Generative AI: Avoiding Jargon Overload

Hey there, LinkedIn community! ??

Ever scrolled through your feed and come across the term "Generative AI" and wondered if it's just another tech buzzword or if there's something genuinely exciting behind it? Well, today, I thought I'd dive into it, break it down, and hopefully make it accessible for all - especially if you're new to the AI world!

1. Generative AI: In Simple Words

Imagine you gave a room full of artists some colored pencils and told them, "Draw something that represents happiness." They would all create different pieces based on what happiness means to them, right? Now, Generative AI is like a virtual artist that does the same thing - but in the digital realm! It "imagines" and creates new content after learning from vast amounts of data.

Generative AI refers to a subset of artificial intelligence models that are designed to produce content. This could be anything: from images, music, and text to more complex outputs like entire videos or 3D models. At its core, Generative AI models learn patterns and data distributions from vast amounts of data and attempt to generate or "imagine" new content that matches the patterns in the data they've seen.

2. Real-World Magic with Generative AI

You might've seen deepfake videos (which, by the way, should be used responsibly!), where one person's face is replaced with another, making it look eerily real. That's the handiwork of a type of Generative AI. But it's not just about creating fake videos:

• Deepfake Videos: Ever seen one person's face seamlessly replaced with another in a video? That’s Generative AI in action!
• Music to Your Ears: Some AI models are now composing unique, original music. ??
• Art for the Ages: Digital artwork created by AI and auctioned for thousands? We're living in that age! ??

When we think of AI, most of us imagine Siri, Alexa, or maybe those recommendation systems on Netflix. These systems take data, process it, and give us an output - like suggesting what movie we might want to watch next. But Generative AI is different. Instead of just processing data, it's creating something NEW from it. Think of it as the difference between reading a recipe and inventing a whole new dish!

3. Generative AI vs. Traditional AI/ML

• Purpose:Generative AI: Its primary aim is to create new content.Traditional AI/ML: Typically focused on classification, regression, clustering, or recommendation.
• Training:Generative AI: Often requires unsupervised learning or a combination of supervised and unsupervised learning, as in GANs.Traditional AI/ML: Mostly relies on supervised learning, where labeled datasets guide the learning process.
• Outcome:Generative AI: Produces entirely new, unseen data or content.Traditional AI/ML: Predicts labels or classes based on previously seen data.

We often hear terms like "Transformers," "Generative AI," and "NLU/NLP" floating around in tech circles. If you've ever wondered what they mean or why they shouldn't be used interchangeably, read on!

4. ?? What is a Transformer?

No, I'm not talking about the action figures or movies! In the AI world, a Transformer is a type of model architecture that has taken the deep learning community by storm. Why? Because it fundamentally changed how models understand and process sequences, like sentences.

• The Magic Word - Attention! The Transformer model introduced the concept of "attention" — essentially, it helps the model decide which parts of a sentence are essential and which can be, well, ignored.

4.1 A Brief Narrative History

The seminal paper, "Attention Is All You Need" by Vaswani et al., unveiled Transformers in 2017. It was a watershed moment, changing the landscape of deep learning research. Want a deeper dive? The original paper, titled "Attention Is All You Need," rocked the AI community in 2017. Read it here .

But how did the tech giants respond?

• Google: Google's BERT (Bidirectional Encoder Representations from Transformers) quickly became the gold standard for various NLP tasks, setting records on benchmarks. Not just that, Google later introduced T5 (Text-to-Text Transfer Transformer), emphasizing the idea that almost all NLP tasks can be perceived as a text-to-text problem.
• OpenAI: They took the Transformer and scaled it up! The result? Models like GPT-2 and GPT-3, which are capable of astonishingly human-like text generation.
• Facebook: Facebook’s BART (Bidirectional and Auto-Regressive Transformers) uses both auto-regressive (like GPT) and auto-encoding (like BERT) training. Then came LLAMAs (Language Model using Adaptive Attention Span), which is Facebook's way of optimizing Transformer's attention span for longer texts.

4.2 How Are Transformers Used?

Transformers were primarily introduced for handling sequences, like sentences or series of events. They have since become indispensable in tasks like:

• Text Translation: Translating one language to another, like English to French.
• Text Summarization: Creating a concise summary from a lengthy article.
• Question Answering: Providing specific answers to user queries.
• Text Generation: Crafting coherent and contextually relevant paragraphs.

5. The Neural Network Beneath

At the heart of the Transformer lies the concept of the attention mechanism, but what neural constructs support this?

• Multi-Head Attention: Instead of having one set of attention weights, why not have multiple? That's the idea! It allows the model to focus on different parts of the input for different tasks or reasons.
• Feed-forward Neural Networks: These are the standard neural networks we've known for a while. In Transformers, they assist in transforming the output of the attention layers.
• Positional Encoding: Since Transformers don't inherently understand the order of sequences (unlike their RNN cousins), they're given a positional nudge to ensure they recognize sequence order.

6. ?? Generative AI (GenAI) vs. NLU/NLP

• Generative AI: Think of it as the AI artist. It creates content, be it text, images, or music. Like a painter, it uses data as its palette and crafts something new and unique.
• NLU/NLP: Stands for Natural Language Understanding and Natural Language Processing. This is more about understanding and processing human language. If AI were a person, NLU/NLP would be its listening and comprehension skills.

?? Remember: GenAI is about creation, while NLU/NLP is about comprehension. Two different roles, both critical!

