Mindsets for AI

Key abilities of AI

  • Automated feature learning. Instead of traditionally manual design of features for various tasks, AI takes data in raw formats and automatically learns the features while optimizing the task objectives.

  • Learning from big data. AI can learn from the accumulated “big” data in many domains that traditional methods are not capable of.

  • Inductive bias (e.g. symmetry preservation). AI models are flexible and can be designed to respect natural laws such as symmetries.

  • Generalizability (to unseen data). After training the AI model, it is expected to generalize to new scenarios and unseen data. In some scenarios, it is also expected to generalize to new dataset or similar task after training with one general dataset or task.

  • Fit high-dimensional function. AI models can fit complex functions, such as free energy surface, Schrodinger equation, etc.

  • Differentiable programming. AI brings a new wave of differentiable programming and pushes forward the development of many tools for automatic differentiation, such as PyTorch, Tensorflow, Theano, etc.

Limitations of AI

  • Overfitting. AI models sometimes overfit into the given data set which hinders their abilities to generalize to other datasets.

  • Data requirement. No free lunch, AI models usually rely on large-scale datasets.

  • Computational cost. AI models usually consume plenty of computational resources, especially with the growth of the model and data sizes.

  • Explainability. AI models usually have poor explainability and are thus considered “black-boxes”, though it is an active area of research.