Bridging the Gap: Exploring the Evolution of Deep Neural Networks and Cognitive Science Towards Human-like Machines
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This document explores the evolution of deep neural networks and cognitive science towards creating human-like machines. It highlights the integration of cognitive science insights into machine learning models, emphasizing challenges such as emotional complexity, contextual understanding, and ethical judgments. The presentation concludes with the potential future implications of smart AI, including personalized education and advanced healthcare solutions.
Bridging the Gap: Exploring the Evolution of Deep Neural Networks and Cognitive Science Towards Human-like Machines
- 1. Bridging the Gap: Exploring the Evolution of Deep Neural Networks and Cognitive Science Towards Human-like Machines
- 2. Introduction This presentation explores the idea of deep neural networks and cognitive science towards human-like machines. We will bridge the gap between and human cognition, examining the journey towards creating intelligent systems.
- 3. Introduction Foundational Work: In 1943, neurophysiologist Warren McCulloch and mathematician Walter Pitts published a seminal paper describing how neurons might work, using electrical circuits as a model. Pioneering Concepts: Their work laid the foundational ideas for neural networks, influencing the development of deep learning technologies decades later.
- 4. Cognitive science is an interdisciplinary field that explores the nature and processes of the mind, integrating insights from psychology, neuroscience, linguistics, philosophy, and artificial intelligence to understand how humans think, learn, and perceive. Understanding Cognitive Science
- 5. Mimicking Human Perception Emotional Complexity: Cognitive science insights into human emotions are crucial for developing machine learning models that attempt to mimic these complex, unpredictable patterns. Contextual Understanding: Cognitive science enhances machine learning's ability to discern and react appropriately to subtle social and cultural contexts, a common challenge in AI interactions. Ethical Judgments: Drawing on cognitive science, embedding nuanced ethical decision-making into machine learning models is intricate due to the deeply subjective nature of morality and values.
- 6. Learning from Human Memory , Encoding Process: Encoding is the initial learning of information; it is a critical step where perceived items are converted into a construct that can be stored within the brain. Memory Storage: After encoding, information is stored in the brain for later retrieval; this involves a complex process where information is distributed across various neural networks. Retention and Recall: The efficiency of memory storage is reflected in the ability to retain and recall information over time, which can be influenced by several factors including attention, repetition, and emotional significance.
- 7. Challenges in Emulating Human Attention Sustained Attention: The ability to maintain focus on a specific task or stimulus over a prolonged period of time without becoming distracted. Selective Attention: The process of focusing on a particular object or task within the environment while ignoring other irrelevant information. Human Attention: A complex cognitive function that is essential for processing sensory information and navigating the environment effectively.
- 8. Bridging the Gap Neural Pattern Simulation: AI neural networks mimic brain activity with significant accuracy, revealing the potential to bridge understanding between artificial and biological neural processing. Functional Insights: By applying AI to cognitive tasks, researchers gain actionable insights into brain functionality, such as differentiating speech and music processing regions. Cognitive Principles in AI: Cognitive science theories inform AI development, leading to enhanced machine learning models that mirror human cognitive abilities in tasks like image classification. Neuroscience-Informed AI: The integration of neuroscience with AI not only fuels the creation of advanced algorithms but also offers a framework for validating AI systems against the human brain's problem-solving capabilities.
- 9. Will AI beat human intelligence ever? ● Risks of Autonomy: As AI achieves self-programming capabilities, there's a rising concern over its uncontrolled growth, with figures like Elon Musk and Stephen Hawking warning against AI systems potentially perceiving humans as a threat. ● Ethical Implications: The proliferation of AI technologies like deep fakes raises issues of privacy and deceit, highlighting the need for ethical oversight and the development of countermeasures to maintain social trust. ● Bias and Impact: The inherent biases within current AI systems, particularly in facial recognition, reveal limitations in judgment and decision-making, necessitating responsible development to prevent societal harm.
- 10. The Road Ahead- Smart AI Smart AI refers to advanced artificial intelligence systems that can learn, adapt, and make decisions autonomously, closely mirroring human cognitive functions. Possibilities of Smart AI: • Customized Learning: AI can dynamically adapt educational content to fit the unique learning pace and style of each student. • Advanced Healthcare: Smart AI can enhance early disease detection and offer innovative solutions like voice synthesis for speech-impaired individuals. • Autonomous Exploration: These systems can independently navigate and conduct research in space, handling complex tasks and decisions without human intervention.
- 11. CONCLUSION In conclusion, the evolution of deep neural networks and cognitive science presents a compelling journey towards creating human-like machines. The fusion of AI and human cognition holds immense potential for shaping the future of intelligent systems.
- 12. Q&A Session
- 13. Thanks!