# Other Challenges

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- Risk, Compliance and Regulation ... Ethics ... Privacy ... Law ... AI Governance ... AI Verification and Validation
- Backpropagation ... FFNN ... Forward-Forward ... Activation Functions ...Softmax ... Loss ... Boosting ... Gradient Descent ... Hyperparameter ... Manifold Hypothesis ... PCA
- Immersive Reality ... Metaverse ... Digital Twin ... Internet of Things (IoT) ... Transhumanism
- Video/Image ... Vision ... Enhancement ... Fake ... Reconstruction ... Colorize ... Occlusions ... Predict image ... Image/Video Transfer Learning
- Symbiotic Intelligence ... Bio-inspired Computing ... Neuroscience ... Connecting Brains ... Nanobots ... Molecular ... Neuromorphic ... Evolutionary/Genetic
- Energy Consumption

## The Wall - Deep Learning

Deep neural nets are huge and bulky inefficient creatures that allow you to effectively solve a learning problem by getting huge amounts of data and a super computer. They currently trade efficiency for brute force almost every time.

Towards Theoretical Understanding of Deep Learning | Sanjeev Arora

- Non Convex Optimization: How can we understand the highly non-convex loss function associated with deep neural networks? Why does stochastic gradient descent even converge?
- Overparametrization and Generalization: In classical statistical theory, generalization depends on the number of parameters but not in deep learning. Why? Can we find another good measure of generalization?
- Role of Depth: How does depth help a neural network to converge? What is the link between depth and generalization?
- Generative Models: Why do Generative Adversarial Networks (GANs) work so well? What theoretical properties could we use to stabilize them or avoid mode collapse?

## The Expert