7. Using "Generative AI" Properly

Avoiding Jargon Overload: Like many areas in tech, AI is riddled with jargon. While it's essential to be precise, overloading conversations or presentations with terms can be off-putting to those not "in-the-know."

When to Use "Generative AI":

• Audience Consideration: If you're addressing a technically proficient audience, then delving into specifics with terms like "Generative AI" or "GANs" is appropriate. For a general audience, consider simpler terms like "AI that creates content."
• Purpose: Use "Generative AI" when distinguishing from other AI models, especially if the context is about content creation.
• Clarification: If using the term, provide a brief explanation or example to clarify its meaning. For instance, "Generative AI, like those models that create lifelike images or write music."

8. Dive Deeper: From Tokens to Hosting

• Tokens: In AI, text is often broken down into chunks called tokens, which can be as short as one character or as long as one word. By processing these tokens, models like Transformers can understand and generate human-like text.
• Supervised Learning: Picture a teacher-student scenario. The teacher (labeled data) guides the student (the model) towards the right answers. In essence, this is supervised learning: training models using data that comes with the answers.
• Training and Hosting Models: Once trained, these models need a home! Platforms like AWS and GCP are like luxurious apartments for these models, providing them the resources they need to function efficiently. Both platforms offer excellent tools, with AWS Sagemaker and Google AI Platform being favorites. I'll more on Amazon BedRock and Meta LLAM in simlified fashion.Resources : MetaAI, Google PaLM2

9. Four Examples of Generative AI for further interest

Generative Adversarial Networks (GANs): Introduced in 2014, GANs consist of two neural networks, the Generator and the Discriminator. The Generator creates images, while the Discriminator evaluates them. They're employed to produce high-quality images, making it hard to distinguish between generated and real photos.

• Application: DeepFake videos, which superimpose existing images and videos onto source images or videos using a machine learning technique.
• Link: NIPS 2014 Paper

RNN-based Text Generation

• Description: Recurrent Neural Networks (RNNs) remember previous inputs in their hidden state, making them adept at tasks that require memory of prior information, like text generation.
• Application: Platforms like ChatGPT and other chatbots or text generation tools.
• Link: Understanding LSTM Networks

WaveGAN

• Description: An adaptation of GANs specifically designed for audio signal generation.
• Application: Generation of music, sound effects, or any audio samples.
• Link: WaveGAN GitHub Repository

VQ-VAE-2

• Description: Developed by DeepMind, it's a generative model that produces high-resolution images with more diversity.
• Application: High-quality image synthesis.
• Link: DeepMind's Research on VQ-VAE-2

StyleGAN & StyleGAN2

• Description: An evolution of GANs, these models focus on styles and features of images, making them capable of producing hyper-realistic images, especially faces.
• Application: The infamous website "This Person Does Not Exist" which showcases faces generated by StyleGAN.
• Link: StyleGAN2 GitHub Repository

DALEE

• Description: A language model by OpenAI, specialized in producing detailed images from natural language descriptions.
• Application: Turning text-based descriptions into complex images, like "a two-story yellow house with white trim and a red door".
• Link: OpenAI's DALL·E Blog Post

10. Conclusion

Generative AI stands out in the vast AI landscape due to its ability to generate novel content. It's crucial to understand its differences from traditional AI and use the term appropriately. As with any technology, ensuring clarity in communication, considering the audience, and being wary of jargon ensures that the incredible capabilities of Generative AI are understood and appreciated by all.

The world of AI is vast and ever-evolving. While it's tempting to use terms interchangeably, understanding their nuances is key. After all, in clarity, there's power!

I hope this article untangled some of the complex webs of AI terminology for you. Until next time, stay curious and keep learning!

Coforge Coforge Solutions #AI #AWS #Meta #GoogleCloud

11. References & Further Reading

1. How Are Transformers Used?

1. Wu, Yonghui, et al. "Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation" . arXiv preprint arXiv:1609.08144 (2016).
2. Nallapati, Ramesh, et al. "A Review of Current Neural Machine Extraction Techniques" . arXiv preprint arXiv:1810.09347 (2018).
3. Devlin, Jacob, et al. "BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding" . arXiv preprint arXiv:1810.04805 (2018).
4. Radford, Alec, et al. "Language Models are Unsupervised Multitask Learners (GPT-2)" . OpenAI (2019).

2. The Neural Network Beneath

1. Vaswani, Ashish, et al. "Attention Is All You Need" . Advances in Neural Information Processing Systems (2017).
2. Goodfellow, Ian, et al. "Deep Learning" . MIT Press (2016). [Chapter 6]
3. Abadi, Martín, et al. "TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems" . arXiv preprint arXiv:1603.04467 (2016).

3. A Brief Narrative History

1. Vasudevan, Vijay, et al. "Transformer - An Open-source Machine Learning Framework for Everyone" . Google AI Blog (2017).
2. Sutskever, Ilya, et al. "Better Language Models and Their Implications" . OpenAI Blog (2019).
3. Lewis, Mike, et al. "BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension" . arXiv preprint arXiv:1910.13461 (2019).

Additional Reads

1. Brown, Tom B., et al. "Language Models are Few-Shot Learners" . Advances in Neural Information Processing Systems (2020).
2. Hochreiter, Sepp, and Jürgen Schmidhuber. "Long Short-Term Memory" . Neural Computation (1997).
3. "The Illustrated Transformer" by Jay Alammar .