Capabilities of OpenAI Models
- Generating natural language
- Generative code
- Generating images
What is Generative AI?
Generative AI is a
... [Show More] type of AI that can generate text, images, or other media in response to prompts. GAI models learn the patterns and structure of their input training data and then generate new data that has similar characteristics, typically using Transformer-based deep neural networks. Potential applications in every industry. Concerns of misuse in the form of deep fakes, fake news
Brainpower
Read More
AI Landscape: Artificial Intelligence
Imitates human behavior by relying on machines to learn and execute tasks without explicit direction on what to output
AI Landscape: Machine Learning
Models take in data, fit data to an algorithm, use framework to make predictions
AI Landscape: Deep Learning
Models use layers of algorithms in the form of artificial neural networks to return results for more complex use cases
The learning process is deep because the structure of artificial neural networks consist of multiple input, output, and hidden layers.
AI Landscape: Generative AI
Models can produce new content based on what is described in the input. The OpenAI models are a collection of generative AI models that can produce language, code, and images
Describe Azure OpenAI
Azure OpenAI service is a product offered by MSFT Azure that provides REST API access to OpenAI's language model including GPT-4, GPT-3, Codex, and DALL-E models. It allows customers to apply large language models and generative AI to a variety of use cases. Runs on Azure global infrastructure - providing Azure security, compliance, and regional availability - Also provides built-in features to ensure you're using AI responsibly
Azure & OpenAI's three main goals in partnership
1) Utilize Azure's infrastructure - including security, compliance, and regional availability - to help users build enterprise-grade applications
2) Deploy OpenAI model capabilities across MSFT products, including and beyond Azure AI products
3) To use Azure to power all of OpenAI's workloads
What 4 components does Azure OpenAI consist of?
1) Pre-trained generative AI models
2) Customization capabilities; the ability to fine-tune AI models with your own data
3) Built-in tools to detect and mitigate harmful use cases/users
4) Enterprise-grade security with role-based access control (RBAC) and private networks
Generative Natural Language: Text Completion:
Generate and edit text
Generative Natural Language: Embeddings:
Search, classify, and compare text
Three groupings of Azure AI's services and tools
1) Azure Machine learning platform
2) Cognitive services
3) Applied AI services
What are the 5 pillars of Cognitive Services?
1) Vision
2) Speech
3) Language
4) Decision
5) Azure OpenAI Service
What are some of the overlapping features shared by Cognitive Service's Language service and OpenAI's service?
Translation, sentiment analysis, and keyword extraction
When do you use Azure's existing language service and when do you use Azure OpenAI's service?
There's no strict guidance - Azure's existing language service can be used for widely known use-cases that require minimal tuning
Azure OpenAI's service may be more beneficial for use-cases that require highly customizable generative models, or exploratory research
What is important to consider when making business decisions around which model to use?
How time and compute needs factor into machine learning training
The 'learning' portion of training requires a computer to ID an algorithm that best fits the data
The complexity of the task the model needs to solve for and desired level of model performance all factor into the time required to run through possible solutions for a best-fit algorithm
Basic ML process
- Feed data into algorithm
- Use this data to train a model
- Test and deploy the model
- Consume the deployed model to do an automated predictive task
Deep learning vs Machine learning
In machine learning, the algorithm needs to be told how to make an accurate prediction by consuming more information
In deep learning, the algorithm can learn how to make an accurate prediction through its own data processing, thanks to the artificial neural network structure
Machine learning requirements
Number of data points: Small amounts of data to make predictions
Hardware dependencies: Can work on low-end machines, does not need a large amount of computational power
Featurization process: Requires features to be accurately ID'd and created by users
Learning approach: Divides the learning process into small steps then combine the results from each step into one output
Execution time: Takes comparatively little time to train, ranging from a few seconds to a few hours
Output: The output is usually a numerical value, like a score or classification
Deep learning requirements
Number of data points: Needs to use large amounts of training data to make predictions
Hardware dependencies: Depends on high-end machines. It inherently does a large number of matrix multiplication operations. A GPD can efficiently optimize these operations
Featurization process: Learns high-level features from data and creates new features by itself
Learning approach: Moves through the learning process by resolving the problem on an end-to-end basis
Execution time: Usually takes a long time to train because a deep learning algorithm involves many layers
Output: The output can have multiple formats, like a text, a score or a sound
What is transfer learning?
A technique that applies knowledge gained from solving one problem to a different but related problem
By repurposing the domain or problem, you can significantly reduce the amount of time, data, and compute resources needed to train the new model [Show Less]