Knowledge Vault 5 /92 - CVPR 2024
The Tip and the Iceberg: Deep Learning and Embodiment
Joshua Bongard
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Concept Graph & Resume using Claude 3 Opus | Chat GPT4o | Llama 3:

graph LR classDef main fill:#f9d4d4, font-weight:bold, font-size:14px classDef biology fill:#d4f9d4, font-weight:bold, font-size:14px classDef robotics fill:#d4d4f9, font-weight:bold, font-size:14px classDef ai fill:#f9f9d4, font-weight:bold, font-size:14px classDef tech fill:#f9d4f9, font-weight:bold, font-size:14px Main[The Tip and
the Iceberg: Deep
Learning and Embodiment] Main --> A[Biological Concepts] Main --> B[Robotics and AI] Main --> C[Technology and Innovation] Main --> D[Hybrid Systems] A --> A1[Bodies prepare for real-world
complexity. 1] A --> A2[Internal growth readies for
surprises. 3] A --> A3[Cells exchange information,
affect development. 15] A --> A4[Cells respond to novel
situations. 16] A --> A5[Living systems follow internal
drives. 23] A --> A6[Cells detect, respond to
environmental stimuli. 22] B --> B1[AI-designed frog cell robots. 2] B --> B2[Evolutionary methods optimize robots. 5] B --> B3[Flexible, adaptable robot components. 8] B --> B4[Machine learning optimizes robot
design. 9] B --> B5[Backpropagation through robot bodies,
controllers. 10] B --> B6[Body structure performs information
processing. 29] C --> C1[Rapidly growing innovations need
management. 17] C --> C2[Manipulating AI systems, needing
defenses. 18] C --> C3[Diverse machine ecosystem prevents
vulnerabilities. 19] C --> C4[Groups participate in robot
creation. 20] C --> C5[Solutions counter existing threats. 27] C --> C6[Transferring simulated behaviors to
reality. 6] D --> D1[Xenobots self-replicate through movement. 7] D --> D2[Manufacturing biological robots using
cells. 12] D --> D3[Combining biological and technological
components. 14] D --> D4[Blurring line between natural,
artificial entities. 24] D --> D5[AI guides biological structure
development. 25] D --> D6[Living systems inspire resilient
machines. 30] A --> E[Additional Concepts] E --> E1[Simplified physics simulating cellular
interactions. 4] E --> E2[Rules governing cellular organization
development. 13] E --> E3[Cells move independently, affect
development. 28] E --> E4[Xenobots clean up aqueous
environments. 21] E --> E5[Reproduction through environmental resource
manipulation. 26] E --> E6[Unique machines enhance resilience
against attacks. 11] class Main main class A,A1,A2,A3,A4,A5,A6 biology class B,B1,B2,B3,B4,B5,B6 robotics class C,C1,C2,C3,C4,C5,C6 tech class D,D1,D2,D3,D4,D5,D6 ai class E,E1,E2,E3,E4,E5,E6 tech


1.- Embodiment: The idea that having a body prepares organisms to handle real-world complexity.

2.- Xenobots: Biological robots created from frog cells, designed by AI to perform specific tasks.

3.- Morphological pre-training: Internal growth and change in organisms prepare them for external surprises.

4.- Biophysical models: Simplified physics engines simulating cellular behavior and interactions.

5.- Genetic algorithms: Evolutionary methods used to design and optimize xenobots and other robots.

6.- Sim-to-real gap: The challenge of transferring behaviors from simulated environments to physical reality.

7.- Self-replication: The ability of xenobots to create copies of themselves through movement and cell collection.

8.- Soft robotics: A field focusing on creating robots with flexible, adaptable bodies and components.

9.- Gradient descent for robot design: Using machine learning techniques to optimize robot morphology and control.

10.- Differentiable physics engines: Simulation tools allowing for backpropagation of error through robot bodies and controllers.

11.- Bespoke AI and robotics: Creating unique, individualized machines to enhance resilience against adversarial attacks.

12.- Biofabrication: The process of manufacturing biological robots or structures using cells and tissues.

13.- Morphogenetic code: The underlying rules governing how cells communicate and organize themselves during development.

14.- Bio-hybrid technology: Combining biological and technological components in robotic systems.

15.- Cellular communication: How cells interact and exchange information, influencing development and behavior.

16.- Zero-shot learning in biology: Cells' ability to respond appropriately to novel situations without prior experience.

17.- Exponential technologies: Rapidly growing and potentially dangerous innovations requiring careful management and countermeasures.

18.- Adversarial attacks: Attempts to manipulate or deceive AI systems, highlighting the need for robust defenses.

19.- Technological heteroculture: Diverse ecosystem of unique machines to prevent widespread vulnerabilities.

20.- Crowd-sourced robot design: Engaging large groups to participate in AI-driven robot creation.

21.- Environmental remediation: Potential application of xenobots for cleaning up aqueous environments.

22.- Cellular sensors: The ability of individual cells to detect and respond to environmental stimuli.

23.- Biological autonomy: The tendency of living systems to behave according to their own internal drives.

24.- Boundary between organisms and machines: The blurring line between natural and artificial entities.

25.- AI-driven biological design: Using artificial intelligence to guide the development of novel biological structures.

26.- Kinematic self-replication: Reproduction through movement and manipulation of environmental resources.

27.- Retroactive technologies: Solutions developed to counter existing threats or problems.

28.- Cellular motility: The ability of cells to move independently, influencing development and behavior.

29.- Morphological computation: Utilizing body structure to perform information processing tasks.

30.- Biologically-inspired robotics: Drawing insights from living systems to create more adaptive and resilient machines.

Knowledge Vault built byDavid Vivancos 2024