The ABCs of AI: A glossary of terms

Let's start with the core concepts that underpin AI and its applications.

Artificial Intelligence (AI)

Refers to the development of computer systems that can simulate human reasoning and perform tasks that typically require human intelligence. It encompasses various techniques, such as machine learning and natural language processing, to enable machines to learn, reason, and make decisions or take actions based on patterns in existing data. AI is a broad field concerned with creating intelligent systems that can perform tasks requiring human-like intelligence, such as problem-solving, pattern recognition, decision-making and natural language understanding.

Microsoft provides a further breakdown of AI types (see more details here):

Artificial narrow intelligence (Narrow AI) - Refers to the ability of a computer system to perform a narrowly defined task better than a human can. This is the highest level of AI development that humanity has reached so far. Examples include autonomous vehicles and personal digital assistants.

Artificial general intelligence (AGI) - Also called “strong AI” or “human-level AI”, refers to the ability of a computer system to outperform humans in any intellectual task. It’s the type of AI that you see in movies where robots have conscious thoughts and act on their own motives.

Artificial super intelligence (ASI) - A computer system that has achieved artificial super intelligence would have the ability to outperform humans in almost every field, including scientific creativity, general wisdom and social skills.

Now, some general “learning” lingo...

Machine Learning (ML)

A subset of AI that focuses on algorithms and models, enabling computers to learn from data, make predictions and take actions without explicit programming. ML algorithms can be applied to various AI tasks, such as image recognition, natural language processing, recommendation systems and many others. Through ML, AI systems can autonomously improve their performance and adapt to new data or situations.

It's worth noting that ML is just one approach to achieving AI; there are other techniques and branches within AI - such as rule-based systems, expert systems and genetic algorithms - that do not rely on ML.

Deep Learning

A subset of ML that leverages artificial neural networks inspired by the human brain. It involves training models with multiple layers to learn complex patterns and representations, enabling breakthroughs in areas like image and speech recognition.

Large Language Model (LLM)

Refers to AI models that can generate natural language texts from large amounts of data. LLMs use deep neural networks, such as transformers, to learn from billions or trillions of words, and to produce texts on any topic or domain.

Fine-tuning

An approach in machine learning to transfer learning in which the weights of a pre-trained model are trained on new data.

Embedding

A relatively low-dimensional space into which you can translate high-dimensional vectors. Embeddings make it easier to do machine learning on large inputs like sparse vectors representing words.

There are three different types of learning algorithms and processes for training AI models.

Supervised learning

Algorithms learn from labeled training data, where each data point has a corresponding label or target. The algorithm learns to make predictions or classifications by mapping inputs to outputs based on the provided labels.

Unsupervised learning

Involves training algorithms on unlabeled data, allowing them to discover hidden patterns and structures autonomously. This type of learning is useful for tasks like clustering, anomaly detection, and dimensionality reduction.

Reinforcement learning

Inspired by behavioral psychology, this involves an AI agent learning through interaction with an environment. The agent receives feedback in the form of rewards or punishments, enabling it to learn optimal strategies to maximize cumulative rewards.

There are many ways in which AI can be used to transform your business; following are several examples.

Natural Language Processing (NLP)

Enables machines to understand and interpret human language. It involves tasks like sentiment analysis, language translation, and chatbot interactions. NLP helps us communicate with AI systems more naturally, bridging the gap between humans and machines.

Computer Vision

Focuses on enabling machines to understand and interpret visual data, including images and videos. It involves tasks like object detection, image recognition, facial recognition, and autonomous vehicle navigation.

Recommendation Systems

Analyze user data and patterns to provide personalized suggestions or recommendations. They are prevalent in e-commerce, streaming services, and social media platforms, enhancing user experiences and increasing engagement.

Conversational AI

A system that can conduct a conversation by understanding and generating human-like language. Example: Chatbots are widely used to engage with customers across multiple digital channels.

Generative AI

A type of AI system capable of generating new text, images or other media in response to prompts. Generative AI systems create output from a probabilistic algorithm based on a combination of the prompt given, the data it was trained on and the level of creativity requested. 

OpenAI

An American artificial intelligence research laboratory consisting of the non-profit OpenAI Incorporated and its for-profit subsidiary corporation OpenAI Limited Partnership. OpenAI conducts AI research with the declared intention of promoting and developing a friendly AI.

Azure OpenAI Service

Gives customers advanced language AI with OpenAI GPT-4, GPT-3, Codex, and DALL-E models with the security and enterprise promise of Azure. Azure OpenAI co-develops the APIs with OpenAI, ensuring compatibility and a smooth transition from one to the other.

chatGPT

An artificial intelligence chatbot developed by OpenAI. It’s built on top of OpenAI's GPT-3.5 and GPT-4 families of large language models and has been fine-tuned using both supervised and reinforcement learning techniques.

As AI becomes more pervasive, it’s crucial to address ethical considerations and potential challenges.

Bias

AI algorithms can inadvertently inherit biases present in the data they are trained on and from the people who created them. This can lead to unfair or discriminatory outcomes. Addressing bias in AI requires careful data selection, preprocessing, and algorithmic fairness considerations.

Transparency and explainability

As AI systems make critical decisions, it's important to understand how and why they arrive at those decisions. Explainable AI aims to provide insights into the decision-making process, ensuring transparency and accountability.

Privacy and security

With the abundance of data used in AI systems, privacy and security are paramount. It's crucial to handle and protect user data responsibly, ensuring compliance with privacy regulations and implementing robust security measures.

AI and copyright

In the context of copyright infringement, the legal implications of using AI are still unclear. Courts across the globe are pondering how to address both Generative AI outputs and AI training processes. There are primarily two concerns:

1. Intellectual Property (IP) Leakage - When using a public service, such as chatGPT from OpenAI, the text or code used in the prompts could be used to train the model and could therefore show up - in some form - in a response to another user. This can be mitigated by using Azure OpenAI, which always runs privately in the Azure tenant, does not update any other model and therefore will not leak IP.
   
   
2. Inadvertent use of copyrighted material - The other side of the coin is accidentally using code or text created by Generative AI that is protected by some form of copyright that only allows the text or code to be used with restrictions. GitHub Copilot has implemented a feature that filters out any code that may be restricted.

Learn how to leverage Microsoft AI technologies to support smarter decisions, personalize customer experiences and optimize operations. Contact our Microsoft AI team today to book a 30-minute discovery call to explore the true value of Azure OpenAI.