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.google.com/search?q=obstacles+challenges+deep+machine+learning+ML ...Google search] | ||
| + | |||
| + | * [[Risk, Compliance and Regulation]] ... [[Ethics]] ... [[Privacy]] ... [[Law]] ... [[AI Governance]] ... [[AI Verification and Validation]] | ||
| + | * [[Backpropagation]] ... [[Feed Forward Neural Network (FF or FFNN)|FFNN]] ... [[Forward-Forward]] ... [[Activation Functions]] ...[[Softmax]] ... [[Loss]] ... [[Boosting]] ... [[Gradient Descent Optimization & Challenges|Gradient Descent]] ... [[Algorithm Administration#Hyperparameter|Hyperparameter]] ... [[Manifold Hypothesis]] ... [[Principal Component Analysis (PCA)|PCA]] | ||
| + | * [[Immersive Reality]] ... [[Metaverse]] ... [[Omniverse]] ... [[Transhumanism]] ... [[Religion]] | ||
| + | * [[Telecommunications]] ... [[Computer Networks]] ... [[Telecommunications#5G|5G]] ... [[Satellite#Satellite Communications|Satellite Communications]] ... [[Quantum Communications]] ... [[Agents#Communication | Communication Agents]] ... [[Smart Cities]] ... [[Digital Twin]] ... [[Internet of Things (IoT)]] | ||
| + | * [[Video/Image]] ... [[Vision]] ... [[Enhancement]] ... [[Fake]] ... [[Reconstruction]] ... [[Colorize]] ... [[Occlusions]] ... [[Predict image]] ... [[Image/Video Transfer Learning]] | ||
| + | * [[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]] | ||
| + | * [[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. | ||
| + | |||
| + | |||
| + | [https://towardsdatascience.com/recent-advances-for-a-better-understanding-of-deep-learning-part-i-5ce34d1cc914 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? | ||
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| + | <youtube>WTnxE0wjZaM</youtube> | ||
| + | <youtube>CLDisFuDnog</youtube> | ||
| + | <youtube>1_KhJv0Em5Y</youtube> | ||
| + | <youtube>rAQ-0wTavfM</youtube> | ||
| + | <youtube>0tEhw5t6rhc</youtube> | ||
| + | <youtube>kpqPFUu9JvU</youtube> | ||
| + | <youtube>Q7ifcUuMZvk</youtube> | ||
| + | <youtube>v3QGgtmAZTE</youtube> | ||
| + | <youtube>kYEUkpHpOKA</youtube> | ||
| + | <youtube>rTawFwUvnLE</youtube> | ||
| + | |||
| + | == The Expert == | ||
| + | |||
| + | <youtube>BKorP55Aqvg</youtube> | ||
Latest revision as of 18:18, 19 March 2024
YouTube search... ...Google search
- 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 ... Omniverse ... Transhumanism ... Religion
- Telecommunications ... Computer Networks ... 5G ... Satellite Communications ... Quantum Communications ... Communication Agents ... Smart Cities ... Digital Twin ... Internet of Things (IoT)
- 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
