Generative AI-Enabled Courses typically range from a few weeks to several months, with fees varying based on program and institution.
Program Name | Duration | Fees |
---|---|---|
Business Analytics for Strategic Decision Making Cohort Starts: 19 Dec, 2023 | 6 Months | $ 2,199 |
Post Graduate Program in DevOps Cohort Starts: 20 Dec, 2023 | 9 Months | $ 3,499 |
Caltech Post Graduate Program In AI And Machine Learning Cohort Starts: 2 Jan, 2024 | 11 Months | $ 3,990 |
Professional Certificate Program in Blockchain Cohort Starts: 3 Jan, 2024 | 4 Months | $ 2,499 |
Post Graduate Program in Data Science Cohort Starts: 4 Jan, 2024 | 11 Months | $ 2,790 |
Post Graduate Program in Data Analytics Cohort Starts: 4 Jan, 2024 | 8 Months | $ 2,140 |
A subset of artificial intelligence (AI), generative AI processes existing data to create newer, unique outputs in video, audio, text, 3D models, images, or as required.
With advancing generative models, generative AI tools can produce complex content, solve problems, create art, and even assist in research. The most recent breakthroughs which have brought generative AI to the forefront are GPT and Midjourney.
AI or artificial intelligence is a machine's capability to perform cognitive functions like the human brain, such as learning, reasoning, interacting, and problem-solving. Traditional AI, or conventional AI or artificial general intelligence, performs tasks according to preset rules.
The most common uses of AI technology include search engines, stock trading, medical diagnosis, etc.
Generative AI, on the other hand, uses existing data for fresh content creation. This could mean image generation, text description, or video creation, similar to the training data.
Generative AI uses neural networks to identify patterns or structures in the input data supplied by human intelligence. The learning could be supervised, semi-supervised, or unsupervised to train AI models.
Unsupervised learning enables generative models to process unlabeled data, saving time and creating foundation models. These foundation models are then used as a base for generative models.
Once the generative AI systems process the training data, the generative models produce fresh content. This could be in the form of generating images, videos, text, etc.
Generative AI is beneficial as it helps:
Generative AI is most commonly distinguished into three types:
These neural networks, generally used for NLP tasks, process sequential data and identify relationships. These are the basis for most foundation models.
This generative AI uses two neural networks to produce realistic content, finding application in art and content creation.
This generative AI finds patterns in a dataset by compressing it into a lower-dimensional space. Further, the AI system learns to generate data by sampling the compressed space.
Training data refers to the data that is given as input to generative AI models. This data is analyzed, processed, and used to create neural networks, based on which the generative AI further performs its tasks.