Get ready to crack the toughest Generative AI Interviews using this Gen AI Interview Questions. This Gen AI Interview Questions guide is suitable for all the levels such as C-Suite executives, Executive Leadership, Engineering Management, Data Scientists, Architects, Product Owners, Business Analysts, Engineering Graduates and to those who want to learn about Gen AI.
1. What is Generative AI?
Generative Artificial Intelligence also called as Generative AI or shortly Gen AI. Generative AI refers to artificial intelligence capabilities that can generate new content and artifacts such as text, images, audio, video, and 3D shapes.
2. What is the key aspect a Generative AI applications powered with?
Foundation Models (FM) form the key powering component behind generative AI applications and their capabilities.
3. What are Foundation Models?
Foundation Models form the backbone of generative AI by learning versatile knowledge from massive datasets that allows them to support diverse capabilities via pretraining and customization. Their scale enables greater contextual understanding but also brings cost and responsible AI challenges.
4. What is the main architecture powering Foundation Models?
FMs are powered by Transformer architecture. The transformer architecture allows efficient parallel training of gigantic neural networks with billions of parameters, unlocking the creation of versatile foundation models that form the core of generative AI.
5. What are different transformer architectures?
There are three types of transformer architectures.
- Encoder-only: Foundation Models with Encoder-only transformer architecture contain just the encoder component which converts the input text into vector representations (embeddings). They are useful for semantic similarity search and retrieval.
Example: BERT (Bidirectional Encoder Representations from Transformers).
- Encoder-decoder: Foundation Models with Encoder-decoder transformer architecture contain both the encoder and decoder components. The encoder creates embeddings from the input text, and the decoder consumes those embeddings to generate output text. These allow text-to-text functionalities like translation.
Example: T5 (Text-To-Text Transfer Transformer) model trained for text-to-text tasks.
- Decoder-only: Foundation Models with Decoder-only transformer architecture contain only the decoder part. They extend an input text sequence by generating continuations and new text. This allows capabilities like text completion and generation.
Example: GPT-3 (Generative Pre-trained Transformer), used for natural language generation.
The choice of transformer architecture shapes the types of capabilities that can be achieved with a foundation model.
6. How are FMs different from Deep Neural Networks?
FMs represent a new paradigm in ML with their scale, architecture, pretraining approach, customizability, and generative abilities - going beyond traditional DNNs. But this also brings challenges around costs, harmful content, and responsible development.
- Scale: FMs contain billions of parameters, allowing them to learn from massive datasets, while traditional DNNs are typically much smaller with millions of parameters.
- Architecture: FMs use transformer architectures which allow parallel training, while DNNs often relied on sequential architectures like RNNs.
- Pretraining: FMs are pretrained on huge unlabeled datasets to learn general knowledge about the world. Traditional DNNs require labeled data for a specific task.
- Customization: FMs can be customized to new tasks with small labeled datasets via fine-tuning. DNNs must be trained from scratch for each task.
- Versatility: FMs can support a wide variety of tasks out-of-the-box via prompts. DNNs are specialized for particular tasks they are trained on.
- Creativity: FMs enable creative generative applications like text and image generation. Traditional DNNs focused more on analysis tasks.
- Infrastructure: Training and running FMs requires optimized ML infrastructure to handle their scale. DNNs have more modest infrastructure needs.
|Deep Neural Networks
|Billions of parameters
|Millions of parameters
|Transformers (parallel training)
|RNNs, CNNs etc. (often sequential)
|On massive unlabeled datasets
|Require labeled data
|Fine-tuning with small labeled datasets
|Train from scratch for each task
|Wide variety of tasks via prompts
|Specialized for trained tasks
|Enable generative applications
|Focus on analysis tasks
|Require optimized large-scale ML infrastructure
|More modest infrastructure needs
The unprecedented scale of training data, parameters and infrastructure needed for state-of-the-art FMs like GPT-3 set them apart, enabling new generative capabilities. But many DNNs also demonstrate impressive versatility and customizability.
7. What are the features of Foundation Models?
- They contain billions of parameters that allow them to capture rich knowledge from large datasets.
- They are pretrained on unlabeled data from sources like web crawling, Wikipedia etc.
- Pretraining allows them to learn general context and relationships from the data.
- This enables FMs to support a broad range of tasks out-of-the-box like text generation, summarization, translation etc.
- FMs avoid the need to build specialized ML models from scratch for each task.
- They can be customized for specific tasks via fine-tuning using small labeled datasets.
8. What is an LLM?
LLM stands for Large Language Model. Large Language Models are a subclass of foundation models specialized in natural language processing tasks by pretraining on massive text corpora to learn generalized linguistic knowledge. Their scale enables advanced generative text capabilities but also poses challenges.
- LLMs are a type of foundation model that contain billions of parameters.
- They are trained on massive amounts of text data.
- The scale of the parameters and data allows them to learn nuanced language knowledge.
- LLMs can support various natural language processing tasks like text generation, summarization, question answering etc.
- Popular examples of LLMs mentioned in the document are BERT, GPT-3, T5, and the new Amazon TITAN LLM.
- LLMs have grown rapidly in size from millions to billions of parameters enabling greater capabilities.
