What is Generative AI and how does it differ from other AI techniques?

1. Artificial intelligence is the overarching system. 2. Machine learning is a subset of AI. 3. Deep learning is a subfield of machine learning, and Generative AI creates new content. Neural networks make up the backbone of deep learning algorithms. It’s the number of node layers, or depth, of neural networks that distinguishes a single neural network from a deep learning algorithm. The GenAI models tend to be fairly generic and trained on a very wide variety of information available on the internet. This type of AI learns patterns from training data and generates new, unique outputs with the same statistical properties.

Here's how you get to Generative AI... ?? Artificial Intelligence ?? Machine Learning ?? Reinforcement Learning, Supervised Learning, Unsupervised Learning ?? Deep Learning, Neural Networks ?? Deep Neural Networks ?? Generative AI (Transformer-Based Language Models, e.g. ChatGPT), Variational Autoencoders (VAEs), Generative Adversarial Networks (GANs))

Generative AI as the name suggests is for generating(read it as predicting) content which is essentially series of next words(token), this generation is not limited to text as it can generate images/text/music etc. It differs from classical AI techniques as it is not finding a mathematical equation/pattern in your data but instead leverages the concept of foundation models i.e. the learning from vast amount of public data as the generative domain is quite generic and applicable to your content as well.

Unpopular opinion: Generative AI is often used as a buzzword to describe the application of deep learning models, such as GANs and diffusion models, to text and image generation, but its real-world use cases go way beyond that. To give a concrete example, one of the big trends in major corporations involves using Generative AI to generate synthetic versions of highly confidential tabular datasets. These generated datasets have all the patterns and statistical properties of the original dataset, without the confidential information, such as customer details. This allows them to be analyzed and even used to train other machine learning models.

Generative AI stands out as a transformative tech, going beyond traditional AI's pattern recognition. It actively creates new content—text, voice, images, and videos—mimicking the input style. Unlike task-focused AI, it fabricates entirely novel outputs inspired by existing data. This includes crafting realistic images, composing music, or generating captivating text. Generative AI's advantage lies in its versatility across applications, reducing deployment time and costs. It can be fine-tuned for numerous tasks, making it a powerful tool for innovation in various fields.

Generative AI is like an artist learning from a vast array of existing artwork. It uses algorithms to understand data patterns, whether in images, music, or text, and then creates new, original pieces. Imagine it as having two artists in some methods: one artist creates a new piece, while the other judges if it looks realistic. This process continues until the artwork is indistinguishable from real ones. In another method, the AI sketches a basic outline and then fills in details differently each time, useful for creating variations of the same theme. Essentially, generative AI is about computers learning to create new, unique content by studying and imitating existing examples.

I'll emphasize on transformers here as they are the reason Gen AI exploded last year. Transformers revolutionized generative AI with their attention mechanism. Reason: transformers can capture long-range dependencies in the data without suffering from issues like vanishing gradients. Transformers also form the basis for many multi-modal models, which leverage their ability to handle sequences of different types of data. OpenAI's CLIP model is a great example of a multi-modal model. It's trained to understand images and text together, learning to map them into a shared embedding space. The success of models like CLIP indicates the rise of multi-modal models is here!

?? Generative AI is like a digital artist. It learns from existing data, like pictures or music, and then creates new, similar pieces. It uses techniques like GANs, where two parts of the AI work together to make realistic creations, and VAEs, which tweak existing data to make something new. Another method, Transformers, are great for sequences like sentences, generating new content that makes sense in context. So, generative AI is really about learning from what exists and then creatively producing something new and similar.

I will strongly propose folks spend time learning about GenAI as it will supercharge your career. 1. The Gen AI for Everyone course by Andrew NG has been super helpful for me. https://www.coursera.org/learn/introduction-to-generative-ai 2. Microsoft offers a Generative AI Professional Certificate program that is available for free through LinkedIn Learning. This program is designed for professionals who are beginners in generative AI skills and want to learn how to apply generative AI to their work.

At the heart of generative AI are sophisticated models like Generative Adversarial Networks (GANs), where two neural networks engage in a unique dance – the generator fabricates data, while the discriminator evaluates its authenticity, fostering a continuous improvement loop. Then there are Variational Autoencoders (VAEs), which masterfully encode data into a latent space and decode it back with added variations, effectively generating new data instances. Transformers, another pivotal architecture in generative AI, utilize advanced attention mechanisms to understand context and dependencies, making them exceptionally adept at producing sequential data, be it text or images.

