2023 has been the year that saw the explosion of generative AI technologies such as ChatGPT, Bard, and Midjourney. From images, video, text, and even music, generative AI has the uncanny ability to bring innate digital elements to life. Everyone is now curious about what generative AI and what its true capabilities are.
But besides the final product that we generate, most people don’t have a clue about what generative AI entails. Whether a curious user, a student doing research, or even a future pro in the industry, this article is right up your alley.
The basis for the new gen generative AI technologies is unsupervised learning. This unsupervised learning takes place in neural networks, which can be ordered in different architecture styles. Traditional machine-learning models use labeled datasets to train themselves to perform certain tasks. Datasets are usually labeled for context, and thus the model can provide a “correct” answer.
Generative AI models use unsupervised learning algorithms to work independently. These models can identify new patterns and relationships.
The contrast between the two types of ML can therefore be seen in their deployment. Supervised models are suitable for classification and regression tasks. Unsupervised models are more commonly used in exploratory data analysis and organizing data into clusters.
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Principal Component Analysis (PCA)
Unsupervised learning models will typically run into problems with the no. of needed variables. This is the exact problem that PCAs help solve in unsupervised learning, i.e. basically chopping down the number of variables. But this isn’t done by selecting certain variables.
This shouldn’t imply that PCAs choose variables. Rather, each original combination is represented by a linear combination of the originals.
Autoencoders are another type of unsupervised neural architecture. They work primarily by encoding compressed representations of input data, and then decoding it back to the original data. Thus, they can extract salient features of the data, reduce dimensionality, and remove noise from models. The quality between the data inputted versus the output is calculated by a loss function.
A typical example the training of a neural network on facial image data. This could be for a facial recognition AI such as in security networks. By learning about the salient features in the images, the autoencoder can generate new images.
Transformer_Architectures and GANs
Generative AI works to either predict or generate new datasets. Here, two key technologies are considered:
Transformer architecture is excellent for learning representations from unlabeled data. Think of ChatGPT that can analyze the context of entire sentences and predict responses or the next word accurately.
Transformers are excellent at producing rich, ordered representations from unlabeled input. In unsupervised learning, this is vital because data structure matters more than the conventional labeling of data.
You’ve probably come across deep fake videos of Donald Trump or some other world leader on the internet. This is the work of GANs, producing data that looks like what you find in the real world. In true competitive fashion, two neural networks are pitted against each other. The generator produces new data instances, while the discriminator judges whether the data is real or fake.
The generator and the discriminator are trained simultaneously. In unsupervised learning, GANs can thus produce highly synthetic data. This is quite true where real data is scarce or diverse datasets are needed.
In neural networks, for example in image recognition software, the aim is sometimes to focus more on one specific part and screen out what isn’t important in the dataset. Let’s say you have a specific part of an image or object you’d like to focus on. Attention mechanisms allow that narrowed focus on specific parts of the image within the GAN. Thus neural networks can achieve improved discretion and focus.
Attention mechanisms are critical in tasks like image classification, object ID, and semantic segmentation. Through weighting and vector compatibility, the relationship of elements to each other in the dataset can be mapped out.
Attention mechanisms assist generative AI in different ways. These include capturing long-range dependencies, improved generational quality, and selective focus. They are also important for multimodal input generation, such as combining text and images.
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Generic and Specific Models
A model such as GPT-3 (Generative Pre-trained Transformer – 3) thrives on large and varied datasets. Thus GPT-3 and other versions can learn continuously from human inputs. Other generative AI such as Mid-journey excels at specialized training tasks, for example, artistic creation.
Generic models are pre-trained on specific datasets which can then be fine-tuned. For example, GPT can learn general language patterns, syntax, and semantic relationships. Generic models can further be deployed to specific tasks or domains, for example, text completion and generation in chatbots.
With that in mind, we therefore see that generic models are generally quite versatile with wide applicability. Specific models such as Midjourney are specialized for a specific domain and are thus more accurate.
Hope that was a good and easy start to your learning of generative AI. We’ve covered the basics of unsupervised learning which is the basis of generative AI. We’ve also learned about different models within the technology such as Transformers and GANs. There are also specific parts to the technology that we’ve covered such as attention mechanisms.
As a growing field, generative AI offers great opportunities for students looking to go in this direction. As models grow more powerful, the need for ML engineers, prompt engineers, and even trainers of generative models will increase.