- But larger scale also increases training costs and raises responsible AI concerns.
9. What are different types of Foundation Models?
Foundation models are large-scale AI models trained unsupervised on vast amounts of unlabeled data. They are designed to learn general-purpose knowledge and skills that can be applied to a wide range of tasks.
|A model that is trained on a large corpus of text and can understand and generate human-like language.
|Machine translation, text summarization, question answering
|Computer vision model
|A model that is trained on a large dataset of images and can recognize and understand visual content.
|Image classification, object detection, image segmentation
|A model that can create new data, such as text, images, or music.
|Creative writing, image generation, music composition
|A model that can process and generate both textual and visual information.
|Image captioning, visual question answering, natural language inference
There are many different types of foundation models, but some of the most common include:
- Language models: These models are trained on large corpora of text and can understand and generate human-like language. They are used for tasks such as machine translation, text summarization, and question answering.
- Computer vision models: These models are trained on large datasets of images and can recognize and understand visual content. They are used for tasks such as image classification, object detection, and image segmentation.
- Generative models: These models can create new data, such as text, images, or music. They are used for tasks such as creative writing, image generation, and music composition.
- Multimodal models: These models can process and generate both textual and visual information. They are used for tasks such as image captioning, visual question answering, and natural language inference.
Foundation models are still under development, but they have the potential to revolutionize many fields, such as healthcare, education, and transportation. They are also being used to develop new forms of creative expression and entertainment.
Here are some specific examples of foundation models:
- BERT: A language model developed by Google AI that is trained on a massive dataset of text and code. BERT can understand the nuances of natural language text and is used for a variety of tasks, such as machine translation, question answering, and text summarization.
- GPT-3: A language model developed by OpenAI that is even larger than BERT. GPT-3 can generate human-quality text and is used for tasks such as creative writing, code generation, and translation.
- DALL-E: A generative model developed by OpenAI that can create images from text descriptions. DALL-E can be used to create realistic images of anything, such as a cat riding a unicorn or a pizza with a landscape on it.
- PaLM: A multimodal foundation model developed by Google AI that is trained on a massive dataset of text, code, and images. PaLM can understand and generate text, code, and images, and is used for tasks such as question answering, code generation, and image captioning.
These are just a few examples of the many different types of foundation models that are being developed. As these models continue to improve, they will have a profound impact on our lives.
10. What is the difference / relation between Foundation Models and LLMs?
Foundation models and LLMs are both large language models (LLMs) that are trained on massive datasets of text and code. However, there are some key differences between the two.
- Foundation models are trained on a wider variety of data than LLMs. Foundation models are trained on a variety of data types, including text, code, and images. This allows them to learn more general-purpose knowledge and skills. LLMs, on the other hand, are typically trained on text data only.
- Foundation models are more adaptable than LLMs. Foundation models are designed to be adaptable to a wide range of tasks. This is because they are trained on a wider variety of data and are able to learn more general-purpose knowledge. LLMs, on the other hand, are typically trained for a specific task and may not be as adaptable to other tasks.
- Foundation models are still under development, while LLMs are more mature. Foundation models are a relatively new field of research, and there are still many challenges that need to be addressed. LLMs, on the other hand, have been around for longer and are more mature.
Despite these differences, foundation models and LLMs are both powerful tools that can be used for a variety of tasks. Foundation models are particularly well-suited for tasks that require general-purpose knowledge and adaptability, while LLMs are better suited for tasks that require specific knowledge or skills.
Following table summarizes the key differences between foundation models and LLMs:
|Data used for training
|Text, code, and images
|Tasks well-suited for
|General-purpose tasks, tasks that require adaptability
|Specific tasks, tasks that require specific knowledge or skills
11. What are the various industry specific use cases for Generative AI?
|Develop new drugs and treatments, create personalized medical plans, and generate realistic medical images for training and diagnosis.
|Insilico Medicine is using generative AI to develop new cancer drugs.
|Create synthetic data for training financial models, identify fraud, and generate personalized financial advice.
|Numerai is using generative AI to create synthetic stock market data.
|Design new products, optimize manufacturing processes, and create virtual prototypes.
|Siemens is using generative AI to design new wind turbines.
|Create personalized product recommendations, generate realistic product images, and optimize inventory management.
|Amazon is using generative AI to recommend products to customers based on their purchase history.
|Media and entertainment
|Create new forms of content, such as movies, TV shows, and video games.
|NVIDIA is using generative AI to create realistic human faces.
|Develop new energy sources, optimize energy consumption, and create virtual power plants.
|Generative Energy is using generative AI to develop new ways to generate solar energy.
|Develop new transportation systems, optimize traffic flow, and create autonomous vehicles.
|Uber is using generative AI to develop new ways to optimize traffic flow.
|Create chatbots that can answer customer questions and provide support.
|LivePerson is using generative AI to create chatbots that can answer customer questions about insurance policies.
|Create personalized learning experiences, generate realistic test questions, and grade student work.
|Knewton is using generative AI to create personalized learning experiences for students.
|Create synthetic data for training government models, identify fraud, and generate personalized government services.
|Palantir is using generative AI to create synthetic data for training models that predict crime.