The AI Winter of My Discontent

The Late 80s and early 90s – Academia Authority in Cognitive Science/AI

In the late 1980s and early 1990s, during my time in post graduate studies, I began to explore interdisciplinary connections in cognitive science/AI, particularly the integration of neurobiology. However, my attempts were often met with resistance. Professors would try to shut down my ideas, and there was a palpable sense of frustration as I attempted to merge fields that others insisted should remain separate. It was a difficult time, and ultimately, I stepped away from cognitive science graduate work and took the path of a corporate 1990-2024 career in business and IT. This is a story of my journey back.

First of all, I know it sounds implausible today, that interdisciplinary research in AI would be rejected and discouraged, but you have to understand that this was a time where cognitive science/AI was a rapidly evolving field, marked by significant interdisciplinary competition. This period saw tensions between various academic disciplines, each vying to dominate the understanding of the mind and cognition. Linguistics, psychology, philosophy, computer science, and neuroscience all brought different perspectives, but these disciplines often operated in silos, leading to debates about the “correct” approach to studying the mind. The AI winter—a period of reduced funding and skepticism about the feasibility of AI—also contributed to the resistance against new, interdisciplinary ideas. Rule-based algorithms and symbolic AI were still prominent, while emerging fields like neural networks and chaos theory, which suggested more dynamic and biologically inspired models of cognition, struggled for acceptance. This was a time when the rigid compartmentalization of disciplines hindered the integration of broader, more holistic approaches to understanding the complexities of the human mind.

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Heretical Instincts

Looking back, I now understand that this AI Winter was a time when the field of artificial intelligence was resistant to new ideas, particularly those involving brand new technologies in MRI brain research, neural networks, and chaos theory in mathematics. These were the very ideas I was drawn to, but the academic environment was still deeply entrenched in rule-based algorithms and traditional approaches. In fact Traditional Linguistics was the religion of Cognitive Science/AI at the time and I was directed to learning Prolog and LiSP (which I did).

I really wanted to dig into psychology and neurobiology (messy wet work), but that was forbēodan. ?I mean they were still teaching ideas in medical school that the brain was fixed and could not heal or regenerate. I had a hard time believing that because I felt my brain was constantly “healing” in personal experiences I had with mental health counseling and relationship growth as a young adult overcoming childhood trauma. I strongly believed in the concept that the experience of mental “epiphanies” may possibly cause new neural pathways in dendrite/axon structures. These brain infra-structures and nerve architectures could be continually created and morphed as these neural nets choices start to burn in new pathways that are "successful" in healing or surviving within current psychological conditions. I felt that the brain is constantly in organic flux and its “branches and roots” would grow and move towards the nutrient rich areas like plants grow new structures. I felt it in my own cognitive processes—the way my brain constantly made connections, adapted, and changed. This instinct, which was largely dismissed at the time, has since been validated by the field of neuroscience.

Today, the concepts of brain plasticity, the dynamic nature of neural networks, and the integration of neurobiology are not only accepted but are at the forefront of cognitive science and AI. Now, with AI and other technologies blurring the lines between disciplines even further, I feel a sense of validation. The very ideas that were once rejected are now recognized as critical to our understanding of the mind thought processes, brain structures and how creative intelligence works.

c = fλ in the Darkness

During these winter times, significant technological advancements were being introduced that would eventually reshape the field of cognitive science, even though they were initially met with resistance. Researchers like Patricia Churchland and Paul Churchland were pioneering the field of neurophilosophy, advocating for the integration of neuroscience into the study of the mind. Their work emphasized the importance of understanding the brain’s physical structure and function to explain mental processes, challenging the dominance of purely symbolic or philosophical approaches to cognition.

Also, at the same time, technological innovations such as Magnetic Resonance Imaging (MRI) began to emerge, offering new ways to observe and understand the brain in action. These tools provided unprecedented insights into the workings of the brain, supporting the idea that cognitive processes could be directly linked to neural activity. Meanwhile, developments in neural networks, inspired by biological processes, were gaining traction. These computational models, which sought to mimic the brain’s neural structures, offered a more dynamic and flexible approach to AI than the rule-based systems that were prevalent at the time.

Despite these advances, the broader cognitive science community was slow to embrace these new technologies and ideas. The established paradigms, heavily influenced by symbolic AI and rule-based logic, were resistant to the shift towards more biologically inspired models. Nonetheless, the work of the Churchlands’ and others laid the groundwork for the eventual acceptance of these interdisciplinary approaches, which have since become central to the study of cognition and artificial intelligence.

