What is Generative AI?
Before starting the quiz ‘Introduction to Generative AI: Quiz’, let’s know that Generative AI is a subset of artificial intelligence that focuses on generating new content based on the prompts input by a human.
It utilizes deep learning techniques, specifically Generative Adversarial Networks (GANs) and Recurrent Neural Networks (RNNs), to generate new and often highly
realistic data. This technology has opened doors for many different uses in different industries.
Examples of Generative AI
Text Generation: Generative AI can make text that looks like it was written by a person, including things like news, blogs, poems, and even computer code. OpenAI’s GPT-3 is a great example of this, as it can create clear and and contextually relevant text.
Image Synthesis: Platforms like DALL-E can generate images from textual descriptions. For instance, you can describe a “fire-breathing panda,” and it will create a unique image based on that description.
Music Composition: Generative AI models like MuseNet can compose original pieces of music in various styles and genres, revolutionizing the music industry.
Art Creation: AI artists like AIVA and DeepDream have the ability to generate stunning pieces of art, offering endless creative possibilities.
Video Generation: AI-driven tools can generate realistic videos, including deepfake technology that can replace faces in videos, which has implications in both entertainment and security.
Job Scope of Generative AI :
The emergence of Generative AI has created a demand for professionals with expertise in this field. Here are some job roles associated with Generative AI:
AI Researcher: Experts in Generative AI often work as researchers, developing new models, algorithms, and techniques to improve generative capabilities.
Data Scientist: Data scientists use Generative AI to create synthetic data for training machine learning models, addressing privacy concerns and data scarcity issues.
Content Creator: Writers and artists can use generative tools to enhance their creative processes, producing content more efficiently.
AI Ethicist: As Generative AI raises ethical concerns, AI ethicists play a crucial role in ensuring responsible and fair use of this technology.
Pros of Generative AI :
Creativity: Generative AI can produce creative content and ideas, which can be harnessed for artistic endeavors, content generation, and product design.
Efficiency: It can automate repetitive tasks like content creation, saving time and resources for businesses.
Data Augmentation: Generative AI can generate synthetic data for training machine learning models, improving their performance and robustness.
Innovation: It opens up new avenues for innovation across industries, from entertainment to healthcare and finance.
Cons of Generative AI :
Ethical Concerns: Generative AI can be used to create fake content, posing ethical challenges such as misinformation, deepfakes, and privacy breaches.
Quality Control: The generated content may not always meet the desired quality standards, requiring human intervention and supervision.
Job Displacement: Automation through Generative AI may lead to job displacement in certain industries, raising concerns about unemployment.
Bias and Fairness: Generative AI can inherit biases from the data it is trained on, potentially perpetuating existing biases and discrimination.
For more information, see Introduction to Generative AI.
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Introduction to Generative AI: Quiz
1. What are foundation models in Generative AI?
A. A foundation model is a small AI model pretrained on a small quantity of data that was “designed to be adapted” (or fine-tuned) to a wide range of downstream tasks, such as sentiment analysis, image captioning, and object recognition.
B. A foundation model is a large AI model pretrained on a vast quantity of data that was “designed to be adapted” (or fine-tuned) to a wide range of upstream tasks, such as sentiment analysis, image captioning, and object recognition.
C. A foundation model is a large AI model pretrained on a vast quantity of data that was “designed to be adapted” (or fine-tuned) to a wide range of downstream tasks, such as sentiment analysis, image captioning, and object recognition.
2. Hallucinations are words or phrases that are generated by the model that are often nonsensical or grammatically incorrect. What are some factors that can cause hallucinations? Select three options.
A. The model is trained on too much data.
B. The model is not trained on enough data
C. The model is not given enough context.
D. The model is trained on noisy or dirty data.
3. What is Generative AI?
A. Generative AI is a type of artificial intelligence (AI) that can create new content, such as text, images, audio, and video. It does this by learning from existing data and then using that knowledge to generate new and unique outputs.
B. Generative AI is a type of artificial intelligence (AI) that can create new content, such as discrete numbers, classes, and probabilities. It does this by learning from existing data and then using that knowledge to generate new and unique outputs.
C. Generative AI is a type of artificial intelligence (AI) that can only create new content, such as text, images, audio, and video by learning from new data and then using that knowledge to predict a discrete, supervised learning output.
D. Generative AI is a type of artificial intelligence (AI) that can only create new content, such as text, images, audio, and video by learning from new data and then using that knowledge to predict a classification output.
4. What is a prompt?
A. A prompt is a short piece of text that is given to the small language model (SLM) as input, and it can be used to control the output of the model in many ways.
B. A prompt is a short piece of text that is given to the large language model as input, and it can be used to control the output of the model in many ways.
C. A prompt is a long piece of text that is given to the large language model as input, and it cannot be used to control the output of the model.
D. A prompt is a short piece of text that is given to the large language model as input, and it can be used to control the input of the model in many ways.
E. A prompt is a short piece of code that is given to the large language model as input, and it can be used to control the output of the model in many ways.
5. What is an example of both a generative AI model and a discriminative AI model?
A. A generative AI model could be trained on a dataset of images of cats and then used to generate new images of cats. A discriminative AI model could be trained on a dataset of images of cats and dogs and then used to classify new images as either cats or dogs.
B. A generative AI model could be trained on a dataset of images of cats and then used to cluster images of cats. A discriminative AI model could be trained on a dataset of images of cats and dogs and then used to predict as either cats or dogs.
C. A generative AI model does not need to be trained on a dataset of images of cats and then used to generate new images of cats, because the images were already generated by using AI. A discriminative AI model could be trained on a dataset of images of cats and dogs and then used to classify new images as either cats or dogs.
D. A generative AI model could be trained on a dataset of images of cats and then used to classify new images of cats. A discriminative AI model could be trained on a dataset of images of cats and dogs and then used to predict new images as either cats or dogs.
Answers:
1: C, 2: B, C, D, 3: A,4: B,5: A