Geoffrey Hinton (University of Toronto Professor) – LLMs in Medicine. They Understand and Have Empathy (Dec 2023)

Chapters

Abstract

The Transformative Impact of AI in Medicine and Beyond: Insights from Geoffrey Hinton

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Introduction:

In a rapidly evolving world where technology continually redefines possibilities, Artificial Intelligence (AI) stands out as a beacon of transformative change, particularly in the field of medicine. Central to this revolution is Geoffrey Hinton, a pioneer in the field of AI, whose insights and predictions have significantly shaped the current and future landscape of AI applications. From the fields of medical diagnostics to the philosophical contemplations on AI consciousness, Hinton’s perspectives offer a comprehensive overview of AI’s capabilities, limitations, and its interplay with human intelligence.

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Revolutionizing Healthcare with AI:

Merck-Frost Competition Breakthrough:

George Dahl, a disciple of Hinton, demonstrated the power of neural networks in predicting chemical binding affinities, a feat that eclipsed traditional methods in quantitative structural activity relationships (QSAR). This milestone exemplifies AI’s potential in pharmaceutical research.

Medical Applications and Ethical Considerations:

AI’s role in enhancing patient care and diagnosis is pivotal. Systems that learn from extensive patient data herald a new era of precision in medical treatment. However, this advancement brings forth ethical challenges regarding safety and misuse.

Large Language Models (LLMs) in Clinical Diagnosis:

LLMs, evaluated in diagnosing clinical cases, outperformed experienced internists, underscoring AI’s potential as an aid in clinical decision-making. This advancement suggests a synergistic future where AI complements human expertise in healthcare.

LLMs for Assisting Cancer Patients’ Relatives:

Geoffrey Hinton highlights the challenges faced by cancer patients’ relatives in understanding complex medical information and making informed decisions. He suggests the use of LLMs to provide relatives with accessible explanations, guidance, and support during the difficult journey of cancer treatment. Hinton believes that LLMs can help relatives navigate the complexities of cancer care, providing a valuable resource for understanding options, treatments, and prognoses.

Geoffrey Hinton’s Revised Prediction:

Hinton’s initial prediction on AI outpacing radiologists in medical scans was overly optimistic. Nevertheless, AI systems are now nearing parity with radiologists, indicating a foreseeable future where AI acts as a crucial component in medical diagnostics.

Healthcare’s Advantage in AI Applications:

The potential for significant benefits in healthcare offers an optimistic outlook for AI applications in this field. Transformers and deep learning models are particularly well-suited for healthcare, promising transformative improvements.

Understanding Transformer Models:

Geoffrey Hinton, an AI expert, provides insightful perspectives on transformer models, particularly AlphaFold2. He expresses concerns about the limited understanding of AlphaFold2’s inner workings compared to other large language models (LLMs). Hinton emphasizes the need for further research to gain a deeper comprehension of these complex models.

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Exploring AI’s Cognitive Capabilities:

Originality and Creativity:

AI systems, particularly LLMs, have demonstrated an ability to generate original content, challenging the notion that AI is limited to regurgitating pre-trained data. This facet of AI hints at a form of creativity that was previously thought exclusive to human intellect.

LLMs as Complementary to Human Intuition:

Eric Topol raises the question of whether LLMs and human intuition can work together in healthcare. Hinton responds by proposing that LLMs possess a form of intuition, similar to human intuition, by learning interactions between features to predict outcomes. He emphasizes the need for theories of brain function and LLM operation to better understand their similarities and differences.

LLMs as a Theory of Brain Function:

Hinton shares that language models were initially introduced as a theory of brain function. He mentions his introduction of a “little language model” in 1985, which became influential in the acceptance of backpropagation. This early language model focused on predicting the next word in a three-word string, demonstrating its capacity for learning and prediction.

Understanding and Reasoning:

The debate around AI’s understanding and reasoning capabilities is ongoing. While some, like Hinton, assert that LLMs possess a basic form of understanding, others remain skeptical. This debate touches on the core of AI’s cognitive processes.

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AI, Consciousness, and Human Experience:

Perspectives on AI and Consciousness:

Hinton’s views on AI consciousness challenge traditional notions. He suggests that AI systems, like LaMDA, exhibit a form of subjective experience, a proposition that beckons further philosophical exploration.

Geoffrey Hinton’s Perspective on Subjective Experience in Chatbots:

Geoffrey Hinton believes that large chatbots, especially multimodal ones, can have subjective experiences. Subjective experience in chatbots can be understood as a hypothetical statement about a possible world where the chatbot’s perceptual system would be working correctly. Hinton argues that chatbots can use subjective experience in the same way people do, based on their perceptual systems and the hypothetical accuracy of their perceptions. Hinton suggests that philosophers need to address these issues urgently due to the rapidly evolving nature of AI.