The biggest challenge I see is people take outcomes for fact. For the majority it almost like magic. But the fact is that tech works on next token prediction. And because it has a randomizer in there as part of the solution you can never control the outcome. As stated by Ilya Sutskever in his interview with Dwarkesh Patel, it lacks reliability and controllability. Because of that a lot of potential use cases can be binned. Or at least not taken without building in a lot of guard rails or just taking the risk to be first adopters.

1.If you go for a proprietary LLM consider how your data is being handled and stored. 2.Vendor outages and deprecated features while relying completely on these models for our applications. 3.Prompt hacking(Some of the solutions exists are use another model to clean the output, repeat instructions or sandwich at the end, enclose the user input with random strings or tags,restrict prompt length),Prompt Injection,Jail breaking:Bypass the moderation rule,Prompt leaking 4.Model hallucination: Due to limitations in understanding, model produce output which directly contradicts the information provided in the source data (Intrinsic hallucination),model produce output which cannot be confirmed by the source data (Extrinsic hallucination).

In my opinion, generative AI presents notable challenges. Bias in outputs is a concern, as models might unintentionally perpetuate biases from the training data. Ethical considerations and bias mitigation strategies are paramount. I believe controlling content diversity is challenging; fine-tuning for specific tasks is resource-intensive. Balancing creativity with accuracy requires ongoing refinement. Responsible development practices are crucial for overcoming these challenges and maximizing the potential of generative AI.

My biggest fear and probably biggest challenge is the misuse of Gen AI like we already see tons of deep fakes and scams happening around. It is; in my opinion, crucial to consider the ethical implications of the models we build and to possibly incorporate safeguards against misuse. Secondly the emerging design pattern of training new generative models with synthetic data created from other generative models may backfire and resulting in echoing bias if not handled carefully (a recent paper experimented this).

Generative AI has several security implications, notably prompt injection, sensitive training data disclosure, input data leak, manipulation of training data (poisoning), manipulated models that are obtained from the supply chain, and output containing injection attacks. There are more issues but these are the main ones. When users over-rely on generative AI, they also underestimate the risk of the model having been manipulated. Therefore, reduction of the impact of false output is important, which includes limiting privileges of the model, algorithmic guard rails and human oversight.

The use of Generative AI has completely transformed the process of data collection in the workplace. Remarkable advances in AI web scraping. techniques have resulted in several key benefits that should not be ignored. One of the most notable benefits is the ability to generate synthetic human-like browsing patterns, which has achieved a 99.9% success rate in bypassing antibot measures. Additionally, Generative AI has streamlined the extraction, auto-parsing, and processing of complex data sets. LLMs have empowered scraping tools to navigate and comprehend complex website structures, track real-time changes, and maintain scraping solutions autonomously. Overall, AI has made data collection efficient and more accurate than ever before.

Generative AI can potentially transform personalized medicine and healthcare with its diverse applications. It can expedite drug discovery through the analysis of biological data, tailor medical treatments via genetic analysis, and improve the accuracy of disease detection through enhanced medical imaging. In addition, it can generate synthetic patient data for research while maintaining privacy, rapidly forecast health trends and disease outbreaks, contribute to genomics research, and enhance medical training with realistic simulations. Lastly, it can streamline and accelerate the design of clinical trials and the writing of clinical protocols.

Here is an example from education: a language learner using a generative AI-powered app experiences a personalized and interactive Spanish learning journey. The AI initially assesses their proficiency, focusing on conversational skills and travel vocabulary. The program offers tailored exercises and simulated dialogues, which are rich in linguistic and cultural content, and these adapt to the learner's progress. As they advance, the AI adjusts the difficulty and introduces new scenarios, keeping the experience challenging and engaging. This approach demonstrates how generative AI can make language learning more immersive, efficient, and enjoyable, catering to individual preferences and needs.

GenAI impacts all businesses, especially in Marketing in these and other ways: ~ Human Service Agents: Scripts and prompts, during phone calls, for tailored replies. ~ Humanoid Chatbots: Personalizes, accurate replies to complex inquiries, in any language. ~ Service Records: Summarizes chats for follow-ups. ~ Sales: Tailors suggestions and deals, boosting loyalty. ~ Conversion/Retention: Emulates human empathy, to build customer trust and rapport. ~ Advertising: Produces drafts and formats of product descriptions, brand adverts, headlines, slogans, and social media posts. ~ Product Search: Personalizes product search via multimodal inputs from text, images, and speech, to help customers find their most relevant products.

Generative AI applications span diverse fields: In art and design, it creates new images, music, and digital artwork. In content creation, it generates written content, from news articles to creative writing. In gaming and virtual reality, it designs levels and environments, enhancing user experience. In fashion, it predicts trends and designs new clothing items. In healthcare, it aids in drug discovery and generates synthetic data for research while maintaining patient privacy. In film and video, it produces realistic special effects and deepfakes for entertainment or educational purposes. It also plays a role in voice synthesis, generating realistic speech for virtual assistants and dubbing in different languages.

