth and Maharashtra’s extraordinary cultural vitality, and how arts, music, design, global leadership, and artificial intelligence can be advanced as an integrated ecosystem, expanding education, opportunity, and constructive diplomacy at scale. I’m sharing a few images from this journey through India, from Mumbai to Delhi, including high-level meetings, movement through the city, and the everyday textures that continually reaffirm why cultural exchange is not symbolic, but strategic. India, thank you for your warmth, clarity of vision, and gracious hospitality. @cmomaharashtra_ @devendra_fadnavis @rgunnell1 @millionpeacemakers @clintvalladares

Self-Driving Labs in Biotech Over the past decade, biologists have begun to build “self-driving labs” for biotech, autonomous experimental loops that fuse robotics with machine learning to explore very large biochemical design spaces. These platforms now support longevity, oncology, and gene-editing programs, compressing screening and formulation timelines from months to days. Here’s How It Works: Think of a biotech SDL as an automated research kitchen. Robots measure and mix tiny droplets, incubators provide the right temperature and atmosphere, and cameras and sensors watch how cells respond. A central software “conductor” keeps track of every plate and setting, makes sure safety rules are followed, and sends the results to an AI model. That AI then suggests the next round of experiments, changing doses, swapping delivery methods, or tweaking gene-editing recipes. The result is a closed Design–Build–Test–Learn loop that can run with minimal human intervention. Why This Matters To You: For translational teams, this means mapping robust design spaces earlier and cutting attrition later in the pipeline. In Chemistry, Manufacturing, Controls, and formulation, SDL’s reveal operating windows that stay stable even when manufacturing conditions drift. In cancer and gene-editing studies, they compress cycles of hypothesis–test–refine while generating structured audit trails that speak the language of regulators. If you’re curious about the research cited and/or want to learn more see the comment section for references below. References: -Abolhasani, M., & Kumacheva, E. (2023). The rise of self-driving labs in chemical and materials sciences. *Nature Synthesis*. -Tobias, A. V., et al. (2025). Autonomous ‘self-driving’ laboratories: A review of technology and policy implications. *Royal Society Open Science*. -Drug Discovery Trends. (2024–2025). Coverage of autonomous formulation platforms and AI in drug discovery.

“Diplomacy is Human Systems” Global Lecture Tour 2025 As I close 2025, I am grateful for a year of sustained dialogue across classrooms, convenings, and public forums with diplomats, academics, creatives, technologists, and civic leaders. What stayed with me most was not any single venue, but the cumulative pattern that emerged when many thoughtful people engage the same questions from different vantage points. This lecture tour clarified a central conviction: “Diplomacy is human systems. It is industry and algorithm agnostic, yet not confined to treaties, speeches, or state-to-state signaling. It is the careful management of interconnected relationships that operate as a system, shaped by feedback loops, incentives, norms, emotions, and credibility. In these settings, trust functions as infrastructure. When trust rises, coordination becomes more feasible and less costly. When trust erodes, everything becomes harder, slower, and more fragile.” I leave the year with a simple synthesis: diplomacy is the governance of human networks, and our most durable progress comes from building trust across complexity with consistency, humility, and care. -Prof. Fernando Garibay

“Diplomacy is Human Systems” Lecture Tour 2025 As I close 2025, I am grateful for a year of sustained dialogue across classrooms, convenings, and public forums with diplomats, academics, creatives, technologists, and civic leaders. What stayed with me most was not any single venue, but the cumulative pattern that emerged when many thoughtful people engage the same questions from different vantage points. This lecture tour clarified a central conviction: “Diplomacy is human systems. It is industry and algorithm agnostic, yet not confined to treaties, speeches, or state-to-state signaling. It is the careful management of interconnected relationships that operate as a system, shaped by feedback loops, incentives, norms, emotions, and credibility. In these settings, trust functions as infrastructure. When trust rises, coordination becomes more feasible and less costly. When trust erodes, everything becomes harder, slower, and more fragile.” I leave the year with a simple synthesis: diplomacy is the governance of human networks, and our most durable progress comes from building trust across complexity with consistency, humility, and care. -Prof. Fernando Garibay

Self-Adapting Language Models Imagine an AI that doesn’t just answer questions, but also rewrites its own study notes after every exam. That is the core idea behind self-adapting learning models. Most large language models are frozen after training: they only adapt through expensive, offline fine-tuning. Self-adapting models, like MIT’s SEAL framework, are designed to improve themselves continuously after deployment by generating their own training signals and update rules. Here’s how it works: Given new data or a task, the model first produces a “self-edit”: a short natural-language plan that says what synthetic examples to create and how to fine-tune its weights. The system then follows that plan: it generates the examples, runs a small update step, evaluates its new performance, and uses reinforcement learning to reward self-edits that actually helped. Over many cycles, the model effectively learns how to learn, a form of meta-learning grounded in its own interactions with data. Why this matters to you: This turns static models into continuously learning systems that can keep up with fast-changing domains and your organization’s internal knowledge. It also foreshadows more autonomous AI agents, making governance, safety constraints, and human oversight design absolutely critical as these systems begin to update themselves. If you’re curious about the research cited and/or want to learn more see the comment section for references below. References: -Zweiger, A. et al. (2025). “Self-Adapting Language Models (SEAL).” arXiv:2506.10943. -Pari, J. (2025). “Self-Adapting Language Models (SEAL) – Project Overview.” -MIT News (2025). “Teaching large language models to absorb new knowledge.” -Wired (2025). “This AI Model Never Stops Learning.” -Hendrich, L. (2025). “Self-Adapting Language Models: A Strategic Milestone in LLM Autonomy.”