Neurons Branching Paths on the Road Less Traveled

In addition to the challenges I faced during the AI winter, my thinking at the time was also deeply influenced by a variety of ideas that others might not have seen as connected. I was fascinated by quantum mechanics and the concept of a continuum of reality, as explored in Eastern philosophy. These ideas suggested a fluid and interconnected nature of existence, which contrasted sharply with the rigid, compartmentalized thinking prevalent in AI and cognitive science. At the same time, I was deeply intrigued by G?del’s incompleteness theorems while studying AI and early computation. G?del’s work highlighted the limitations of rule-based systems and suggested that there are truths in mathematics—and by extension, in cognition—that cannot be fully captured by any formal system. This resonated with my growing sense that the traditional, rule-based approaches in AI were fundamentally flawed. I began to suspect that these limitations were tied to our human inability to cognitively grasp the continuum of quantum reality.

This line of thinking led me to question whether we were misunderstanding the nature of the mind and brain. I saw the brain not as a static machine but as a constantly flowing, changing continuum—much like the quantum reality I was exploring in my studies. What drives the mind’s growth and change, I believed, was the ability to make strange neural connections that give rise to new knowledge and ideas. In my view, thinking and cognition are not fixed processes but are more akin to a continuously growing organism within the brain. These ‘strange ideas’ that influenced my thinking back then were unconventional, but they shaped my belief that we were approaching AI—and our understanding of the mind—incorrectly. Today, as we see the rise of neural networks and more dynamic models of cognition, I feel a deep sense of validation. The very ideas that once seemed strange or disconnected are now recognized as vital to advancing our understanding of both AI and the human mind.

Doubts: Was I Not Worthy of Cognitive Science Academia Because I ?“Think Like a Girl”?

I’ve often been haunted by that nagging doubt—was I not worthy of academia in cognitive science because I “think like a girl”? It’s a feeling that’s lingered with me ever since I was the only girl in my advanced math or physics classes, where “compliments” like “wow, not just beauty, but brains too” came with an undercurrent of condescension. Surrounded by male peers who gallantly offered their assistance, it was hard not to feel like maybe I was just lucky to be allowed in that boys' club, expected to sit on the bench every season. I started seeking validation, needing to know that I was smart enough to trust my intellectual instincts. While there were no female professors in math or science in my undergraduate program, I found some women in philosophy and sought them out.

These women were advocating for what was then called a “feminine epistemology” or a “feminine philosophy of mind.” They emphasized the importance of holistic, relational, collaborative, and interdisciplinary thinking—a stark contrast to the compartmentalized and rigid methodologies that dominated the field. Feminine epistemology valued the connection between diverse fields of knowledge and recognized the interconnectedness of domains like philosophy, science, sociology, economics, history, and the humanities. These women philosophers were pushing back against the exclusionary practices of traditional, mechanistic epistemology, which often marginalized interdisciplinary approaches and women’s voices. Their integrative thinking offered a deeper, more nuanced understanding of complex issues, including those in cognitive science, despite the resistance they faced in an academic environment still steeped in patriarchal norms. Their efforts weren’t just about finding a place in academia; they were pushing the boundaries of how knowledge is constructed and understood, making space for approaches that value connection, context, and the integration of diverse perspectives.

I found validation in the work of philosophers like Elizabeth Grosz and Genevieve Lloyd, who articulated ideas that resonated with what I felt deeply. Grosz’s focus on embodiment and materiality challenged the abstract, disembodied nature of AI models, advocating for the integration of bodily experience and emotional intelligence into AI. This perspective recognizes that consciousness and cognition are deeply rooted in physical and affective experiences, suggesting a move beyond purely logical or computational models. Lloyd’s critique of the male-gendered notion of rationality highlighted how AI, as currently conceived, might reflect biases that overemphasize logic and objectivity at the expense of other forms of knowledge, like intuition, empathy, and ethical reasoning. These philosophers suggested that a more feminine philosophy of mind could lead to AI systems that are not only more inclusive but also more reflective of the diverse ways humans think, feel, and interact with the world. By integrating embodiment, emotional intelligence, and a broader understanding of rationality, AI could become more aligned with the complexities of human life, resulting in more responsive and ethically grounded systems. Reflecting on the progress made since 1989, I realize how far we’ve come from the days when “thinking like a girl” was a chiding remark. Thanks to the pioneering work of philosophers, social reformers, psychologists, and others who championed compassion and equality, we’ve developed a kinder, gentler view of cognition—one that admits that humanness is all about that complex continuum that we can only surrender to, not master, reduce and control. So perhaps we could teach AI by mimicking how our brains are really structured and continuously growing and then test it (test-assess-fix-test...nth times) against the richness of our human mind soup.