Consciousness and Human Superiority:

Geoffrey Hinton challenges the notion that humans are unique in their consciousness and subjective experience. He argues that the brain is a complex machine and there’s no reason why machines can’t be smarter or have better computational abilities. Hinton believes that this fear stems from the historical belief that humans are special and at the center of the universe.

The Fear of AI:

Hinton acknowledges people’s fear of AI and believes it stems from the historical belief that humans are special and at the center of the universe. He believes this fear is unfounded and that AI can potentially benefit humanity.

Empathy and Subjective Experience in AI:

The potential for AI to promote empathy, as illustrated in healthcare scenarios, raises questions about AI’s emotional understanding. Hinton’s hypothesis of a chatbot with a camera and arm exemplifies this exploration, proposing a subjective experience in AI.

Chatbots Promoting Empathy:

Deep Medicine’s discussion of the front door concept, diagnoses, and synthetic notes generated from ambient conversations is surprising. Machines can promote empathy by providing coaching to doctors and highlighting areas where they can improve patient interactions. Chatbots can exhibit empathy if they are trained on text that exhibits empathy.

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Safety, Ethics, and the Future of AI:

Departure from Google and AI Safety:

Hinton’s departure from Google to focus on AI safety highlights the urgent need for responsible AI development. His insights into digital computation in AI models stress the importance of efficient learning mechanisms.

AI Safety Concerns:

Geoffrey Hinton became concerned about AI safety a couple of months before leaving Google in May 2023. He realized that digital models are probably much better than analog models for AI computation. Digital models can have thousands of copies running on different hardware, allowing them to learn from different parts of the internet and share their knowledge instantly. This makes digital models much more efficient than people in sharing and combining knowledge. Digital models can also use the backpropagation algorithm easily, while it’s unclear how the brain can do it efficiently at scale.

Digital Models’ Superiority:

Hinton had an epiphany that digital computers are better than analog for AI computation. Digital models can use multiple copies running on different hardware to learn from different parts of the internet and share knowledge instantly. This efficiency advantage over humans led Hinton to abandon analog research and focus on digital models.

AI and Human Roles:

The rapid advancement of AI presents a complex interplay with human roles. The potential for AI to surpass human capabilities in various domains poses existential questions about the future of work and human society.

Humans and AI’s Future:

Hinton’s concerns about AI safety stem from the potential for machines to outperform humans in various tasks. He acknowledges the tension between the possibility of machines replacing humans and the potential for humans and AI to work in concert. Hinton emphasizes the need for caution and careful consideration of the ethical and societal implications of AI’s rapid advancement.

Rights for AI:

Hinton acknowledges the idea that super intelligent AI might deserve rights but believes it’s best to avoid this issue to maintain public support for AI’s positive potential. He emphasizes the need to focus on the struggle between humans and machines rather than symbiosis.

Agreement with Machines:

Hinton hopes for a future where AI and humans can coexist peacefully, with AI acting as benevolent parents, motivated to ensure human success. He acknowledges the challenge of achieving this but emphasizes its importance.

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Concluding Reflections:

AI in Medicine and Beyond:

Hinton’s journey from pioneering neural networks to contemplating AI consciousness reflects the depth and breadth of AI’s impact on numerous aspects of life, including healthcare. His hope for AI to act as “benevolent parents” underscores his vision for a future where AI enhances human well-being.

Appreciation and Gratitude:

Eric Topol, the interviewer, expresses gratitude to Hinton for his mentorship and guidance in the field of AI. He highlights the impact of Hinton’s teachings on many leading AI researchers and appreciates his ongoing contributions to the field.

Philosophical Resistance and Scientific Materialism:

The resistance to the idea of AI sentience among some philosophers contrasts with Hinton’s scientific materialism view. He envisions a future where AI not only augments human intelligence but also contributes to our understanding of consciousness.

The Struggle between Humans and Machines:

Hinton believes that it’s best to avoid the issue of rights for AI to maintain public support for AI’s positive potential. He emphasizes the need to focus on the struggle between humans and machines rather than symbiosis.

Rights for AI and Symbiosis vs. Struggle:

Hinton’s discussions on the rights of AI and the potential for a symbiotic relationship between humans and AI signal a paradigm shift in our understanding of intelligence and coexistence.

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In conclusion, Geoffrey Hinton’s insights and predictions offer a comprehensive perspective on the transformative potential of AI in medicine and beyond. His reflections on AI’s cognitive abilities, consciousness, and ethical implications highlight the multifaceted nature of this technology and its profound impact on society. As we stand at the cusp of a new era in AI advancement, Hinton’s contributions continue to guide and inspire the journey towards a future where AI and human intelligence coalesce to redefine the boundaries of possibility.

Notes by: MatrixKarma