Question

define the following computer science terms

1. diffusion models
2. prompt engineering
3. catastrophic forgetting
4. few - shot learning
5. neural radiance fields
6. zero trust architecture
7. homomorphic encryption
8. supply chain attack
9. credential stuffing
10. sandboxing
11. bloom filter
12. hashgraph
13. federated learning
14. gradient clipping
15. backpropagation through time
16. infrastructure as code
17. blue - green deployment
18. observability
19. container orchestration
20. immutable infrastructure
21. webassembly
22. progressive web app
23. edge computing
24. digital twin
25. quantum supremacy

Explanation:

1. Diffusion Models: Generative models that work by gradually adding noise to data and then learning to reverse the process to generate new data.
2. Prompt Engineering: The practice of crafting effective prompts to get desired outputs from language - based AI models.
3. Catastrophic Forgetting: In machine learning, when a model forgets previously learned information when trained on new data.
4. Few - shot Learning: The ability of a model to learn from a small number of examples.
5. Neural Radiance Fields: A method for representing 3D scenes in a way that can be used for rendering and view synthesis.
6. Zero Trust Architecture: A security model where no user or device is trusted by default, whether inside or outside the network perimeter.
7. Homomorphic Encryption: Allows computations to be performed on encrypted data without decrypting it first.
8. Supply Chain Attack: An attack where malicious actors target the supply chain of software or hardware products.
9. Credential Stuffing: An attack where hackers use stolen username - password pairs to try to access other services.
10. Sandboxing: Isolating software or processes in a secure, restricted environment to prevent them from causing harm to the main system.
11. Bloom Filter: A space - efficient probabilistic data structure that is used to test whether an element is a member of a set.
12. Hashgraph: A consensus algorithm for distributed ledger technology that provides fast transaction processing and high - throughput.
13. Federated Learning: A machine - learning approach where models are trained across multiple decentralized devices or servers while keeping the data local.
14. Gradient Clipping: A technique in neural network training to prevent the problem of exploding gradients by limiting the magnitude of gradients.
15. Backpropagation Through Time: A method for training recurrent neural networks by unfolding the network over time and applying backpropagation.
16. Infrastructure as Code: Treating infrastructure (servers, networks, etc.) as code that can be version - controlled, deployed, and managed.
17. Blue - Green Deployment: A deployment strategy where two identical environments are used to minimize downtime during software deployments.
18. Observability: The ability to understand the internal state of a system by examining its external outputs.
19. Container Orchestration: Managing the deployment, scaling, and networking of containerized applications.
20. Immutable Infrastructure: Infrastructure that cannot be modified once deployed; instead, new instances are created when changes are needed.
21. WebAssembly: A binary - format instruction set that allows code to run in web browsers and other environments with high performance.
22. Progressive Web App: A web application that provides a native - app - like experience, including offline access and push notifications.
23. Edge Computing: Performing data processing and computation at or near the source of data (e.g., on edge devices) instead of sending all data to a central cloud.
24. Digital Twin: A virtual representation of a real - world entity (e.g., a physical object, a process) that can be used for simulation, monitoring, and optimization.
25. Quantum Supremacy: The point at which a quantum computer can perform a calculation that is not feasible for classical computers.

Answer:

1. Diffusion Models: Generative models that reverse noise - addition process for data generation.
2. Prompt Engineering: Crafting effective prompts for AI model outputs.
3. Catastrophic Forgetting: Model forgetting old info when learning new data.
4. Few - shot Learning: Learning from a small number of examples.
5. Neural Radiance Fields: 3D scene representation for rendering.
6. Zero Trust Architecture: No - trust - by - default security model.
7. Homomorphic Encryption: Computation on encrypted data.
8. Supply Chain Attack: Malicious targeting of product supply chains.
9. Credential Stuffing: Using stolen credentials for access.
10. Sandboxing: Isolating software in a secure environment.
11. Bloom Filter: Probabilistic data structure for set membership testing.
12. Hashgraph: Consensus algorithm for distributed ledgers.
13. Federated Learning: Training models across multiple local devices.
14. Gradient Clipping: Limiting gradient magnitude in neural network training.
15. Backpropagation Through Time: Training RNNs by time - unfolding backpropagation.
16. Infrastructure as Code: Treating infrastructure as version - controlled code.
17. Blue - Green Deployment: Minimizing downtime with two environments.
18. Observability: Understanding system state via external outputs.
19. Container Orchestration: Managing containerized app deployment.
20. Immutable Infrastructure: Non - modifiable deployed infrastructure.
21. WebAssembly: Binary instruction set for web and other environments.
22. Progressive Web App: Web app with native - like experience.
23. Edge Computing: Data processing at the data source.
24. Digital Twin: Virtual representation of real - world entity.
25. Quantum Supremacy: Quantum computer outperforming classical ones.