Define : Machine learning
This is often the foundation for an AI system, and is the way we “teach” a computer model to make predictions and draw conclusions from data.
Define: Computer vision
Capabilities within AI to interpret the world visually through cameras, video, and images.
Define: Natural language processing
Capabilities within AI for a computer to interpret written or spoken language, and respond in kind.
Define: Document intelligence
Capabilities within AI that deal with managing, processing, and using high volumes of data found in forms and documents.
Define: Knowledge mining
Capabilities within AI to extract information from large volumes of often unstructured data to create a searchable knowledge store.
Define: Generative AI
Capabilities within AI that create original content in a variety of formats including natural language, image, code, and more.
What are some ways AI can be used?
- Speech to text to help people with hearing loss
- Used to identify key features of animals to understand their characteristics
- Text to speech to help blind people
Define: Data Scientists
People that study ways AI can be used/developed
How does Microsoft Azure use /provide AI to its customers?
Microsoft Azure provides the Azure Machine Learning service - a cloud-based platform for creating, managing, and publishing machine learning models. Azure Machine Learning Studio offers multiple authoring experiences such as:
- Automated machine learning: this feature enables non-experts to quickly create an effective machine learning model from data.
- Azure Machine Learning designer: a graphical interface enabling no-code development of machine learning solutions.
- Data metric visualization: analyze and optimize your experiments with visualization.
- Notebooks: write and run your own code in managed Jupyter Notebook servers that are directly integrated in the studio.
What are Computer Vision’s capabilities and what are some of its use cases?
Most computer vision solutions are based on machine learning models that can be applied to visual input from cameras, videos, or images. The following table describes common computer vision tasks.
- Image classification (classified based on the images contents)
- Object detection/differentiation (Identifies the presence of an object or item in front of it/ what category it falls into car, bus, etc)
- Semantic segmentation (Advanced object detection that highlights individual pixels on a screen/ maps exact orientation of the object)
- Image analysis (Explains/describes an image)
- Face detection, analysis, and recognition (describes and detects facial characteristics)
- Optical character recognition (OCR) (Image to text when text in image is present. Basically reads text in an image)
How does Microsoft Azure use Compute vision?
You can use Microsoft’s Azure AI Vision to develop computer vision solutions. The service features are available for use and testing in the Azure Vision Studio and other programming languages.
- Image Analysis: capabilities for analyzing images and video, and extracting descriptions, tags, objects, and text.
- Face: capabilities that enable you to build face detection and facial recognition solutions.
- Optical Character Recognition (OCR): capabilities for extracting printed or handwritten text from images, enabling access to a digital version of the scanned text.
What can NLPs do?
- Analyze and interpret text in documents, email messages, and other sources.
- Interpret spoken language, and synthesize speech responses.
- Automatically translate spoken or written phrases between languages.
- Interpret commands and determine appropriate actions.
How does Azure use NLPs
You can leverage Microsoft’s Azure AI Language to create natural language processing (NLP) solutions. It offers capabilities such as text analysis, training conversational models to understand spoken or written commands, and developing intelligent applications.
Microsoft’s Azure AI Speech service enables building audio-based NLP solutions, with features like speech recognition, speech synthesis, real-time translation, and conversation transcription.
Azure AI Translator uses a Neural Machine Translation (NMT) model, which considers the semantic context of text to provide more accurate translations.
You can experiment with Azure AI Language in the Azure Language Studio and with Azure AI Speech in the Azure Speech Studio, where these features are accessible for testing in various programming environments.
What is document intellect?
Basically where AI structures and processes unstructured data so preexisting systems can process them more efficiently so the system can understand data better
How does Azure play a role in document intellect?
Microsoft offers a service called Azure AI Document Intelligence that helps you build systems to efficiently collect and process data from scanned documents. It can automate document-related tasks in apps and workflows, making data-driven strategies easier to implement.
Key features include:
- Prebuilt models for processing common document types such as invoices, receipts, health insurance cards, and tax forms.
- The ability to create custom models using your own labeled data for specialized documents.
- These tools can improve document search, speed up processing, and integrate into various applications.
You can explore and test these features in the Document Intelligence Studio and other programming environments, allowing you to use them flexibly across different programming languages.
What is Knowledge mining?
Knowledge mining is the term used to describe solutions that involve extracting information from large volumes of often unstructured data to create a searchable knowledge store.
Azure’s Knowledge Mining contribution
One Microsoft knowledge mining solution is Azure AI Search, a private, enterprise, search solution that has tools for building indexes. The indexes can then be used for internal only use, or to enable searchable content on public facing internet assets.
Azure AI Search can utilize the built-in AI capabilities of Azure AI services such as image processing, document intelligence, and natural language processing to extract data. The product’s AI capabilities makes it possible to index previously unsearchable documents and to extract and surface insights from large amounts of data quickly.
Generative AI Input/how it works
Generative AI works by taking an audio, Image, text, or speech input that then creates a unique output relating to your/ a systems needs.
Azure’s Contribution to generative AI
In Microsoft Azure, you can use the Azure OpenAI service to build generative AI solutions. Azure OpenAI Service is Microsoft’s cloud solution for deploying, customizing, and hosting generative AI models. It brings together the best of OpenAI’s cutting edge models and APIs with the security and scalability of the Azure cloud platform.
Azure OpenAI supports many foundation model choices that can serve different needs. The service features are available for use and testing in the Azure AI Studio and other programming languages. You can use the Azure AI Studio user interface to manage, develop, and customize generative AI models.
What are some challenges that come with the use of AI?
- Bias based on human rights/differences (Our AI should not be bias towards or against groups of people)
- Errors in processing/output/understanding (Tesla’s AI should not crash into other cars)
- Data Exposure (Medical data cannot be released/exposed to ensure safety
- Solutions may not work for everyone (AI robot not giving audio signals to blind person)
- Users must trust AI Pred (AI must have credibility for its predictions)
- Liability for AI (Innocent is convicted by AI, who is liable?)
What are the 6 Responsibilities of AI Developers?
- Fairness (equal treatment of all human beings based on ethnicity, gender etc)
- Reliability + Safety (AI Systems should be reliable so people can use AI in their everyday lives such as self driving cars)
- Private and Secure (AI Systems should be Secure and should not leak people’s personal details)
- Inclusivity (AI systems should include and empower all groups of people and benefit everyone)
- Transparency (Users should know what the companies are doing with their data and how the AI works for their own well-being)
- Accountability (AI Developers should be accountable for their actions when developing AI. Developers should be legal and ethical)
Responsibility of Developers:
Artificial Intelligence enables the creation of powerful solutions to many kinds of problems. AI systems can exhibit human characteristics to analyze the world around them, make predictions or inferences, and act on them in ways that we could only imagine a short time ago.
With this power, comes responsibility. As developers of AI solutions, we must apply principles that ensure that everyone benefits from AI without disadvantaging any individual or section of society.
