Difference between revisions of "Other Challenges"
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[https://www.youtube.com/results?search_query=obstacles+challenges+Deep+Learning YouTube search...] | [https://www.youtube.com/results?search_query=obstacles+challenges+Deep+Learning YouTube search...] | ||
[https://www.google.com/search?q=obstacles+challenges+deep+machine+learning+ML ...Google search] | [https://www.google.com/search?q=obstacles+challenges+deep+machine+learning+ML ...Google search] | ||
| − | * [[Privacy]] | + | * [[Risk, Compliance and Regulation]] ... [[Ethics]] ... [[Privacy]] ... [[Law]] ... [[AI Governance]] ... [[AI Verification and Validation]] |
* [[Backpropagation]] | * [[Backpropagation]] | ||
* [[Gradient Descent Optimization & Challenges]] | * [[Gradient Descent Optimization & Challenges]] | ||
| − | * [[ | + | * [[Immersive Reality]] ... [[Metaverse]] ... [[Digital Twin]] ... [[Internet of Things (IoT)]] ... [[Transhumanism]] |
| − | + | * [[Video/Image]] ... [[Vision]] ... [[Enhancement]] ... [[Fake]] ... [[Reconstruction]] ... [[Colorize]] ... [[Occlusions]] ... [[Predict image]] ... [[Image/Video Transfer Learning]] | |
| − | * [[Occlusions]] | + | * [[Symbiotic Intelligence]] ... [[Bio-inspired Computing]] ... [[Neuroscience]] ... [[Connecting Brains]] ... [[Nanobots#Brain Interface using AI and Nanobots|Nanobots]] ... [[Molecular Artificial Intelligence (AI)|Molecular]] ... [[Neuromorphic Computing|Neuromorphic]] ... [[Evolutionary Computation / Genetic Algorithms| Evolutionary/Genetic]] |
| − | * [[Bio-inspired Computing]] | ||
* [[Energy]] Consumption | * [[Energy]] Consumption | ||
| − | + | ||
== The Wall - Deep Learning == | == The Wall - Deep Learning == | ||
Revision as of 19:23, 10 July 2023
YouTube search... ...Google search
- Risk, Compliance and Regulation ... Ethics ... Privacy ... Law ... AI Governance ... AI Verification and Validation
- Backpropagation
- Gradient Descent Optimization & Challenges
- 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