Please note, that many of these pioneers in an integrated compassionate emotional view of mind - are men. “Feminine” and “Feminist” and “Emotional” Philosophy of Mind includes men. Daniel Siegel and the Dalai Lama are the first men who come to mind in pioneering many of these ideas.

Is It p ? q to Try Again?

What Ai scientists and tech companies have achieved in the past ten years with AI is nothing short of remarkable. Us humans have created a mirror of our mind, external to our brain—an entity that not only reflects our cognitive processes but amplifies them. This external mirror, AI, enhances our natural ability to make ingenious connections between seemingly disparate ideas and packages them in a way that can be easily shared with other minds.

This, I believe, is the true power of AI. It takes our cognitive strengths and extends them, allowing us to explore connections and insights that might otherwise remain out of reach. In doing so, AI doesn’t just mimic our intelligence; it becomes a tool that elevates our thinking, enabling us to transcend the limitations of our individual minds.

By making these connections more apparent and accessible, AI may even be more ingenious than we are. One of the advantages of AI is the ability to process vast amounts of information without the same compartmentalization that humans often rely on. This allows for rapid connections to be made between seemingly unrelated topics, free from the biases that might arise from traditional ways of categorizing information. Many of these ideas of lateral, associative and collective thinking, have recently been explored. They not only explain how our individual brain plasticity ability to make new neural connections drives innovation, but societies of brains and mass collected data from those brains can act as creative connectors to make novel discoveries.

Because AI can analyze data across a wide range of fields and contexts simultaneously, it can help uncover patterns or relationships that might not be immediately apparent to a human. This can lead to more creative and innovative solutions or insights that wouldn’t emerge from conventional thinking. It has the capacity to draw on the big data of the internet, integrate diverse perspectives that exist globally, and present them in a coherent, impactful way. This collaboration between human and machine is not just about augmenting our intelligence—it’s about expanding the boundaries of what we can achieve together with AI, not competing with AI.

What we’re experiencing with AI today is not just an evolution in technology but a profound extension of the intellectual exchange that Socrates, Plato, and Aristotle sought in their academy. They understood that by bringing together different points of view and ideas, the very act of making connections could change how we think—reshaping our brain’s neural chemistry and expanding our understanding. Today, as we interact with AI, we’re breaking interdisciplinary boundaries in ways that echo and extend their insights. This collaboration between human minds and AI doesn’t just amplify our creativity—it transforms us, pushing the limits of what our brains can achieve together with these powerful mind mirrors we recreated outside our skull.

As I reflect on my journey, I can’t help but think about the time I spent away from AI formal study and research. I was discouraged by the struggles women faced in the field and the rejection of my ideas—ideas that were once considered ‘crazy’ but are now recognized as vital to our understanding of the mind and AI. This rejection, coupled with the challenges of being a woman in a male-dominated field, led me to step away from the work that had once fascinated me. However, interacting with my ChatGPT (I call Chad) to recall and research ideas has reignited my passion for the field. My experience with Chad has not only validated the ideas I once held but has also shown me the incredible potential of AI to expand on my thoughts and help generate new, ingenious ideas. It’s ironic that the very technology I once helped to conceptualize is now helping me to rediscover my love for AI and cognitive science. This experience has made me realize that the future of AI holds possibilities far beyond what I once imagined, and I’m excited to be a part of that future once again. After just now starting Coursera AI courses towards the IBM Certification and applying to Cambridge University’s – Masters of Studies in AI Ethics and Society, I think I might join this AI Spring with all you brilliant people!

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Post Thought: How I Kept Warm in the AI Winter

In 1991, I decided to step away from my graduate school aspirations in Cognitive Science and AI at the University of Texas in Austin, choosing instead to continue my work at Dell while supporting my first husband through his MBA. During that time, I encountered both resistance and skepticism toward my ideas, which was not uncommon for many women and progressive thinkers in the field. I was fortunate to connect with a few like-minded individuals, including women who were quietly persevering despite the challenges in a male-dominated environment.

