Why are we here related to Generative Models and Deep Learning? All you need to know.

Boeing Associate Technical Fellow /Engineer /Scientist /Inventor /Cloud Solution Architect /Software Developer /@ Boeing Global Services

Generative models are crucial in AI research, enabling creativity, data synthesis, and novel applications. As you delve deeper, you’ll discover exciting possibilities in this dynamic field!

Generative models are neural networks designed to approximate complex, high-dimensional probability distributions using a large number of samples.

When trained successfully, these models can estimate the likelihood of each observation and create new samples from the underlying distribution.

Deep learning[1–2] is a subset of machine learning that uses artificial neural networks to learn from data.

The term “deep” refers to the use of multiple layers [3] in the neural network[4].

Origins of Generative Models:

• Generative models aim to create new data samples that resemble a given dataset. The concept dates back to the 1950s when researchers began exploring probabilistic models.
• Early generative models included Hidden Markov (HMMs) for speech recognition and natural language processing.

The Rise of Neural Networks:

• In the 1980s, neural networks gained prominence. However, training deep neural networks was challenging due to the vanishing gradient problem.
• Restricted Boltzmann Machines (RBMs) emerged as generative models capable of learning hierarchical representations.

Autoencoders and Variational Autoencoders (VAEs):

• Autoencoders, introduced in the 1990s, learned compact representations of data. They consist of an encoder and a decoder.
• Variational Autoencoders[5] (VAEs), developed in the 2010s, added probabilistic components, enabling them to generate new data points.

Generative Adversarial Networks (GANs):

• Ian Goodfellow proposed GANs in 2014. GANs consist of a generator and a discriminator[6].
• The generator learns to create realistic data, while the discriminator[7–8] distinguishes between actual and generated samples.
• GANs have revolutionized generative modelling, producing impressive results in image synthesis, style transfer, and more.

Deep Learning and Transformers:

• Deep learning improved generative capabilities, especially with convolutional neural networks (CNNs).
• Transformers[9–13], introduced in 2017, transformed natural language processing. Models like BERT and GPT (such as ChatGPT) excel at text generation.

Applications of Generative Models:

• Image Synthesis: GANs generate realistic images, e.g., StyleGAN, for creating lifelike faces.
• Text Generation: Transformers produce coherent text, from chatbots to story writing.
• Drug Discovery: Generative models explore chemical space for potential drugs.
• Music Composition: AI generates music compositions.
• Anomaly Detection: Generative models identify unusual patterns.

Challenges and Future Directions:

• Mode Collapse: GANs sometimes generate similar samples.
• Ethical Concerns: Deepfakes and Misuse.
• Hybrid Models: Combining GANs and VAEs.
• Continual Learning: Adapting to new data over time.

Generative AI is the broader field where artificial intelligence systems create new content or data without human intervention.

It can produce a variety of novel artifacts, such as:

• Images
• Videos
• Music
• Speech
• Text
• Software code
• Product designs

Generative AI leverages techniques like foundation models (such as ChatGPT), which are trained on large unlabeled datasets and can be fine-tuned for specific tasks.

Enterprise use cases for generative AI include innovations in drug design, chip development, and material science.

In summary, generative models provide the underlying techniques for creating new data, while generative AI applies these techniques to automate content generation across various domains[14–15].

References:

1.- LeCun, Y., Bengio, Y. and Hinton, G. E. (2015), Deep Learning, Nature, Vol. 521, pp 436–444: https://www.cs.toronto.edu/~hinton/absps/NatureDeepReview.pdf

2.- Five different types of artificial intelligence: https://identicalcloud.com/blog/web-stories/5-diffrent-types-of-artificial-intelligence/

3.- Apostolidis, E, et al. “Video Summarization Using Deep Neural Networks: A Survey.” Proceedings of the IEEE, 2021: https://www.researchgate.net/publication/355839573_Video_Summarization_Using_Deep_Neural_Networks_A_Survey

4.- Artificial Neural Network: Artificial Neural Network — an overview | ScienceDirect Topics

5.- Understanding Variational Autoencoders (VAEs): https://towardsdatascience.com/understanding-variational-autoencoders-vaes-f70510919f73

6.- The GANfather: The man who’s given machines the gift of imagination: https://www.technologyreview.com/2018/02/21/145289/the-ganfather-the-man-whos-given-machines-the-gift-of-imagination/

7.- Young-Tak, Kim, et al. “Generating Synthetic Dataset for ML-Based IDS Using CTGAN and Feature Selection to Protect Smart IoT Environments.” Applied Sciences, vol. 13, no. 19, 2023, p. 10951: Applied Sciences | Free Full-Text | Generating Synthetic Dataset for ML-Based IDS Using CTGAN and Feature Selection to Protect Smart IoT Environments (mdpi.com)

8.- Wang, Xinghua, et al. “A Scenario Generation Method for Typical Operations of Power Systems with PV Integration Considering Weather Factors.” Sustainability, vol. 15, no. 20, 2023, p. 15007. : https://www.researchgate.net/publication/374837118_A_Scenario_Generation_Method_for_Typical_Operations_of_Power_Systems_with_PV_Integration_Considering_Weather_Factors

9.- Attention Is All You Need: https://arxiv.org/abs/1706.03762

10.- All you need to know about ‘Attention’ and ‘Transformers’ — In-depth Understanding — Part 1 | by Arjun Sarkar | Towards Data Science

11.- All you need to know about ‘Attention’ and ‘Transformers’ — In-depth Understanding — Part 2 | by Arjun Sarkar | Towards Data Science

12.- Transformer Architecture explained | by Amanatullah | Medium

13.- Understanding the Transformer Model: A Breakdown of “Attention is All You Need” | by Srikari Rallabandi | MLearning.ai | Medium

14.- Explained: Generative AI | MIT News | Massachusetts Institute of Technology

15.- Generative AI: What Is It, Tools, Models, Applications and Use Cases (gartner.com)