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Comment by artemonster | Hacker News Reader

While we are on the topic, can somebody give a TLDR what breakthroughs made current AI advancements? From what I understand the "foundation" is exactly the same as it was 40 years ago - same neural networks, same activation functions, same architectures, same gradient descent. If I ask some "skeptical" crowd they say: "nothing is new, we just started using GPUs". Some say there were breakthroughs in learning algorithms to facilitate deep learning (i.e. that features are trained and learned by deeper layers automatically). Can someone elaborate on this, please? I tried googling and I only get crap articles that just "wave hands"

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I studied machine learning 20 years ago, and have only been a casual observer since. I hope someone else corrects me where I'm wrong.

I'd say the big change was first deep nets, mostly CNNs. 20 years ago, there was a lot of talk about fully connected hidden layers. And how they are equivalent to an ANN with one hidden layer, if you allow exponential scaling of the number of perceptrons. They took forever to converge. CNNs reduce the number of parameters, and hence the training by quite a lot. They also make it obvious that even if multi-hidden layer nets are equivalent theoretically, in practice, you can cheat a lot using convolutions, thereby making this theory pointless.

Then came embeddings that segmented training into an embedding stage and a prediction stage, further reducing training time.

RNNs never really took off as a name, and seems to have been replaced with LSTM, being a subset of the vague RNN term. Having a well-understood way of introducing memory is very useful for sequences, and makes scaling a model to sequences much cheaper. So yet another input size reducing thing. (I guess we're basically building Markov processes on top of ANNs now? When I was in school, it was an either-or choice.)

On a meta-level, there are ensemble models, which is just taking many models, running them in parallel and combining the outputs. Simple idea, but apparently effective enough that people keep doing it. :)

Then there was attention being added, which adds a stage where you find similarities between entities. This means the ANN doesn't have to figure clustering, which again speeds up convergence. (I think it also makes the models more modular, but I'm not sure.)

Reinforcement learning (Q-learning from the late 1980s) arguably benefits tremendously with all the improvements to the ANN models, so I think time will tell how awesome this will be. For now, it seems we're all amazed by how much just the ANN (Q-function) can do on its own.

All of this is enabled by vastly improved matrix arithmetic hardware, software libraries and RAM prices. (Aside from each of these techniques just reducing training time/size.)

And then there's the inevitable hype bubble that just turns it all up to 11 for a while, because people with money want to invest and people without money want to pretend to know things. I wouldn't discount the availability of money as a "news" here, though.

This is a good question for ChatGPT

Here you go.

Sure, I can provide a brief overview of the key breakthroughs and advancements that have contributed to the current state of AI, particularly in the domain of deep learning.

1. Availability of data: The explosion of digital data, especially from the internet, has provided a massive amount of training data for AI models. This has allowed AI systems to learn patterns, features, and representations from various data sources more effectively than before.

2. Hardware improvements: The introduction of GPUs (Graphics Processing Units) and specialized hardware, like TPUs (Tensor Processing Units), has significantly accelerated the training of large neural networks. These advancements enable researchers to experiment with larger and more complex models, leading to improved performance.

3. Algorithmic innovations: Key algorithmic advancements have been made to train deep neural networks more efficiently. Some notable examples include: a. Backpropagation: This algorithm is used to train neural networks by minimizing the loss function through gradient descent. Although it was introduced in the 1980s, it became more widely used and optimized in recent years. b. Activation functions: Non-linear activation functions like ReLU (Rectified Linear Unit) have been crucial in addressing the vanishing gradient problem and improving training efficiency in deep networks. c. Dropout: This regularization technique helps prevent overfitting by randomly dropping out neurons during training, encouraging the network to learn more robust features.

4. Architectural advancements: The development of various neural network architectures has led to improved performance in specific tasks. Some prominent architectures include: a. Convolutional Neural Networks (CNNs): These networks are especially effective at image recognition tasks due to their ability to capture spatial patterns and hierarchical features. b. Recurrent Neural Networks (RNNs) and Long Short-Term Memory (LSTM): These architectures excel at handling sequence data, such as time series or natural language processing tasks. c. Transformers: Introduced in 2017, the transformer architecture has become a key component in state-of-the-art natural language processing models like BERT and GPT, due to its self-attention mechanism and ability to handle long-range dependencies.

5. Transfer learning and pre-training: Instead of training models from scratch, researchers have found it effective to pre-train models on large datasets, followed by fine-tuning them on specific tasks. This approach reduces training time, requires less labeled data, and often leads to better performance.

These breakthroughs and advancements, combined with a growing research community and increased investment in AI, have led to the current state of AI, where deep learning models can achieve human-level or near-human-level performance on a variety of tasks.

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