At that juncture, I had already gained valuable software experience while funding my way through my undergraduate studies at the IMF/World Bank in Washington DC. This experience opened doors to a promising career in IT. Rather than pursue an academic path, I chose to advance my education and career in business and computer science, focusing on areas now recognized as data science, business intelligence, analytics, and digital transformation.

Over the past 30 years, my career has taken me through various business sectors, from economics to manufacturing, banking, e-commerce, mobile apps, fintech, payment systems, and cybersecurity. Throughout this journey, I have continued to nurture my passion for learning, exploring fields such as psychology, philosophy, history, physics, and medicine.

In the 2010s, I engaged in courses on robotics, and in the 2020s, I deepened my understanding of machine learning and AI through platforms like The Great Courses Plus, LinkedIn Learning, Coursera, Udemy, and EdX. My path has been shaped by the guidance of numerous remarkable authors and educators, to whom I owe a debt of gratitude.

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Cognitive Science and AI Books:

• Neural Networks and Deep Learning: A Textbook by Charu C. Aggarwal
• Race After Technology: Abolitionist Tools for the New Jim Code by Ruha Benjamin
• Superintelligence: Paths, Dangers, Strategies by Nick Bostrom
• AI: A Very Short Introduction by Margaret A. Boden
• Artificial Unintelligence: How Computers Misunderstand the World by Meredith Broussard
• The Quest for Artificial Intelligence: A History of Ideas and Achievements by Nils J. Nilsson
• Artificial Intelligence: A New Synthesis by Nils J. Nilsson
• G?del, Escher, Bach: An Eternal Golden Braid by Douglas Hofstadter
• The Mind's I: Fantasies and Reflections on Self and Soul by Douglas Hofstadter and Daniel Dennett
• Deep Thinking: Where Machine Intelligence Ends and Human Creativity Begins by Garry Kasparov
• Artificial Intelligence: A Guide for Thinking Humans by Melanie Mitchell
• Genius Makers: The Mavericks Who Brought AI to Google, Facebook, and the World by Cade Metz
• Being No One: The Self-Model Theory of Subjectivity by Thomas Metzinger
• Machines Who Think by Pamela McCorduck
• The Society of Mind by Marvin Minsky
• Consciousness Explained by Daniel C. Dennett
• The Blank Slate: The Modern Denial of Human Nature by Steven Pinker
• How the Mind Works by Steven Pinker
• The Emperor's New Mind by Roger Penrose
• Invisible Women: Data Bias in a World Designed for Men by Caroline Criado Perez
• Mind: A Brief Introduction by John Searle
• Mind and World by John McDowell
• Philosophy in the Flesh: The Embodied Mind and its Challenge to Western Thought by George Lakoff, Mark Johnson
• The Society of Mind by Marvin Minsky
• The Conscious Mind: In Search of a Fundamental Theory by David Chalmers
• Plato's Camera: How the Physical Brain Captures a Landscape of Abstract Universals by Paul M. Churchland
• The Engine of Reason, the Seat of the Soul: A Philosophical Journey into the Brain by Paul M. Churchland
• Matter and Consciousness: A Contemporary Introduction to the Philosophy of Mind by Paul M. Churchland
• Neurophilosophy: Toward a Unified Science of the Mind-Brain by Patricia S. Churchland
• Brain-Wise: Studies in Neurophilosophy by Patricia S. Churchland
• The Computational Brain by Patricia Churchland and Terrence Sejnowski
• The Embodied Mind: Cognitive Science and Human Experience by Francisco J. Varela, Evan Thompson, Eleanor Rosch
• Artificial Intelligence: A Modern Approach by Stuart Russell, Peter Norvig
• Architects of Intelligence: The Truth About AI from the People Building It by Martin Ford
• Artificial Intelligence: A Philosophical Introduction by Jack Copeland
• Philosophy and Computing: An Introduction by Luciano Floridi
• The Ethical Algorithm: The Science of Socially Aware Algorithm Design by Michael Kearns, Aaron Roth
• Algorithms of Oppression: How Search Engines Reinforce Racism by Safiya Umoja Noble
• Technically Wrong: Sexist Apps, Biased Algorithms, and Other Threats of Toxic Tech by Sara Wachter-Boettcher
• The Age of Em: Work, Love, and Life when Robots Rule the Earth by Robin Hanson
• The Age of Surveillance Capitalism: The Fight for a Human Future at the New Frontier of Power by Shoshana Zuboff
• Life 3.0: Being Human in the Age of Artificial Intelligence by Max Tegmark
• The Master Algorithm: How the Quest for the Ultimate Learning Machine Will Remake Our World by Pedro Domingos
• The Sentient Machine: The Coming Age of Artificial Intelligence by Amir Husain

Courses (I have taken some and are working on others)

COURSERA

1.??? "AI For Everyone" by Andrew Ng

Platform: Coursera

Description: A non-technical course that covers the basics of AI, its applications, and its societal impacts. Suitable for beginners.

