What is the primary goal of neuro-symbolic AI?

• Translate natural language to SQL

• Replace all symbolic systems with neural networks

• Combine logical reasoning with perception-based learning

• Increase GPU usage in training

• Encode symbolic logic into attention layers

Which of the following best describes a symbolic reasoning system?

• Is based on convolutional architectures

• Is primarily trained using reinforcement learning

• Generates embeddings using backpropagation

• Learns features directly from raw data

• Uses predefined rules and logic structures

Which domain is particularly well-suited for neuro-symbolic approaches?

• Legal reasoning and compliance automation

• Video compression

• Real-time game rendering

• Blockchain consensus algorithms

• Memory optimization in OS kernels

What is one of the main challenges of symbolic AI alone?

• Too dependent on GPU acceleration

• Lack of labeled training data

• Too many parameters

• Difficulty handling ambiguity and noise

• Cannot scale to big data

In a neuro-symbolic pipeline, what role does the neural network typically play?

• Optimizing inference speed

• Reasoning over logical graphs

• Parsing unstructured input like images or text

• Writing rules

• Encoding symbolic formulas

What is the benefit of adding symbolic reasoning to a neural system?

• Better handling of structured knowledge and explainability

• Faster gradient descent

• Larger datasets

• Obfuscation of the model logic

• Increased GPU usage

Which of the following is an example of a neuro-symbolic application in healthcare?

• Diagnosing diseases using patient data + expert rules

• Predicting stock prices

• Matching ads to users

• Real-time translation

• Fingerprint authentication

Which system is known for pioneering neuro-symbolic approaches in visual question answering?

• AlphaGo

• ResNet

• ChatGPT

• Neuro-Symbolic Concept Learner (NS-CL)

• YOLO

How do symbolic components in neuro-symbolic AI contribute to explainability?

• By offering human-readable reasoning steps

• By hiding the model weights

• By using attention heads

• By minimizing loss functions

• By generating data

What is an example of a symbolic representation commonly used in neuro-symbolic reasoning?

• GAN latent space

• Transformer layers

• Principal component vectors

• Fourier Transforms

• First-order logic