?? Generative AI spans several domains based on what it creates. In image generation, it can make realistic or artistic images, like scenes or portraits. Sound generation is another area, where it produces voices, music, or other sounds, either realistically or synthetically. Text generation is a big one too, enabling the creation of coherent or imaginative written content. Video generation is also part of its skill set, creating realistic or animated videos. Beyond creating from scratch, generative AI can also manipulate existing images, sounds, texts, and videos, altering aspects like color, shape, or tone. Each domain showcases the AI's ability to not only generate but also transform data in creative ways.

An interesting aspect to explore is how these domains can intersect and complement each other. For example, generative AI can be used to create a multimedia experience by combining text, images, sound, and video. This is particularly relevant in fields like virtual reality (VR), augmented reality (AR) and gaming, where an immersive experience is created by synthesizing elements from multiple domains.

Generative AI spans various domains based on the data or content it produces. Image generation crafts realistic or stylized images of objects, scenes, or people. Sound generation creates lifelike or synthetic sounds, including voices, music, or noises. Text generation generates coherent or creative texts, spanning words, sentences, or paragraphs. Video generation produces realistic or animated videos depicting scenes, actions, or events. Additionally, Generative AI can manipulate existing images, sounds, texts, and videos by altering color, shape, pitch, tone, and more. Its versatility across these domains showcases its capacity for creative and adaptive outputs.

The easiest acronym that I have self-invented to remember various domains of Gen AI is "LAVO". Language, Audio, Visual, and others. 1. Language-Large language models (LLMs), which can generate coherent and diverse texts for various tasks, such as summarization, translation, and dialogue. 2. Audio- Generative models can be used to create realistic and expressive sounds, such as music compositions, sound effects, voice cloning, and speech synthesis. 3. Visual- Techniques used for visual generative AI are GANs, VAEs, and Deep Reinforcement Learning. 4. Other- Generative AI can also be applied to other domains, such as software code, product design, data augmentation, and synthetic medical images.

Generative AI encompasses a wide range of domains, including art and design, where it creates visual artworks, music, and audio for composing and sound effects, natural language processing for writing and content generation, video and film production for animations and effects, gaming for dynamic environment creation, 3D modeling for realistic models, healthcare for medical data and research, and fashion for designing new styles and patterns.

When considering generative AI, it's important to address ethical concerns, such as ensuring content accuracy and avoiding biases in generated outputs. Intellectual property rights and the potential for misuse, like deepfakes, must be carefully managed. The environmental impact of training large models, along with data privacy and security issues, also warrants attention. Continuous improvement in algorithm efficiency and transparency in AI processes is crucial. Lastly, maintaining a balance between human creativity and AI assistance is key to leveraging generative AI's potential responsibly.

In addition, enterprises are continuing to adopt generative AI at a gradual pace and have largely acknowledged its significant role in the future of their businesses. I believe that Large Language Models (LLM), if done right, holds the biggest opportunity for us to democratize the future of open data. We can expect knowledge graphs to enable greater adoption of this technology. A knowledge graph is an information-rich structure that provides a view of entities and how they interrelate. Expressing these relationships as a graph can uncover facts that were previously obscured and lead to valuable insights. You can even generate embeddings from this graph that can be used in machine learning pipelines or as an integration point to LLMs.

Generative AI is a powerful tool, but its effectiveness is tied to specific types of problems. It thrives in well-defined scenarios with ample data but may struggle with complex or novel issues. The choice between ready-made solutions like Copilot and bespoke developments is crucial. Pre-built tools offer ease and instant deployment, backed by extensive research. However, they might not fully align with unique project needs. Custom solutions provide tailored functionality but require more resources and time. The optimal approach often combines GenAI with other AI and Machine Learning techniques. This hybrid strategy maximizes GenAI's potential, ensuring adaptability and success across diverse applications.

The environmental cost and impact of Generative AI should also be considered. For some Gen AI models, energy consumption and carbon emissions are quite significant, not only for training but also for inference. Using Gen AI for 1 image generation with some models/tools could be equivalent to a full charge of a cellular phone. I would recommend to take a look at this really good research paper: https://arxiv.org/abs/2311.16863

One additional section I would like to add here is Evaluating the quality and diversity of generative models which is now a complex problem, especially with rise of LLM's and multi modalities. Although we have some standardized metrics available to evaluate the models but the race is far from over. Also in terms of GANs, training a model can be unstable and challenging. Techniques to improve stability like gradient penalty, spectral normalization, or different training dynamics can be useful.