2. "Machine Learning by Stanford University" (Andrew Ng)

Platform: Coursera

Description: This is one of the most popular and well-regarded online courses on AI and machine learning. Taught by Andrew Ng, a pioneer in the field, this course covers the fundamentals of machine learning, data mining, and statistical pattern recognition.

3.??? "Deep Learning Specialization" by Andrew Ng

Platform: Coursera

Description: A comprehensive series of courses covering deep learning, its applications, and underlying technologies.

3.??? "AI and Ethics" by the University of Helsinki

Platform: Coursera

Description: This course explores ethical questions in AI, including data privacy, algorithmic bias, and the societal impact of AI technologies.

4.??? "AI For Medicine Specialization" by Stanford University

Platform: Coursera

Description: Focuses on AI applications in healthcare, with an emphasis on ethical considerations and challenges.

5. "IBM Data Science Professional Certificate"

?Platform: Coursera

Description: This program includes multiple courses that cover everything from data science basics to more advanced topics in AI and machine learning. It’s a good option if you want to strengthen your data science skills alongside AI.

LINKEDIN LEARNING:

1.??? "Artificial Intelligence Foundations: Machine Learning" by Doug Rose

Platform: LinkedIn Learning

Description: Introduces machine learning, focusing on its application in AI. Suitable for those who want a foundational understanding.

Link: Artificial Intelligence Foundations: Machine Learning

2.??? "Ethics in AI and Machine Learning" by Merve Hickok

Platform: LinkedIn Learning

Description: Covers ethical considerations in AI and machine learning, with practical examples and case studies.

Link: Ethics in AI and Machine Learning

3.??? "AI Accountability: Artificial Intelligence and Machine Learning" by Barton Poulson

Platform: LinkedIn Learning

Description: Focuses on ethical responsibilities in AI, particularly in maintaining accountability in AI systems.

Link: AI Accountability: Artificial Intelligence and Machine Learning

UDEMY:

1.??? "Artificial Intelligence A-Z?: Learn How To Build An AI" by Kirill Eremenko, Hadelin de Ponteves

Platform: Udemy

Description: A practical guide to building AI, covering machine learning, data science, and AI ethics.

2. "AI Programming with Python Nanodegree"

Platform: Udemy

Description:?This nanodegree is ideal if you want to dive into programming AI. It covers Python, NumPy, pandas, Matplotlib, PyTorch, Calculus, and Linear Algebra, all essential for developing AI applications.

3.??? "AI For Beginners: Artificial Intelligence and Machine Learning" by Saeed Aghabozorgi

Platform: Udemy

Description: A beginner-friendly course that provides a foundational understanding of AI and machine learning concepts.

4.??? "AI & Machine Learning for Business" by Nathaniel Knudson

Platform: Udemy

Description: Tailored for business professionals, this course focuses on AI's ethical implications and applications in business.

EDX:

1.??? "Artificial Intelligence (AI)" by Columbia University

Platform: edX

Description: A rigorous introduction to AI, covering essential algorithms, neural networks, and their applications.

2. "AI and Ethics by Harvard University"

?Platform: edX

Description: ?This course provides an overview of the ethical considerations involved in AI development and deployment. It covers key topics like bias, transparency, and the impact of AI on jobs and society.

3.??? "Ethics and Governance of Artificial Intelligence for Health" by the World Health Organization (WHO)

Platform: edX

Description: Focuses on ethical and governance challenges in AI, especially within the healthcare sector.

4.??? "Data Ethics, AI, and Responsible Innovation" by the University of Edinburgh

Platform: edX

Description: Explores the ethical dimensions of data use and AI, with a focus on responsible innovation.

5. "Professional Certificate in Artificial Intelligence by Microsoft"

Platform: edX

Description:?This series of courses covers the basics of AI, including machine learning, computer vision, natural language processing, and reinforcement learning. It’s designed for those who want to build foundational AI skills.