From: lexfridman
Training deep neural networks is a complex endeavor that involves several significant challenges. These challenges encompass various aspects such as computational power, algorithm efficiency, and understanding of the data space in which the networks operate.
1. The Need for Large Models
To achieve intelligence at or above human levels, machines require a vast amount of information about the world. Although this is currently not feasible due to the limitations in model size and complexity, future advancements in machine learning may enable the training of significantly larger models that exceed current capabilities [00:00:59]. These models would need efficient algorithms to represent complex functions, introducing the challenge of developing machine learning algorithms that can handle highly intricate representations efficiently.
2. Curse of Dimensionality
One of the most pressing challenges in training deep neural networks is defeating the curse of dimensionality. This refers to the exponentially large number of configurations that space variables can take, making it impossible to learn about the world without specific assumptions or compositionality in models [00:02:49]. Overcoming this challenge requires designing models that are both compositional and capable of representing complex functions with a manageable number of parameters [00:04:09].
3. Compositional Models
Deep learning architectures rely on compositional models, which involve distributed representations and multiple levels of abstraction. These distributed representations allow networks to learn exponentially many configurations with linearly growing parameters [00:05:10]. The challenge lies in constructing these models so that they generalize well without having seen all possible configurations of input features [00:15:16].
4. Optimization Challenges
Another significant challenge is the optimization of these networks. Although the optimization landscape of neural networks was initially deemed problematic due to the presence of numerous local minima, recent studies show that high-dimensional networks predominantly encounter saddle points rather than problematic local minima [00:27:07]. Despite these advances, optimization remains difficult, particularly for complex tasks like machine translation and reasoning tasks, where models can get stuck due to ill-conditioned landscapes [00:34:14].
5. Unsuitability for Every Task
Deep learning may not always be the optimal choice for every machine learning problem. The no-free-lunch theorem suggests that without the assumption that the world is compositional, no single model, including deep learning, is universally superior [00:05:55].
6. Unsupervised Learning
Unsupervised learning remains a largely unresolved challenge. Despite its potential to learn from vast amounts of unlabeled data, it has yet to be an obvious component of most industrial applications [00:39:00]. There is a need to improve unsupervised learning to make significant breakthroughs, especially because it could lead to better model-based reinforcement learning and help machines understand the world as humans do [00:48:45].
New Perspectives on Training
Concepts such as disentangling factors of variation and developing layers of abstraction are essential for understanding how the world operates through deep neural networks [00:54:19]. These will enable better generalization from data and handling of structured output problems, akin to how human intelligence functions [00:45:36].
In conclusion, while training deep neural networks presents numerous challenges, understanding and addressing these issues is critical for advancing machine intelligence and enabling machines to perform complex tasks involving reasoning, perception, and unsupervised learning. Further research into the foundational aspects and novel training methods will continue to pave the way for overcoming these obstacles.